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+IntroductionAir traffic demand is projected to increase significantly in the upcoming years [1].In order to meet the forecasted levels, the human workload associated with conflict detection and resolution must be reduced to assure system safety and performance.Automated separation assurance systems are proposed as a way to efficiently separate aircraft in highly dense traffic situations up to two to three times current levels.There are numerous algorithms proposed to provide separation assurance in the future air traffic system [2].With any automated resolution tool, the type of resolution selected is based on some cost function.Two costs associated with conflict resolution maneuvers are delay and fuel burn.These two costs are closely correlated, but not necessarily the same.There have been several previous studies of conflict resolution algorithms in which the preferred resolution is selected based on delay [3,4].These studies demonstrate that the algorithms are robust to high traffic demand and can find resolutions that have low average delay, but they have not been examined for fuel efficiency.The purpose of the current study is to compare the system performance of a conflict resolution algorithm in realistic traffic scenarios when selecting resolutions based on minimum delay to system performance when selecting resolutions based on minimum fuel burn.The total costs in terms of fuel burn and delay are compared between the two resolution selection criteria.Also, the total number of conflicts is compared to determine if there is any adverse impact on safety.Finally, an examination of the different types of resolutions selected in the two cases is performed to understand how the selection criteria may affect specific resolution scenarios.The paper is organized as follows: a brief overview of the simulation setup is provided in the next section and is followed by the equations that govern fuel burn.The results are then presented along with a discussion of their implications, concluding with a summary of the findings and a recommendation for future work.
+Simulation SetupIn this study the Airspace Concept Evaluation System (ACES) is used to simulate the National Airspace System (NAS) in a fast-time simulation.The conflict resolution algorithm evaluated is the Advanced Airspace Concept (AAC) Autoresolver [4,5].For this simulation ACES computes the delays and the fuel burn values used by the Autoresolver to select preferred resolutions.
+Test BedACES is a fast-time, agent-based simulation of the NAS that uses four-degree-of-freedom equations based on the Base of Aircraft Data (BADA) to generate aircraft trajectories [4].ACES was developed specifically to provide a general purpose environment for evaluating future air traffic management and control concepts, including automated resolution algorithms.Essential to the simulation of resolution algorithms is the ability to generate 4D trajectories.In ACES these trajectories begin at the departure fix and end at the arrival fix.By using aircraft-type-specific performance data together with guidance and navigation models, the ACES trajectory engine can generate representative trajectories for many aircraft.In the ACES simulation environment these aircraft trajectories are entirely deterministic; aircraft conflicts can be predicted with perfect accuracy and resolution trajectories are guaranteed to be followed precisely by the simulated aircraft.In addition to deterministic aircraft trajectories, simplifications were made in the modeling and execution of the experiment.Negotiation of resolution trajectories between aircraft operators and/or the air navigation service provider were not modeled.Neither were data link transmission delays or pilot-action delays.Once a resolution trajectory was determined by the automation it was executed immediately and precisely [3].
+Test Article: AAC AutoresolverThe AAC Autoresolver is a strategic conflict resolution algorithm designed to deconflict aircraft that are predicted to lose separation more than two minutes in the future.For this study, every minute of simulation time the future trajectories of all the aircraft are computed and processed to determine if there are any predicted losses of separation where two aircraft come within 5 nautical miles horizontally and 1,000 feet vertically of one another.The Autoresolver receives a list of aircraft conflict pairs ordered by predicted time to first loss of separation.For each conflict in the conflict list, the Autoresolver follows an iterative approach for resolution.Taking into account characteristics such as aircraft type, speed and airspace boundaries, the resolver calculates a future route composed of waypoints, speeds and altitudes which may possibly resolve the conflict.Figure 1 shows the types of future routes attempted by the Autoresolver grouped by whether they are horizontal, vertical, or speed resolutions.This future route is then sent to a trajectory engine that computes a trial resolution trajectory based on this route.In order for the resolution to be viable, it must resolve the primary conflict, be free of predicted losses of separation with the primary aircraft in the conflict, as well as any other aircraft in the simulation for a specified period of time.If these conditions are met, the Autoresolver has successfully generated a candidate resolution trajectory and stores it.If the resolution is not free of primary or secondary conflicts, the Autoresolver computes a new resolution route and checks if it is successful.For each resolution type this iteration is continued until a successful resolution is found or all possibilities of that type have been tried.For each successful resolution, both the associated delay and the fuel burn are calculated.A common spatial point on the original trajectory and the resolution trajectory is found.To calculate the delay, the time on the original trajectory at the common point is subtracted from the time on the resolution trajectory at the common point.Similarly for the fuel burn, the weight of the aircraft at the common point for the resolution trajectory is subtracted from the aircraft weight for the original trajectory.A discussion of how the aircraft weight is calculated and converted to fuel burn is given in a subsequent section.The resolver will generate up to seven successful resolutions per aircraft in conflict for a total of fourteen available between the two aircraft.In this study, the algorithm selects a resolution from among the set of successful resolutions using either the minimum delay or the minimum fuel burn criterion, depending on how the algorithm is configured.The selected resolution is then sent to ACES for implementation.Further discussion regarding the design of the algorithm and the types of resolutions that are generated is presented in [4,5].
+ProcedureTo illustrate the differences between selecting conflict-resolution maneuvers based on delay and selecting resolution maneuvers based on fuel burn, a test plan was developed to isolate this variable.Two cases were simulated for each specific scenario: one with resolution selection based on minimum delay and one with resolution selection based on minimum fuel burn.
+Demand SetFlight operations over a 36-hour period were simulated based on Aircraft Situation Display to Industry (ASDI) data recorded March 8, 2007.ASDI data comes from the FAA's Enhanced Traffic Management System (ETMS) and contains information about flights controlled by air traffic control.The data set included 62,970 flights, their associated routes, and their departure times.This dataset had mixed aircraft types representing the current fleet mix.The specific day was selected because it represented a "low weather," high volume day in the NAS.
+Simulated AirspaceFor this study, the Autoresolver provided conflict resolution services for a single Air Route Traffic Control Center at a time.Three centers were used to analyze the algorithmic performance with different types of air traffic flows.These centers were Indianapolis (ZID), Minneapolis (ZMP) and Atlanta (ZTL).The simulation included all types of air traffic for each center: departures, overflights and arrivals for air carrier, business and general aviation.Each of the demand sets provided thirty six hours of simulated air traffic transitioning through the selected airspace.Two simulations were run per center, one for fuel burn and one for delay for a total of six simulations.Although the data set used in the simulations consisted of 62,970 flights, the number of flights that passed through each center differed because of differences in the size and layout of the airspace along with the traffic volume and composition.Table 1 shows the experiment test matrix and number of flights that passed through each center.
+Center
+Fuel Burn EquationsBy default, the AAC Autoresolver selects the preferred conflict resolution based on minimum delay.For this study, the algorithm was modified to allow for selection of the preferred resolution based on minimum fuel burn.Since the computation of fuel burn is critical to the results presented here, the equations used to calculate this fuel burn will now be discussed.The fuel burn required for a resolution for this simulation is computed by ACES using aircraft-specific coefficients selected from the Base of Aircraft Data [7].The BADA is comprised of the performance and operating procedure coefficients of 295 aircraft types.These coefficients encompass those that are used to calculate thrust, drag, and fuel flow along with those used to specify nominal cruise, climb and descent speeds.The BADA fuel model uses the thrustspecific fuel consumption, η, measured in kilogram/minute/kilonewton and the thrust, T , to determine the nominal fuel flow, f nom .This is given by:f nom = ηT,(1)where η, for jet aircraft, is:η = C f l 1 + V T AS C f 2 . (2)In this equation, C f 1 and C f 2 are two thrust-specific fuel consumption coefficients reported in the BADA dataset and V T AS is the true airspeed.The thrust depends on the aircrafts phase of flight.For the majority of the resolutions discussed in this study, the aircraft is in the level cruise portion of flight.In this phase, the thrust is equal to the drag and can be represented by the following:T = ρC D S(V T AS ) 2 2 , (3)where ρ is the air density, C D is the drag coefficient reported by BADA, and S is the wing reference area.For idle thrust descent conditions the fuel flow, f min , is measured in kilogram/minute as:f min = C f 3 1 - h C f 4 , (4)where h is the altitude above sea level in feet, C f 3 is the first descent fuel flow coefficient and C f 4 is the second descent fuel flow coefficient.For climb portions of flight, the fuel flow is still given by equation 1, but the thrust is computed based on the type of climb performed by the aircraft.These equations show that, among other things, the fuel burn is a function of thrust, airspeed, and altitude.Even though these equations are only an approximation of the actual fuel burn of an aircraft, they will be used as the true fuel burn for the results which follow.
+ResultsThe six simulation runs presented in Table 1 were performed, and the results were analyzed.Three aspects of the performance of the conflict resolution algorithm were compared between the delay cases and the fuel burn cases: system safety, system efficiency, and resolution selection.Although results were compiled and analyzed on an individual Center basis, no significant differences were observed between Centers.Accordingly, the results are presented in the aggregate.
+SafetyThe main focus of this study is on how the resolution selection criterion affects system efficiency and resolution selection.However, it is also important to determine whether this selection criterion impacts safety.As a first-order look at safety, two metrics were analyzed: the total number of conflicts and the percentage of conflicts successfully resolved.A significant increase in the number of conflicts as a result of selecting resolutions based on fuel burn might suggest increased risk.The total number of conflicts for the delay cases and the fuel burn cases is presented in Table 2. Selection based on fuel burn leads to approximately 5% more conflicts than selection based on delay.This increase may be a by-product of the resolution selection process, but it is not considered large enough to have an impact on system safety.To illustrate this point, only one conflict remained unresolved in all of the delay cases, and only one conflict remained unresolved in all of the fuel burn cases.So, over 99.98% of all conflicts were resolved when either selection criterion was used.
+Delay
+EfficiencyThe operational efficiency of the resolution trajectories produced by the algorithm is important in understanding the advantages and disadvantages of resolution selection based on fuel burn or delay.Successful resolution trajectories that require less fuel or reduce delay are preferable to those that cause an increase in either quantity.
+Cumulative DelayDelay is the time associated with executing resolution maneuvers.Figure 2 shows the cumulative delay for the system when selecting resolution trajectories based on delay or fuel burn.As expected, the results show that the cumulative delay when selecting resolutions based on delay is 25% less than the cumulative delay when selecting resolutions based on fuel burn.This reduced delay can result in economic and system efficiency advantages to selecting resolutions based on minimum delay.The histograms in Figure 3 provide insight into how the delay imposed by the algorithm is distributed for the two resolution selection criteria.These histograms are divided into 30-second time bins with negative times corresponding to resolutions which generate time savings relative to the selected original trajectory.Negative delay results when Direct-To resolutions (Figure 1(a)) are included within the successful resolutions.Direct-To resolutions resolve conflicts by redirecting the aircraft to a downstream waypoint.This directly bypasses a dogleg in the flight plan.Figure 3(a) shows the delay for the cases where resolutions are selected based on minimum delay, and Figure 3(b) shows the delay for the cases where resolutions are selected based on minimum fuel burn.The mean delay for resolutions in Figure 3(a) is 18 seconds.Over 22% of the resolutions in these cases result in a time savings.These values can be contrasted with the results for fuel-burn selection cases shown in Figure 3(b).The mean delay for these resolutions is 40 seconds, and only 11% of the resolutions result in a time savings.It can be seen that for the fuel-burn cases the histogram is more heavily weighted to the right.
+Cumulative Fuel BurnFigure 4 shows the cumulative fuel burn required for conflict resolution for the system when selecting resolutions based on delay or fuel burn.As expected, when the algorithm is selecting based on fuel burn the cumulative fuel burn of the system is 27% less than the fuel burn for delay selection.This fuel-burn reduction could lead to environmental and economic reasons for selecting resolutions maneuvers based on minimum fuel burn.There are tradeoffs evident when comparing Figure 2 for delay selection and Figure 4 for fuel burn selection, and these tradeoffs will be discussed further in the Environmental and Economic Impact Section.The mean fuel burn for the delay selection case is 22 pounds.It can be seen from Figure 5(a) that, for the delay case, the fuel burn distribution is more heavily weighted to the right side of zero, with only a small percentage of resolutions resulting in a fuel savings.In contrast, when selecting resolutions based on fuel burn (Figure 5(b)), the distribution is more evenly weighted with nearly half of the resolutions producing a fuel savings.For this case the mean fuel burn is 12 pounds.
+Resolution SelectionIn the previous sections, the system-wide trade-offs of selecting conflict resolutions based on minimum fuel burn or minimum delay were presented.Since these two different cases produce different results for total delay and total fuel burn, it is interesting to try to comprehend the mechanism for this difference.As a first attempt to understand the underlying differences, the impact of this selection criterion on the types of resolutions that are likely to be selected will now be analyzed.For this analysis, the many different types of resolutions attempted by the Autoresolver will be categorized in three groups according to the dominant method of conflict resolution: horizontal maneuvers, vertical maneuvers, and speed maneuvers.Figure 6 shows the percentage of each type of maneuver selected for the two cases.When selecting based on fuel burn, the percentage of vertical maneuvers is about equal to the percentage when selecting based on delay.The percentage of speed maneuvers based on fuel burn is higher by 3.5% and the percentage of horizontal maneuvers is lower by 3%.
+Figure 6. Selected maneuver typesTo understand the causes of this difference in maneuver selection, the relationship between delay and fuel burn are plotted for a single aircraft type for the Atlanta Center case. Figure 7 shows this relationship for all resolutions (not just selected ones) for Airbus 319 aircraft in the simulation.A single aircraft type was selected to reduce the fuel flow variance.Generally, it might be thought that reducing the delay will reduce fuel burn.Figure 7(a) shows that this is indeed the relationship for horizontal maneuvers.There is a linear variation where increasing delay leads to increasing fuel burn.The multiple trend lines evident in the figure are from resolutions at different altitudes and at different cruise speeds.The relationship is a bit more complex for vertical resolutions (Figure 7(b)).Many of the resolutions plotted in this figure show a linear positive correlation, but there are some cases where resolutions with negative delay led to positive fuel burn.These are probably from altitude-hold resolutions where the aircraft maintains a lower altitude than the cruise altitude for a certain amount of time to avoid a conflict.For speed resolutions (Figure 7(c)) the relationship between delay and fuel burn does not show clearly identifiable trends therefore a linear regression was included to aid in identification.There are many resolutions where increases in delay lead to decreases in fuel burn.These are speed resolutions that command a reduction of cruise speed to avoid the conflict.This speed reduction results in less fuel burn, but greater delay.The relationships between delay and fuel burn for speed resolutions illustrate the differences between the resolution selections shown in Figure 6 as well as the differences in cumulative delay and fuel burn shown in Figures 2 and4.
+Environmental and Economic ImpactThe development of algorithms in support of automated separation assurance should not only be concerned with safe and efficient operations but also be environmentally and economically responsible.In recent years public concern has grown regarding the potential impact of the byproducts of aviation, particularly noise and emissions.It is estimated that aircraft world-wide contribute about 3.5% of the total radiative forcing (a measure of change in climate) off all human activities, and this percentage is projected to grow [8].A contributor to this projected growth is the impending expansion in the level of air traffic demand in the NAS.Ensuring safe and environmentally responsible systems is of utmost importance if the aviation industry is to meet projected levels of growth and demand.
+Fuel BurnThe potential reduction in fuel burn presented in the results section amounted to 10 pounds per resolution when selecting resolutions based on fuel burn.Expanding on this result using a jet fuel (Jet A) price of 220.1 cts/gal and 5,233 the number of resolved conflicts in the three centers from Table 2, the total savings in US dollars over the course of the 36-hour period is $16,963 which would amount to approximately $4 million per year [9].This fuel cost translates to a savings in carbon dioxide emissions.From the above it can be seen that the selection based on minimum fuel consumption would save approximately 52,330 pounds of fuel.Using the weight of the jet fuel, the amount of carbon dioxide released can be determined.The Energy Information Administration estimates that burning a gallon of jet fuel emits 21 pounds of carbon dioxide [10].One gallon of jet fuel weighs on the order of 6.79 pounds per gallon, which would bring the amount of carbon dioxide saved to 161,845 pounds.The projected savings would come at no cost to airlines, as they do not require any modification of existing aircraft or controller practices.The reduction in emissions and cost stems from a change in the way resolutions are selected within the automated conflict resolver.
+DelayThe effects of delay play an important role in airspace management and decision making.For shorter delays the system-wide impact can be relatively small and result in longer flight times that influence the direct operating cost of the airline.However, longer delays can propagate through the system as the day progresses.These delays can prove to be disruptive to activities such as crew scheduling, gate scheduling and even delay later flights.Although selecting resolutions based on minimum fuel burn results in fuel savings, delay is increased.The mean delay of resolutions when selecting based on fuel burn is 40 seconds.Using a delay cost of $20 per minute for the number of resolved conflicts in the three centers from Table 2, and assuming the number of seats in the aircraft is between 65-150, the cost of the delay is $69,773 [11].This is significantly more than the cost of the fuel savings associated with the same number of resolved conflicts when selecting conflict resolutions based on minimum fuel burn.However, cost is only one of several factors that must be taken in to account when evaluating resolution selection criterion.
+ConclusionThe AAC algorithm was modified to select the preferred resolution based on minimum fuel burn by com-paring the aircraft weight at a point along the original trajectory with a common point downstream.In fast-time simulation of three airspace regions, the resolution trajectories were found to incur an average of 40 seconds more delay when selecting conflict resolutions based on minimum fuel burn.This represents a 25% increase over resolutions selected based on minimum delay.Similarly, the trajectories required an average of 10 pounds more fuel when selecting based on delay when compared to selection based on fuel burn.A preference for speed maneuvers was established when selecting resolutions based on minimum fuel burn.Horizontal and vertical maneuvers were found to be less fuel efficient than speed maneuvers when selecting based on fuel burn.When executing horizontal and altitude maneuvers, optimization based on delay was found to be more efficient.Changing the selection criteria from delay to fuel burn was found to have no impact on the ability of the algorithm to successfully detect and resolve conflicts.Despite the modifications, the algorithm was able to successfully detect and resolve 99.98% of all conflicts regardless of the resolution selection criterion.
+Future WorkIn this study, speed maneuvers were found to be the most fuel efficient when selecting resolutions based on minimum fuel burn.However, the number of speed resolutions executed in comparison to horizontal and vertical resolutions is significantly less.Further modification of the algorithm to generate a greater number of speed resolutions would yield higher fuel savings.Similarly, an additional reduction in fuel consumption can be achieved by combining Direct-To resolutions with speed changes.This would serve to reduce the speed by an amount that would cancel the negative delay of the resulting Direct-To resolution.Figure 1 .1Figure 1.Resolution trajectories of type (a) horizontal, (b) vertical, and (c) speed [5].
+Figure 2 .2Figure 2. Cumulative delay.
+Figure 3 .3Figure 3. Delay histograms for (a) minimum delay and (b) minimum fuel burn.
+Figure 4 .4Figure 4. Cumulative fuel burn.
+Figure 5 .5Figure 5. Fuel burn histograms for (a) minimum delay and (b) minimum fuel burn.
+Figure 7 .7Figure 7. Fuel burn versus delay for Airbus 319 aircraft for (a) horizontal maneuvers, (b) vertical maneuvers, and (c) speed maneuvers.
+Table 1 .1Experiment test matrix and simulated flights.CaseFlights SimulatedZIDDelay5413ZIDFuel Burn5413ZMPDelay8577ZMPFuel Burn8577ZTLDelay10049ZTLFuel Burn10049
+Table 2 .2Conflict resolution results.Fuel Burn
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+IntroductionAir traffic demand is projected to double in the next 20 years (ref.1).The human workload associated with conflict detection and resolution is expected to limit this increase and thereby limit the economic growth that aviation facilitates.Automated separation-assurance systems are proposed as a way to safely and efficiently separate aircraft in highly dense traffic situations up to two to three times current levels, thereby fostering increased economic growth for the nation.Numerous algorithms have been proposed to provide separation assurance in the future air traffic system (ref.2).Maintaining safe separation is the first-order objective of all such algorithms; the second-order objectives vary, but most of the proposed algorithms optimize the selection of conflict resolution maneuvers to minimize airborne delay in order to mitigate the effect on schedule.An alternative objective is to optimize based on fuel burn (refs. 2 and 3).The Advanced Airspace Concepts (AAC) Autoresolver is strategic conflict resolution algorithm capable of deconflicting aircraft.AAC is a concept for automating separation assurance in the future that includes multiple layers of separation assurance for increased reliability.One component of AAC is the Autoresolver, a strategic problem-solving tool that is responsible for strategic separation assurance as well as weather avoidance and arrival metering, although for this study the focus is only on the separation-assurance function (refs. 4 and 5).In reference 6, the system performance of a conflict resolution algorithm that selected resolutions based on minimum delay was compared to the system performance of the same algorithm when selecting resolutions based on minimum fuel burn.The most effective resolution maneuver when optimizing for airborne delay was a Direct-to maneuver, which identifies wind-favorable shortcuts along the planned route of an aircraft that reduce its flying time while resolving the predicted conflict (ref.7).The most effective resolution maneuver when optimizing for fuel burn was a speed reduction maneuver, which employs a temporary speed reduction to resolve the predicted conflict.However, speed reductions were selected less frequently by the algorithm than other, lessfuel-efficient maneuvers.Additionally, when utilized, these maneuvers significantly increase the cumulative delay.It is hypothesized that the availability of a compound maneuver combining a Direct-To maneuver with the fuel efficiency of a speed reduction would improve the performance of the separation-assurance algorithm.This study compares the system performance of a conflict resolution algorithm in realistic traffic scenarios with and without the availability of a compound Direct-to/speed-reduction maneuver, hereafter referred to as a Variable Speed Direct-To maneuver.The objective is to quantify the operational benefit of adding the proposed new maneuver to the set of maneuvers already available to the automated separation-assurance algorithm.The next section describes the conflict resolution algorithm under test and the new compound maneuver.Then the experimental approach, procedure, and assumptions are discussed.The results are then categorized according to safety and efficiency.Lastly, a summary of the study findings is given, along with suggestions for future research.
+Test ArticleThe conflict resolution algorithm evaluated in this study is the Advanced Airspace Concept (AAC) Autoresolver (refs. 4 and 5) .It is a groundbased algorithm that resolves conflicts in pairwise fashion and can be configured to select resolutions based on minimum delay or minimum fuel burn.The Autoresolver selects a maneuver from one of the following categories: horizontal, vertical, altitude, Direct-To, or compound.For this study, only conflicts with en-route flight maneuvers are analyzed; arrivals are not included because they adhere to additional constraints such as metering.In the following study only one compound maneuver is enabled: the Variable Speed Direct-To maneuver.
+AAC AutoresolverThe AAC Autoresolver is a strategic conflict resolution algorithm designed to deconflict aircraft that are predicted to lose separation more than 2 minutes in the future.For aircraft en route, the look-ahead time is 8 minutes and up to 20 minutes for arriving aircraft.In this study, for every minute of simulation time the future trajectories of all aircraft are computed and processed to determine if there are any predicted losses of separation where two aircraft come within 5 nautical miles horizontally and 1,000 feet vertically of one another.The Autoresolver receives a list of aircraft conflict pairs ordered by predicted time to first loss of separation.For each conflict in the con-flict list, the Autoresolver follows an iterative approach for resolution.Accounting for characteristics such as aircraft type, speed, and airspace boundaries, the resolver calculates a future route composed of waypoints, speeds, and altitudes that may possibly resolve the conflict.Figure 1 shows the types of future routes attempted by the Autoresolver, grouped by whether they are horizontal, vertical, or speed resolutions.This future route is then sent to a trajectory engine that computes a four-dimensional (4-D) trial resolution trajectory based on this route.In order for the resolution to be viable, it must resolve the primary conflict and be free of predicted losses of separation with the primary aircraft in the conflictas well as any other aircraft in the airspacefor a specified period of time.If these conditions are met, the Autoresolver has successfully generated a candidate resolution trajectory and stores it.If the resolution is not free of primary or secondary conflicts, the Autoresolver computes a new resolution route and checks to determine if it is successful.For each resolution type this iteration is continued until a successful resolution is found or all possibilities of that type have been exhausted.For each successful resolution, both the associated delay and the fuel burn are calculated.A common spatial point on the original trajectory and the resolution trajectory is found.To calculate the delay, the time on the original trajectory at the common point is subtracted from the time on the resolution trajectory at the common point.Similarly for the fuel burn, the weight of the aircraft at the common point on the resolution trajectory is subtracted from the aircraft weight at that point, after the aircraft has flown the original trajectory.Figure 2 shows an example trajectory with a resolution maneuver represented by segments 3a and 3b.The algorithm evaluates the cost of segments 1, 2, and 3 versus the cost of segments 1, 2, 3a, and 3b.A discussion of how the aircraft weight is calculated and converted to fuel burn is given in a subsequent section.The resolver will generate up to 18 successful resolutions per aircraft in conflict for a maximum of 36 candidate resolution maneuvers between the two aircraft.In this study, the algorithm selects a resolution from among the set of successful resolutions using either the minimum delay or the minimum fuel-burn criterion, depending on how the algorithm is configured.The selected resolution is then implemented via fast-time, closed-loop simulation as discussed in the following sections.Further discussion regarding the design of the algorithm and the types of resolutions that are generated is presented in references 4 and 5 .
+Variable Speed Direct-To ManeuverThe Autoresolver was modified to allow for the combination of a Direct-To maneuver with a reduction in speed.The reduced speed is chosen to exactly negate the time savings normally associated with a Direct-To maneuver; this reduction in speed produces a fuel-burn benefit while maintaining the flight-plan schedule.This compound maneuver is referred to as a Variable Speed Direct-To maneuver.A Direct-To maneuver resolves a conflict by taking an aircraft directly to a downstream waypoint, thus bypassing a dogleg in the flight plan.This modification augmented the existing Direct-To maneuver, thus allowing the algorithm to continue to have the option to utilize a Direct-To maneuver when efficient.The equation that describes a Direct-To maneuver is shown in reference 1, where ∆t [[need to fix all these symbol callouts]] represents delay in hours, D 1 is the previous distance along the route in nautical miles, D 2 is the new distance in nautical miles, and S is speed in knots:∆t = D 1 S - D 2 S(1)Augmenting equation ( 1) to produce a maneuver that results in zero delay requires setting d to zero, yielding equation (2), where S new represents the new (slower) speed in order to result in a Variable Speed Direct-To maneuver.The algorithm abides by the original Direct-To constraints where the maneuver will not be considered if:• the aircraft is less than 20 minutes from the arrival fix,• the aircraft cannot return to the route within 50 n.mi. of the final fix,• the path of the aircraft along the Direct-To route is greater than 250 n. mi.(dotted line in fig.1(a)), and• the point where the aircraft rejoins the trajectory is within 50 n.mi. of the current Air Route Traffic Control Center boundary.In addition, it will not attempt to execute the maneuver if S new is within 5 knots of the original speed.S new = ( D 2 D 1 )S(2)For example, a Variable Speed Direct-To maneuver by an aircraft traveling 450 knots that will reduce the distance along the route from 400 to 360 n. mi.would reduce the speed to 405 (by 45) knots in order to produce no delay.When performing a Variable Speed Direct-To maneuver, the intent is for the aircraft to recapture the route at the same time it would have if it had not performed the maneuver.Figure 3 illustrates the Variable Speed Direct-To maneuver where A1 and A2 are aircraft predicted to conflict.To avoid this conflict, A1 is selected to execute a Variable Speed Direct-To maneuver.The new trajectory for A1 (dashed line) removes several waypoints and reduces the speed as shown in the neighboring profile.The Mach number of A1 is decreased for the duration of the maneuver and eventually returns to its original speed after clearing the conflict.
+Experiment DesignThis section describes the simulation approach and the metrics used.
+Simulation EnvironmentThe Advanced Concepts Evaluations System (ACES) is a fast-time, agent-based simulation of the National Airspace System (NAS) that uses four-degree-of-freedom equations based on the Base of Aircraft Data (BADA) to generate aircraft trajectories (ref.8).ACES was developed specifically to provide a general-purpose environment for evaluating future air traffic management and control concepts, including automated resolution algorithms.Essential to the simulation of resolution algorithms is the ability to generate 4-D trajectories.In ACES these trajectories begin at the departure fix and end at the arrival fix.By using aircraft-type-specific performance data together with guidance and navigation models, the ACES trajectory engine can generate representative trajectories for many aircraft.In the ACES simulation environment these aircraft trajectories are entirely deterministic; aircraft conflicts can be predicted with perfect accuracy, and resolution trajectories are guaranteed to be followed precisely by the simulated aircraft.In addition to deterministic aircraft trajectories, simplifications were made in the modeling and execution of the experiment.Negotiations of resolution trajectories between aircraft operators and/or the air navigation service provider were not modeled, nor were data-link transmission delays or pilot-action delays.Once a resolution trajectory was determined by the automation, it was executed immediately and precisely.
+Simulated AirspaceIn this study, the Autoresolver resolved conflicts in three pairs of adjacent Air Route Traffic Control Centers (ARTCCs).Each of the airspaces was simulated independently of each other and was selected based on its operational conflict properties as defined in reference 9.These properties fall into three categories that characterize the conflict, its relationship between two or more conflicts, and the locations of the conflicts within the NAS.In this study statistical clustering analysis was employed to categorize ARTCCs based on normalized conflict properties.As a result, three ARTCC pairs: Oakland-Los Angeles (ZOA-ZLA), Indianapolis-Chicago (ZID-ZAU), and Boston-New York (ZBW-ZNY) were identified that provided a wide representation of conflict properties.In order to create three distinct NAS regions, an adjacent center was chosen for each pair, creating (ZOA-ZLA) as representative of West Coast air traffic flow, (ZAU-ZID) as representative of Midwest air traffic flow, and (ZBW-ZNY) as representative of East Coast air traffic flow.By using the clusters shown in figure 4, we can model behavior seen over the entire NAS, thus allowing a more complete assessment of the performance of the algorithm.Figure 4: The ARTCCs simulated in this study.• CL/CL -Both aircraft are climbing.
+Demand Set• CL/CR-One aircraft is climbing while the other is cruising.• CL/DE -One aircraft is climbing while the other is descending.• CR/CR-Both aircraft are cruising.• CR/DE -One aircraft is cruising while the other is descending.• DE/DE-Both aircraft are descending.
+Independent VariablesTo evaluate the difference between the current state-of-the-art conflict resolution algorithm and the addition of a Variable Speed Direct-To maneuver, a test plan was developed that examines the behavior of the algorithm with and without this maneuver enabled in three pairs of ARTCCs under two conflict resolution optimization schemes.Table 1 shows the independent variables and settings.Each of the possible permutations is representative of a simulation run.
+Dependent VariablesThe dependent variables for the experiment were the number of conflicts and the airborne delay and fuel burn incurred by flying the conflict resolution trajectories.In the development of a robust, efficient algorithm for implementation in the Next-Generation Air Transportation System (NextGen), safety is of the utmost concern.The number of conflicts is the metric used here to reflect the safety of the system.Efficiency in terms of delay and fuel burn is important once safety is assured.The fuel consumed per resolution is computed by ACES using aircraft-specific coefficients selected from the BADA (ref.8).The BADA comprises the performance and operating procedure coefficients of 295 aircraft types.These coefficients encompass those that are used to calculate thrust, drag, and fuel flow along with those used to specify nominal cruise, climb, and descent speeds.Further discussion of the specific equations used to calculate the fuel burn is included in references 6 and 8. Evaluating the number of conflicts per simulation provides insight into the impact of the modifications made to the algorithm.A significant increase in the number of conflicts as a result of the availability of the Variable Speed Direct-To maneuver suggests increased risk.The safetyand efficiency-related results are presented in the section Results.
+ResultsThis experiment seeks to evaluate the benefit of augmenting the AAC Autoresolver to consider a Variable Speed Direct-To maneuver when resolving a given conflict.The subsequent results address the safety and efficiency of potential implementation.
+SafetyThe primary safety metric for the experiment is the number of conflicts.A conflict occurs when aircraft are predicted to come within 5 n.mi.horizontally and 1,000 feet vertically from each other in en-route airspace.As expected, the addition of the Variable Speed Direct-To maneuver did not adversely affect the safety of the system, as measured by the total number of predicted conflicts.Figure 6 shows that in none of the test airspaces did the number of conflicts significantly increase when the Variable Speed Direct-To maneuver was enabled.On average, the percent difference between the baseline number of conflicts and the Variable Speed Direct-Toenabled scenario is less than 1%, suggesting that the inclusion of this maneuver does not adversely affect the ability of the algorithm to resolve conflicts, and there are no major gaps in its implementation.
+Efficiency
+Fuel BurnWhen a Variable Speed Direct-To maneuver is executed, the maneuvered aircraft is slowed by an amount such that it will traverse its now, shorter Direct-To route in the same amount of time that it planned to traverse its original route.Figure 7 shows the distribution of speed-reduction magnitudes for ZID-ZAU.Seventy-five percent of all speed reductions observed in the experiment were less than 30 knots.A typical Boeing 737 aircraft at 35,000 feet will cruise between Mach 0.72 (415 knots) and Mach 0.76 (438 knots), approximately a 30-knot variation, indicating that most of the speed-reduction values required to obtain the desired fuel benefit are reasonable.Within our simulation, the range observed adhered to aircraft performance limitations.The speed-reduction ranges vs. the number of Variable Speed Direct-To maneuvers for the selected airspaces are shown in appendix A. To evaluate the fuel burn associated with a resolution maneuver, the weight of the aircraft at the termination point on the resolution trajectory (where the aircraft rejoins the original trajectory) is subtracted from the aircraft weight at the same point, after the aircraft has flown the original trajectory.Fuel-burn savings were higher by 92% in ZID-ZAU, 55% in ZBW-ZNY, and 47% in ZLA-ZOA when resolving conflicts with the Variable Speed Direct-To maneuver enabled.Figure 8 shows the average fuel burn per resolution for the selected airspaces.The negative fuel burn seen in ZBW-ZNY and ZID-ZAU is an indication that the modification made to the algorithm causes it to outperform the nominal case when selecting resolutions based on minimum fuel burn.The average fuel burn per resolution in ZID-ZAU is 4.01 pounds less than when selecting resolutions based on minimum fuel burn with Variable Speed Direct-To maneuvers enabled.Similarly, in ZBW-ZNY the average fuel burn per resolution is 7.04 when optimizing for fuel burn with the maneuver enabled, a 2.43pound-per-resolution decrease.In ZLA-ZOA the average fuel burn per resolution is 2.73 pounds, 2.41 pounds less than when Variable Speed Direct-To is disabled.Though these numbers are small, they are not insignificant when extrapolated to potential savings per year.In this study there were 3,276 conflicts in ZID-ZAU over the course of the day.Each of these conflicts requires one of the two aircraft to be maneuvered.Considering the average fuel savings of 4 pounds per resolution in ZID-ZAU, this savings amounts to roughly 4.8 million pounds of fuel per yearenough fuel to fill the tank of a Boeing 737-700 approximately 100 times.Furthermore, 20 ARTCCs within the continental United States could benefit from these savings.Variation in traffic density and route length accounts for most of the difference in the magnitude of savings between the centers.ZID-ZAU center executed nearly twice as many resolution maneuvers as ZLA-ZOA and ZBW-ZNY, suggesting that the fuel efficiency of the resolutions the algorithm selects increases with the air traffic demand.However, the improvement seen in the delay cases is not as significant.When selecting resolutions based on delay, the algorithm finds Direct-To maneuvers to be more efficient.This increase in efficiency can be attributed to the fact that the selection of a Direct-To maneuver can result in negative delay and thus a time savings, whereas the most time-efficient zero-delay solution is zero and will not yield a time savings.Figure 9 shows the resolutions selected by the algorithm for ZID-ZAU for fuel-burn optimization with Variable Speed Direct-To maneuvers enabled and disabled.Overall, the number of resolutions other than Direct-To or Variable Speed Direct-To remains consistent between scenarios.When Variable Speed Direct-To maneuvers were disabled, 306 Direct-To maneuvers were executed.When enabled, 181 Direct-To and 147 Variable Speed Direct-To maneuvers were executed, representing a 41% decrease in the number of Direct-To maneuvers.When optimizing for minimum fuel burn, the algorithm frequently selected Variable Speed Direct-To maneuvers over traditional Direct-To maneuvers.However, in a small number of cases, a Direct-To maneuver was selected despite the fact that a Variable Speed Direct-To maneuver was available.In these instances, the additional fuel savings did not outweigh a decrease in flight time.The maneuver types for delay and fuel-burn optimization for each of the ARTCCs are shown in appendix B.
+DelayAirborne delay is defined as the difference in time between the arrival time of an aircraft as given in the flight schedule and its actual arrival time.Although there are many sources of delay (e.g., air traffic control, weather, maintenance, crew availability), in the following analysis the source of delay is attributed to time difference between the modified trajectory and original trajectory of an aircraft at a common point in the en-route airspace.Positive delay occurs when the modified trajectory incurs additional flight time to avoid a loss of separation, akin to a detour.Negative delay is a reduction in flight time that can occur when a dogleg in the flight plan is eliminated or more favorable winds are encountered.As expected, the inclusion of a Variable Speed Direct-To maneuver has almost no impact on delay when selecting resolutions based on delay.Figure 10 shows the average delay per resolution.When selecting resolu-tions based on minimum fuel burn, the average delay per resolution with the Variable Speed Direct-To maneuver enabled for ZID-ZAU is 10.86 seconds.For ZLA-ZOA under the same conditions, the average delay is 16.84 seconds, and it is 11.08 seconds for ZBW-ZNY.This delay translates to a 20% increase in cumulative delay in ZID-ZAU, but the absolute difference is only 4.7 minutes.Likewise, in ZLA-ZOA and ZBW-ZNY the difference is less than 1% when selecting resolutions based on delay.This finding supports the initial assertion that cumulative delay would only marginally increase with the availability of the Variable Speed Direct-To maneuver when resolution trajectories are optimized for airborne delay.Selecting resolutions based on minimum fuel burn increases the cumulative delay in each center.Figure 11 shows the cumulative delay per center for each optimization.This effect can be attributed to the selection of Variable Speed Direct-To maneuvers to resolve the associated conflict.As opposed to Direct-To maneuvers that can potentially yield a time savings, these maneuvers result in zero delay benefit.Additionally, when optimizing for fuel burn, the algorithm prefers speed-reduction maneuvers that tend to increase delay within the system.Each implementation of Variable Speed Direct-To changes the way the primary and, consequently, secondary conflicts are solved.Because only one aircraft of the pair will be maneuvered to avoid a conflict, the average delay per resolution can be thought of as per aircraft.Generally, increasing the delay is considered to be undesirable.However, there are strategic instances in which this increase could be of value, such as an aircraft that needs to be slowed in order to meet the requested time of arrival.The magnitude of additional delay per resolution is small when compared to the 15-minute FAA definition of a reportable delay (ref. 11).The largest amount of delay per resolution observed when optimizing for fuel burn utilizing the Variable Speed Direct-To maneuver was 4 minutes.Even if marginal, the system-wide effects of an increase in delay are difficult to determine.
+ConclusionsTwelve conditions were simulated to evaluate the benefit of modifying the AAC Autoresolver to consider a Variable Speed Direct-To maneuver when resolving a given conflict.Two methods of resolution selection were used: minimum delay and minimum fuel burn.The experiment was conducted in a fast-time environment using data representing a reasonable traffic day in the NAS.The results showed that augmenting the existing algorithm to include the compound maneuver did not significantly influence the ability of the algorithm to resolve conflicts, nor did it affect the number of conflicts observed.The inclusion of Variable Speed Direct-To increased the cumulative fuel-burn savings by 92% in ZID-ZAU, 55% in ZBW-ZNY, and 47% in ZLA-ZOA when selecting resolutions based on minimum fuel burn.In these results, the average penalty in delay per aircraft was on the order of a few seconds.Further analysis is required to determine the effect of increasing the delay as well as the balance between delay and fuel-burn benefit.The cumulative fuel-burn savings observed in this study suggests that the Variable Speed Direct-To maneuver could provide significant fuel savings with no significant effect on safety or schedule.Figure 1 :1Figure 1: Resolution trajectories of type horizontal (a), vertical (b), and speed (c).
+Figure 2 :2Figure 2: Delay and fuel burn estimation.
+Figure 3 :3Figure 3: Variable Speed Direct-To maneuver.
+Flightoperations over a 24-hour period were simulated based on Aircraft Situation Display to Industry (ASDI) data recorded March 8, 2007.ASDI data come from the Federal Aviation Administrations (FAA's) Enhanced Traffic Management System (ETMS) and contain information about flights controlled by air traffic control.The dataset included 62,970 flights, their associated routes, and their departure times.This dataset had mixed aircraft types representing the current fleet mix.The data used in this study represent reasonable daily traffic in the NAS.The Rapid Update Cycle wind data were used to model winds in the selected ARTCCs (ref.10). Figure 5 shows the conflict types represented within the demand set by ARTCC. Figure 5 illustrates a diversity of traffic flow types, with the East Coast containing primarily transitioning traffic, the Midwest predominately cruising traffic, and the West Coast a mix of all traffic types.The conflicts are coded as follows:
+Figure 5 :5Figure 5: Conflict types per center.
+Figure 6 :6Figure 6: Number of conflicts.
+Figure 7 :7Figure 7: Variable Speed Direct-Toenabled speed reduction for ZID-ZAU; fuel burn optimal.
+Figure 8 :8Figure 8: Average fuel burn.
+Figure 9 :9Figure 9: Resolution types in ZID-ZAU when optimizing for fuel burn.
+Figure 10 :10Figure 10: Average delay
+Figure 11 :11Figure 11: Cumulative delay
+FigureFigure A2: -Variable Speed Direct-Toenabled speed reduction, ZBW-ZNY.
+FigureFigure A3: -Variable Speed Direct-Toenabled speed reduction, ZLA-ZOA
+FigureFigure B2: -Maneuver types for all centers, fuel-burn optimization.
+
+
+TABLE 1 .1-INDEPENDENT VARIABLES
+Table 1 .1INDEPENDENT VARIABLES.Independent VariablesSettingsVariable Speed Direct-To ManeuverEnabled, DisabledOptimizationDelay, Fuel BurnAirspaceZID-ZAU, ZBW-ZNY, ZOA-ZLA
+		
+		
+
+			
+AcknowledgmentsThe authors wish to acknowledge Dr. Todd Lauderdale, whose contributions to this work were invaluable.The authors also thank Todd Farley and Drs.Antony Evans and Banavar Sridhar for their insightful suggestions and thoughtful review.
+			
+
+			
+Appendix AA.0 A.0 This appendix includes supplemental plots for speed distribution, maneuver types, and distance from final fix.When a Variable Speed Direct-To maneuver is executed, the maneuvered aircraft is slowed by an amount such that it will traverse its now, shorter Direct-To route in the same amount of time that it planned to traverse its original route.Figures A-1 through A-3 show the distribution of speed-reduction magnitudes for the simulated airspaces.The majority of all speed reductions observed in the experiment were less than 30 knots.
+REPORT DOCUMENTATION PAGE
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+PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.Standard Form 298 (Rev.8/98)Prescribed by ANSI Std.Z39.18
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+TITLE AND SUBTITLEA Fuel-Efficient Conflict Resolution Maneuver for Separation Assurance  An electronic version can be found at http://ntrs.nasa.gov.
+ABSTRACTThis experiment seeks to evaluate the benefit of augmenting a conflict detection and resolution algorithm to consider a fuel-efficient, Variable Speed Direct-To maneuver when resolving a given conflict based on either minimum fuel burn or minimum delay.Twelve conditions were tested in fast-time simulation conducted in three airspace regions with mixed aircraft types and nominal traffic.Inclusion of this maneuver had no appreciable effect on the ability of the algorithm to safely detect and resolve conflicts.Cumulative fuel-burn savings were significantly higher when selecting resolutions based on minimum fuel burn; average delay per resolution was only marginally higher.			
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+	
+		Table 3: The percentages of the top 20% NMI features from each omics data.
+		10.7717/peerj.9440/table-3
+		N: 100 Max: 4164 nmi 75%: 1318.75 nmi Median: 394 25%: 236.5 nmi Min: 132 nmi
+	
+	
+		Federal Aviation Administration
+		
+			PeerJ
+			2011
+		
+	
+	Tech. rep
+	Operational Data Reporting Requirements (OPSNET). Tech. rep., Federal Aviation Administration, 2011. N: 100 Max: 4164 nmi 75%: 1318.75 nmi Median: 394 25%: 236.5 nmi Min: 132 nmi
+
+
+
+	
+		Figure 3: A C major scale, starting from C4 and finishing at C5, and going back to C4.
+		10.7717/peerjcs.229/fig-3
+	
+	
+		Distance from fix
+		
+			PeerJ
+			null
+			4
+		
+	
+	Figure C4: -Distance from fix, ZBW-ZNY.
+
+
+				
+			
+		
+	
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+I. IntroductionA ir traffic demand is projected to increase significantly in the upcoming years. 1 The human workload associated with conflict detection and resolution is expected to limit this increase and thereby limit the economic growth that aviation facilitates.Automated separation assurance systems are proposed as a way to safely and efficiently separate aircraft in highly dense traffic conditions up to two to three times current levels.Numerous algorithms have been proposed to provide separation assurance in the future air traffic system. 2,3 aintaining safe separation is the first-order objective of all such algorithms; however, the secondorder objectives can vary and are the focus of much research in the field of air traffic management.With any automated resolution tool, the resolution selected is based on some criterion function.The majority of the proposed algorithms optimize the selection of conflict resolution maneuvers to minimize airborne delay in order to mitigate the effect on schedule.However, the true cost of operations is more complex with considerations beyond delay.With the tremendous rise in fuel price over the past few years, examination of the implications of fuel price has increased in relevancy.A additional objective, then, is to optimize based on fuel burn.Prior research, as in Ref. 4, showed that the minimum-delay solution was rarely the same as the minimum-fuel-burn solution.In Ref. 4, the system performance of a conflict resolution algorithm that selected resolutions based on minimum-delay was compared to the system performance of the same algorithm when selecting resolutions based on minimum-fuel-burn.The most effective maneuver when minimizing for fuel burn was a speed reduction maneuver, which employs a temporary speed reduction to resolve the predicted conflict.However, speed reductions were selected less frequently than other, less fuel-efficient maneuvers.Additionally, when utilized, speed reduction maneuvers significantly increased the cumulative delay.When selecting resolutions based on minimum-fuel-burn, a 40% reduction in fuel burn was realized as compared to the the conventional minimum-delay approach.However, the delay incurred with those more fuel-efficient resolution maneuvers was nearly twice that observed with the minimum-delay approach.The stark contrast suggests there could be value to an approach that considers the cost of both delay and fuel burn.The desire to balance the costs of delay and fuel burn is evident in today's Flight Management Systems (FMS).Most airlines use a ratio of the two costs to determine the economy speed for a given flight on a given day.This ratio is called the Cost Index, and it determines the "economy" speed profile for a flight by minimizing the total cost of operation.The Cost Index is the ratio of the time-related operating costs of the aircraft vs. the cost of fuel.This process can be applied when determining how best to resolve a conflict.Where previous studies have explored resolution selection based on minimizing either delay or fuel burn, the algorithm in this study was modified to minimize cost given a parameterized expression of their relative importance.This study examines the system performance of a conflict resolution algorithm capable of selecting maneuvers based on minimum cost in realistic traffic scenarios.The paper is organized as follows.Section II presents the cost function that considers the price of fuel and the"price" of airborne flight delay.The conflict resolution algorithm into which this cost function was embedded is described in Section III.Section IV sets forth the experiment design, and Section V presents the results.A summary of the conclusions and discussion of future work conclude the paper.
+II. Balancing Delay and Fuel BurnThis study evaluates the performance of modifying the resolution selection method to evaluate fuel and delay together.An approach to accomplish this is to design a cost function that normalizes delay and fuel burn.Since delay and fuel burn produce a cost in a true sense of the term, the monetary amount of resolutions is modeled within our system and used as the optimization criteria in our resolution selection scheme.Furthermore, a mechanism to vary the weight of delay and fuel burn in a cost function creates a separation assurance feature similar to the Cost Index of a FMS.This section describes an approach for quantifying the cost of a resolution as a function of delay and fuel burn, and selecting the least-cost resolution for a given conflict.Often, the cost of delaying a flight differs from flight to flight.This cost can be most accurately estimated by the airlines; unfortunately these models are not regularly available.In Ref. 5, the cost of different types of delay (airborne, ground, etc.) were estimated for an array of aircraft sizes.The average airborne delay price of $20.00 per minute, for passenger aircraft of 100 seats or more, was used as the nominal delay price for this study.Since most conflict resolutions produce delays of less than a minute, delay price translated to $0.33 per second.Present-day fuel price of approximately $0.43 per pound was used for resolution selection.The structure of the employed conflict resolution algorithm allows for the tabulation of both delay and fuel burn for each resolution considered.Using these metrics, a cost function descriptive of the relationship between the price of fuel and the amount of fuel and the price of delay and the amount of delay was derived.Equation (1) describes the operational cost:C O = (F B × P F B ) + (D × P D ) (1)where FB is the fuel burn in pounds, P F B is the fuel price in dollars per pound, D is the delay in seconds and P D is the price of delay.For the purpose of this study a resolution cost function, C R , was developed.The relative importance of delay to fuel burn within C R is represented by the inclusion of a user specified weight parameter: alpha.In this scenario, the user represents the Federal Aviation Administration (FAA).The availability of the alpha parameter could allow the FAA to balance system wide preferences for delay and fuel burn thus allowing national optimization of the air traffic control system.For example, the user could shift alpha to favor fuel burn savings when flights are not constrained by time (i.e. a flight is early and would otherwise be delayed because of traffic flow management or the gate is not ready) and shift to delay for aircraft that need to be scheduled more closely.The range of alpha is shown in Eq.( 2):0 ≤ α ≤ 1 (2)Using alpha to represent the weight of a given parameter to another, the resolution cost [Eq.(1)] can be expressed as Eq.(3):C R = [α(D × P D ) + ((1 -α) × (F B × P F B ))](3)where α=0 represents minimum fuel burn optimization and α=1 is minimum delay optimization.The calculation of fuel burn and delay is discussed in Section IV.E.For this study, resolution cost, C R , is used as the criteria in which the optimal resolution is selected to resolve a conflict.The results of this process is controlled by the user-specified alpha value based on the importance of fuel burn and delay.Since resolution cost is a theoretical term, in later sections, operational cost, C O , is analyzed to represent the actual price to the airspace users.
+III. Implementation
+A. Advanced Airspace Concept AutoresolverThe Advanced Airspace Concept Autoresolver (AAC Autoresolver) is a strategic conflict resolution algorithm designed to deconflict aircraft that are predicted to lose separation more than two minutes in the future.The Autoresolver resolves aircraft conflict pairs ordered by predicted time to first loss of separation.For each conflict in the conflict list, the Autoresolver follows an iterative approach for resolution.These trajectories take into account characteristics such as aircraft type, speed and airspace boundaries.The Autoresolver calculates future trajectories composed of waypoints, speeds and altitudes which may possibly resolve the conflict.Figure 1 shows the types of trajectory changes attempted by the Autoresolver grouped in terms of horizontal, vertical, or speed maneuvers.The dashed lines in Figure 1 indicate the suggested trajectory changes to avoid the predicted conflict.This trajectory change is then sent to a trajectory engine that computes a corresponding trial resolution trajectory.A resolution trajectory is considered viable, successful (and stored), if it resolves the primary conflict, and is free of predicted losses of separation with all aircraft for a specified period of time.If the trial resolution is not conflict free, the Autoresolver computes a new trial resolution and checks if it is successful.For each resolution type this iteration is continued until a successful resolution is found or all possibilities of that type have been tried.For each successful resolution, both the associated delay and the fuel burn are calculated.The Autoresolver will generate up to 18 successful resolutions per aircraft in conflict for a total of up to 36 between the two aircraft.In this study, the algorithm selected a resolution from among the set of successful resolutions by calculating the cost per resolution and selecting the resolution with the lowest cost.The selected resolution was then implemented via fast-time, closed-loop experiment as discussed in the following sections.Using the equation formulated in the previous section, the result of the AAC computations is a list of resolutions and their associated costs.Further discussion regarding the design of the algorithm and the types of resolutions that are generated is presented in Refs.6, 7.
+IV. Experiment DesignThis section describes the fast-time simulation environment, test parameters, and the metrics used in the study.
+A. Simulation EnvironmentThe Airspace Concept Evaluation System (ACES) is a fast-time, agent-based simulation of the National Airspace System (NAS) that uses four-degree-of-freedom (4 D.O.F) equations of motion based on the Base of Aircraft Data (BADA) to generate aircraft trajectories. 8ACES was developed specifically to provide a general purpose environment for evaluating future air traffic management and control concepts, including automated resolution algorithms.Each flight's trajectory is simulated from the departure fix associated with its original airport and ends at the arrival fix associated with its destination airport.By using aircrafttype-specific performance data together with guidance and navigation models, the ACES trajectory engine can generate representative trajectories for many aircraft.For the purposes of this study, the aircraft trajectories were entirely deterministic with no trajectory uncertainty.Aircraft conflicts were predicted with perfect accuracy, and resolution trajectories were guaranteed to be followed precisely by the simulated aircraft.In addition to deterministic aircraft trajectories, certain simplifications were made in the modeling and execution of the experiment: negotiation of resolution trajectories between aircraft operators and/or the air navigation service provider were not modeled, and neither data link transmission delays nor pilotaction delays were modeled.Once a resolution trajectory was selected by the automation it was executed immediately and precisely.
+B. Airspace and Traffic
+C. Test MatrixTable 1 shows the test matrix used in this study to investigate the benefits of selecting conflict resolution maneuvers based on minimum cost.The matrix includes two independent variables: alpha and price index.Nine test points were chosen for alpha evenly distributed between 0 and 1 at 1/8 increments.Three test points were chosen for price index: Nominal, Double the fuel price, and Double the cost of delay."Nominal"represents a fuel price and delay costs at current-day values.Double the Fuel Price and Double the Delay Price describe test points for which P F B or P D is doubled, respectively.
+D. Dependent VariablesThree metrics were selected for comparison: the number of conflicts per flight hour, delay and fuel burn.A conflict is said to occur when two aircraft are predicted to come within 5 nautical miles horizontally and 1,000 feet vertically from each other some time in the future (i.e.20 minutes).The flight hour metric is calculated by summing the total flying time within ZAU and ZID of every flight in the simulation.In the study, the number of conflicts per flight hour is used as a proxy for complexity.The delay metric is defined as the additional delay incurred per resolution, in seconds, as compared to the original (i.e., conflicted) trajectory.The fuel-burn metric is defined as the additional fuel burned per resolution, in pounds, as compared to the original trajectory.Fuel burn is modeled as a function of thrust, true airspeed, and altitude using BADA.
+E. Delay and Fuel Price ParametersThe cost of airborne delay used in this study was approximated from the values for airborne delay costs presented in Table 4
+V. ResultsThis study evaluates the effects of a cost-based resolution selection criterion on system efficiency.Metrics for complexity and cost are examined to quantify the impact of modifying the AAC Autoresolver.The cost metrics and resolution-type cost results are presented parametrically in terms of the delay cost and fuel burn cost of selected resolutions.The cost associated with various resolution types is investigated, and their implications are discussed.
+A. ComplexityIn order to assess how the resolution selection criterion (alpha) affects the complexity of the conflict resolution problem generally, the number of conflicts per flight hour was examined.The number of conflicts per flight hour provides insight into the algorithm's response to the inclusion of cost-based resolution selection.A significant increase in the number of conflicts per flight hour, as a result of including the minimum-cost resolution selection approach, might suggest an increase in problem complexity.Figure 3 shows that using cost-based resolution selection does not significantly increase the observed number of conflicts per flight hour.The small increase in the number of conflicts per flight hour observed between α=0 and α=1 may be a by-product of the resolution selection process.In Bowe et al. (Ref.4) minimum-delay resolution selection was found to favor timesaving maneuvers such as route shortcuts.When selecting resolutions based on minimum fuel burn the algorithm displayed a preference for speed reduction maneuvers which were shown to increase the cumulative delay.
+B. Costs Associated with Balancing Delay and Fuel BurnMost flight management systems in operation today have configurable cost index settings to select the most efficient speed profile according to the users' needs.This allows the user to weigh the importance of saving time or saving fuel per flight.For example, if a given flight is ahead of schedule, and connecting flights are in question, a user may change the cost index in the FMS to favor fuel savings.Likewise, if a flight is behind schedule the user could change the index to increase the importance of delay.Similar to this cost index paradigm for favoring delay or fuel burn in certain cases, the results of this experiment can be modeled in a way that allows one metric to be weighed more heavily than the other.Figure 4(a) shows the cost as a function of alpha.As expected, when α=0 the fuel burn cost is minimized, and when α=1 delay cost is minimized.When α=1 the contribution of the fuel cost to the operational cost equation is zero but each resolution still has an associated amount of fuel burn.Conversely, in the minimum fuel burn case, the total cost is dominated by the delay cost.The lowest total operational cost roughly occurs when α=0.5.The figure reveals the minimum delay case to be the most expensive overall, as reflected by the Total Operational Cost curve, which is primarily dominated by the fuel cost.This result suggests that optimizing conflict resolution maneuvers for minimum delay may be the least cost effective approach.Figure 4(b) shows the total operational cost as a function of alpha for the three price indices.The observed trends suggest that, when the cost of delay is doubled, the overall operational cost is higher than when the fuel price is doubled, with the exception of when alpha is between 0.875 and 1.As expected, the nominal price index produces the lowest total operating cost and the least dramatic fluctuation in cost over the range of alpha, until alpha is greater than 0.875.Of interest in Figure 4(b) is the increase in total operational cost when the price of delay is doubled and α=0.5.Further investigation revealed a large disparity in the total operational cost for en route and arrival conflicts for this simulation setting.An arrival conflict is defined when a maneuvered aircraft is predicted to conflict with another within 20 minutes of its arrival fix.All other instances of conflict are considered en route.Figure 5 shows the total operational cost when the price of delay is doubled for en route and arrival conflicts.The rise in operational cost is directly due to the spike in operational cost for arrival conflicts when α=0.5.Results show that when delay and fuel costs were evenly balanced in the Double the Delay Price simulation runs, a large amount of speed reductions were selected as the optimal cost-based resolution, thus creating arrival sequencing congestion where additional resolutions were required to separate the flow.More analyses are needed to evaluate the impact of optimizing cost when resolving arrival conflicts.The curve for total operational cost across alpha considering only en route conflicts is much smoother and almost symmetrical at α=0.5.This indicates that as the importance of delay and fuel costs becomes more unbalanced, operational cost increases, and this trend is generally the same regardless which parameter (delay or fuel burn) is favored.
+C. Resolution Type CostsFurther analysis illustrates the influence that the selection of resolution type has on aircraft operating costs.When resolving conflicts there are several categories of maneuvers that can be utilized to prevent a predicted conflict.The AAC Autoresolver captures most of the different resolution types used in the field, and these types are illustrated in Figure 1.For most resolution types their impact on the performance of delay and fuel burn can be generally hypothesized through intuition into the physics of the maneuvers.For instance, maneuvers such as a Direct-to which identify wind favorable shortcuts along the planned route are known to save time and fuel, step altitude climbs generally reduce fuel burn, step altitude descents generally increase fuel burn, path stretches are known to increase delay and fuel burn, and speed reductions save fuel, but increase delay.The performance of delay and fuel burn does not have a direct correlation based on resolution type, however the operational cost of resolutions creates a normalization of the two metrics and comparisons can be made.In this section, the operational cost among 13 different resolution types are investigated in an attempt to uncover which resolutions theoretically cost more than others.Figure 6(a) provides the average operational cost per resolution executed for each resolution type.The data was computed using simulation results of α=0.5, in order to evenly balance the cost of delay and fuel burn, and the nominal price index.As expected, the two best resolutions with respect to operational cost are Direct-to and Variable Speed Direct-to ($36.70 and $55.70 savings, respectively).Both maneuvers result in a shorter horizontal path thus saving fuel and delay, and Variable-Speed Direct-to (D2Speed ) initiates a speed reduction simultaneously with a Direct-to for added benefit.The most expensive resolution types are the horizontal maneuvers: path stretch, offset, and horizontal vector turn (HVT ).Path stretches produce the overall highest price with $48.40 per resolution.Interestingly, seven of the 13 resolution types produce a mean price savings (negative cost), however it should be noted this does not translate to a total price savings as the magnitude of horizontal maneuvers and speed reductions overtake any savings for a incurred cost.In the speed domain, increases tend to save $8.30 on average and reductions incur a price of $3.20 on average.Both step altitude descents and climbs produce a mean price savings with descents saving approximately four times more than climbs.Furthermore, temporary altitude descents incur a mean price of $3.20, by contract temporary altitude climbs save $7.40.A maneuver not illustrated in Figure 6 called an extended temporary altitude (ExtTempAlt), is a maneuver where in order to resolve a conflict an aircraft, already performing a temporary altitude, remains at the current temporary altitude for a specified period of time (i.e., 12 minutes).These maneuvers produce a mean price savings of $3.40.The selection rate of each resolution type is important when making comparisons, especially when a broad distribution exists as shown in Figure 6(b).In our simulations, path stretches were the single most utilized maneuver when resolving conflicts.These maneuvers are the most frequently selected (approximately 29% of the time), because they are extremely successful at creating the separation minima required to clear a conflict.Consequently, they are also the most expensive resolution, and the dominant factor in the overall price of resolving conflicts.By contrast, the Direct-to maneuver significantly reduces operational cost when selected, however is only utilized 3.1% of the time.Regardless of the optimization problem or advancements to conflict resolution algorithms, the performance of resolutions will never be more important than the criticality of avoiding actual losses of separation.It is likely there will always be a net price to resolving conflicts.In order to uncover which cost, delay or fuel burn, has a greater effect on the results in Figure 6(a), the percentage that derives from the price of fuel is shown in Figure 6(c).For example, the value of 57% for the path stretches means 57% of the $48.40 per resolution comes from the price of fuel, thus 43% of the price derives from additional delay.Eight of the 13 resolution types produce mean costs mostly made up by the price of fuel burn, i.e. fuel burn price percentages greater than 50% Most of the price savings for Variable-Speed Direct-to maneuvers come from fuel burn (98%).This is expected because the maneuver employs a speed reduction to produce zero delay when performing a Direct-to, thus no delay change to benefit from.The price of path stretches and Direct-to maneuvers are nearly impacted evenly between delay and fuel burn.Moreover, temporary altitude descents and speed increases were primarily impacted by their respective delay price (savings).
+VI. ConclusionsFast-time simulations of current-day traffic levels in two regional airspaces under nominal weather conditions were simulated to evaluate the benefit of modifying a conflict resolution algorithm to select resolution maneuvers based on minimum cost.The study employed the use of a parameter, alpha, to represent the relative importance of fuel price to the cost of delay, similar to FMS cost index.In terms of operational cost, the most efficient choice of alpha is roughly 0.5, i.e., when the cost of fuel and delay are weighted evenly.The operational cost is highest when the cost of fuel is ignored by the algorithm (i.e., α=1), which is the case for most conflict resolution algorithms in the literature, including the baseline prototype (AAC Autoresolver) that was modified for this study.The most cost-effective resolution maneuvers were the Direct-to and the Variable-Speed Direct-to, owing to the fact that both maneuvers result in a shorter horizontal path, thus saving time and fuel.Conversely, the most costly maneuver type was the Path Stretch.The cost of fuel burn is the predominant factor in the total operational cost of most (eight) of the 13 resolution maneuver types.
+VII. Future WorkAn approach for evaluating cost based conflict resolution was presented.The context for the evaluation was an approximation of delay and fuel cost.However, the price of delay varies based on the number of passengers per aircraft.In the future, an investigation of how different aircraft fleet mixes (low, medium, and high-occupancy aircraft) affect the results of selecting resolutions based on minimum cost could be performed.Furthermore, other ARTCCs could be simulated to test the results of different conflict geometries (i.e different resolution type distribution).Each simulation in this study used the same value of alpha for every, conflict therefore, each conflict had the same defined importance of delay vs. fuel burn.Further research into calculating an optimal alpha value for each conflict based on characteristics of the flight pair could improve the operational practicality of approach.For example, a flight approaching its arrival fix may tend to prefer to minimize delay (higher alpha value), especially if it is already behind schedule and there are no apparent arrival sequencing issues.By contrast, a flight that is early may tend to prefer a fuel-efficient resolution (lower alpha value), because any further timesaving may be nullified by additional delays in the terminal.Figure 1 :1Figure 1: Resolution trajectories of type horizontal (a), vertical (b) and speed (c & d)
+In this study, the Autoresolver resolved roughly 1,885 conflicts in two Air Route Traffic Control Centers (ARTCCs): Indianapolis (ZID) and Chicago (ZAU).The ARTCCs selected contain primarily cruising traffic which is of interest as this study focused on the resolution of en route conflicts.Flight operations over a 6-hour period were simulated based on Aircraft Situation Display to Industry (ASDI) data recorded March 8, 2007 which represented a"low weather," high volume day in the NAS.The data set included 23,000 flights of varying types, their associated routes, and their departure times.The Rapid Update Cycle wind data was used to model winds in the selected ARTCCs.Figure2shows a subset of the ARTCCs in the central region of the United States with ZID and ZAU shaded.
+Figure 2 :2Figure 2: The ARTCCs studied in this experiment.
+Figure 3 :3Figure 3: Conflicts per Flight Hour vs alpha.
+Figure 4 :4Figure 4: (a) Total Cost vs. alpha, (b) Operational Cost vs. alpha
+Figure 5 :5Figure 5: Total Operational Cost vs. alpha for En Route and Arrival Conflicts for Double Delay Price Index.
+Figure 6 :6Figure 6: Comparison of results by distinct resolution types for α=0.5 and nominal price.(a) mean operational cost per resolution type, (b) percentage of all conflicts each resolution type was selected, (c) fuel burn price ratio.
+Table 1 .1Experiment Factors and Levels.Experiment FactorsLevelsOptimizationResolution Costα0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1Price IndexNominal, Double the Fuel Price, Double the Delay Price
+Table 2 .2Delay and fuel burn prices for the various Price Index levels.Price IndexDelay Price ($/seconds) Fuel Price ($/pound)Nominal0.330.43Double the Fuel Price0.330.86Double the Delay Price0.660.43of Ref.5for passenger aircraft greater than 100 persons.The operational data used to generate the cost figures in the referenced study were collected from European Airlines.A summary of the cost of delay to airlines during various trip segments is presented in Ref. 9. The fuel price used in this study was taken from the International Air Transport Association (IATA) Jet Fuel Price Monitor.10Thisstudyused a price from August 2012 to represent the Nominal Cost Index.The delay and fuel prices for the different Price Index levels used in this study is presented in Table2.
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
+			of 10 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5416
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+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
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+			2013
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+			VikramManikonda
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+		
+		
+			MohamadRefai
+		
+		
+			RichardDupee
+		
+		10.2514/6.2011-6373
+	
+	
+		AIAA Modeling and Simulation Technologies Conference
+		Portland, OR
+		
+			American Institute of Aeronautics and Astronautics
+			2011
+		
+	
+	George, S., Satapathy, G., Manikonda, V., Palopo, K., Meyn, L., Lauderdale, T. A., Downs, M., Refai, M., and Dupee, R., "Build 8 of the Airspace Concept Evaluation System," AIAA Modeling and Simulation Technologies Conference, Portland, OR, 2011.
+
+
+
+	
+		Evaluating the True Cost to Airlines of One Minute of Airborne or Ground Delay
+		
+			PRUnit
+		
+	
+	
+		Tech. rep., University of Westminster Final Report
+		
+			May 2004. August 2012
+		
+	
+	Unit, P. R., "Evaluating the True Cost to Airlines of One Minute of Airborne or Ground Delay," Tech. rep., University of Westminster Final Report, May 2004. 10 International Air Transport Association Jet Fuel Price Monitor, August 2012.
+
+
+				
+			
+		
+	
diff --git a/file104.txt b/file104.txt
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+	
+	
+		
+IntroductionAir traffic demand is projected to increase significantly in the upcoming years [1].The human workload associated with conflict detection and resolution is expected to limit this increase and thereby limit the economic growth that aviation facilitates.Automated separation assurance systems are proposed as a way to safely and efficiently separate aircraft in highly dense traffic situations up to two to three times current levels, thereby fostering increased economic growth for the nation.Numerous algorithms have been proposed to provide separation assurance in the future air traffic system [2,3].Maintaining safe separation is the first-order objective of all such algorithms; the second-order objectives vary, however.The majority of the proposed algorithms optimize the selection of conflict resolution maneuvers to minimize airborne delay in order to mitigate the effect on schedule.An alternative objective is to optimize based on fuel burn.In [4] the system performance of a conflict resolution algorithm that selected resolutions based on minimum delay was compared to the system performance of the same algorithm when selecting resolutions based on minimum fuel burn.The most effective resolution maneuver when optimizing for airborne delay was a "direct-to" maneuver, which identifies wind-favorable shortcuts along an aircraft's planned route that reduce its flying time while resolving the predicted conflict [5].The most effective resolution maneuver when optimizing for fuel burn was a "speed reduction" maneuver, which employs a temporary speed reduction to resolve the predicted conflict.However, speed reductions were selected less frequently than other, less fuel-efficient maneuvers.Additionally, when utilized, these maneuvers significantly increase the cumulative delay.It is hypothesized that the availability of a compound maneuver combining a Direct-To maneuver with the fuel efficiency of a speed reduction would improve the performance of the separation assurance algorithm.This study compares the system performance of a conflict resolution algorithm in realistic traffic scenarios with and without the availability of a compound Direct-to/speedreduction maneuver, hereafter referred to as a Zero-Delay Direct-To maneuver.The objective is to quantify the operational benefit of adding the proposed maneuver to the set of maneuvers already available to the automated separation assurance algorithm.This paper is organized as follows.Section 2 describes the conflict resolution algorithm under test and the new compound maneuver.Section 3 presents the experimental approach, procedure, and assumptions.The results are then categorized according to safety and efficiency.Lastly, a summary of the study's findings is given, along with suggestions for future research.
+Test ArticleThe conflict resolution algorithm evaluated in this study is the Advanced Airspace Concept (AAC) Autoresolver [6,7].It is a ground-based algorithm that resolves conflicts in pairwise fashion and can be optimized for airborne delay or for fuel burn.The Autoresolver selects a maneuver from one of the following categories: horizontal, vertical, altitude, Direct-To or compound.For this study, only conflicts where en route flights were maneuvered are analyzed; arrivals were not included because they adhere to additional constraints such as metering.In the following study only one compound maneuver is enabled: the Zero-Delay Direct-To maneuver.
+Zero-Delay Direct-To ManeuverThe Autoresolver was modified to allow for the combination of a Direct-To maneuver with a reduction in speed.This compound maneuver is referred to as a Zero-Delay Direct-To maneuver.This modification augmented the existing Direct-To maneuver, thus allowing the algorithm to continue to have the option to utilize a Direct-To maneuver when efficient.The equation that describes a Direct-To maneuver is shown in (1) where d represents delay in hours, D 1 is the previous distance along the route in nautical miles, D 2 is the new distance in nautical miles and S is speed in knots:! d = D 1 S " D 2 S (1)Augmenting the above equation to produce a maneuver that results in zero delay requires setting d to zero.This yields equation 2 where S new represents the new (slower) speed in order to result in a Zero-Delay Direct-To maneuver.The algorithm abides by the original Direct-To constraints where the maneuver will not be considered if the aircraft ( 1) is less than 20 minutes from the arrival fix, (2) cannot return to the route within 50 nmi of the final fix, (3) path along the Direct-To route is greater than 250 nmi (dotted line in Figure 1), and (4) if the point where the aircraft rejoins the trajectory is within 50 nmi of the current Air Route Traffic Control Center boundary.In addition, it will not attempt to execute the maneuver if S new is within 5 knots of the original speed.!S new = D 2 D 1 " # $ % & ' S (2)For example, a Direct-To maneuver by an aircraft traveling 450 knots that will reduce the distance along the route from 400 to 360 nmi would reduce the speed to 405 (by 45) knots in order to produce no delay.When performing a Zero-Delay Direct-To maneuver, the aircraft would recapture the route at the same time it would had it not performed the maneuver.Figure 1 illustrates the Zero-Delay Direct-To maneuver where A 1 and A 2 are aircraft predicted to conflict.To avoid this, A 1 is selected to execute a Zero-Delay Direct-To maneuver.The new trajectory for A 1 (dashed line) removes several waypoints and reduces the speed as shown in the neighboring profile.The Mach number of A 1 decreases for the duration of the maneuver and eventually returns to its original speed after clearing the conflict.
+Resolution Selection CriteriaThe resolver will generate up to 18 successful resolutions per aircraft in conflict for a total of 36 available between the two aircraft.More specifically, the algorithm selects a successful resolution in each of the following categories for each aircraft:• Vector Left • Vector Right • Climb • Descend • Speed Increase • Speed Decrease • Direct-To • Zero-Delay Direct-To • Left Horizontal Vector Turn • Right Horizontal Vector TurnIn this study, the algorithm selects a resolution from among the set of successful resolutions using either the minimum delay or the minimum fuel burn criterion, depending on how the algorithm is configured.The selected resolution is then implemented via fast-time, closed-loop experiment as discussed next.Further discussion regarding the design of the algorithm and the types of resolutions that are generated is presented in [7].
+Experiment
+Test BedThe Airspace Concept Evaluation System (ACES) was used to simulate the National Airspace System (NAS) in a fast-time simulation [8].ACES was also used to compute and archive the dependent variables: the number of losses of separation and the airborne delay and fuel burn incurred flying the conflict resolution trajectories.
+ProcedureTo evaluate the difference between the current state-ofthe-art conflict resolution algorithm and the addition of a Zero-Delay Direct-To maneuver, a test plan was developed that examines the behavior of the algorithm with and without this maneuver enabled in two pairs of Air Route Traffic Control Centers (ARTCCs) under two conflict resolution optimization schemes.In [9], statistical clustering analysis was employed to categorize ARTCCs based on normalized conflict properties.The two ARTCC pairs selected for this experiment-Indianapolis (ZID) -Chicago (ZAU) and Los Angeles (ZLA) -Oakland (ZOA) -were chosen because they provide a wide representation of conflict properties.Table 1 shows the independent variables and settings.
+Independent Variables SettingsZero-Delay Direct-To maneuver Available, Unavailable Optimization Delay, Fuel Burn Center ZID-ZAU, ZLA-ZOA Table 1.Independent Variables
+Demand SetFlight operations over a 24-hour period were simulated based on Aircraft Situation Display to Industry (ASDI) data recorded March 8, 2007.ASDI data comes from the FAA's Enhanced Traffic Management System (ETMS) and contains information about flights controlled by air traffic control.The data set included 62,970 flights, their associated routes, and their departure times.This dataset had mixed aircraft types representing the current fleet mix.The data used in this study represents reasonable daily traffic in the NAS with a relatively small amount of weather induced delay.The Rapid Update Cycle wind data was used to model winds in the selected ARTCCs.
+Dependent VariablesThe dependent variables for the experiment were the number of losses of separation and the airborne delay and fuel burn incurred by flying the conflict resolution trajectories.In the development of a robust, efficient algorithm for implementation in the Next Generation Air Transportation System (NextGen), safety is of the utmost concern.The number of losses of separation is the metric used here to reflect the safety of the system.Those results are presented in Section 4.1.Efficiency in terms of delay and fuel burn are important once safety is assured.To calculate the delay imposed by executing a resolution maneuver, the time on the original trajectory at a common point is subtracted from the time on the resolution trajectory at the common point.Similarly, to estimate the fuel burn associated with a resolution maneuver, the weight of the aircraft at the common point for the resolution trajectory is subtracted from the aircraft weight for the original trajectory.The fuel consumed per resolution is computed by ACES using aircraft-specific coefficients selected from the Base of Aircraft Data (BADA) [10].The BADA is comprised of the performance and operating procedure coefficients of 295 aircraft types.These coefficients encompass those that are used to calculate thrust, drag, and fuel flow along with those used to specify nominal cruise, climb and descent speeds.Further discussion of the specific equations used to calculate the fuel burn is included in [4].The efficiencyrelated results are presented in Section 4.2.
+ResultsThis experiment seeks to evaluate the benefit of augmenting the Autoresolver to consider a Zero-Delay Direct-To maneuver when resolving a given conflict.The subsequent results address the safety and efficiency of potential implementation.
+SafetyThe primary safety metric for the experiment is the number of losses of separation.A loss of separation occurs when aircraft are less than 5 nmi horizontally and 1,000 feet vertically from each other in en route airspace.As expected, the addition of the Zero-Delay Direct-To maneuver did not adversely affect the safety of the system, as measured by losses of separation.Evaluating the number of conflicts per simulation provides insight into the impact of the modifications made to the algorithm.A significant increase in the number of conflicts as a result of the availability of the Zero-Delay Direct-To maneuver might suggest increased risk.Figure 2 shows that enabling the compound maneuver does not significantly increase the number of conflicts in either ARTCC.On average, the percent difference between the baseline number of conflicts and the Zero-Delay Direct-To enabled scenario is less than 1%.This suggests that the inclusion of this maneuver does not adversely affect the ability of the algorithm to resolve conflicts, and there are no major gaps in its implementation.
+EfficiencyThe following section uses fuel burn and delay as a metric to evaluate the efficiency of the addition of the Zero-Delay Direct-To maneuver to the base algorithm.This is different than discussing delay and fuel burn as an optimizing factor because for each resolution implemented these metrics are computed.Although the algorithm is selecting resolutions based on fuel burn or delay, both the delay and fuel burn values per maneuver were tabulated, thus allowing for comparison.
+En Route Delay MetricFlight arrival delay is defined as the difference in time between the arrival time of an aircraft as given in the flight schedule and its real arrival time.Although there are many sources of delay (e.g., air traffic control, weather, maintenance, crew availability), in the following analysis the source of delay is attributed to time difference between the aircraft's modified trajectory and original trajectory at a common point in the en route airspace.Positive delay occurs when the modified trajectory incurs additional flight time to avoid a loss of separation, much akin to a detour.Negative delay is a reduction in flight time that can occur when a dogleg in the flight is eliminated or more favorable winds are encountered.The inclusion of Zero-Delay Direct-To maneuver has almost no impact on delay when selecting resolutions based on delay.Figure 3 shows the average delay per resolution.When selecting resolutions based on minimum fuel burn the average delay per resolution with the Zero-Delay Direct-To maneuver enabled for ZID-ZAU is 10.86 seconds.For ZLA-ZOA under the same conditions the average delay is 16.84 seconds.This translates to a 20.29% increase in cumulative delay in ZID-ZAU, but the absolute difference is only 4.7 minutes.Likewise, in ZLA-ZOA the difference is less than 1% when selecting resolutions based on delay.This supports the initial assertion that cumulative delay would only marginally increase with the availability of the Zero-Delay Direct-To maneuver when resolution trajectories are optimized for airborne delay.Selecting resolutions based on minimum fuel burn with the Zero-Delay Direct-To maneuver available increased the cumulative delay by 45.12% in ZID-ZAU and 10.49% in ZLA-ZOA.The increase in cumulative delay is caused by the selection of fewer Direct-To's due to the fact that Zero-Delay Direct-To maneuvers are more optimal than Direct-To's when optimizing for minimum fuel burn.There is a greater negative effect in ZID-ZAU than ZLA-ZOA because the traffic in ZID-ZAU had a greater number of Direct-To's that were no longer implemented.This finding will be further discussed in the next section.Selecting resolutions based on minimum fuel burn appears to result in an increase in cumulative delay in each center.This effect can be attributed to secondary conflicts.Each implementation of Zero-Delay Direct-To changes the way the primary and consequently, secondary conflicts are solved.Because only one aircraft of the pair will be maneuvered to avoid a conflict, the average delay per resolution can be thought of as per aircraft.Generally, increasing the delay is considered to be undesirable.However, the magnitude of additional delay per resolution is small when compared to the 15-minute Federal Aviation Administration (FAA) definition of a "reportable" delay [11].Even if marginal, the system-wide effects of an increase in delay are difficult to determine.
+Fuel Burn MetricWhen a Zero-Delay Direct-To maneuver is executed the maneuvered aircraft is slowed by a specified amount.Figure 4 shows the distribution of speed reduction magnitudes for ZID-ZAU.75% of all speed reductions observed in the experiment were less than 30 knots.A typical Boeing 747-400 aircraft at 35,000 feet will cruise between Mach 0.8 (533 knots) and Mach .85(566 knots) approximately a 30-knot variation.This indicates that the majority of the speed reduction values required to obtain the desired fuel benefit are reasonable.Within our simulation the range observed adhered to aircraft performance limitations.To evaluate the fuel burn associated with a resolution maneuver, the weight of the aircraft at a common point on the resolution trajectory is subtracted from the aircraft weight for the original trajectory.The utilization of Zero-Delay Direct-To maneuvers increases the fuel burn savings by 91.85% in ZID-ZAU and by 47.48% in ZLA-ZOA when resolving conflicts.Figure 5 shows the average fuel burn per resolution for ZID-ZAU and ZLA-ZOA.The negative fuel burn seen in ZID-ZAU is an indication that the modification made to the algorithm causes it to outperform nominal case when selecting resolutions based on minimum fuel burn.The average fuel burn per resolution in ZID-ZAU is 4.01 pounds less than when selecting resolutions based on minimum fuel burn with Zero-Delay Direct-To maneuvers enabled.In ZLA-ZOA the average fuel burn per resolution is 2.73 pounds, this is 2.41 pounds less than when Zero-Delay Direct-To is disabled.Though these numbers may seem insignificant the potential fuel benefit is great when considering the savings per year.In this study there were 3,276 conflicts in ZID-ZAU over the course of the day.Each of these requires one of the two aircraft to be maneuvered.Considering the average fuel savings of 4 pounds per resolution in ZID-ZAU, this amounts to roughly 4.8 million pounds of fuel per year.This is enough fuel to fill the tank of a Boeing 737-700 approximately 100 times.Furthermore, there are 20 ARTCCs within continental United States that could benefit from these savings.Variation in traffic density and route length accounts for most of the difference in the magnitude of savings between the two centers.ZID-ZAU center executed nearly twice as many resolution maneuvers as ZLA-ZOA, suggesting that the fuel efficiency of the algorithm increases with the air traffic demand.However, the improvement seen in the delay cases is not as significant.When selecting resolutions based on delay the algorithm finds Direct-To maneuvers to be more efficient.This can be attributed to the fact that the selection of a Direct-To can result in negative delay and thus a time saving whereas the most time-efficient Zero Delay solution is zero and will not yield a time savings.Figure 6 shows the resolutions selected by the algorithm for ZID-ZAU for fuel burn optimization with Zero-Delay Direct-To maneuvers enabled and disabled.Overall, the number of resolutions other than Direct-To or Zero-Delay Direct-To remains consistent between scenarios.When Zero-Delay Direct-To maneuvers were disabled there were 306 Direct-To's executed.When enabled there were 181 Direct-To's and 147 Zero-Delay Direct-To's.This represents a 41% decrease in the number of Direct-To maneuvers.When optimizing for minimum fuel burn, the algorithm frequently selects Zero-Delay Direct-To maneuvers over traditional Direct-To maneuver.However, in a small number of cases, a Direct-To maneuver is selected despite the fact that a Zero-Delay Direct-To maneuver is available.In these instances, the additional fuel savings does not outweigh a decrease in flight time.
+ConclusionEight conditions were simulated to evaluate the benefit of modifying the AAC Autoresolver to consider a Zero-Delay Direct-To maneuver when resolving a given conflict.Two methods of resolution selection were used: minimum delay and minimum fuel burn.The experiment was conducted in a fast-time environment using data representing a reasonable traffic day in the NAS.The results showed that augmenting the existing algorithm to include the compound maneuver did not significantly influence the algorithm's ability to resolve conflicts or effect the number of losses of separation observed.The inclusion of Zero-Delay Direct-To increased the cumulative fuel burn savings by 91.85% in ZID-ZAU and 47.48% in ZLA-ZOA when selecting resolutions based on minimum fuel burn.In this scenario, the average penalty in delay per aircraft is on the order of seconds.Further analysis is required to determine the effect of increasing the delay as well as the balance between delay and fuel burn benefit.The cumulative fuel burn savings observed within this study suggests that the Zero-Delay Direct-To maneuver could provide significant fuel savings in future systems while maintaining safety and schedule.
+Future WorkIn en route airspace, aircraft operate within desired performance envelopes and operational speed limitations.To address these factors a survey concerned with evaluating the effects of distinct performance envelopes on the feasibility of the Zero-Delay Direct-To maneuver is planned.In addition, the operational soundness of the speed reduction distribution requires validation by subject matter experts.Furthermore, the prior work introduced the addition of a Zero-Delay Direct-To maneuver within the Advanced Airspace Concept Autoresolver.The experiment then looked at the performance of the Zero-Delay Direct-To maneuver when selecting resolutions for either minimum fuel burn or minimum delay.This leaves a gap in coverage for a follow-on simulation to explore a hybrid selection scheme where resolution selection is based on a tradeoff between the two cost functions.Figure 1 .1Figure 1.Zero-Delay Direct-To
+Figure 2 .2Figure 2. Number of Conflicts
+Figure 3 .3Figure 3. Average Delay
+Figure 4 .4Figure 4. Zero-Delay Direct-To Enabled Speed Reduction ZID-ZAU Fuel Burn Optimal
+Figure 5 .Figure 6 .56Figure 5. Average Fuel Burn
+		
+		
+
+			
+AcknowledgmentThe authors wish to acknowledge Dr. Todd Lauderdale whose contributions to this work were invaluable.The authors also thank Todd Farley, and Drs.Antony Evans, and Banavar Sridhar for their insightful suggestions and thoughtful review.
+			
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+		New Concepts and Methods in Air Traffic Management
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+	Erzberger, H., McNally, B. D., Foster, M., Chiu, D., and Stassart, P., "Direct-To Tool for En Route Controllers," ATM '99: IEEE Workshop on Advanced Technologies and their Impact on Air Traffic Management in the 21st Century, Capri, Italy, 26-30 Sep. 1999.
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+		Automated Conflict Resolution for Air Traffic Control
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+		25th International Congress of the Aeronautical Sciences (ICAS)
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+			2006
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+	Erzberger, H., "Automated Conflict Resolution for Air Traffic Control", 25th International Congress of the Aeronautical Sciences (ICAS), Hamburg, Germany, 2006.
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+		Automated conflict resolution, arrival management, and weather avoidance for air traffic management
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+		10.1177/0954410011417347
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+		Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
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+		0954-4100
+		2041-3025
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+		Build 4 of the Airspace Concept Evaluation System
+		
+			LarryMeyn
+		
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+			RobertWindhorst
+		
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+			KarlinRoth
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+			DonaldVan Drei
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+			VikramManikonda
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+			SharleneRoney
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+		Analysis of the Aircraft to Aircraft Conflict Properties in the National Airspace System
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+	Paglione, M. M., Santiago, C., Crowell, A., Oaks, R.D., "Analysis of the Aircraft to Aircraft Conflict Properties in the National Airspace System", American Institute of Aeronautics and Astronautics Guidance, Navigation, and Control Conference, AIAA 2008-7143, Honolulu, Hawaii, August 18-21, 2008.
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+		Sensitivity Analysis of Aviation Environmental Impacts for the Base of Aircraft Data (BADA) Family 4
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diff --git a/file105.txt b/file105.txt
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+
+	
+	
+		
+I. IntroductionLTHOUGH a number of airport surface movement models exist 1,2,3 and have been successfully used for analysis of airport operations, validation of these models has been a challenge due to a lack of advanced airport surface surveillance.With such data, it is now possible to conduct detailed validation of these models.In this study we have conducted a limited set of analyses to empirically derive operational techniques that are used by controllers in sequencing flights on the airport surface at taxiway intersections.These operational techniques provide greater insight regarding airport surface traffic control and can be used to validate and enhance airport simulation modeling capabilities.We have used the Surface Operations Data Analysis and Adaptation (SODAA) tool 4 to collect and analyze these detailed airport surface operations.
+ATo model airport surface operations with detail and accuracy, it is necessary to consider current techniques and strategies used to determine the taxi route of an aircraft and to establish the sequence to be used whenever two or more aircraft place demand on a taxiway or runway resource simultaneously.Until recently such analysis could only be conducted through visual observation of sequencing decisions, 5,6 whereas now it is possible to analyze such details using airport surface surveillance data through the use of the SODAA tool.The SODAA tool supports NASA's NextGen research 7,8 with a focus on advanced airport surface and terminal operations.SODAA provides the infrastructure and information necessary for NASA researchers and industry analysts to achieve a deep level of knowledge and understanding of airport surface operations.This tool provides data querying and analysis capabilities, as well as advanced data mining features to support analysis of taxi routing, sequencing, and congestion management strategies used by air traffic controllers.The objective of this paper is to describe a novel method of airport surface operations analysis and to provide initial results demonstrating the viability of the technique.The analysis was conducted using airport surface operations data from the Dallas/Fort Worth International (DFW) airport.The Surface Management System (SMS) 9 installation at the North Texas Research Station (NTX) was used to collect surface operations data, which was then analyzed using SODAA to empirically derive sequencing practices.The first section of this paper describes this new methodology used for this work.The second section presents the initial results, and the third section discusses future opportunities and research direction.
+II. MethodologyAirport surface operations at DFW airport provide a useful case study environment for sequencing analysis.For the analyses presented here, we focus on the intersection of taxiways K and EL, as shown in Fig. 1.Taxiway K is a primary north/south taxiway located just to the east of the ramp area for DFW terminals A, C, and E. This taxiway is used for both departures and arrivals as they leave the ramp and taxi to their assigned runway for departure or as the flights taxi toward the ramp after landing.The EL taxiway is one of the primary routes used by flights that have arrived on runway 17C and are crossing runway 17R to reach their parking areas.Thus, the K/EL intersection appears to be an interesting case for a sequencing study.During periods of peak airport demand, both arrival and departure taxi times tend to increase.This is due to departure queuing, communication frequency congestion, and traffic congestion on the airport surface.Any time two flights are in contention for the same intersection at roughly the same time, the Ground Controller must decide which flight passes through first and which must hold.Once this decision is made, the trailing flight must wait.The total elapsed wait time can be broken into the time required for several events to occur: 1) The leading aircraft must reach the intersection.
+K/EL2) The lead aircraft must then pass through the intersection.3) A certain amount of following distance must be established (if following will occur).4) If not previously provided, the trailing aircraft must obtain clearance to continue.The first three steps must happen in sequential order, while the last step may be handled concurrently if the controller gives the direction to proceed after the traffic crosses.This sequencing decision regarding which aircraft leads and the resulting delay experienced by the following aircraft has a significant impact on surface operations.For example, as departing aircraft taxi toward a departure runway, the sequencing decision will ultimately determine the departure order.A difference of one position in the departure order will change the taxi time for a particular flight by a minimum of one or two minutes.For arriving flights, the additional time spent waiting for crossing traffic is the primary consideration when modeling taxi time.A special case of sequencing delay incurred more often by American Institute of Aeronautics and Astronautics Using surface surveillance data, it is possible to determine the location on the taxiway at which flights wait for runway crossings or other sequencing decisions.Figure 2 shows a portion of the taxi path for a single flight that must cross runway 17R and through the intersection between taxiways K and EL on its way to its parking gate.As shown in the figure, the flight stops and waits at both points B and D. However, it is not possible to determine from this information alone what the reasons were for the decision to hold the flight at each of those points.In many cases, a flight is held on the airport surface to implement a sequencing decision.Such sequencing decisions are the focus of this study.We have a two-fold approach for determining how sequencing decisions are made.The first step is to detect situations where flights are in contention for the same intersection and to identify the intersections of interest-those at which sequencing decisions are actually being made versus those where sequencing is merely First-Come-First-Served (FCFS).The second step is to analyze the relevant intersections and corresponding sequencing events to determine the factors that influence the sequence order.American Institute of Aeronautics and Astronautics 3 At any taxiway intersection, SODAA automatically identifies situations in which two aircraft could have crossed the intersection at the same time along crossing or converging paths, as illustrated in Fig. 3.This figure shows a plan view of several DFW terminals, taxiways and runways.The yellow and green lines show the paths of different aircraft that land on different runways, and share a common path during taxi in to the ramp area.In this case, a decision had to be made by a controller to direct one of the aircraft to go first, while the other aircraft would give way to allow the first aircraft to cross.After collecting hundreds or thousands of such sequencing events at each sequencing intersection, SODAA can perform automatic, detailed data mining analysis to find parameters and correlations that provide the strongest indicator of which aircraft would be selected to proceed and which one would be held.For example, at a given intersection, SODAA may find that aircraft on taxiway A are given priority over aircraft on taxiway F over 90% of the time if the aircraft on A can reach the sequencing intersection at or before the time the contending aircraft on taxiway F arrives.Once determined by SODAA, these sequencing parameters can be directly applied to improve the airport surface modeling capabilities of SMS or other fast-time models used to evaluate the benefits of future airport operational procedures.In order to analyze current airport operations, and to also provide the ability to model procedural changes to implement NextGen concepts, it is necessary to develop a modeling system that can both mimic current operational characteristics and implement future procedures.Airport surface sequencing behavior must be a model parameter.For example, future research may identify a novel runway crossing procedure.In order to evaluate the benefits of this procedure, the airport modeling system must be able to conduct both a baseline case model run without the new procedure and a future case model run with the new procedure.SODAA sequencing analysis utilizes recorded target positions on the airport surface to determine locations (e.g., taxiway intersections) that were used by a pair of aircraft.For each situation in which a common intersection was found, SODAA calculates the earliest time that each of the aircraft could have reached the common intersection based on a nominal taxi speed.This earliest crossing time and the actual crossing time for each of the aircraft are used to determine whether the two aircraft could have been at the intersection at the same time.If the following aircraft could have been at the intersection at or before the time that the leading aircraft actually crossed the intersection, then a sequencing event is identified by SODAA.Once a sequencing event has been identified by SODAA, quantitative characteristics of the event are computed and recorded.Example sequencing statistics include the following:-the actual separation time between the two aircraft at the common intersection; -the initial time offset between the two aircraft at the common intersection, which indicates how much earlier one aircraft could have reached the common intersection than the other; and -the amount of delay experienced by each of the aircraft in their taxi from their starting point to the common intersection.To accomplish this analysis, we have extended SODAA to populate two new tables in the SODAA database when flight data is processed.The first table will contain one record for each (flight, node) pair for all nodes through which a flight actually passed.This table stores the earliest estimated time for crossing that node and the actual time that node was crossed.The second table contains one record for each combination of flight, node, and time, where "time" corresponds to a surface surveillance update.For each surveillance update, we calculate the distance to each node remaining in the actual taxi route and estimate the earliest time that flight may reach each of those nodes by dividing distance by a relatively fast nominal taxi speed.After we have the distance and time data populated, we can create "waterfall charts" by plotting, for one node, the distance versus time profile of all flights as they approach that common node.Thus, if a flight stops on the taxiway, its distance to the common node will remain constant as time progresses, and the waterfall diagram will show a flat line.A flight taxiing at a nominal taxi speed will appear in the waterfall diagram as a descending line.This will aid in the exploratory analysis of how the flights behave.Figure 4 shows a sample waterfall diagram.Note that in this sample waterfall diagram, we are only showing the time and distance relative to the intersection at a very limited set of discrete points along the taxi path.In a full waterfall diagram, we would expect to see flat spots in the diagram for flights that are stopped on their taxi path, and we expect to see many instances of crossing lines close to the X axis for intersections where sequencing is not simply based on the order of arrival (i.e., FCFS).If there are many instances of crossing lines far away from the node of interest, we expect that we may have to traverse the network to upstream nodes to determine whether sequencing decisions are made there.If we start the analysis at an intersection where sequencing is known to occur, such as the threshold of a departure runway, we can learn how to graphically identify sequencing events.We may then recursively move through the network to identify sequencing events at upstream intersections.
+American Institute of Aeronautics and Astronautics
+III. Initial ResultsFollowing the methodology described above, SODAA was used to analyze multiple sets of airport surface sequencing operations at DFW.The first analysis that we conducted evaluates sequencing characteristics at all intersections at DFW over a six-hour period.Figure 5 compares the initial predicted arrival time of each aircraft in the sequencing event pair at the common intersection.We computed the difference between the two aircraft arrival times to generate the histogram.Positive differences indicate the aircraft that was originally predicted to be able to reach the common intersection first was actually sequenced first.Negative results indicate that the flights crossed the intersection in a non-FCFS order because the flight that crossed the common intersection first was originally predicted to reach the common intersection after the second flight.All intersections that were found to have a sequencing event are included in this set of results.The figure indicates that an FCFS sequence was used in the majority of cases.However, there were some cases in which the sequencing decision resulted in a non-FCFS sequence (at least according to our definition and computation method).American Institute of Aeronautics and Astronautics Figure 6 shows a histogram of the actual separation times observed in surface surveillance data over a 24-hour period at the intersection of taxiways K and EL.The figure shows the distribution of separation times between aircraft that required sequencing on the airport surface.Note that this data only applies to situations in which the two aircraft could have been at the same intersection at the same time.This separation time data provides valuable information about the throughput capacity of an intersection.If all aircraft were able to move freely through the intersection and to continue taxiing without delay, how much time separation would be required between successive aircraft?Physically, this depends on the taxi speed, length of the aircraft, and required buffer distance.As shown in the figure, this information can be derived empirically.Using the detailed data that has been computed, including the predicted crossing time at this intersection for each flight as a function of time, we have analyzed controller decision-making regarding the sequence of flights through this intersection.To accomplish this analysis, we created a set of geospatial regions in the SODAA tool and used a query to obtain the first entry time of each flight into each of the geospatial regions.The geospatial regions were located on the taxi routes approaching the K/EL intersection, as shown in Fig. 7.As flights taxi through each of the geospatial regions, the SODAA query provides the time of entry.Using a nominal taxi speed and the distance from each of the geospatial regions to the K/EL intersection, we compute the earliest crossing time of the K/EL intersection for each flight at each of the regions.
+Initial Predicted Time Offset at
+Time Separation Between Flights in IntersectionSequencing Events
+Figure 6. Actual separation times between aircraft at common intersection (K and EL).Although these calculations do not give us a full waterfall diagram for the flight, we can analyze the time of intersection crossing predicted at each of the geospatial regions that the flight crosses to evaluate whether or not the flight is sequenced at the intersection in an FCFS order.American Institute of Aeronautics and Astronautics 7 Note that the layout of the geospatial regions has been designed to monitor multiple approaches to the K/EL intersection.Our hypothesis in designing these geospatial regions in this manner is that the direction and route that is used to approach the intersection has a significant impact on the controller decision-making process regarding the flight sequence.On the left side of Fig. 7, three geospatial regions have been created that encompass a group of 'spots'.A 'spot' or apron entrance/exit point marks a location on the airport surface at which flights transition from the Airport Movement Area (AMA) to the ramp area.The spots that are included in the geospatial regions are 42, 43, and 44 in the first group, 45 and 46 in the second region, and 47 and 48 in the last region.Departure flights stop and hold in these regions waiting for taxi clearance from the tower to proceed onto taxiway K. On taxiway K, we have multiple geospatial regions.A geospatial region at the intersection of K and EM is shown in the figure.The largest portion of traffic through K/EL goes through the K/EM intersection.The traffic through this intersection includes arrival flights heading for their parking gates and departure flights that have left spots further south of the EM taxiway.
+K/EL
+Runway 17RTaxiway EL Taxiway K The geospatial regions used for this analysis also include intersections on taxiway EL.This taxiway is used by arrival flights to cross runway 17R and to proceed toward their gates.We have created two geospatial regions-one immediately before the flights cross runway 17R, and one after 17R has been crossed and before the intersection with taxiway L.
+K/EMBy identifying the earliest time at K/EL for each flight at each of these geospatial regions, we determine whether or not flights are handled at K/EL in an FCFS order.If a flight's earliest crossing time at K/EL is earlier than the actual crossing time of the flight ahead of it at the intersection, then we consider the flight to have been handled in a non-FCFS order.As the flight enters each geospatial region, we compute the earliest time of arrival at K/EL.Since flights may progress towards K/EL with varying average velocities, the predicted K/EL sequencing order will change from one geospatial region to the next.Figure 8 shows sequencing analysis results for 414 flights that traversed the K/EL intersection during a 24-hour period.In the figure, a pair of numbers is shown for each of the geospatial regions.The number below the line is the total number of flights that went through the indicated geospatial region on the way to the intersection of taxiways K and EL.The number above the line is the number of flights that were handled at K/EL in non-FCFS order.For example, 21 flights out of 89 that waited east of 17R on EL were not handled in FCFS order at K/EL.Of those 21, three of them took their non-FCFS sequencing delay before getting to the geospatial region east of the L taxiway.The other 18 took their non-FCFS delay between the geospatial region on EL east of L and the K/EL intersection.This result indicates, as would be expected, that flights taxiing on the K taxiway, which is the primary route for departure and arrivals, are more likely to be sequenced ahead of flights that are merging onto the K taxiway.Flights that are leaving the spots seem to have a higher percentage of cases in which they are sequenced out of FCFS order.Although we have not analyzed the taxi route pairs for each sequencing decision, based on the number of flights traveling on the K taxiway, we expect that most of the sequencing decisions for flights coming out of the spots are made with respect to flights taxiing north on the K taxiway.It is reasonable to expect that flights on the K taxiway would receive some preference because they are more likely to be up to speed, whereas the flights leaving the spots are more likely to be at a full stop while they wait for a taxi clearance.It appears from the data that flights taxiing across runway 17R and entering the K taxiway are handled in a non-FCFS order at a higher frequency than those traveling north on the K taxiway.This may be due to the fact that flight crews must be on the Local Controller's frequency while crossing runway 17R, and then (usually) they must switch to the Ground Controller's frequency before receiving the remainder of their taxi clearance.The data indicate that aircraft often stop short of taxiway K after crossing runway 17R, which would be a common result of the need to change frequency and receive further clearance to taxi into the ramp.
+American Institute of Aeronautics and AstronauticsNote that the results shown in Fig. 8 include all flights crossing the K/EL intersection during a 24-hour period.During that period of time, however, there would be many flights that crossed through the K/EL intersection that did not require a sequencing decision to be made at all because there were no other flights that were competing for access to the intersection.By limiting the data to only those pairs of flights that could have arrived at the K/EL intersection within one minute of each other, we can more accurately evaluate the decision-making and sequencing techniques used by the controller when sequencing is necessary.Figure 9 shows the results when we consider only flights that require a sequence decision to be made.Notice that the flights leaving the spots are even more likely to be held for other traffic when there a sequencing decision to be made.This version of the results also illustrates another surface operations phenomenon.Note that there are 49 flights that pass through the geospatial region on EL east of runway 17R that are considered to be part of a sequence event at the K/EL intersection.However, after the flights have held and waited to cross runway 17R, only 43 of the original 49 flights are still involved in a sequencing event at the K/EL intersection.This is an example of the upstream effects on a downstream intersection that must be considered when formulating conclusions about controller decision-making at a given intersection.Finally, in Fig. 10, we present an analysis of the delay allocated to aircraft that are sequenced in a non-FCFS order.Although there are many different reasons that flights may be held on the airport surfaceincluding a parking gate that is not available, a Traffic Flow Management (TFM) restriction, or a mechanical issue-we have designed this analysis to exclusively evaluate the amount of delay assigned to a flight because of the sequencing of that flight behind another.The results shown in the figure indicate that the sequencing delay is generally less than two minutes.
+Histogram of Delay for Non-FCFS Sequences
+IV. ConclusionsIn this study we have conducted a limited set of analyses to empirically derive operational techniques used by controllers in sequencing flights on the airport surface at intersections between taxiways.While controller techniques may vary, initial results suggest that consistent sequencing patterns can be identified.Further, the results indicate that sequencing decisions are dependent on the flight status (in motion vs. stopped) and taxiway location.For example, our results for this particular case study at DFW indicate that almost 90% of flights that are established on a major taxi route (taxiway K) are handled in an FCFS order, while only 50% of flights leaving the spots and merging onto the taxiway are handled in an FCFS order.These initial results, and the analysis techniques that have been developed through this study, provide the means by which airport surface decision support tools and airport surface models can be improved to accurately represent microscopic decisions on the airport surface that can have significant effects on the flow of the overall air transportation system.American Institute of Aeronautics and Astronautics
+V. Future WorkFuture work will include analysis to characterize the decision factors involved in sequencing situations.The present study has identified some indicators of controller technique at specific intersections at DFW.In future studies we intend to construct a logistic regression model to predict the likelihood of a flight being the next one to proceed through an intersection.Logistic regression is a generalized linear model that fits a binomial response variable to a linear combination of independent variables.The basic model is described by Eqs.(1-3) below.In our case, π would represent the probability of being the next flight to use the intersection.) exp( 1) exp( ) ( The independent variables (x 1 …x k ) may be designed to represent a combination of categorical or numeric values.Figure 11 shows the shape of the linking function in Eq. ( 2), which maps the linear model to the non-linear probability.In a fashion somewhat similar to multiple linear regression, we can test the fit of the model with and without each factor to identify those that provide a statistically significant improvement in the fit of the model.We plan to build the model data set by sampling at random times from a relatively large time interval of data, where the data for one intersection at one time will consist of all flights that meet all of the following conditions at the sample time:1 1 0 1 1 0 k k k k x x x x x β β β β β β π Κ Κ + + + + + = (1) ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ - = ) ( 1 ) ( ln ) ( x x x g π π (2) k k x x x g ) ( β + β = 1 1 0 + Κ β1) The actual taxi path includes the intersection, 2) The flight is active (meaning we have taxi surveillance data), and 3) The flight has not yet reached the intersection.The set of state variables to be used is as yet undetermined, but will likely include the following:1) Distance to the intersection; 2) Current taxiway link; 3) Speed, possibly categorized into {stopped, slow, fast, etc..}; 4) Timeliness of flight, possibly categorized into {early, on-time, late, etc...}; 5) Aircraft type; 6) Airline; 7) Controlled departure time, if any (departures only); and 8) Departure fix/procedure.To these state variables, we can add prior knowledge of whether or not the flight actually was next to pass through the intersection, which is the binomial independent variable we are trying to fit.Once we compute probabilities for being the next in sequence, we must develop a model that applies them to automatic sequencing decisions.For example, we may choose to simply pick the flight with the highest probability or combine the probabilities of potential candidates into an odds ratio and use that to decide.The development of that model will depend heavily on the outcome of the logistic regression and our ability to create a model that will accurately estimate the likelihood of being the next in the sequence.Other aspects of sequencing decisions will be evaluated as well, such as decisions regarding the sequencing of arrivals and departures on a runway, as well as the sequencing of aircraft crossing runways with arrival and departure traffic.Figure 1 .1Figure 1.DFW airport east-side taxiway layout.
+Figure 2 .2Figure 2. SODAA display showing taxi track and speed on the surface.arrivals is the crossing of active runways.In this case, the arriving aircraft must wait for departures, and possibly arrivals, to use the runway prior to obtaining clearance from the Local Controller to cross.Using surface surveillance data, it is possible to determine the location on the taxiway at which flights wait for runway crossings or other sequencing decisions.Figure2shows a portion of the taxi path for a single flight that must cross runway 17R and through the intersection between taxiways K and EL on its way to its parking gate.As shown in the figure, the flight stops and waits at both points B and D. However, it is not possible to determine from this information alone what the reasons were for the decision to hold the flight at each of those points.In many cases, a flight is held on the airport surface to implement a sequencing decision.Such sequencing decisions are the focus of this study.We have a two-fold approach for determining how sequencing decisions are made.The first step is to detect situations where flights are in contention for the same intersection and to identify the intersections of interest-those at which sequencing decisions are actually being made versus those where sequencing is merely First-Come-First-Served (FCFS).The second step is to analyze the relevant intersections and corresponding sequencing events to determine the factors that influence the sequence order.
+Figure 3 .3Figure 3. Intersecting aircraft surface tracks at DFW.
+Figure 4 .4Figure 4. Sample waterfall diagram for intersection K and EL (point E in figure).
+Figure 5 .5Figure 5.Initial predicted arrival time difference at common intersections.
+Figure 7 .7Figure 7. Geospatial regions surround the intersection of taxiway K and taxiway EL.
+Figure 8 .8Figure 8. Flights sequenced in non-FCFS order compared to total flights.
+Figure 9 .9Figure 9. Flights sequenced in non-FCFS order when sequencing is necessary.
+Figure 10 .10Figure 10.Sequencing delay for flights that are handled in non-FCFS order.
+of fitted model coefficients 0...k x are the independent variables included in the model 1...k π (x) is the binomial response variable being fit g (x) is the linking function
+Figure 11 .11Figure 11.Shape of non-linear linking function.
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+I. IntroductionW ith the expected introduction of unmanned aircraft to move goods [1] and people [2-4] and conduct other novel operations like structural monitoring, surveying, etc, future airspace could be filled with traffic orders of magnitude higher than it can bear today.How many such aircraft operations can be accommodated in low-altitude airspace given a set of technological capabilities, operational requirements and protocols, while maintaining safety, stability, performance efficiency and an optimal flow of traffic?In this paper, we address that question by proposing a throughput-based airspace capacity metric.Historically, airspace capacity has been constrained by manual air traffic controller workload [5][6][7][8].The system has evolved with very stringent requirements on safety, as any loss of flight is catastrophic.This may change for unmanned operations for two main reasons.First, the constraint of a manual controller is relaxed.Automated traffic management should accommodate higher traffic densities.It has been shown to do so to some extent even for manned aviation [9,10].Second, not all crashes will be catastrophic.Most may instead result in property damage and not injury or death.Hence, this opens up the opportunity to explore new approaches to estimate capacity for operations in low-altitude airspace.Our throughput idea is inspired by the concept of the fundamental diagram [11], a component of kinematic wave theory.An extensive application of the concept in road transportation [12] relates the freeway traffic flow to the traffic density (Fig. 1).This has been researched for over seven decades and is well understood and utilized in road transportation [13].Further, the expected future demand of over 100,000 flights per day [14] (just for package delivery in a single metropolitan region) is closer to volumes traditionally handled in road transportation.Hence, it provides a reasonable starting point for further research into estimating airspace capacity for novel air traffic operations.Intuitively, as inflow into an airspace volume increases from zero, the throughput (i.e.number of aircraft traversing the airspace per unit time) increases as well.This induces a corresponding rise in accumulation (density).However as aircraft begin to excessively impede each other to avoid losses of separation, the traffic becomes congested and throughput decreases.The aircraft must slow down or deviate significantly from their intended path.The throughput should eventually drop to a minimum steady-state value at a maximum aircraft density that preserves safety.The presence of a peak throughput value suggests that operating beyond that regime will be inefficient, even if it is still safe.Therefore, this constrains the capacity of the airspace.In our current work, we study whether such traffic behavior is actually exhibited by aircraft traversing an airspace.The study is restricted to small Unmanned Aircraft Systems (sUAS) traffic in this paper.Furthermore, capacity is a function of technology.Technology dictates the conflict detection and resolution (CD&R) [15] capability and the allowable minimum separations between the aircraft.Hence, we evaluate the throughput behavior for different CD&R algorithms and separation minima and use a simulation paradigm to produce the results.The rest of this paper is structured as follows.We first present a review of related work that motivates this effort under section II.Section III lays out our approach in detail.Metrics, CD&R methods and the simulation platforms are discussed under section IV.In V we present the results showing the peak throughput behavior with different CD&R methods and separation minima.Section VI concludes this paper with a discussion of proposed extensions of the work.
+II. Literature ReviewApproaches in manned aviation literature frequently estimate capacity as a function of controller and pilot workload [5][6][7][8].Capacity is derived from air traffic complexity measures such as Monitor Alert Parameter (MAP) [16], the maximum number of aircraft an Air Traffic Control (ATC) controller can handle at any given time and Dynamic Density (DD) [17,18], a weighted summation of factors that affect the air traffic complexity.There is an inherent assumption of a structured airspace and Air Traffic Management (ATM) that includes monitors, sectors and airways [19][20][21].Capacity is then estimated using fast-time and real-time simulation methods [22] in a highly subjective manner biased by the judgment of human air traffic controllers during the experiments, who are also assumed to be the bottleneck of the system.A second approach called the Eurocontrol Care-Integra models the ATM system as a combination of several information processing agents, each with an associated information processing load (IPL) [23].The system reaches capacity when one of the agents overloads.This is deterministic for machine agents but again needs subjective judgment for human agents.However, it finds the bottleneck in the system instead of assuming it.Road transportation practice uses a third approach that pins down the bottleneck by measuring the change in lane throughput as a function of the freeway traffic density (see Fig. 1).
+Fig. 1 Fundamental Diagram of Traffic FlowOur throughput-based capacity metric is inspired by the latter two approaches.Further, the fundamental diagram approach was recently studied for highly structured onedimensional sUAS traffic flow in sky lanes in urban areas and shown to exhibit a threshold behavior [24].We want to explore if this also holds true for unstructured free-flow traffic in an area (2D) and eventually in an airspace volume.Future operations both for sUAS and Urban Air Mobility (UAM) [4] may be free flight in nature; i.e. individual flights could be responsible for determining their own courses, independent of a global plan or system.Unmanned Aircraft Systems (UAS) Traffic Management (UTM) should therefore support userpreferred flight trajectories to the extent possible.Any chosen metrics should account for this.ATM architectures that transfer some of the separation responsibility to the cockpit for manned free flight were researched by Bilimoria et al. as part of their Distributed Air/Ground Traffic Management (DAG-TM) concept [9,10,25,26].Based on their work, the following types of metrics can potentially be used to evaluate any UTM architecture for free flight (the sample measures used for manned ATM [9] are listed in parenthesis): Safety (number of actual conflicts and conflict alerts), Performance (Change in direct operating cost), Stability (number of forced conflicts (domino effect)) and DD (aircraft density, average proximity and average point of closest approach).Of these, we focus on the first two as the basis of our chosen comparative metrics.
+Fig. 2 CDR geometry based on choosing the lower cost choice between the frontside and backside maneuver [9]Next comes the choice of a CD&R algorithm.CD&R methods in aviation literature have been primarily developed for large aircraft [15] flying at higher altitudes and lower densities than the expected future sUAS traffic.An example of a simple rule used by Krozel et al. [9] is shown in Figure 2. Smaller unmanned aircraft provide a unique opportunity for simpler conflict resolution algorithms.Proposed future operations [27] might primarily be done by aircraft that have Vertical Take Off and Landing (VTOL) capability and better maneuverability.Under these considerations, we chose three simple algorithms in this work.We discuss these further under section IV-IV.B Finally, we need a simulator that can simulate sUAS traffic densities so that the throughput behavior can be studied.Existing simulation and evaluation tools developed for ATM (like BlueSky, TMX, ACES, AEDT, FACET) may not fit for our purposes -they are designed to handle manned-aircraft with a much lower traffic density than our study.They take into account interactions with a variety of actors (air traffic controllers, etc.) that are not needed in low-altitude UTM questions of traffic behavior and capacity.The fast-time Fe3 simulator developed by NASA Ames [28] provides the capability of statistically analyzing the high-density, high-fidelity, and low-altitude traffic system.It can be used for effectively evaluating policies and concepts, and performing parameter studies in a higher-fidelity environment like the one in which we are interested.Hence, we use it in conjunction with a simple kinematic model-based Matlab simulator developed for simpler algorithms.
+III. ApproachAirspace capacity is the maximum number of aircraft that can traverse an airspace in a given time under a set of requirements.Throughput is a way of quantifying the capacity per unit time.Prior analysis [14] suggested that demand for sUAS package deliveries could be as high as 100,000 flights per day in a metropolitan region like the San Francisco Bay Area.A threshold-based definition was used to study these estimates and establish airspace capacity for such a metropolitan region in terms of "flights per day" considering safety and performance efficiency [29].Such a macroscopic approach, although useful for long-term planning and design of an airspace system, provides no direct method of real-time control.On the other hand, the flow density relation is used as a tool to control road traffic by regulating inflow in real time and improve throughput.Hence, if the peak throughput behavior is exhibited by air traffic, a similar air traffic control method could also be explored for operating the airspace at or close to capacity.There is no empirical data on sUAS traffic in the airspace today on which to base our methods.Hence, we start by considering a representative area, subjecting it to increasing steady state inflow rates of air traffic and measuring the mean outflow rate, which we call throughput of the airspace.Next, to study the feasibility of the throughput metric, we pick other metrics to compare against, use parameters that model the technology, and develop a computational process that quantifies the metrics as a function of the technology parameters.The primary goals of air traffic management are to maximize safety, capacity, and efficiency.In Section IV we discuss the safety and efficiency metrics that are evaluated along with the throughput metric.We make the following operational assumptions about the aircraft and their operations: (a) All aircraft are sUAS with strictly VTOL capability; (b) Their flight plans are straight line paths from entry to exit on the boundary of the study area.These paths change as aircraft fly through the airspace and avoid conflicts with other aircraft using a given CD&R algorithm; and (c) All sUAS have nominal and maximum speeds constrained by the capabilities of typical sUAS in use today.Our setup is two dimensional.Any losses of separation are horizontal (a simplification to evaluate the throughput-based capacity metric).We plan to extend this to a volumetric study in the future.The detailed simulation setup and the chosen metrics and CD&R methods are described next.We first define the notion of a conflict and loss of separation.Any sUAS should stay out of a minimum separation exclusion zone (a disc with radius D) around another sUAS.A loss of separation occurs when two sUAS come within this minimum separation.Given their projected paths in the horizontal plane, if an sUAS is predicted to eventually enter within the minimum separation of another sUAS, the two aircraft are said to be in conflict.Figure 3 illustrates a loss of separation occurring between two sUAS.
+IV. SimulationWe make the following assumptions.We only consider multicopters, which means that the aircraft can hover.The nominal flight speed is assumed to be 15 ms -1 with a maximum value of 20 ms -1 .The maximum acceleration that today's sUAS can achieve is about 2g.If 1g is used to overcome the weight, close to 1g is available for horizontal maneuvers while keeping the aircraft in safe operational limits.To avoid pushing aircraft to their maximum capability all the time, we limited the maximum acceleration to 0.5g.We chose a representative area as a square of 0.5km width.The origins and destinations of aircraft are uniformly distributed along the edges and are spaced such that two aircraft don't enter or exit within loss of separation distance.These are randomly connected to form the flight paths such that no aircraft has an origin and destination on the same edge.This ensures that every aircraft enters the study area.Finally, we estimate the different metrics for two different separation minima -5 meters and 10 meters.
+A. Metrics
+ThroughputOur primary metric -Trip Exits per min captures the average traffic outflow rate through the area (i.e.throughput).Measuring trip exits per second would be too small to capture substantial intended boundary crossings and measurements over an hour would be too long to provide any real-time control over an area.
+SafetySafe operation of the airspace is of utmost importance.Following the proposed requirements by MITRE [30] and its use in our prior macroscopic capacity estimation work, we choose the necessary safety metric as the Total Losses of Separation observed over the simulation interval.
+Performance EfficiencyHigher operating costs (fuel, wear, etc.) lower performance.They are typically caused by longer travel times and distances, which are in turn usually the byproduct of safer operation.We capture this in the current work by measuring the Percentage Extension in Travel Time.This is a direct derivative of the Change in Direct Operating Cost as proposed by Krozel et al. [9].
+B. CD&R methodsApproaches to CD&R may be broadly classified into three categories -force field based, trajectory projection based and offline look-up table-based.We chose three simple CD&R methods that capture different types of control, represent different categories of CD&R approaches and can together encompass most types of aircraft.This makes them flexible for future extensions of this study to different classes of aircraft.First is avoidance by slowing down to "Hover."This captures the effect of pure speed control and encompasses aircraft that can stop in flight.It uses a trajectory projection-based approach.Second is a simplified implementation of "Potential Field" method as used by Mueller [31].This uses a simultaneous speed and direction control.Since the minimum speed can be set greater than 0, it captures all aircraft with a stall speed constraint.It belongs to the broad category of force field-based CD&R approaches.The third is an algorithm derived from the ICAROUS [32] architecture, that is based on DAIDALUS [33], a reference implementation of RTCA-228 Minimum Operational Performance Standards (MOPS)(Appendix G) for UAS DAA (Detect and Avoid) [34].This also uses speed and direction control and is extendable to all aircraft with a stall speed constraint.It acts as a more complex example of trajectory prediction-based approaches under formal consideration.In the rest of this paper, we will refer to this ICAROUS-based algorithm as "ICb" for brevity.We used a kinematic model-based simulator in Matlab to study the throughput behavior for Hover and Potential Field and used the same flight data to study ICb as implemented on the fast-time simulation platform Fe3.Fe3 is highlyparallelized and implemented on Amazon Web Services(AWS) Graphical Processing Unit(GPU) instances.It includes various six-degree-of-freedom vehicle models and CD&R algorithms and also incorporates vehicle communication and sensor models and wind models.Although other components, such as no-fly zones, near-ground static and dynamic obstacles and avoidance, and community effect via noise and pollution, are still under development, Fe3 provides essential functionality necessary for our study.
+V. ResultsIn this section, we present our results that describe the throughput-based capacity metric.Figures 4, 5 and6 show the variation of throughput for hover, potential field and ICb.The figures on the left compare throughput to number of losses of separation during the simulation and the figures on the right compare it to the percentage extension of travel time.In all the figures, solid line and dotted line represent 5 meters and 10 meters minimum separation, respectively.The metrics are evaluated as a function of different steady-state inflow rates.The average area outflow rate measured as Trip Exits per min is plotted in blue.The losses of separation and the mean percentage extension of travel time measured over the entire simulation are plotted in orange on the left and right figures respectively.We observe the following general trends.In all figures, a peak throughput behavior is exhibited at an intermediate steady-state traffic inflow (between 60 to 80 flights per min for Hover and ICb and between 40 to 60 flights per min for Potential Field).Any losses of separation and noticeable extensions of travel times occur at or beyond the peak throughput.In other words, peak throughput is achieved even before safety of the system is compromised.Therefore, in this airspace, the optimal inflow to be maintained is decided by throughput rather than safety.When the tolerance is higher (smaller minimum separation), the throughput is also higher as the aircraft can be safely packed closer together.Under the same steady-state inflow conditions, for example between 60 and 80 flights per min, the highest throughput is shown by both Hover and ICb (about 50 trip exits per min) but Hover exhibits it at a slightly higher inflow rate.Potential Field shows lower peak throughput than the other two.It also peaks at an inflow rate of 40 trips per min, almost half that of the other two.However, this loss comes at a much higher level of safety.This is shown by the loss of separation numbers at and beyond peak throughput.Both Hover and ICb start deteriorating in terms of safety beyond their respective throughput peaks, while Potential Field maintains its low losses of separation (below 4) well beyond.Further, the number of losses of separation rise rapidly for the Hover and ICb cases but they stay low for potential field.Practically, this gives flexibility to the system to operate at peak, while for the other two, from a risk standpoint, it is preferable to operate to the left of the peak.Next we compare the performance efficiency.ICb fares better than Hover in terms of travel time extension.For example, close to the peak throughput, hovering extends mean travel time by 1% for the 5m separation case, while the value for ICb is around 0.8%.The percentage extension is almost thrice at peak (about 3%) for Potential Field.The difference is more pronounced beyond the peaks.In the entire simulation, Hover exhibits a maximum mean travel time extension of 3%.The same metric for ICb is 1.2%, while it is slightly more than thrice for Potential Field at 10%.But as stated earlier, what Potential Field loses in efficiency, it compensates for in safety.These behaviors are explained as follows.Our hover approach slows down aircraft to a stop without deviating them from their trajectory.Hence beyond peak throughput, first there is an excessive slowdown that reduces the throughput.Next, when all aircraft begin to stop while the inflow is still maintained, several aircraft don't have enough distance/time to stop safely.Hence, they start entering each others' minimum separation distances, especially closer to the boundaries.The number of losses of separation rises fast and exit rates continue to fall.However, the aircraft within separation minima are either stopped or moving very slowly.This is comparable to a jam on a freeway where cars are bumper to ICb picks the resolution maneuver from the recovery bands provided by DAIDALUS that has the least secondary conflicts and simultaneously minimizes the deviation from the nominal trajectory.This results in low travel time extensions and higher throughput.As more aircraft accumulate in the airspace, the recovery bands become narrower and hence lead to higher number of losses of separation.In the potential field approach, the aircraft at or close to minimum separation have large repulsive forces, which ensure that the aircraft are kept away from each other and hence very safe.The small number of losses of separation happen when excessive aircraft have accumulated in the system and either the repulsive forces start overwhelming the aircraft operation limits or an aircraft trying to reach its destination ends very close to an originating aircraft.Both of these scenarios can be minimized by implementing appropriate entry and exit rules or providing buffer zones at high inflow rates.Safety is achieved by spreading the aircraft beyond the primary study region boundaries.To understand this better, let us assume that the study region was an urban area.This approach pushes out aircraft at the edges of the area into suburban airspace.Hence, a higher amount of contingency airspace is required.Based on the above insights we find that the throughput metric is not only useful to understand airspace capacity as a function of technology, but our comparative approach also provides a basis for evaluating CD&R methods in terms of the capacity, safety and efficiency they can achieve at high density system level operations.
+VI. Conclusions and Future WorkWe have developed a throughput-based airspace capacity metric for unmanned air traffic in low-altitude airspace.Throughput, safety and performance metrics were evaluated for uniformly distributed air traffic inflow over a square area of 0.5 km width.We used three CD&R methods -Hover, Potential Field and ICb, and two separation minima -5 meters and 10 meters in our simulations.Our results show the throughput behavior as a function of the steady-state air traffic inflow in a representative area.The system stays safe (i.e.no losses of separation) without excessive impact on performance (less than 5.5% mean extension of travel time) until after the accumulation of air traffic has lead to a reduction in throughput.This suggests that the throughput peak may quantify the airspace capacity.The CD&R algorithms themselves exhibited different throughput peaks.Further, smaller separation requirements allowed better throughput no matter which algorithm was used.We also observed that measuring throughput in comparison to safety and efficiency metrics could be used as a tool to evaluate the adequacy of a CD&R algorithm for large-scale operations.A next step in the evaluation of this approach is to use other other robust CD&R methods such as Airborne Collision Avoidance System X (ACAS X) developed for multi-copters [31].This would capture an offline look-up table-based CD&R method, which we didn't explore in this paper.We also need to measure the effect of sensor and navigational uncertainties (such as deviations from trajectory, delays in aircraft detection, wind, etc), static and dynamic obstacles (e.g., buildings and temporary flight restrictions) and specific traffic flow patterns.Fig. 33Fig. 3 Conflict and Loss of Separation.Ao -Own sUAS, Ai 1 & Ai 2 -Intruder sUAS.The aircraft are shown in relative frame of reference
+Fig. 4 Fig. 545Fig. 4 Results for Hover to Avoid
+		
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+AcknowledgmentWe express our sincere gratitude to Dr. Alex A. Kurzhanskiy from UC, Berkeley, Dr. Parimal H. Kopardekar from the NASA Ames Research Center for valuable insights on the basic ideas.We thank Joseph Silva and the entire Fe3 team at NASA Ames for their ongoing contributions to the fast-time simulator used in the current work.This work is funded in part by the USRA NAMS Student R&D Program.
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diff --git a/file107.txt b/file107.txt
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+		
+I. IntroductionNASA, in collaboration with government and industry partners, is developing concepts for Urban Air Mobility (UAM) vehicles and technologies to support UAM vehicle operations across a network of takeoff and landing areas (TOLAs) in metropolitan regions.These UAM operations of the future will need FAA approval.Hence, the simplest approach to begin operations might be to start with already-existing approved routes, especially for flying into major airports.Can UAM vehicles fly these routes with minimal impact on present-day commercial air traffic (hereafter referred to as conventional traffic)?This paper presents a preliminary modeling and analysis of interactions between proposed UAM operations and conventional traffic, if UAM operations were restricted to FAA-approved helicopter routes and altitude ceilings.UAM operations in Dallas/Fort-Worth (DFW) terminal airspace is chosen for the study.The goal of this work is to assess the extent to which proposed initial UAM operations may trigger Traffic alert and Collision Avoidance System (TCAS) resolution advisories (RA) aboard conventional aircraft in the DFW terminal area.A range of operational scenarios at DFW is evaluated with combinations of UAM vehicle route, speed, altitude, and direction along the DFW "spine route."The analysis is done for both South flow and North flow configurations of DFW.First, results are obtained under the assumption that UAM vehicles fly their routes precisely (i.e.no uncertainty).Then, the impact of altitude uncertainty on these results is also evaluated.Different route alternatives are evaluated between DFW and Frisco, Texas in this paper.DFW-Frisco operation was selected as a test case based upon recommendations derived from traffic demand studies from potential UAM operators in the region.The focus of research presented here is only the impact of operations for this test case on triggering TCAS RAs on conventional aircraft.Interaction with Air Traffic Control(ATC) and other route alternatives and procedures are not studied.Associated work by Verma et.al. [1] explored potential routes and procedures in a Human-In-The-Loop (HITL) experiment expanding the above study region to include Dallas Lovefield (DAL) and Addison (ADS) airspace and Dallas downtown.The rest of this paper is divided into five sections.Section II, Traffic alert and Collision Avoidance System (TCAS), provides a description of TCAS and how its logic is used in this study.Section III, DFW Airport Terminal Area Traffic, describes DFW runways and operational configurations.Section IV, Study Approach, provides a breakdown of modeling assumptions, UAM operational route scenarios, selection of study days and description of simulation set up.Section V, Results, first summarizes the results of the geometric analysis of the routes, followed by their verification using simulations without trajectory uncertainties.It also presents the results with altitude uncertainty.Section VI, Conclusions, discusses the overall findings and recommendations for future studies.
+II. Traffic alert and Collision Avoidance System (TCAS)In conventional operations, TCAS is the last layer to prevent mid-air collisions between aircraft other than the pilot.The design of the TCAS logic is a trade-off between providing necessary safe separation between aircraft and preventing unnecessary advisories.Unnecessary advisories reduce trust in the system and may increase crew and controller workload to unacceptable levels.The system monitors the amount of horizontal and vertical separation and uses the rates of change in horizontal and vertical separation to predict the closest point of approach between the ownship and surrounding aircraft.A Traffic Advisory (TA) or a Resolution Advisory (RA) is issued based on thresholds for estimated time for an intruder aircraft to enter a protected volume of airspace around a TCAS equipped aircraft (τ mod ) and time to co-altitude (τ vert ) [2].The boundary of the protected volume is defined by a slant range distance called DMOD.During a TA, on a conventional aircraft, TCAS displays the intruder aircraft and notifies the pilot about a potential conflict through visual and audio alerts.The pilot is expected to respond to a TA by (1) establishing visual contact with the intruder and other aircraft in the vicinity and (2) maintaining current assigned clearance.During an RA, TCAS issues maneuvers to the pilot to increase or maintain current vertical separation.The pilot is expected to immediately respond to the indicated maneuvers unless doing so would unduly jeopardize the safe operation of the flight.When both aircraft are equipped with TCAS II, aircraft coordinate their RA's through Mode S datalink to ensure that complementary RAs are selected.Due to the assumption that TCAS II is available only on conventional aircraft, RA coordination functionality will not be discussed in this work.To balance the tradeoff between necessary protection and unnecessary advisories, TCAS uses an altitude-based Sensitivity Level (SL), which controls the tau (time) thresholds for TA and RA issuance, and therefore the dimensions of the protected airspace around each TCAS-equipped aircraft (Table 1).DMOD and ZTHR are the slant range and vertical separation threshold of the protected airspace as per the TCAS II manual [2].The higher the SL, the larger the amount of protected airspace and the longer the alerting thresholds.While an aircraft is in close proximity to ground, the SL of TCAS alert and avoidance parameters depends on the altitude of the ownship aircraft above ground level (AGL).TCAS does not provide RAs below 1000ft AGL (SL=2).Between 1000-2350 ft AGL (SL=3), TCAS issues RAs, if both τ mod and τ vert are less than 15 seconds threshold, or τ mod is less than 15 seconds and current altitude separation is less than 600 feet (vertical separation threshold).
+III. DFW Airport Terminal Area TrafficThe study described in this paper uses nominal operation assumptions and procedures for DFW airport as described in this section.
+Fig. 1 DFW Runway Map
+A. DFW RunwaysThe DFW airport has seven physical runways shown in figure 1.These runways are operated in the South-flow and North-flow configurations.The runways in South-flow configuration are designated as, 13L, 13R, 17L, 17C, 17R, 18L and 18R.The runways in the North-flow configuration are designated as, 31L, 31R, 35L, 35C, 35R, 36L and 36R.These designations indicate the runway heading with respect to north.The two inner runways 18L/36R and 17R/35L are primarily used for departures.Runways 18R/36L and 17C/35C are primarily used for arrivals [3].Adjacent primary departure and arrival runways are about 0.2 miles (1200 ft) apart.In North flow operations, runway 35R is used for arrivals as the amount of traffic increases but it is less desirable being shorter and far from terminals and its use requires aircraft to cross runways.As a rule of thumb, easterly arrivals use runway 17C/35C and westerly arrivals use runway 18R/36L.Considering the geometry of the runways, arrival and departure procedures at DFW and the expected nominal trajectories of UAM vehicles along helicopter routes (see figure 2), interactions between commercial aircraft and UAM are most likely to happen between aircraft arriving/departing on runways (18R,17C)/(18L,17R) in South flow and arriving/departing from runways (36L,35C)/(36R/35L) in North Flow.
+B. DeparturesNinety five percent of all departures from DFW are jets using RNAV routes [4].Departures typically are provided temporary level-off altitudes of 10,000ft, if their track crosses under arriving aircraft.After crossing they are cleared to climb to their cruise altitude.The average duration of a 10,000 ft level off is about 1.7 minutes, traveling a distance of 9.4 nmi based on an assumed airspeed of 331 knots [4].
+C. ArrivalsThe arrivals use Runways 13R, 18R, 17C and 17L in South flow and 31R, 35C, 35R, and 36L in North flow configurations.All approaches follow a 3 degree glide slope procedure [5].Hence, aircraft arriving with a stable approach are below 1000 ft AGL, 3.16 nm before the touchdown zone.
+IV. Study ApproachIn this study, two routes for UAM flights operating between the city of Frisco, Texas and DFW airport were analyzed.Frisco was selected based on recommendations derived from traffic studies by potential UAM operators in the region.UAM-conventional aircraft interactions were evaluated from a TCAS perspective.UAM-UAM interactions were not studied as they are out of scope for this work.
+A. Assumptions and Test CasesThe following assumptions were made:-All UAM vehicles in the simulation were modeled as the same aircraft type.-All UAM fly along published helicopter routes.-UAM trajectories are completely deterministic in the simulation for the first set of results and identification of sensitive areas.-Only altitude uncertainty is considered for the second set of results for the identified sensitive areas.-The altitude errors are assumed to be normally distributed with zero mean and varying standard deviations.-Conventional aircraft are modeled with TCAS II version 7.1.-UAM vehicles are not modeled with the above TCAS system but do provide the state information required by the TCAS system on board the conventional aircraft.-Conventional aircraft adhere to published area navigation (RNAV) routes.Same aircraft type assumption has low impact on the relevance of this study, as we only need a representative performance model.In future, a wider range of vehicles could be summoned based on trip length and traffic demand.However, manufacturing and maintenance costs alone will likely push towards a preferred UAM vehicle.Existing helicopter routes are a useful starting point as they are already designed for vehicles that operate at low altitudes with Vertical Take-Off and Landing (VTOL) capability.Eventually, other routes and procedures can be explored (for example see [1]).Since vertical separation was identified as a major factor that could potentially trigger RAs, from the first set of results, altitude uncertainty was a good first candidate to study.Four UAM operational route scenarios were evaluated: Nominal days were characterized by moderate meteorological (temperature between 85 o F -95 o F and low precipitation) and traffic flow conditions.The off-nominal days had maximum temperatures close to 100 o F , clear skies, and minimal weather impacted operations, allowing for the highest traffic flow with least impact on airport operations.On these days, owing to higher temperatures, conventional aircraft may not have been able to climb as quickly as they could on nominal days, which was expected to cause closer encounters and potentially more TCAS RAs.Furthermore, August 7 had the highest conventional traffic in North Flow configuration for the entire year and therefore was also used for the altitude uncertainty study.These test cases together account for different routes, flow directions of UAM traffic between Frisco and DFW and flow variations of conventional traffic.Additionally, in the vicinity of the airport, all UAM flying into DFW were modeled to cruise at 1000 ft MSL, and all UAM flying out were modeled to cruise at 900 ft MSL.This separates UAM vehicles flying into DFW by 100 ft from UAM vehicles flying out.The cruise airspeed of all UAM vehicles was modeled at 130 knots.Although it is important to assess whether this UAM-UAM vertical separation is sufficient, it is beyond the scope of this study.However, it ensures that UAM vehicles taking off and flying against arriving conventional traffic, are at a higher vertical separation, compared to UAM vehicles flying in the same direction.
+C. Analysis ApproachThe analysis was accomplished in three stages.First, TCAS sensitivity level in the simulator was higher than a real system.The value of sensitivity parameter DMOD was 0.66 nm as per UAS in NAS project [7], which would be highly conservative for the near surface operations in this study.τ mod measures the time it takes two aircraft to come closer than DMOD distance.Second, scenarios that produce RAs from above were filtered using geometric aircraft configuration data from the simulator, with the thresholds for a SL 3 TCAS operation, even below 1000 ft AGL (which would technically be SL 2 -No RAs).Third, the correct TCAS SL was used, based on actual AGL altitude of both aircraft consistent with what would be used by a real TCAS II system on a conventional aircraft.If there are no RAs in a higher analysis stage, there can't be any in the next stage.The first two stages are useful for identifying the sensitive areas in the system.However, the results presented in the next section are based on the third and least conservative stage, which is how TCAS would behave in a real scenario.As an example, a sample encounter scenario is shown in figure 3 with the associated encounter parameters.Recall that if τ mod is less than the threshold and either τ vert is less than the threshold or the current vertical separation is less than the threshold, then a TCAS RA is issued.TCAS τ mod (Sim) is evaluated using a DMOD of 0.66 nm as used in UAS-NAS project [7]; in this example, that results in a value of 1 sec, which is below all τ mod thresholds.From the first stage of analysis, the simulation will flag an RA because both τ mod and τ vert are less than the threshold of 15 sec.TCAS τ mod (SL) is derived based on SL-specific DMOD.For SL 3, DMOD is 0.2 nm.At the second stage of analysis, an RA is also issued because both times are below the threshold of 15 sec (SL 3).However, since DMOD is smaller, the projected time for slant range to go below it is greater.Finally, in a real scenario, since the commercial aircraft is below 1000 ft AGL, no RA shall be issued.Results derived following this analysis are presented in the next section.
+V. ResultsOutside the DFW surface-to-2500 MSL class B airspace, under the assumption that conventional aircraft adhere to published RNAV routes, they are far above the 1000-ft MSL ceiling/cruise altitude of the UAM aircraft and therefore well separated (> 1000 ft) in altitude.Hence, the analysis here is focused only on the interactions near DFW.The region of interest along with the sensitive areas is shown in figure 4. The thick blue lines indicate the vertical planes between which the arriving conventional traffic is below 1000 ft AGL.They are 3.16 nm from the touchdown zone of their respective runways.This distance was computed from the 3-degree approach glide slope described in section III.C.All analysis for deterministic UAM trajectories is primarily geometric and hence applies to both nominal and off-nominal days.However, those days were simulated as a secondary confirmation to ensure that no corner cases were neglected.The elevation of DFW airport is 607 ft MSL.TCAS will not issue RAs when the conventional aircraft is below 1607 ft MSL (i.e.<1000 ft MSL).It will operate at SL=3 for conventional aircraft between 1607-2957 ft MSL.The UAM vehicles simulated in this study are below 1607 ft MSL during their entire trajectory portion under consideration, i.e. in the vicinity of DFW.Thus, in principle, RAs can technically be issued only at SL=3 in the worst case.If the conventional aircraft is above 2957 MSL (SL=4), a TCAS RA will not be triggered with a UAM.In order for the conventional aircraft at 2957 MSL to become co-altitude with the UAM vehicle at 1607 MSL within the τ vert threshold of 20 seconds, the conventional aircraft would have to descend faster than 4000 ft/min.This is much higher than a typical descent rate of 800 ft/min that such an aircraft would use on a 3-degree final approach glide slope.Even before the aircraft has intercepted the final glide slope, it would descend at a much lower final descent rate [9].Therefore, the modeling assumptions for this study prevent the time and altitude separation thresholds from ever being violated, if the conventional aircraft is above 2957 MSL near DFW.
+A. Departures AnalysisDeparting conventional aircraft primarily use runways 18L and 17R in South Flow (35L and 36R in North Flow) (Figure 1) and are above 1000 ft AGL 20 secs after departure, based on a climb rate of roughly 50 ft/sec [4].Since the departure runways have a minimum separation of 0.44 nm from the UAM flight paths in their take-off zones, departing conventional aircraft are well separated horizontally from the UAM aircraft.After take-off, the conventional aircraft climb at rates between 2000-3000 ft/min, much faster than the 500 ft/min ascent/descent rate assumed for UAM flights.They are above the incoming UAM flight altitudes in less than 10 sec.Hence, they are always diverging and well separated by 1000ft AGL, the altitude where the TCAS system would start producing any RAs.Therefore, under the assumptions in this study, departing conventional aircraft will not produce RAs, even with UAM trajectory uncertainties.
+B. Arrivals AnalysisResults in this section were determined assuming no UAM trajectory uncertainties.In every case where τ mod was violated and either the vertical separation threshold or τ vert was violated, the conventional aircraft was always below 1000 ft AGL, where TCAS RAs are inhibted.Therefore, under the assumptions of this study, arriving conventional aircraft will also not produce any RAs, if the UAM trajectories are deterministic.North of DFW (figure 5), UAM flights departing against the direction of arriving conventional traffic are separated horizontally by a minimum of 0.44 nm (0.64 nm from the outer runway -0.2 nm separation between the adjacent parallel runways) and primarily follow parallel trajectories.For interaction scenarios south of the blue line, the aircraft are therefore well separated horizontally.Furthermore, conventional aircraft are below 1000 ft AGL with TCAS operating at SL 2 as described at the start of this section.Hence, they issue no RAs.North of the blue line in the same figure, the conventional aircraft are above 1000 ft AGL and have TCAS operating at SL 3.Even where τ mod goes below thresholds (e.g.where the UAM turns right and flies below conventional aircraft arrival path), the vertical separation is greater than 600 ft and the time to co-altitude is greater than the SL 3 threshold of 15 seconds to trigger any RAs.Such a sample encounter where τ mod goes below thresholds is shown in figure 6.The evolution of vertical separation (solid blue line) is plotted on the left axis.At SL 3, the vertical separation threshold, ZTHR is 600 ft (dashed blue line).τ mod (solid red line) and τ vert (dashed red line) are plotted on the right axis.The SL 3 time thresholds are violated below Tau = 15 sec, i.e. when τ mod and τ vert are between the dotted red lines.When τ mod is between 0 and 15 sec, neither the vertical separation threshold nor the τ vert threshold is violated.Hence, this encounter will not trigger an RA. Figure 7 illustrates this is true even at a high descent rate.Assume that the conventional aircraft is at AGL altitude A C in feet (>1000 ft) and the UAM is cruising in level flight at AGL altitude A U in feet.Any aircraft above 1000 ft, under normal operation, should not descend at a rate (ft/min) greater than its AGL altitude, i.e. maximum descent rate or maximum vertical closure rate, V ZC = A C in ft/min.Hence time to co-altitude, τ vert = separation/(closure rate per min/60) = separation/(closure rate in sec) = ((A C -A U )/A C )*60 = (1 -A U /A C )*60 seconds.For maximum A U = 400 ft and minimum A C > 1000 ft, minimum τ vert > 36 sec.Hence time to co-altitude is always greater than 36 seconds, which is more than double the time threshold for SL 3. The vertical separation threshold of 600 ft is also not violated because A C -A U > 600 ft.South of DFW (figure 8), the UAM flight route intersects with conventional aircraft runway approaches after the conventional aircraft is already below 1000 ft AGL (north of blue line) with TCAS operating at SL 2. Hence, no RAs will be issued.South of blue line (region not shown in figure 8), the conventional aircraft are separated by at least 0.5 nautical miles horizontally and more than 600 ft vertically.Following the same explanation as the SL 3 situation north of DFW (see figure 7), the vertical violation criteria is never satisfied.Hence, no RAs are issued.These results cover all combinations of UAM vehicle routes, altitudes and directions studied.They also account for variations in UAM vehicle cruise speeds.When conventional aircraft are below 1000 ft AGL, there will be no RAs irrespective of the closure rates (slant and vertical).When they are above 1000 ft AGL, the lack of RAs is due to vertical closure rates and adequate vertical separation.The entire analysis is therefore agnostic to UAM cruise speeds.
+C. Altitude Uncertainty AnalysisAlthough τ vert was never violated when the conventional aircraft were above 1000 ft AGL, it is noteworthy that the vertical separation threshold (600 ft) was very close to violation at the intersection of the UAM route and the conventional aircraft arrival path south of DFW, when the UAM vehicles fly into DFW (at 1000 ft MSL).A sample encounter from this sensitive area is shown in figure 9.Although there is a time when τ mod is less than 15 sec and the vertical separation is less than 600 feet, the conventional aircraft is already below 1000 ft AGL at that time.Hence, TCAS will ignore the UAM vehicle and not trigger an RA.However, if the UAM vehicle had a vertical position error of even 10 ft, it would have triggered an RA in this scenario.Hence, the natural next step is to explore the impact of trajectory uncertainties.In this paper, only altitude uncertainty was explored.Vertical GPS errors with available technologies today are less than 10 m (≈30 ft) even in the worst case.This was simulated by introducing errors in the altitude of UAM vehicles.The errors were assumed to be normally distributed with zero mean and standard deviation (StD) varying from 5 ft to 30 ft.The maximum error allowed was thrice of the chosen StD.Hence, even though the maximum errors in reality shouldn't exceed 30 ft, errors were simulated up to 90 ft.It is noteworthy that UAM vehicles departing DFW were modeled to cruise at 900 ft MSL (293 ft AGL) and hence, even with a maximum error of 90ft, they wouldn't trigger RAs at the sensitive area identified above.Hence, analysis Fig. 9 A sample encounter South of DFW (north of blue line in figure 4).Note: Actual onboard TCAS II RA logic will ignore diverging aircraft, hence τ mod and τ vert will only be computed when they are non-negative.was only done for Scenario 4, where UAM vehicles fly from Frisco to DFW and enter DFW from the South, passing below the conventional aircraft arrival paths at 1000 ft MSL (393 ft AGL).Furthermore, encounters were simulated with the conventional aircraft track data for August 7, 2017.That day had the highest North Flow traffic of the year and hence, maximum potential encounters.Figure 11 top row shows the probability of triggering an RA for a UAM vehicle departing Frisco at the times shown on the horizontal axis, as the altitude error StD varies from 5 ft to 30 ft.The probability variation followed the conventional traffic demand change through the day.During lean times (before 9a and after 7:30p), even a 30 ft error StD did not produce more than forty percent risk of triggering an RA.During the peak conventional traffic rush between
+Fig. 10 Risk of triggering RAs on a typical day of operation -Noon-3p9a and 7:30p, it is observed that even a 5-ft error StD produced over twenty percent risk of triggering RAs at certain times.This risk increased to over forty percent, when the error StD was increased to 10 ft or more.A potential solution to this problem could be that the UAM fly into DFW at a slightly lower altitude below the arrival paths.To justify this recommendation, the above analysis was repeated by lowering the UAM vehicle mean altitude by 5 ft and 10 ft, respectively.It was observed that, in general, lowering the mean altitude by twice the allowable error StD substantially reduced the risk of triggering RAs.As an example, in figure 11, third row, lowering the UAM vehicle mean altitude by 10 ft, reduced the chance of triggering an RA to under ten percent throughout the day, even with a 10-ft error StD (maximum error 30 ft).For clarity, figure 10 shows a zoomed version of the variation from noon to 3p.
+VI. ConclusionsThe study found that using a basic model of UAM performance, UAM vehicles simulated to operate from Frisco to DFW utilizing existing helicopter routes as shown in figure 2, triggered no RAs on conventional aircraft.These results were obtained under the assumption of deterministic UAM vehicle trajectories, i.e. zero error in observed and true position of the UAM vehicles.Meteorological conditions, such as wind, which could affect this accuracy, were ignored.These results suggest a very high navigational performance requirement on UAM, if operations were to be enabled with high UAM trajectory determinism.From the altitude uncertainty study, it was also observed that the above performance requirements could be slightly relaxed by operating the UAM vehicles at or below 990 ft MSL (383 ft AGL), if they can adhere to a maximum altitude error of 15 ft from their trajectory.Furthermore, the primary reason for the lack of RAs is that conventional aircraft are either already, by procedure, well separated horizontally and vertically; or are below 1000 ft AGL otherwise (which suppresses RAs).Even though this is true for DFW based on its particular runway configuration, a similar analysis can be performed at any other airport to determine the sensitive regions for TCAS RA alerts.It should be noted that TCAS II ignores intruders below 360 ft AGL.This means any UAM flights below 967 ft MSL (around DFW) are automatically ignored by current TCAS operation criteria.This can be interpreted in two ways.In the short term, UAM flights around any airport in the country can be kept below 360 ft AGL to enable early operations, if necessary, without triggering RAs.In the long term, this could create potential issues with high density of near-ground traffic and therefore, might necessitate an update to the TCAS logic to account for the same.Therefore, this is also an important area for further investigation.1 )1Frisco, Texas to DFW entering DFW from the North (Figure 2, Scenario 1); 2) DFW to Frisco exiting DFW towards the South (Figure 2, Scenario 2); 3) DFW to Frisco exiting DFW towards the North (Figure 2, Scenario 3); and 4) Frisco to DFW entering DFW from the South (Figure 2, Scenario 4).Each of these scenarios was studied for every DFW runway operation condition listed below for a total of 16 simulation test cases (4 UAM operation scenarios X 4 runway operation conditions).Conventional traffic data for each runway operation condition was derived from the dates mentioned in parenthesis.-Nominal Day in South Flow (June 03, 2017).-Nominal Day in North Flow (November 11, 2017).-Off-Nominal Day in South Flow (July 20, 2017).-Off-Nominal Day in North Flow (August 7, 2017).
+Fig. 22Fig. 2 Flight routes between Frisco, Texas and DFW airports overlaid on FAA Sectional Charts [6] (Clockwise from top left: Scenario 1, Scenario 2, Scenario 4 and Scenario 3)B.Simulation Platform and Software ComponentsStudy simulations used the SaaControl fast-time simulation software developed by NASA as a testing tool for modeling Detect-And-Avoid (DAA) capability of Unmanned Aircraft Systems (UAS-NAS project[7]).Conflict avoidance algorithms (TCAS as one), surveillance and atmospheric models, and pilot response models have been integrated into its core module.SaaControl is capable of running faster-than-real-time NAS-wide simulations.In this study, it detects potential conflicts from raw input traffic data: flight plans for UAM and track data files for conventional aircraft.For the TCAS logic, FAA-supplied TCAS II version 7.1 software was used with a software wrapper developed by NASA, to integrate it into NASA simulation platforms.The wrapper packages TCAS II into a JAVA library callable by clients.It takes aircraft states from a client, calls TCAS II, and returns TCAS responses to the client.
+Fig. 3 A3Fig. 3 A Sample Scenario (CPA is Closest Point of Approach)
+Fig. 44Fig. 4 Region of Interest [8].UAM flight routes in green (North approach/departure -fluorescent green, South approach/departure -dark green), conventional aircraft paths in orange and red and the sensitive areas for analysis, North (figure 5) and South (figure 8) of UAM DFW vertiport, marked with purple boxes.Between the North and South blue lines, conventional aircraft are below 1000 ft AGL.
+Fig. 5 Fig. 6 A56Fig. 5 Sensitive Area North of DFW.UAM flight route in green, conventional aircraft landing and take-off paths in orange and dark red.Bright red arrows denote horizontal separations.South of blue line, conventional aircraft are below 1000 ft AGL.
+Fig. 77Fig. 7 Worst case scenario, when conventional aircraft is above 1000 ft AGL
+Fig. 1111Fig. 11 Risk of triggering RAs on a typical day of operation
+
+
+
+Table 1 TCAS II Version 7.1 Sensitivity Levels and Thresholds for Resolution Advisories [2] Ownship Altitude SL Tau(sec) DMOD(nmi) ZTHR(ft) (ft1)<1000 (AGL)2N/AN/AN/A1000-2350 (AGL)3150.206002350-50004200.356005000-100005250.5560010000-200006300.8060020000-420007351.10700>420007351.10800
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+I. IntroductionThe airspace of the future is expected to support Unmanned Aircraft Systems (UAS) aircraft operations that are orders of magnitude higher than conventional aviation traffic the National Airspace System (NAS) handles today [1][2][3].An important question is if there is a traffic density at which the airspace becomes too complex to operate.This paper presents an approach to estimate the complexity of a given UAS traffic scenario with an associated traffic density.The availability of airspace to meet traffic demand in a safe and efficient manner is central to airspace operations both today and in the future with increasing number of unmanned and manned vehicles sharing the airspace with commercial air traffic.Measures of airspace complexity are used by air traffic management to schedule flights and resolve conflicts.New measures of airspace complexity are needed to make traffic flow decisions as controller workload limitations are enhanced or removed in certain parts of the airspace by increased automation.In conventional aviation, air traffic complexity is evaluated from controller and pilot workload [4][5][6][7].Monitor Alert Parameter (MAP) [8], the maximum number of aircraft an Air Traffic Control (ATC) controller can simultaneously handle, is an example.Another example is Dynamic Density (DD) [9,10], a weighted summation of factors that affect the air traffic complexity.The complexity metrics are defined based on an assumption of a structured airspace and Air Traffic Management (ATM) that includes controller displays, sectors and airways [11][12][13].Fast-time and real-time simulation methods [14] are then used to evaluate a given traffic scenario.Intrinsic metrics have also been developed to estimate complexity in a sector independent of controller workload.Delahaye et.al. [15,16] proposed a geometrical approach based on the properties of relative positions and relative speeds of aircraft in a sector to obtain time histories of traffic divergence, convergence, and sensitivity.They also developed entropy based velocity vector field methods [16,17] to compute complexity maps for given traffic scenario snapshots.Future UAS traffic and its management would differ from conventional traffic in several ways.First, the high number of proposed operations suggests a need to shift from a human to an automated controller, negating the use of cognitive measures.Second, future operations may be free flight by nature i.e. using a predefined on-board conflict resolution model, they may prefer responsibility for determining their course independent of a global plan or system [2,18].Furthermore, a good and quick approximation of UAS traffic complexity, without the need for a full-scale simulation, would support a real-time assessment of traffic scenarios [19], re-planning of flight routes and schedules to alleviate traffic bottlenecks, and mitigation of operation risk.A set of different complexity metrics can together also help classification of traffic scenarios for traffic management studies.This paper extends an earlier approach by Xue [20] that introduced a scenario complexity metric based on the number of potential conflicts weighted by the associated conflict resolution cost.In this work, an impedance-based complexity metric is proposed.When the number of conflicts is high, a scenario is expected to be complex.However, it may not necessarily be so if those conflicts are isolated.The pattern of traffic flow also contributes to the complexity of the scenario, if the aircraft begin to impede the conflict resolutions of others in the airspace.The proposed impedance metric therefore accounts both for the number of conflicts and the free space available around aircraft in conflict.Furthermore, it simplifies the complexity computation process.First, the metric is tuned for a given type of aircraft and conflict resolution method, using high-fidelity simulation data as baseline.Then the impedance for new scenarios can be computed assuming the aircraft as point masses, without having to simulate the vehicle dynamics or actual conflict resolution maneuvers for each of the scenarios.A high-fidelity fast-time simulator, Fe 3 [21], was used to simulate over a thousand randomly generated scenarios and measure the baseline for complexity.The impedance metric was then evaluated separately for the same scenarios and compared against the baseline data and the metric from previous study.The results showed that the correlation between the proposed impedance metric is better than the previous metric.It also produces impedance maps showing regions of high complexity which provides better spatial information for managing air traffic.The rest of the paper is organized in the following way.The test scenario description, and the metric and its evaluation in detail are presented in Section II.Detailed results and discussion are presented in Section III.Summary of the paper, as well as identification of directions for further research is presented in Section IV.
+II. MethodologyIn this paper, the focus is on estimating the complexity of operations in a two-dimensional horizontal portion of airspace that has no constraints such as controlled airspace, temporary flight restrictions, geo-fences or terrain.In the next two sections, the test scenarios and the evaluation of the impedance metric are described in detail.
+A. Test ScenariosThe test scenario generation remains the same as used in [20].To evaluate the complexity metrics, random scenarios with a large variety of complexities were generated.Then the metric referred to as "number of resolution maneuvers" was evaluated in the Fe 3 [21] simulator.The high-fidelity simulator uses dynamic models of aircraft and a pairwise conflict resolution method that employs a combination of speed and direction changes to simulate the trajectories and encounters of aircraft typical to a real scenario.The aircraft are therefore actually diverting during close encounters instead of a prescribed course change.Using the simulation-generated measurements as the baseline for the complexity of the scenario, the proposed complexity metric, Impedance (I), was then analyzed and compared using statistical methods.Note, the number of conflict resolution maneuvers measures the resolution moves issued during the simulation.Since the time step size in Fe 3 is 0.5 seconds, the number of conflict resolution maneuvers also reflects the resolution duration.For the generated scenarios, several criteria were used to ensure high traffic intensity and comparability in scenarios.First, a 1.3x1.3nautical mile region was defined (shown as the red box in Fig. 1), and all flights were required to go through the predefined region with origin and destination outside of the region.Second, at most one turning point was allowed other than the origin and destination in a flight plan.Third, all flights were set to depart within a five-minute window.Lastly, this study focused on low-altitude small UAS traffic.Hence, the target ground speeds of all flights were set in the range of 5 meters per second to 20 meters per second.Fig. 1 shows a sample scenario with 30 vehicles, where the circle, cross, and diamond markers represent origins, destinations, and mid-points, respectively.In [20], the number of aircraft in these scenarios was varied from 5 to 50 (or in density from 3 to 30 vehicles/nmi 2 ).Fig. 2 shows the percentage of scenarios with and without conflicts during the process of generating scenarios.When the traffic density increases, the likelihood of having conflicts increases and reaches 100 % at approximately 15 Fig. 1 A Sample Scenario with 30 flights vehicles/nmi 2 .Additionally, scenarios with aircraft densities from 50 to 100 were also generated in increments of 2. Consequently, a total of 1045 scenarios were created and used.From 5 to 50, 20 scenarios were generated at each level of density and from 52 to 100, 5 scenarios were generated at every alternate level of density.Scenarios without conflicts are defined as having zero scenario complexity based on the proposed metric.Therefore, only the scenarios with potential conflicts are used in experiments.
+Fig. 2 Likelihood of Conflicts at Different Density Levels
+B. The Impedance Metric and Its EvaluationTo evaluate the complexity of a scenario, first a notion of conflict is defined.Two aircraft are assumed to be in conflict at a given time if they are within a distance h sep = k.D wc of each other, where D wc is the well-clear distance (arbitrarily chosen at 50 feet or 15.24 meters) and k is a rational number (≥ 1) multiplication factor.h sep is referred to as Conflict Distance in this paper.This approach is used to calibrate the Impedance evaluation to the region of influence of the conflict resolution method that would have been used in an actual traffic simulation.For a given scenario, the Impedance metric is computed as follows:Let R be the pre-defined region of interest in two-dimensional Euclidean space, i.e.R ∈ R 2 .Grid the region into square cells C xy with a side length l, where x is the row number and y is the column number.Each cell has n adjacent cells C axy , where n ∈ [3,5,8] depending on the location of the cell (corner, edge, interior) in the grid.The letter a is used to denote adjacent.At each instant of time t i in the entire duration of the scenario, compute the aircraft occupancy graph/map O t i =[O xy,t i ], where O xy,t i is the number of aircraft in a cell at that time.Also, at each t i , compute the aircraft conflict graph/map C t i ,c =[C xy,t i ,c ], the set of all cells that have at least one conflict in them.For each C xy,t i ,c , let m of the adjacent cells C axy,t i ,c have at least one aircraft in them over the next dt seconds.This can be obtained for each adjacent cell by summing its occupancy, O xy,t j for t j ∈ [t i + 1,t i + dt].The Impedance of a Cell in space (location xy) at time t i , I xy,t i = m/n.Thus at each time instant t i , there is a colored grid/map produced, called the Impedance MapI t i =[I xy,t i ],where the color of a cell indicates its impedance I xy,t i ∈ [0, 1].Now, to get a single snapshot of the region, the time slices need to be collapsed over the entire period of the scenario.This produces the Impedance graph/map of the Scenario I xy .An example is shown in Fig. 3.This can be done by either taking the time mean or a percentile value of each cell's impedance.To understand the severity of impedance in each cell over the entire scenario, in this study, the p th percentile value of I xy,t is used to collapse the graphs in time.Finally, to get a single impedance number for the whole scenario, both the space and time dimensions need to be collapsed.To do this, compute the percentage of cells in the time collapsed map with I xy ≥ P , where P is the chosen impedance threshold.This gives the Impedance, I for the entire scenario.For example, suppose choosing the 99 th percentile value for time collapse and an impedance threshold, P = 20%, results in an impedance value of 0.3 for a scenario.This can be interpreted as -30% of the region has conflicts that are impeded by nearby aircraft in one-fifth (20%) of the vicinity, 1% of the time.In other words, conflicts in almost one-third of the region are impeded in the scenario.The metric computation uses two parameters: the conflict distance parameter k and the time window parameter dt.A set of traffic scenario complexities evaluated in a high-fidelity simulator like Fe 3 , with a given conflict resolution model, is used to tune the impedance metric parameters to achieve the maximum correlation.Then, the tuned parameters can be used to evaluate the complexity for any new scenario (comprising the same type of aircraft and the same conflict resolution model) without the need for high-fidelity simulations.The metric captures the effect of aircraft dynamics and resolution models in its parameters.For example, an aircraft with low maneuverability will need more space and the conflict resolution method will have a high conflict distance when evaluating the baseline data in Fe 3 .That in turn means the Impedance metric tuned for that type of aircraft and conflict resolution method will have a different value of k and dt where it is most correlated with the baseline data.In other words, the tuning of the Impedance metric parameters is tied to the type of aircraft and conflict resolution method that will be used in the real scenarios.
+C. AssumptionsIn addition to the scenario assumptions stated earlier, the chosen values of different parameters are defined as follows:-The cell edge length l = 100m (0.054nmi).-For each scenario, impedance is computed by varying the k value between 1 and 5, to ensure that the conflict distance is less than the cell edge length.-The time window dt is varied between 3 seconds and 17 seconds with 2-second increments.Since each aircraft flies at a speed between 5 to 20 meters per second, an aircraft will leave a cell anytime between the next time step to 20 seconds at most.Hence, time window values are chosen between those numbers, ignoring the smallest and largest values.On average it will take about 5 seconds to reach an adjacent cell.-The percentile number p = 99.9.This is done to capture the worst impedances observed at every cell over the entire duration of the scenario.-The impedance threshold P is varied from 10% to 80%.In other words, if 10% impedance is considered bad, having a third aircraft in the vicinity is considered bad and every moderate to bad cell will contribute to the scenario complexity.This is typically what might be considered bad in a realistic scenario today.On the other hand, if only 80% or more impedance is considered bad, only the worst of the bad cells will contribute.This could be the case for a highly futuristic scenario where multi-aircraft conflicts are operationally acceptable.-Since this study focuses on scenario intrinsic complexity, uncertainties on wind, communication, navigation, and surveillance were not included in the simulations.After the impedance metric for each scenario is evaluated, the Pearson method is used to compute the correlation between the impedance measures and the number of resolution maneuvers (baseline) for each scenario.This varies as a function of the chosen k, P and dt values and is discussed under results.Furthermore, since the Pearson method is designed for checking linear correlations, a maximal correlation method [22] is also used to capture any non-linear association, and the Alternative Conditional Expections (ACE) method implemented in Matlab is used to compute such maximal correlations.
+III. ResultsImpedance maps were generated for each scenario at different conflict distances and time windows.For any given time window, two common trends were observed.For a fixed conflict distance, h sep , as the traffic density was increased, the spread and value of the impedance increased (the cells became yellower/brighter) (Fig. 4).For a fixed traffic density, as the conflict distance was increased, again the spread and value of impedance increased (Fig. 5).However, the effect was less pronounced.This indicates that the metric is more sensitive to the traffic density than the conflict distance.For computing the final impedance number for each scenario, the cell impedance threshold was varied from 10% to 80%.This was repeated at different conflict distances.For each time window, to determine the best combination of conflict distance and cell impedance threshold, correlations were computed between the impedance numbers and the baseline data.In general, as the conflict distance was increased, the peak correlation was observed at higher impedance thresholds.It was found that a conflict distance, h sep of 75 feet and a threshold, P of 10% had the best correlation coefficient when compared with the number of resolution maneuvers from the actual simulation, irrespective of the time window.As the time window was increased, the correlation improved up to a time window of 9 seconds, and then deteriorated.The best Pearson correlation observed was 0.9207 for the 9-second time window (Fig. 6).The correlation obtained for the same using the ACE method was 0.9325.The corresponding best correlation coefficients for the weighted conflict complexity metric from our earlier work were 0.9 and 0.913, respectively [20].The impedance metric therefore performed better.
+A. DiscussionThe impedance metric serves two purposes.First, the space and time collapsed impedance of a scenario provides a single complexity number in the usual sense of measuring airspace complexity.It captures the impact of both number of conflicts, and the relative spatial distribution of aircraft in conflict with respect to other aircraft in vicinity, which could impede the performance of the conflict resolution strategy.Second purpose is the impedance map, which provides visual information to identify the hot spots: regions with limited conflict resolution capability in the scenario.This is useful in not only flagging a scenario as too complex but also pin-pointing where the problem is.Consequently, more informed air traffic management decisions can be taken.Suppose an arbiter runs a scenario and the impedance is above a threshold, then the maps show her where the hot spots are, and she could either deny the whole scenario, or just the flights which go through that area, or provide a reroute to flights going through hot spots, and so on.
+IV. ConclusionsIn this paper, an impedance-based metric was introduced to represent the complexity of a given unmanned aircraft system traffic scenario.There were 1045 scenarios analyzed, and their impedance metric was computed and compared against the baseline data produced from high-fidelity simulation.The metric was evaluated for varying conflict distances and traffic scenarios.It was found that the proposed metric had a high correlation of 0.92 (Pearson) and 0.9325(ACE) Additionally, the metric provides a way to account for both the number of aircraft and the traffic flow pattern.The impedance maps produced as part of the impedance computation process identified areas of concern in a given scenario.Such information may be helpful for developing traffic management strategies such as adjusting and re-planning only flights that pass through the most impeded areas.The air traffic services in a UAM environment may be provided by one or more operators.Each operator needs real-time tools to assess the safety and efficiency of operations and make adjustments to changing traffic demands.The impedance metric provides a tool to identify hot spots, regions with limited conflict resolution capability, in the airspace operations.It can therefore also be used to assess if and how the hot spots vary with uncertainties like sudden changes in demand, wind speed variations and low visibility.Similarly, it could be used to reallocate demand to maintain safety.The results presented in this paper investigated the top 0.1% impedances in each cell for a scenario.In other words, the metric is only studying the worst 1 minute for every 1000 minutes in an area.Other levels of impedance can be explored further and tested against the baseline.Also, for this analysis, a fixed grid cell edge length was assumed.Grids with varying edge lengths could also be explored.Finally, this work extends our earlier work that introduced a weighted conflict-based scenario complexity metric.The impedance metric performed marginally better than the weighted conflict metric.This is part of an extended effort to develop complexity metrics that can be computed in real time without the need for scenario simulation.This therefore can be used for jointly classifying a scenario as acceptable, unacceptable or acceptable with changes made to flight plans that pass through high complexity regions and then applying necessary air traffic management strategies.Fig. 33Fig. 3 The Impedance Map of the Sample Scenario with 30 flights at conflict distance, h sep = 60.96m, for a time window, dt = 5 sec
+Fig. 44Fig. 4 Impedance maps with varying traffic density and fixed conflict distance, h sep = 45.72m for a time window, dt = 9 sec
+Fig. 55Fig. 5 Impedance maps with varying conflict distance, h sep and fixed traffic density for a time window, dt = 9 sec
+Fig. 66Fig. 6 Correlation between Impedance and Number of Resolution Maneuvers (baseline) as a function of conflict distance and impedance threshold at dt = 9 sec
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diff --git a/file109.txt b/file109.txt
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+
+	
+	
+		
+BACKGROUNDIn support of the Dynamic Interface Modeling and Simulation System (DIMSS) task in the Navy's Joint Shipboard Helicopter Integration Process (JSHIP) program, a real-time, piloted simulation experiment was conducted in the Vertical Motion Simulator (VMS) facility at NASA Ames Research Center.The purpose of the experiment was to develop and evaluate the capability of simulation to conduct dynamic interface testing, in order to establish the operational envelope for helicopters landing on ships in various wind conditions and sea states.The experiment simulated a UH-60 helicopter landing on an LHA ship, and incorporated an unsteady (timevarying) ship airwake model computed by Computational Fluid Dynamics (CFD) techniques.
+THE VERTICAL MOTION SIMULATORThe VMS is the world's largest R&D motion-base flight simulator.To study a variety of different aircraft, the VMS uses interchangeable cabs.For this particular study, an interchangeable cab was created to emulate the UH-60 helicopter.Barco rear-projection displays were installed to give a wide-angle out-the-window view approximating that seen from the cockpit of a Black Hawk, and a close representation of its instrument panel was generated using computer graphics.A DEC Alpha simulator host computer ran the Open VMS operating system.The simulation also included an Evans and Sutherland ESIG 4530 computer-generated "out-the-window" display system; graphical displays generated by Silicon Graphics computers, simulating the aircraft instruments; a hydraulic control loader system to simulate the control inceptors; and an ASTi sound simulation system.For this study, two other items were integrated into the simulator: a CATI PCbased XIG "out-the-window" display system, for comparison with the ESIG 4530; and a dynamic seat.The Evans and Sutherland ESIG 4530 was upgraded to include an "ocean wave" simulation capability.The waves were specified in terms of modal period and significant wave height.A graphical model of an LHA ship was developed for the simulation, and a real-time version of the CARDEROCK Ship Motion Program (SMP) was integrated into the simulator host computer to provide realistic ship motion.A Landing Safety Enlisted (LSE) man, with limited hand-signaling capability, was also programmed into the ESIG.
+UH-60 MODELThe UH-60 helicopter was simulated using a bladeelement model originally developed by Sikorski Aircraft and documented under contract from NASA. 1 The helicopter model consists of a main rotor model, aerodynamic models of the fuselage, horizontal and vertical tail surfaces, and tail rotor, as well as a simulation of the engine, drive train, flight controls, and landing gear.The blade-element main rotor model used five "equal annuli" segments on each of the four blades.All calculations for the helicopter flight model, including the airwake, were performed at an update rate of 100 Hz.
+CFD AIRWAKE MODELThe CFD airwake model consisted of a matrix of time histories at each of 56,661 grid vertices in the region around the ship.The airwake model data were developed by NAVAIR's Advanced Aerodynamics Branch, at Patuxent River Naval Air Station, using a Navier-Stokes formulation of the viscid flow equations in the vicinity of the ship. 2 This simulation produced time-varying values for the three components of the airwake velocity at each of the spatial points.Each of these time histories consisted of 30 seconds of data, sampled every 0.1 second, for a total of 300 points.Therefore, the total number of data values was 50,994,900.
+STORING THE DATAEach of the floating-point data values in the CFD airwake model required 4 bytes of storage in the computer.This would constitute a total file size of nearly 204 Megabytes if the data were stored as a binary file.However, binary files are not computerportable.In order to make the data computer-portable, a special format was developed that would be readable on any system, but would be the same size as a binary file.The data were inspected, and it was determined that the values were always less than 300 feet per second.It was also decided that an accuracy of 0.01 feet per second would be acceptable.Therefore, if the values were multiplied by 100 and truncated to an integer, the result would always be less than 32767.This meant that the data could be scaled and stored in memory as a signed 4-byte integer; and it could be written to a disk file under FORTRAN Z4 format, consisting of four ASCII characters.Thus, the data could be transported in files of the same size as binary files; but, since these files actually consisted of ASCII characters, they would be totally computer-portable.Another issue was the media on which to store the data.Compact disks (CD-ROM's) were the media of choice, since several sets of data could be stored on each one.The following compact disc specifications were adopted to minimize the difficulties in transferring the data:(1) The media type should be CD-R, not CD-RW.(2) The logical format should be ISO 9660, but may use the Joliet extensions (long file names).(3) The CD should not be CD-XA format.
+LOADING THE DATADue to the size of the data files, it was found that special techniques had to be implemented in order to minimize the time required to change data files.This was necessary because the experimental test plan called for frequent changes of wind azimuth (requiring a change of the data file).The technique used was developed specifically for the Open VMS operating system.It is based on the use of a Global Data Section, which is a section of memory that is allocated and named, and filled with data to be used by other programs.A pre-processor was written that would read the data, convert it from INTEGER*4 to REAL*4 and scale it by a factor of 100, and then output it to a file in the special binary format required for the Global Data Section.Whenever a different wind azimuth was desired, a simulation engineer would run a program that would create a Global Data Section using the desired data file.A command issued within the simulation would map an array in the code to the data in the Global Data Section.By using this technique, the time to change from one azimuth to another was reduced from about 20 minutes to a matter of seconds.Although this technique is dependent on specific Open VMS utilities, similar techniques could be developed for other operating systems.
+COORDINATESIn developing and implementing the airwake model and integrating it with the simulation, a number of different coordinate systems are involved:
+CFD Wind CoordinatesThe CFD data were generated using a set of coordinates originating at deck level, on the centerline of the ship, at the most forward part of the bow.The x-axis points aft, parallel to the centerline; the y-axis is toward the starboard side of the ship; and the z-axis is upward, perpendicular to the deck.This forms a right-handed coordinate system.The velocity components are positive when they blow along the positive axes; in other words, a wind blowing from bow to stern, port to starboard, and upward, has all positive components.
+CFD Grid CoordinatesThe CFD grid coordinates are different from the coordinates in which the velocities are represented.The grid coordinates also originate at deck level at the bow on the centerline; however, the coordinates form a left-handed system: the I-axis points aft, the J-axis points to port, and the K-axis is upward.The CFD grid is non-uniform; that is, the blocks of the grid have different sizes, depending on the location.Basically, the grid blocks get larger as the distance from the ship gets larger, as shown in Figure 1.The Earth Axes used in the simulation are local North, East and Down.Their origin, initialized to an arbitrary location relative to the ship, is used for the location of the aircraft.
+Ship AxesThe ship axes used in the simulation originate at the C.G. of the ship, pointing forward, starboard, and downward.
+Aircraft AxesThe aircraft axes originate at the aircraft C.G., pointing forward, right, and downward.
+Blade AxesThe helicopter blade axes are the axes in which the data are used in the rotor model.These axes are tangential, radial and perpendicular to the blade.For the airwake code, the transformation to this axis system was simplified by ignoring flapping and lagging angles.
+INTEGRATING THE AIRWAKE MODEL INTO THE SIMULATIONFor the airwake velocity components to affect the simulated aircraft, the velocity components need to be turned into forces and moments at the center-of-gravity (C.G.) of the vehicle.To do this, the airwake components were calculated at each of the aerodynamic centers of the helicopter.These consist of the fuselage aerodynamic center, the horizontal and vertical tail, the tail rotor, and at each of the main rotor blade segments.In order to look up the airwake velocity components, it was first necessary to locate each of the aircraft aerodynamic centers in the grid coordinates.The airwake data were extracted from the database at eight points adjacent to the aerodynamic center.The values were interpolated to the current time (modulo 30), and then interpolated on the spatial coordinates.Then the velocity components were related to the proper axis system.In order to keep the number of table lookups reasonable for real-time computation, a different algorithm was used to interpolate to the blade-elements of the main rotor.For this, the five points extracted from the database were the rotor hub, and the center of the outermost segment of each blade, as shown in Figure 2.
+Figure 2. Main Rotor Data Lookup PointsThe airwake velocity components at each of the other blade elements were found by interpolating along the blades.In this way, the total number of table lookups was kept to 432.This consists of one for each of the 5 points on the main rotor, plus one for each of the other 4 aerodynamic centers (giving 9 locations); times 2 for the number of time points to be interpolated for each point; times 8 for the number of adjacent points for the spatial interpolation; times 3 for the number of velocity American Institute of Aeronautics and Astronautics components at each point.If the data were to be looked up for each of the twenty blade elements, it would have been necessary to perform 1152 lookups.Although the CFD data had been computed with the ship stationary, it was allowed to steam along a straight course in the simulation.In order to simulate this situation correctly, it was necessary to separate the ambient wind from the airwake.This was done by solving for the ambient wind which, combined the ship speed and direction, would yield the desired wind-overdeck speed and azimuth corresponding to one of the sets of CFD data.The ambient wind was then added into the calculation of the airspeed of the aircraft, and the wind-over-deck was subtracted from the airwake data.The resulting airwake velocity perturbation components at each of the aerodynamic centers were then transformed to the proper coordinates (usually aircraft body axes; except that for the main rotor, blade axes were needed), and then added to the local velocity used to calculate the aerodynamics at that location.Another option that was provided was the capability to simulate wind-over-deck speeds different from the CFD data.All the CFD data had been run at a speed of 30 knots.In order to simulate different wind speeds, it was necessary to scale both the magnitude and frequency spectrum (or time).Scaling the magnitude was simply a matter of multiplying the airwake velocity components by the ratio of the wind-over-deck speed divided by the nominal speed (30 knots).In order to scale the frequency spectrum, the independent time variable used to look up the data in the time history was scaled by the same factor.
+SIMULATION VERIFICATIONThe airwake velocity components integrated into the simulation were verified by comparison with the raw CFD data at specified locations.The total effect was evaluated by experienced UH-60 pilots in the simulator, and was found to be generally realistic and to produce a workload representative of a shipboard landing.One pilot said, "Better than a generic model," and another said, "...better than any training simulator."Some negative comments were also received, but they can be explained by some of the known errors that were present in the airwake model at the time of the evaluation, but that have since been corrected.
+FREQUENCY ANALYSISIn order to better understand the effects produced by the introduction of the time-varying airwake into the UH-60 math model, frequency analysis was performed using a software tool known as Comprehensive Identification from Frequency Responses 3 or CIFER®.Three cases were simulated: one in still air, one with 30 knots of wind-over-deck (but without airwake turbulence), and one with 30 knots of wind-over-deck and airwake turbulence.Each case with wind was run at the two wind-over-deck azimuths of 0 and 60 degrees.Each simulation run was 30 seconds in length, in order to utilize the full 30 seconds of airwake data, thus preserving the low frequency data.Each run was made with the aircraft C.G. at an altitude of 10 feet over the deck of the LHA, hovering over landing spot #5 -just to port of the forward part of the "island" (superstructure) of the ship.For each run, a software flag was set to disable the integration of acceleration to velocity and velocity to position in the equations of motion.This effectively "froze" the aircraft in space, so that the forces and moments exerted on the aircraft by the airwake, and their resulting accelerations, could be analyzed without spurious transition to different locations during the run.For each of these runs, the ship was stationary, with a heading of 0 degrees (North).The aircraft also had a heading of 0 degrees, with zero ground speed (hovering over the deck).Figures 3 through 14 are Power Spectral Density (PSD) plots of the linear and rotational accelerations.Each plot shows three cases.The solid line shows the still air case, the dotted line represents the case with wind but not airwake, and the dashed line shows the case with the airwake.The series of plots on the left is for a wind azimuth of 0 degrees; the plots on the right are for a wind azimuth of 60 degrees.Note the peaks at 108 and 216 radians per second.These are the fundamental and second harmonic of n/rev: there are 4 blades, and the rotation rate is 27 radians per second (257.8rpm).In each case, the effect of adding steady wind does not significantly affect the frequency or energy content in the acceleration responses, except for some additional low frequency energy in some cases.The addition of airwake turbulence is significant in all cases, however, producing a substantial increase in total energy over a wide bandwidth.This effect contributes to pilot workload when landing on a ship in windy conditions.One pilot wrote, "Workload frequency (and magnitude, to a lesser extent) increased markedly in all axes to counteract turbulence."American Institute of Aeronautics and Astronautics
+RECOMMENDATIONSIn order to validate this model, a series of flight tests should be conducted, in which the helicopter is mounted on a stationary test stand attached to the deck of a ship.Instrumentation should include measurements of the forces and moments acting on the airframe and test stand.The tests should be conducted in a variety of wind-over-deck conditions, with the controls fixed, rotor turning at normal RPM, and the collective set so that the total lift force approximates the aircraft weight.Frequency analysis could then be used to compare the test data with a simulation of the same conditions.The volume in which the airwake is computed is necessarily bounded in order to keep the data storage requirements reasonable.In order to avoid transients at the boundary, the lookup algorithm held the function values at the boundary for positions outside the volume.Over the deck, the top of the volume was at about 51 feet above the deck, extending to 300 feet at 1000 feet from the ship.Although the experimental test plan called for the pilots to stay within the boundaries, they occasionally exceeded these limits in their familiarization flights.Since the volume over the deck was limited to 51 feet above deck height (but the island extends to about 80 feet above the deck), the pilots noticed that they did not reach a freestream airflow in a vertical ascent past the top of the island.In order to make the simulation more realistic, a heuristic fadeout should be applied to the airwake turbulence outside of the volume in which the CFD data are defined.
+CONCLUSIONSSignificant techniques to simulate a helicopter flying through ship airwakes have been developed and demonstrated in piloted simulation.Subjective pilot evaluation has shown that the techniques produce a realistic environment, with appropriate increases in pilot workload.Frequency analysis of acceleration histories show that the simulated airwake produces much more frequency content and total energy than steady simulated winds.Figure 1 .1Figure 1.Non-uniform CFD Grid
+c) 20012001American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
+c) 20012001American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)1 Sponsoring Organization.
+Figure 3 .Figure 6 . 1 FrequencyFigure 9 .Figure 13 .361913Figure 3. Rolling Acceleration -Wind Azimuth 0 Degrees Figure 4. Rolling Acceleration -Wind Azimuth 60 Degrees
+		
+		
+			
+
+				
+
+
+	
+		UH-60A Black Hawk Engineering Simulation Program: Volume I -Mathematical Model
+		
+			JJHowlett
+		
+	
+	
+		NASA CR-166309, United Technologies, Sikorski Aircraft
+		Stratford, CT
+		
+			December 1981
+		
+	
+	Howlett, J.J., "UH-60A Black Hawk Engineering Simulation Program: Volume I -Mathematical Model," NASA CR-166309, United Technologies, Sikorski Aircraft, Stratford, CT, December 1981.
+
+
+
+	
+		Time-accurate computational simulations of an LHA ship airwake
+		
+			SusanPolsky
+		
+		
+			ChristopherBruner
+		
+		10.2514/6.2000-4126
+		AIAA-2000-4126
+	
+	
+		18th Applied Aerodynamics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			August, 2000
+		
+	
+	Polsky, S., and Bruner, C., "Time-Accurate Computational Simulations of an LHA Ship Airwake", AIAA-2000-4126, August, 2000.
+
+
+
+	
+		Frequency-Response Method for Rotorcraft System Identification: Flight Applications to BO 105 Coupled Rotor/Fuselage Dynamics
+		
+			MarkBTischler
+		
+		
+			MavisGCauffman
+		
+		10.4050/jahs.37.3
+	
+	
+		Journal of the American Helicopter Society
+		j am helicopter soc
+		2161-6027
+		
+			37
+			3
+			
+			July 1992
+			American Helicopter Society
+		
+	
+	Tischler, M.B., and Cauffman, M.G., "Frequency- Response Method for Rotorcraft System Identification: Flight Applications to BO-105 Coupled Rotor/Fuselage Dynamics," Journal of the American Helicopter Society, Vol. 37, No. 3, pp. 3-17, July 1992.
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+In order to develop an aerodynamic database representing a tail-less vehicle, the CFD (computational fluid dynamics) team re-calculated the data using the same geometry but with the tail removed.Arbitrary, undefined control devices (which could be split-ailerons or other asymmetric drag devices) were assumed to replace the yawing moment of the rudder.These surfaces were not modeled in the CFD analysis; rather, the yawing moment coefficient due to the yaw control devices was arbitrarily set to a value that would yield approximately the same yaw control power that the rudder had produced.The flight control system was then modified to augment the directional stability of the tail-less aircraft, and the gains were re-optimized.The aircraft math model was then tested in the Vertical Motion Simulator (VMS) facility at NASA-Ames.This not only showed the quick turnaround possible with the RITE process, but also demonstrated the feasibility of the tail-less re-entry vehicle concept, and provided insight into the required control effectiveness of the yaw control devices.
+RAPID INTEGRATION TEST ENVIRONMENTThe RITE (Rapid Integration Test Environment) process was developed at Ames Research Center to promote rapid turnaround in the aircraft design cycle. 1 In this process, a multi-disciplinary integrated team developed the design using design optimization tools, calculated the aerodynamic data using CFD techniques, designed a flight control system architecture, and optimized the control gains using an off-the-shelf control design software tool.Then the vehicle was simulated in the VMS facility.
+A SHARP RE-ENTRY VEHICLEThe CTV8 vehicle was a modification of the final geometry developed in the RITE-3 project. 2 It is a crew transfer concept vehicle, smaller than the Space Shuttle and intended only for transport of personnel to and from the International Space Station, not for cargo delivery.The CTV8, like its predecessors, incorporates SHARP (Slender Hypersonic Aerothermodynamic Research Probes) technology. 3,4,5By using ultra high temperature ceramics (UHTC) for the leading edges, the leading edges can be made sharper than is the case in current re-entry vehicle design, which yields a higher hypersonic lift-to-drag ratio, thus allowing a larger potential landing footprint.The CTV8 design is shown in Figure 1.
+Figure 1 The CTV8 DesignA modification was made to the control surface configuration of the vehicles tested in RITE-3.In those simulations, it was found that the split-rudder used as a speed brake caused excessive pitch-up moment, which made it difficult to control when the speed brakes were opening.To mitigate this problem, upper and lower body flaps were added for use as speedbrakes.Each of the flaps produced a smaller pitching moment due to their proximity to the plane of the center-of-gravity of the vehicle, and simultaneous deflection of the upper and lower flaps causes their pitching moments to partially cancel each other.
+AERODYNAMIC MATH MODELA simplified aerodynamic math model of the vehicle was developed using computational fluid dynamics simulations 6 .The methods used included both the Navier-Stokes (viscous) and Euler (inviscid) formulations of the flow equations. 7,8A vortex lattice method was used to calculate the dynamic stability derivatives. 9The resulting file was then uploaded to an Internet-based data management system, to allow easy access by all interested parties to the project.The aerodynamic data tables were converted to the format required by the Function Table Processor (FTP) used in the NASA-Ames Vertical Motion Simulator facility.The data tables were then downloaded to the simulation host computer, and processed by the FTP.The FTP compiles the aerodynamic data into code that provides efficient table lookup with linear interpolation for real-time simulation.
+FLIGHT CONTROLSAs in the previous CTV simulations, the flight control system was developed using SimuLink and the CONDUIT® control optimization tools. 10like most other control optimization tools, CONDUIT® accepts flying qualities specifications defined by the user, and attempts to optimize the control gains to meet those specifications.This provides a user-friendly environment, and allows vehicles having different aerodynamic characteristics and/or different control system architectures to be optimized to meet a common set of specifications.The pitch control system used the same Nz-Q architecture as the CTV simulations in the previous year's RITE experiment (see Figure 2).This blended feedback system, previously used in the HL-20 (a reentry vehicle design concept investigated at NASA-Langley), provides an approximate glideslope angle rate command by scaling the normal acceleration by the inverse of the airspeed, and augments the pitch damping with pitch rate feedback.The roll control system provided augmented roll damping by feeding back roll rate to the ailerons (see Figure 3).The aileron command was scheduled inversely with dynamic pressure to maintain relatively constant performance throughout the approach.An "aileron fail" mode was provided, as well, to demonstrate the capability of the RITE process to assist in the development of fault-tolerant flight controls.This mode used the elevators differentially for roll control, as well as symmetrically for pitch control.This capability was implemented in an aileron-elevon control mixer (see Figure 4).
+Figure 4 Aileron-Elevon MixerThe yaw control system used in the previous RITE/CTV simulations had used sideslip rate (beta dot) feedback (see Figure 5).This was found to provide excellent tracking over the runway in the presence of gusty winds.However, in order to do that, the control system caused the aircraft to make rapid yaw corrections in response to the gusts.The astronaut pilots felt that this would be unacceptable, since it would be disorienting to a "de-conditioned" pilot returning from a long space mission.Therefore, alternatives were developed.One of these was to use a complimentary filtering technique to combine the low frequency components of the sideslip rate with the high frequency components of the yaw rate.This worked well; but, somewhat surprisingly, did not seem to have any real advantage over a classical yaw damper, consisting of a simple washed-out yaw rate feedback to the rudder.These different mechanizations were implemented by varying the feedback gains in the yaw control system.In each mechanization, the rudder command was scheduled inversely with dynamic pressure in order to maintain constant performance.
+Figure 5 Yaw Control SystemAs with the previous simulations, a speed control system was included.This system modulated the speedbrake deflection in order to provide an airspeed hold capability (Figure 6).Because the vehicle configuration used upper and lower body flaps as speedbrakes, it was expected that the pitching moment changes produced by speedbrake deflection would be negligible.However, when the astronaut pilots began to fly the simulation, they discovered an objectionable tendency of the aircraft to overshoot the desired attitude in the pre-flare maneuver.It was determined that this was caused by the changing pitching moment produced by the speedbrakes, which were closing because of the increasing drag due to the change in attitude.This was mitigated by scheduling the lower body flap as a function of the average deflection of the upper body flaps in such a way as to minimize the net pitching moment.This was successful, and the tendency to overshoot in the pre-flare was barely detectable with the modified schedule.It should be noted that no design tradeoff was done in this study, so no conclusions have been reached regarding whether a tail-less design would really be better than the more conventional design with a vertical stabilizer and rudder.This study was just a first look at the tail-less option, to see if it would be potentially controllable, and if reasonable handling qualities were potentially achievable.In order to provide an aerodynamic model of the modified vehicle, the CFD team developed a design in which the only difference was that the vertical tail (including both the vertical stabilizer and rudder) was removed (see Figure 7).Due to lack of time and funds, the vehicle geometry was not optimized as a tail-less design.Also, the new yaw control surfaces required to replace the rudder were not designed, nor were they included in the CFD analysis.New CFD simulation runs were made, using the baseline CTV8 configuration with the vertical tail removed, without yaw control devices, and the data were integrated into the piloted simulation.
+Figure 7 The Tail-Less CTVOf course, such a design requires an active stability augmentation system (SAS) to provide adequate weathercock stability.This means that the yaw SAS must operate full-time, and must have adequate redundancy to mitigate the possibility of failure.For the purposes of this preliminary study, it was assumed that suitable yaw control devices (such as "split ailerons" or other differential drag devices) could be developed, having sufficient yaw control power to provide the needed stability and control.Then, if the vehicle could be made stable and controllable, a first cut approximation of the necessary yaw control power could be specified to the designers as a requirement.In order to provide weathercock stability, a sideslip angle feedback path was added to the directional control system, using proportional plus integral compensation (see Figure 8).The architecture of the flight controls for the other axes was not changed; however, the gains for all axes were re-optimized, since the aerodynamic data had changed.
+Figure 8 Modified Yaw Control SystemThe resulting vehicle simulation turned out to have very good handling qualities.In addition, it was determined that the new vehicle also worked well with the "failed aileron" flight control system, using the elevators differentially for roll control, as well as symmetrically for pitch control, as was done with the nominal CTV8 configuration.This was good news, since it meant that the tail-less vehicle would not require ailerons for roll control.That meant that yaw control devices could be installed in place of the CTV8's ailerons.This would, however, remove some control redundancy from the design.Or, if "split ailerons" were used to provide both roll and yaw control, then the elevators could still provide control redundancy as on the CTV8.
+THE SIMULATION EXPERIMENTThe tail-less CTV configuration was simulated in the Vertical Motion Simulator at NASA-Ames Research Center.The simulator cockpit was configured as it would be for the Space Shuttle simulation (Figure 9).The simulation was compared with simulations of the CTV8 and the Space Shuttle.Both CTV configurations were found to be much more maneuverable in roll than the Space Shuttle, and to have excellent handling qualities in all axes.
+Figure 9 Simulator Cab Interior
+CONCLUSIONSThe Virtual Flight -RITE process resulted in a rapid design cycle.The tail-less CTV was developed and tested in piloted flight simulation in less than one week from the time that the decision was made to do it.The simulation experiment showed that a tail-less vehicle similar to the SHARP CTV conceptual designs developed at NASA-Ames would not be difficult to control, and could potentially be made to have excellent handling qualities.It was found that yaw control devices with approximately the same yaw moment control power as the rudder on the CTV8, driven by actuators with time constants of 0.025 seconds, would be adequate to stabilize the tail-less version.However, no analysis was performed to estimate the control power that could be achieved with physically realizable control surfaces.It was also demonstrated that adequate roll control performance could be achieved using the elevators as elevons, so that the ailerons could be replaced by differential drag yaw control devices.
+RECOMMENDATIONSSince the aerodynamics of the assumed yaw control devices were never calculated, any future work on such a vehicle should include the design of possible yaw control devices and calculation of their effect on the aerodynamics.Also, since the CTV8 was never optimized to be a tail-less configuration, optimization should be carried out.The mass distribution for the tailless vehicle should also be determined, and new moments of inertia should be calculated.This would also allow determination of any potential weight savings.Finally, a design tradeoff study should be conducted to compare the costs and benefits of a tailless configuration to the standard configuration with a tail.Figure 22Figure 2 Pitch Command Generator
+Figure 33Figure 3 Roll Command Generator
+Figure 66Figure 6 Speed Control System
+11Pitch RateLead LagQb GainLon StickStick ShapingStick Gain-+ +XElev Gain+ +AutoTrimElev CmdLowDelta NzXLead LagNz GainPassVtGain SchedDyn PressElevator Gain Sched
+Reference Dyn Press Yaw Rate Yaw Rate Gain Washout Filter Beta Dot Rudder Pedals Beta Dot Gain Rudder Pedal Gain + + + Low Pass Filter Kdr Dyn Press Rudder Actuator Rudder
++10 Non-linear Gearing Lt. Upper Body Flap Actuator Rt. Upper Body Flap Actuator Lt. Upper Body Flap Speedbrake CommandEquiv Airspeed+ -Kp+ +Proportional + IntegralCompensationH > 1200VholdKi1/sH < 120015%-0.4+ +Lower Body Flap ActuatorLower Body Flap-10/+3010.-30/Rt. UpperBody Flap
+Reference Dyn Press Rt. Yaw Actuator Rt.Yaw Device Lt. Yaw Actuator Lt.Yaw Device Yaw Rate Yaw Rate Gain Washout Filter Beta Dot Beta Dot Gain Low Pass Filter Rudder PedalsBetaBeta Gain+ + +Rudder Pedal Gain+KyawDyn Press
+		
+		
+
+			
+ACKNOWLEDGEMENTSThe author would like to acknowledge the following people for their significant contributions to this work: Jorge Bardina, Kenny Cheung, Susan Cliff, Arsenio Dimanlig, Ron Gerdes, Veronica Hawke, Jeff Homan, Dave Kinney, Julie Mikula, Joe Ogwell, Chun Tang, Alex Te, Mark Tischler,and Dan Wilkins, as well as the astronaut pilots who participated in the flight simulation tests: Eric Boe, Ken Ham, Charlie Hobaugh, Scott "Doc" Horowitz, Greg "Ray Jay" Johnson, Steve Lindsey, Barry Wilmore, and George Zamka.
+			
+
+			
+
+				
+
+
+	
+		Rapid Integration Test Environment: An Integrated Process for Aircraft Design
+		
+			JohnBunnell
+		
+		
+			JulieMikula
+		
+		10.2514/6.2002-4479
+		AIAA-2002-4427
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Monterey, CA
+		
+			American Institute of Aeronautics and Astronautics
+			August 2002
+		
+	
+	Bunnell, J.W., and Mikula, J.A., "Rapid Integration Test Environment: An Integrated Process for Aircraft Design," AIAA-2002-4427, Monterey, CA August 2002.
+
+
+
+	
+		Vehicle Design of a Sharp CTV Concept Using a Virtual Flight Rapid Integration Test Environment
+		
+			FannyZuniga
+		
+		
+			SusanCliff
+		
+		
+			DavidKinney
+		
+		
+			VeronicaHawke
+		
+		
+			ChunTang
+		
+		
+			StephenSmith
+		
+		10.2514/6.2002-4881
+		AIAA-2002-4881
+	
+	
+		AIAA Atmospheric Flight Mechanics Conference and Exhibit
+		Monterey, CA
+		
+			American Institute of Aeronautics and Astronautics
+			August 2002
+		
+	
+	Zuniga, F.A., Cliff, S.E., Kinney, D.J., Hawke, V.M., and Tang, C.Y., "Vehcile Design of a Sharp CTV Concept Using a Virtual Flight Rapid Integration Test Environment," AIAA-2002-4881, Monterey, CA, August 2002.
+
+
+
+	
+		A reusable space vehicle design study exploring sharp leading edges
+		
+			JamesReuther
+		
+		
+			DavidKinney
+		
+		
+			StephenSmith
+		
+		
+			DeanKontinos
+		
+		
+			PeterGage
+		
+		
+			DavidSaunders
+		
+		10.2514/6.2001-2884
+		AIAA-2001-2884
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Reuther, J.J., Kinney, D.J., Smith, S.C., Kontinos, D.A., Saunders, D., and Gage, P., "A Reusable Space Vehicle Design Study Exploring Sharp Leading Edges," AIAA-2001-2884, June 2001.
+
+
+
+	
+		Conceptual design of a 'SHARP' - CTV
+		
+			DavidKinney
+		
+		
+			JeffBowles
+		
+		
+			LilyYang
+		
+		
+			CathyRoberts
+		
+		10.2514/6.2001-2887
+		AIAA-2001-2887
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Kinney, D.J., Bowles, J.V., Yank, L.H., and Roberts, C.D., "Conceptual Design of a 'SHARP' CTV," AIAA-2001-2887, June 2001.
+
+
+
+	
+		Temperature constraints at the sharp leading edge of a Crew Transfer Vehicle
+		
+			DeanKontinos
+		
+		
+			KenGee
+		
+		
+			DineshPrabbu
+		
+		10.2514/6.2001-2886
+		AIAA-2001- 2886
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Kontinos, D.A., Gee, K., Prabhu, D.K., "Temperature Constraints at the Sharp Leading Edge of a Crew Transfer Vehicle," AIAA-2001- 2886, June 2001.
+
+
+
+	
+		Spatial Convolution Neural Network for Efficient Prediction of Aerodynamic Coefficients
+		
+			TRajkumar
+		
+		
+			JBardina
+		
+		10.2514/6.2021-0277.vid
+	
+	
+		Proceedings of FLAIRS 2002
+		FLAIRS 2002Florida
+		
+			American Institute of Aeronautics and Astronautics (AIAA)
+			2002
+		
+	
+	Rajkumar, T., and Bardina, J., "Prediction of Aerodynamic Coefficients Using Neural Network for Sparse Data," Proceedings of FLAIRS 2002, Florida, 2002.
+
+
+
+	
+		Unsteady aerodynamic simulation of static and moving bodies using scalable computers
+		
+			RobertMeakin
+		
+		
+			AndrewWissink
+		
+		10.2514/6.1999-3302
+		AIAA-99- 3302
+	
+	
+		14th Computational Fluid Dynamics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			
+		
+	
+	Meakin, R.L., and Wissink, A.M., "Unsteady Aerodynamic Simulation of Static and Moving Bodies Using Scalable Computers," AIAA-99- 3302.
+
+
+
+	
+		Robust and efficient Cartesian mesh generation for component-based geometry
+		
+			MJAftosmis
+		
+		
+			MJBerger
+		
+		
+			JEMelton
+		
+		10.2514/3.13918
+		AIAA 97-0196
+	
+	
+		AIAA Journal
+		AIAA Journal
+		0001-1452
+		1533-385X
+		
+			36
+			
+			January 1977
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Aftmosis, M.J., Berger, M. J., and Melton, J.E., "Robust and Efficient Cartesian Mesh Generation for Component-Based Geometry," AIAA 97-0196, January 1977.
+
+
+
+	
+		A Generalized Vortex Lattice Method for Subsonic and Supersonic Flow Applications
+		
+			LRMiranda
+		
+		
+			RDElliot
+		
+		
+			WMBaker
+		
+		NAS1-12972
+		
+			December 1977
+		
+	
+	NASA CR-2866
+	Miranda, L.R., Elliot, R.D., and Baker, W.M., "A Generalized Vortex Lattice Method for Subsonic and Supersonic Flow Applications," NASA CR- 2866, Contract No. NAS1-12972, December 1977.
+
+
+
+	
+		A multidisciplinary flight control development environment and its application to a helicopter
+		
+			MBTischler
+		
+		10.1109/37.777786
+	
+	
+		IEEE Control Systems
+		IEEE Control Syst.
+		1066-033X
+		1941-000X
+		
+			19
+			4
+			
+			August 1999
+			Institute of Electrical and Electronics Engineers (IEEE)
+		
+	
+	Tischler, M.B., et al, "A Multidisciplinary Flight Control Development Environment and Its Application to a Helicopter," IEEE Control Systems Magazine, Vol. 19, No. 4, pg.22-33, August 1999.
+
+
+
+	
+		Investigation of the launch pad abort capabilities of the HL-20 lifting body
+		
+			EBJackson
+		
+		
+			RobertRivers
+		
+		
+			RajivChowdhry
+		
+		
+			WARagsdale
+		
+		
+			DavidGeyer
+		
+		10.2514/6.1993-3690
+	
+	
+		Flight Simulation and Technologies
+		Langley Research Center, Hampton, VA
+		
+			American Institute of Aeronautics and Astronautics
+			July 1992
+		
+	
+	Jackson, E.B., Cruz, C.L., and Ragsdale, W.A., "Real-Time Simulation Model of the HL-20 Lifting Body," NASA TM-107580, Langley Research Center, Hampton, VA, July 1992.
+
+
+				
+			
+		
+	
diff --git a/file111.txt b/file111.txt
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+	
+	
+		
+BACKGROUNDThe traditional process for aircraft design is sequential, where each step is completed before the next one begins.This facilitates scheduling each of the facilities (such as wind tunnels and simulators).However, it also guarantees that the knowledge gained during the simulation tests will not be used to refine the aerodynamic configuration -at least not until a subsequent design cycle, or a later model is designed.This is because there is no room in the process for lessons learned in the flight simulation phase to be fed back to the vehicle designers in time to make a difference.Some iteration may be done on the flight control system design during the flight simulation, but usually not on the aerodynamic shape of the vehicle.In order to solve this problem, the simulation phase of the process must be introduced earlier in the design cycle.It is this goal that has driven the RITE project team to develop a new, integrated process, in which pilots (in this case, astronaut-pilots) have an input early in the cycle by evaluating a flight simulation of the design before the design has been finalized.Recent advances in computer speed, computational fluid dynamics technology, and modern control techniques, have made it possible to rapidly make changes to the design, calculate new math model parameters, re-optimize control gains, and integrate the new data into a flight simulator.This allows the simulator test results to be fed back to the vehicle designer, and a modified design to be created and retested during the simulation test period.
+THE RITE PROCESSThe RITE process is an extension of the traditional design and analysis process in that it adds the dimension of piloted flight simulation to the decision making process.In this environment, design cycle times are shortened by using a number of modern techniques, including codes that facilitate rapid development of parametric geometries and the resulting surface and volume grids of vehicle designs; an integrated information system to allow rapid distribution of data; and control design tools to allow rapid re-optimization of the control system parameters when the vehicle design is changed.These capabilities allow design modifications to be accomplished rapidly during piloted flight simulation testing.The RITE process, like traditional development, begins with a conceptual design of the aircraft.The design is American Institute of Aeronautics and Astronautics drawn using a graphic design tool, then lofted in CAD software, and the aerodynamic characteristics of the design are determined using a combination of computational methods and wind tunnel tests.The aerodynamic model is then developed in a form usable in a real-time flight simulator.If the aircraft were powered, a simplified engine model would be developed.Since the particular aircraft studied in this project does not have an engine, this step was bypassed.Next, a flight control system must be developed, and the gains optimized for desired performance.Then the various parts of the model are integrated into the flight simulator, and a simulation experiment is conducted to evaluate the total vehicle performance.Several iterations may be needed to refine the flight controls, after which tests may be performed to determine the handling qualities of the vehicle.The results of these tests are then fed back to the designers, who now have an opportunity to improve the design.CFD simulations are once again used to calculate the aerodynamics of the modified design, the control gains are re-optimized, and the modified vehicle is tested again in the flight simulator.This process is shown in Figure 1.
+VEHICLE DESIGN AND OPTIMIZATIONIn order to develop this process, a conceptual aircraft design of interest to NASA-Ames researchers was chosen as a test case.This aircraft was a Crew Transfer Vehicle (CTV). 1,2A CTV is a re-entry vehicle that could be used to return astronauts to earth following a space station mission -or in the case of a mission abort, possibly due to a launch vehicle failure.This particular vehicle incorporated sharp leading edges, made of high-temperature ceramics, in order to improve the hypersonic lift-to-drag (L/D) ratio. 3The improved L/D would give the vehicle the capability to land within a larger footprint on the earth, thus giving the crew more options. 4e baseline aircraft design for the project, designated V-7, was developed by the Systems Analysis Branch of the Aeronautical Projects and Programs Office at NASA-Ames Research Center.Five modifications were made to this baseline, and each was tested in the Vertical Motion Simulator (VMS) with a pilot-in-theloop.Five of these configurations are shown in Figure 2, where the baseline is shown in the center as CTV0.In the figure, the models are color coded by pressure coefficient at Mach 0.3, at an angle-of-attack of 10 degrees.
+Figure 2. The Aircraft ConfigurationsThere are subtle differences in the configurations shown in Figure 2. Wing twist and camber were modified to produce the CTV1 configuration.In CTV2, the concavity of the upper surface was eliminated.In an attempt to stabilize the Dutch roll mode, more dihedral was added to the configuration of CTV3. 5 CTV4 was developed using an optimization code to vary the wing twist, dihedral and sweep. 6The CTV5 configuration (not shown) was developed during the simulation period, using feedback from the piloted tests.These models were designed at Mach 6 and Mach 0.3 using unsteady aerodynamic shape optimization.
+AERODYNAMIC DATA GENERATIONComputational fluid dynamics (CFD) simulations and wind tunnel tests were used to develop mathematical models of the aerodynamics of several variations of the conceptual vehicle.
+Computational Fluid DynamicsSeveral forms of computational fluid dynamics codes were used in the RITE process.These included a vortex lattice method, as well as both the Euler (inviscid) and Navier-Stokes formulations of the flow equations. 7,8,9e vortex lattice method, simplest and fastest of the computational methods employed, was used to obtain preliminary estimates of the aerodynamics.This method was also used to develop approximations to the dynamic derivatives (such as roll moment due to roll rate), since computing them with the more sophisticated CFD techniques would have been very costly and timeconsuming.The Euler method is computationally faster, and therefore cheaper, than the Navier-Stokes method.However, the Navier-Stokes formulation is usually more accurate, especially when flow separation is a factor.Both methods were used: the Navier-Stokes formulation was used to compute the "clean" aerodynamics (without control surface deflections), and the Euler method was used to compute the control effectiveness and the ground effect model.
+Wind Tunnel TestingOne problem with wind tunnel testing is the delay caused by the need to construct a physical model of the aircraft.This delay was minimized by using a stereolithography technique to create the model.This process uses two computer-aimed lasers, shining into a vat of resin, which cause the resin to harden where the lasers intersect.The hardened resin forms a model for use in the wind tunnel.Two such models were tested manufactured and tested in the Ames 32 inch by 48 inch atmospheric low speed wind tunnel. 10This manufacturing technique is most useful for small-scale, low-speed tests where structural loads on the model are minimized.For tests demanding greater structural strength, other rapid-prototyping techniques are now available to hasten model fabrication.
+DATA TRANSFERAn internet-based data management system was used to allow all members of the design/test team ready access to the aerodynamic data during the development of the mathematical model.The data were then converted to the Function Table Processor (FTP) format used in the VMS simulation facility.The Function Table Processor compiles function table data into a run-time database, with linear interpolation.This database system allows up to seven independent variables, can either use equally spaced or arbitrary breakpoints, and provides a number of features that enhance real-time computational efficiency.
+FLIGHT CONTROLSIn order to test an aircraft design in a real-time, piloted flight simulator, a flight control system model is required.SimuLink® and the CONDUIT® control design tools were used to facilitate the development of suitable control laws to complete the mathematical model of the vehicle.CONDUIT® provides a relatively user-friendly environment for optimizing control system gains to meet flying qualities specifications defined by the control engineers. 11This was essential to the RITE process, as it allowed rapid re-optimization to account for changes to the aerodynamic design.The flight control system design began using a slightly modified version of the HL-20 flight controls. 12The pitch control system, shown in Figure 3, utilized an Nz-Q command, with a blend of pitch rate and normal acceleration feedback.This approximates a flight path command, since the airspeed is held nearly constant.
+Figure 3. Pitch Control SystemThe roll command system, shown in Figure 4, used roll rate damping, together with bank angle command (from the guidance system) and bank angle feedback.
+Figure 4. Roll Control SystemThe yaw control system, shown in Figure 5, originally used washed-out yaw rate feedback to augment the yaw damping.However, it was found that the Dutch roll mode was not sufficiently well damped, so an alternative system, consisting of inertial sideslip and sideslip rate feedback, was tried.This system worked very well, and it was found that the sideslip gain could be set to zero.The resulting system, using only inertial sideslip rate feedback, had the interesting property of automatically compensating for side gusts.
+Figure 5. Yaw Control SystemIn order to provide an airspeed hold function, a split rudder was used as a speed brake.The speed control system, using equivalent airspeed feedback with proportional plus integral compensation, is shown in Figure 6.This system worked well to control airspeed, but introduced objectionable pitch transients in the flare maneuver.
+FLIGHT SIMULATIONA real-time, piloted flight simulation was conducted in the VMS, using astronauts as the subject pilots to test the conceptual vehicle designs in approach and landing tasks on the Kennedy Space Center runway, and the results were fed back to the designers.The various CTV configurations were compared to both the Space Shuttle and the HL-20, a re-entry vehicle concept previously studied at NASA-Langley Research Center. 12The simulator cab was configured exactly as it normally is when simulating the Space Shuttle for astronaut training (Figure 7).
+Figure 7. Simulator Cab InteriorThe Space Shuttle Orbiter simulation was used as a calibration point for the pilots.It was found that there was a significant difference between the handling qualities ratings given by astronauts who had piloted the Space Shuttle on an actual orbital mission, versus those who had been trained in the simulators but had not yet flown the real Space Shuttle.It was found useful to have every pilot fly and rate the Orbiter simulation, (which all had flown in their training) as this provided insight into their ratings of the CTV configurations.A suite of tools known as the Virtual Laboratory (Figure 8) allowed members of the design/test team to participate in the simulation experiments in real time from a remote site. .
+Figure 8. The Virtual LaboratoryThe RITE process then allowed the designers to make changes based on the simulation results, and to perform evaluations with the integrated modifications.Significantly, within a few weeks the flying qualities were improved from barely controllable to excellent.
+LESSONS LEARNEDThere were several different categories of lessons learned during this experiment: information about the specific aircraft; information about the class of aircraft; information about the experiment; and information about the process.About the Specific Configurations Each of the configurations tested was determined to be acceptable, with Level I flying qualities, after proper optimization of the control system.However, some configurations required less control activity than others.This could indicate that those configurations might be able to use less powerful actuators, with consequent weight savings.More details on the results pertaining to each of the configurations have been published in another paper. 5out the Class of Aircraft The CTV is a lifting-body re-entry vehicle, a class of aircraft that usually has a low L/D compared to winged aircraft.It also has no engine, so power cannot be used to control rate of descent at touchdown.This complicates the landing task, and there is no possibility for a go-around.Therefore, the L/D in ground effect is critical to pilot's ability to control the rate of descent at touchdown.Since go-around is not possible, and the pilot is returning from the physical and mental stress of a space mission (possibly aborted), the flying qualities of the re-entry vehicle must be excellent.The pilot-astronauts who participated in this study are among the best pilots in the world, and they are well trained to fly the Space Shuttle.As expected, their performance with the Shuttle landing task was consistently excellent.Nevertheless, they consistently gave the Shuttle mediocre handling qualities ratings, and said that the next generation of re-entry vehicles must have better flying qualities.It was also found that the guidance information on the Head-Up Display (HUD) was critical to the pilot's ability to meet the touchdown performance criteria.Since there is no engine in this vehicle, the trajectory must be accurately followed in order to control touchdown point, rate of descent, and landing airspeed.
+About the ExperimentSince L/D is such an important factor in landing, in simulation it was found that the ground effect model is critical to the pilot's ability to control rate of descent at touchdown.It therefore had a large effect on pilot ratings for the landing task.Initially, there was no plan to develop a new set of ground effect data, but rather to use the ground effect model from the HL-20 simulation for all configurations (except the Space Shuttle).When the astronauts discovered that they were having difficulty controlling the descent rate at touchdown, they suggested two experiments to determine the cause of the problem.First, the ground effect model was "turned off" in the simulation math model.The pilots found that the vehicle was equally difficult to land either way, implying that there was very little effect from the ground effect model.Next, they suggested a test in which the L/D was set to an arbitrarily high value of 6 for the entire run.This was done by setting the drag calculated by the simulation math model equal to the lift divided by 6, without regard for whether such a high value of L/D would be attainable with this type of vehicle.This test resulted in a simulated aircraft that was easy to land.Based on these tests, it was postulated that the difficulty in controlling rate of descent at touchdown was due to lack of a good ground effect model.Runs were made rapidly, using unstructured grid based Euler CFD methods, to generate a more realistic ground effect model.The new ground effect data were found to produce a greater L/D, which improved the touchdown performance significantly.Another finding from the experiment was that the split rudder used for speedbrakes in the simulation produced too much nose-up pitching moment.This required it to be opened slowly and to remain at a fixed deflection when the aircraft was near the runway.Therefore, this speedbrake mechanization was not useful for manual control inputs.Finally, the Rotational Hand Controller used to fly the simulated aircraft caused difficulty for the flight control optimization.This device is the same one that is used in the Space Shuttle.It was developed for maneuvering in space, but the astronauts don't like it for approach and landing.In addition, since there is no Military Specification for such a control inceptor, controller gains had to be determined by trial and error.
+About the ProcessThe process worked well, but it could be improved by having a more systematic test procedure.The different configurations were tested in the simulator by having the pilot perform landings, either straight-in with no winds, offset laterally, or straight-in with gusty winds.The pilot then gave Cooper-Harper handling quality ratings. 13This procedure showed how good each configuration was for the landing tasks, compared with the other configurations.But it did not provide any indication of how the configuration might be improved unless the pilot happened to make some observation (as they did in the case of ground effect) concerning the aerodynamic cause of deficiencies.So, a more systematic procedure should be developed that would have more probability of pointing out areas for possible improvements to the aerodynamic configuration.
+FEEDBACK FROM SIMULATIONDuring the simulation, feedback was provided to the design and CFD teams regarding a number of issues.In one case, when the configuration was first flown, it exhibited excessive adverse yaw.When this characteristic was described to the CFD team, they were able to identify and correct an error that had been made in creating the data.The simulation then showed minimal yaw due to roll.
+American Institute of Aeronautics and AstronauticsAnother issue that was fed back to the CFD team was the ground effect problem.The team was able to rapidly generate ground effect data for the baseline CTV configuration, using the vortex lattice method, and this resulted in significantly better Cooper-Harper ratings.One astronaut commented, "With the new ground effect, my touchdown speeds are slower and my sink rate is slower.Overall, it would be easier."During the experiment, a new configuration (CTV5), based on feedback from the piloted simulation, was implemented.New CFD data were calculated, function tables were generated, the control gains were reoptimized, and the simulation was flown again to evaluate the performance of the new configuration.The original plan was to try to do the re-design over a weekend, and test it in the simulator during the following week.Due to scheduling priorities, it was actually flown for the first time on Thursday, but the complete cycle required less than four days of work.This demonstrated the capability of fast turnaround.The new configuration also incorporated body flaps that could be used as speedbrakes, in an attempt to reduce the pitching moment due to speedbrakes.This new feature was not tried, however, due to lack of time.
+RECOMMENDATIONSSince the RITE process is intended to provide a design team with guidance about how to improve its design, it would be advantageous to use a methodical test procedure that would show what could be improved.In this experiment, the test pilots were able to discover some potential improvements.However, there was no systematic procedure to look for areas of potential improvement to the aerodynamics of the vehicle.Some method should have been devised to systematically investigate what aerodynamic changes to the vehicle might improve its performance.With hindsight, it is postulated that a procedure could be developed in which incremental changes to the various aerodynamic tables could be programmed into the simulation math model code.By evaluating the effect of each incremental change, data could be produced that would show the design team where improvements might be made.For example, an increment could be added to the roll damping in the rolling moment equation.If this hypothetical aerodynamic data set produced a better Cooper-Harper rating, that could suggest that the design might be improved by increasing the roll damping.In fact, such a procedure was used to some extent to investigate the ground effect problem in this experiment, by arbitrarily varying the L/D of the simulated vehicle, as described previously.The approach proved to be very useful, and will be incorporated into future RITE experiments.
+CONCLUSIONSThis project has demonstrated the feasibility of the Rapid Integration Test Environment process for aircraft design.The strength of the RITE process is that it allows the vehicle designers to get feedback about the vehicle configuration before the design must be finalized.It also provides a way for pilots to be involved in the design process.This approach has a number of benefits to the design process.First, it facilitates the rapid discovery of any errors that may have occurred in the calculation of the aerodynamic data.Second, it aids in the identification of any other math model deficiencies.Third, it provides insight into the handling qualities of the design, and allows the designers to make improvements and tradeoffs.For these reasons, the RITE process should become the standard for aircraft design.Figure 1 .1Figure 1.The RITE Process
+AIAA Modeling and Simulation Technologies Conference and Exhibit 5-8 August 2002, Monterey, California AIAA 2002-4479 2
+		
+		
+
+			
+ACKNOWLEDGEMENTSThe authors would like to acknowledge the following people for their significant contributions to this work: Fanny Zuniga, Dave Kinney, Steve Smith, Veronica Hawke, Chun Tang, Susan Cliff, Jorge Bardina, Joe Ogwell, and Dan Wilkins, as well as the test pilots and astronauts who participated in the flight simulation tests.
+			
+
+			
+
+				
+
+
+	
+		A reusable space vehicle design study exploring sharp leading edges
+		
+			JamesReuther
+		
+		
+			DavidKinney
+		
+		
+			StephenSmith
+		
+		
+			DeanKontinos
+		
+		
+			PeterGage
+		
+		
+			DavidSaunders
+		
+		10.2514/6.2001-2884
+		AIAA-2001-2884
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Reuther, J.J., Kinney, D.J., Smith, S.C., Kontinos, D.A., Saunders, D., and Gage, P., "A Reusable Space Vehicle Design Study Exploring Sharp Leading Edges," AIAA-2001-2884, June 2001.
+
+
+
+	
+		Conceptual design of a 'SHARP' - CTV
+		
+			DavidKinney
+		
+		
+			JeffBowles
+		
+		
+			LilyYang
+		
+		
+			CathyRoberts
+		
+		10.2514/6.2001-2887
+		AIAA 2001-2887
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Kinney, D.J., Bowles, J.V., Yank, L.H., and Roberts, C.D., "Conceptual Design of a 'SHARP'- CTV," AIAA 2001-2887, June 2001.
+
+
+
+	
+		Temperature constraints at the sharp leading edge of a Crew Transfer Vehicle
+		
+			DeanKontinos
+		
+		
+			KenGee
+		
+		
+			DineshPrabbu
+		
+		10.2514/6.2001-2886
+		AIAA-2001- 2886
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Kontinos, D.A., Gee, K., Prabhu, D.K., "Temperature Constraints at the Sharp Leading Edge of a Crew Transfer Vehicle," AIAA-2001- 2886, June 2001.
+
+
+
+	
+		Crew Transfer Vehicle trajectory optimization
+		
+			DavidSaunders
+		
+		
+			GaryAllen, Jr.
+		
+		
+			PeterGage
+		
+		
+			JamesReuther
+		
+		10.2514/6.2001-2885
+		AIAA-2001-2885
+	
+	
+		35th AIAA Thermophysics Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2001
+		
+	
+	Saunders, D., Allen, G. Jr., Gage, P., Reuther, J.J., "Crew Transfer Vehicle Trajectory Optimization," AIAA-2001-2885, June 2001.
+
+
+
+	
+		Vehicle Design of a Sharp CTV Concept Using a Virtual Flight Rapid Integration Test Environment
+		
+			FannyZuniga
+		
+		
+			SusanCliff
+		
+		
+			DavidKinney
+		
+		
+			VeronicaHawke
+		
+		
+			ChunTang
+		
+		
+			StephenSmith
+		
+		10.2514/6.2002-4881
+		AIAA-2002-4881
+	
+	
+		AIAA Atmospheric Flight Mechanics Conference and Exhibit
+		Monterey, CA
+		
+			American Institute of Aeronautics and Astronautics
+			August 2002
+		
+	
+	Zuniga, F.A., Cliff, S.E., Kinney, D.J., Hawke, V.M., and Tang, C.Y., "Vehicle Design of a Sharp CTV Concept Using a Virtual Flight Rapid Integration Test Environment," AIAA-2002-4881, Monterey, CA, August 2002.
+
+
+
+	
+		Aerodynamic Shape Optimization Using Unstructured Grid Methods
+		
+			SusanCliff
+		
+		
+			ScottThomas
+		
+		
+			TimothyBaker
+		
+		
+			AntonyJameson
+		
+		
+			RaymondHicks
+		
+		10.2514/6.2002-5550
+		AIAA-2002-5550
+	
+	
+		9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization
+		Atlanta, GA
+		
+			American Institute of Aeronautics and Astronautics
+			September 2002
+		
+	
+	Cliff, S.W, Thomas, S.D, Baker, T.J., Jameson, A., and Hicks, R.M., "Aerodynamic Shape Optimization Using Unstructured Grid Methods," AIAA-2002-5550, Atlanta, GA, September 2002.
+
+
+
+	
+		A Generalized Vortex Lattice Method for Subsonic and Supersonic Flow Applications
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+		
+negatively impacts the NAS by increasing controller workload along with aircraft fuel burn and emissions.Departuretime approval requests (APREQs) provide center-approved departure times to allow for smooth stream insertion.For many years APREQs have used land-line voice communications.Each day en-route centers send a generalinformation message to certain towers indicating that Call-For-Release (CFR) is required for departures to specific destinations.When an affected flight is ready to depart, the control tower traffic manager calls the adjacent en-route center to request approval for a time that reflects the best estimate of when the flight will be able to depart.The center traffic manager enters the requested time in TBFM and responds with a departure time predicted to enable the flight to fit into the overhead stream of traffic.Tower controllers maneuver the aircraft on the airport surface to meet the time; the FAA considers the eventual release compliant if the departing aircraft's takeoff rotation is within two minutes prior to one minute after the approved time.ATD-2 draws from prior NASA research geared toward replacing CFR with electronic center-tower coordination for APREQ scheduling.Most recently, the NASA Precision Departure Release Capability [2,4,5] led to the FAA's Integrated Departure Arrival Capability (IDAC) implemented within TBFM.IDAC includes departure-demand monitoring, slot identification, and semi-automatic and automatic modes for requesting release times from towers equipped with the Integrated Departure Scheduling Tool (IDST) [6].The tower component of the ATD-2 surface system, called the Surface Trajectory-Based Operations (STBO) Client, encompasses the IDST functionality.In addition, the STBO Client provides the capability to leverage a surface traffic schedule and airline-provided Earliest Off-Block Times (EOBTs) to calculate the Earliest Feasible Takeoff Time (EFTT) that the tower traffic manager should request for a given flight.Thus, release-time requests made using the STBO Client can consider not only slotavailability in the overhead stream, but also the feasibility of departing at the requested time.CLT Tower traffic managers began using the fielded ATD-2 system to electronically negotiate APREQ times with Washington Center (ZDC) in November 2017.For the initial 41-day introductory period from 23 November 2017 to 2 January 2018, electronic coordination was used for more than half of eligible flights, and ZDC traffic managers approved electronic requests, on average, in less than one minute [7].These data also showed that average compliance with electronically negotiated release times and the average tactical delay assigned did not differ significantly from those of release times coordinated using CFR.In addition, traffic managers also used electronic negotiation to reschedule release times.The present research extends the analysis in Ref. [7] to examine APREQs during daily operations at CLT from 1 January 2018 to 28 February 2019.During this period the ATD-2 system underwent numerous enhancements, and in October 2018 operations expanded to include electronic APREQ negotiation with Atlanta Center (ZTL).In addition to providing comparisons with prior results, this paper examines APREQ rescheduling and compliance improvements.The paper first provides background on APREQs at CLT and electronic APREQ negotiation using the STBO Client.It then presents the results of the analysis, followed by conclusions and topics for future investigation.
+II. BackgroundThe ATD-2 system became operational at CLT in September 2017.The CLT airport surface layout is shown in Fig. 1.During typical operations runway 18R/36L is dedicated to arrivals, 18C/36C is primarily dedicated to departures, and 18L/36R serves both arrivals and departures.Surface traffic management challenges at CLT stem from limited ramp area, the dual-use runway, and arrivals taxiing across the dedicated departure runway.Construction on the 5/23 runway has prevented southconverging operations since May 2018.A key IADS information-sharing and coordination focus area entails linking Traffic Management Initiatives (TMIs) developed using TFMS to the TBFM scheduling capabilities.TMIs implemented to manage demandcapacity imbalances include ground delay programs, ground stops, required re-routes, miles-in-trail restrictions, Expect Departure Clearance Times (EDCTs), and APREQs [8].Like APREQs, EDCTs are controlled departure times, but EDCTs are imposed NAS-wide by the FAA Command Center, and have a larger compliance window, from five minutes earlier to five minutes later than the assigned departure time.Tactical departure scheduling via APREQs is particularly important at CLT owing to its location underneath busy overhead traffic streams entering ZDC and ZTL airspace.ZDC and ZTL impose daily APREQ restrictions on CLT departures to busy airports such as the New York metroplex airports and Atlanta.Complying with approved release times helps the CLT departures merge smoothly into packed traffic flows (Fig. 2).The STBO Client (Fig. 3) supports APREQ TMIs through specialized display of relevant information on its runway timelines.Similar to IDST, STBO Client timelines depict green and red areas that reflect where slots are available and unavailable, respectively, in the relevant center's TBFM schedule (Fig. 4).This helps the tower maintain awareness of the center's demand and request release times the center is likely to approve.Moreover, because the timelines also show runway demand, including arrivals, the tower traffic manager can request release times that account for other surface traffic management considerations, potentially increasing the likelihood of compliant releases.Timeline symbology for aircraft subject to APREQs also indicates whether semi-automatic or automatic electronic-negotiation modes are available, or whether circumstances dictate the use of CFR.The principal difference between semi-automatic and automatic coordination is that under automatic mode TBFM/IDAC automatically sends an approved release time back to the tower STBO Client without input from the center traffic manager.In either mode, the tower traffic manager can right-click the flight data tag on the STBO Client timeline and choose one of two release-time request methods from a context menu: 'Select Slot on Timeline' or 'Request Release Time.'The former enables the traffic manager to then click within the red/green area of the timeline to transmit a requested time to the center, while the latter directs STBO to automatically choose an EFTT and request it.Active requests are indicated with a yellow arrow next to the flight's data tag.At the center, TBFM/IDAC produces an audible alert and highlights the flight on the TBFM timeline.Under semi-automatic mode, the center traffic manager can adjust the requested time before sending an approved time to the tower.The timeline symbology for a flight changes to reflect receipt of an approved release time.If the approved time differs from the originally requested time, the STBO Client produces both audible and visual alerts.The tower traffic manager can acknowledge the new time and clear the alert symbol by clicking it or selecting a context-menu item.To be compliant APREQ flights must depart the runway within a compliance window from two minutes earlier to one minute later than the approved release time.Some flights may be subject to both EDCT and APREQ restrictions; the STBO Client also shows EDCT compliance windows for selected flights (Fig. 5), so that requested times can also honor the EDCT compliance window.Once a flight has an approved release time (or times), the STBO Client colorcodes the labels at the end of the flight's data tag according to the flight's projected compliance (see Ref. [7]).The compliance indications aid the tower traffic manager in identifying flights that may benefit from a rescheduled release time.Circumstances may also arise in which a specific flight may be excluded from an APREQ restriction, or have a previously approved release time removed (referred to as a 'free release'); the STBO Client also supports these operations.The ATD-2 deployment at CLT began with a focus on efficiency and predictability improvements in airport surface and departure operations enabled by data integration and sharing, surface movement scheduling, and tactical departure scheduling.Additional system-integration elements, including integration with Advanced Electronic Flight Strips (AEFS), the aforementioned introduction of IDAC at ZTL, extending the scope of 'prescheduling' operations with ZTL, and surface scheduler improvements have all contributed toward improved APREQ management: AEFS automatically shows APREQ release times to tower controllers, IDAC at ZTL further reduces the need for CFR operations, and scheduler enhancements improve pushback-time advisories.Prescheduling refers to assigning release times based on a flight's airline departure time, rather than waiting until the pilot calls to indicate the flight is ready to push back from the gate; ZTL has implemented prescheduling operations with CLT for many years.The analysis presented in the following section highlights some of these impacts.
+III. Field-Data AnalysisATD-2's data-integration focus has yielded a rich, electronically-logged data set covering the January 2018 through February 2019 study period.This section first generally describes CLT operations and data included for analysis, then presents a series of results pertinent to electronic APREQ negotiation.
+A. CLT OperationsThe raw data for the 423-day study period include 627,516 CLT flight operations (313,984 arrivals and 313,532 departures).To focus the analysis on normal operations, calendar days with departure counts outside [1.5 * interquartile range] were identified; removing those 21 days from consideration leaves 402 days encompassing 303,729 departures.Table 1 describes the distribution of departures per day for the reduced data set used for subsequent analyses.CLT is a major hub for American Airlines (AAL); most CLT departures are operated by AAL and its regional carriers.CLT flights to Atlanta, Newark, LaGuardia, and John F. Kennedy airports are subject to APREQ restrictions throughout each day, with other major destinations including Chicago O'Hare, Washington Dulles, and Philadelphia also frequently subject to APREQs.All are among the top ten most frequent destinations of CLT departures.Scheduled AAL operations at CLT are organized into banks, which leads to periods of surface congestion interspaced with lulls.Fig. 6 shows the departure-bank structure reflected in the departure runway-utilization local time, summed over all days in the 402-day data set.
+B. APREQ and EDCT DeparturesFrom January 2018 through February 2019, there were 32,337 flights (10.6% of all departures) with controlled release times due to APREQs, EDCTs, or both.Of these, 26,733 flights (8.8% of all departures) were subject to APREQ restrictions, including those also subject to EDCTs.Fig. 7 shows the counts and proportions of controlled departures in each category.Overall, 82.6% of controlled departures were APREQ flights.More APREQ flights were negotiated with ZDC (61.1%) than with ZTL (38.9%).The larger proportion of APREQ flights negotiated with ZDC reflects the large number of flights to the U.S. Northeast that are subject to daily APREQ restrictions from ZDC.Fig. 8 shows the airport configuration in use at takeoff during each month of the study period for 26,436 APREQ flights (98.9% of all APREQ flights) for which these data were available.The south-converging ('South_Conv') flows utilizes runway 23 for arrivals, which adds complexity to surface traffic management.Due to the 5/23 construction noted above, the predominant airport configuration for the latter part of the study period was the more standard north-flow, with 36C and 36R used for departures.The north-flow configuration affords more room for APREQ flights to wait on the airport movement area away from ramp-area congestion; by contrast, the south-flow runways (18C and 18L) are considerably closer to the ramp area surrounding the main terminal building near the top of Fig. 1.Flights through ZDC are likely to use the eastern departure runways (18L/36R), while flights through ZTL are likely to depart from the western departure runway (18C/36C).Overall, 22.8% of APREQ flights used runway18L and 36.3%used 36R, while 15.8% used 18C and 25.1% used 36C.The higher utilization of 18L and 36R again reflects the typical use of the eastern runway for ZDC APREQs.
+C. Electronic APREQ Coordination and Release Time Request MethodsTo perform electronic APREQ negotiation, the center traffic manager must first enable it in TBFM IDAC by specifying whether semi-automatic or automatic mode should be used, or whether CFR is required.When semiautomatic or automatic mode is available, tower traffic managers have the option to select the desired release time manually via the 'Select Slot on Timeline' (SSOT) method or allow the STBO Client to automatically request a release time using 'Request Release Time' (RRT).Figs. 9 and 10 show the methods used by CLT tower traffic managers to negotiate release times in semi-automatic or automatic mode with ZDC and ZTL, respectively.'OFF' indicates electronic negotiation was turned off, so that CFR was required.Center release-mode data became available in March 2018, and except for a few test periods beginning in July 2018, ZTL only used CFR prior to October 2018 when IDAC was officially introduced there.Semi-automatic mode was used predominately at both centers, indicating a desire on the part of center traffic managers to approve requested release times manually; however, both centers increased the use of automatic mode toward the end of the study period.Anecdotal evidence suggests some center traffic managers may prefer the flexibility to add slack to schedules under certain circumstances (e.g., if they anticipate unscheduled flights or expect flights will require scheduling soon); this requires semi-automatic mode.The ZTL data also depict the introduction of prescheduling, in which the ATD-2 system automatically requests release times for ATL flights.All of the ZTL 'Request Release Time' usage in automatic mode stems from prescheduling; all but a small fraction stems from prescheduling in semi-automatic mode (note large proportions of 'SEMI, RRT' and 'AUTO, RRT' in Fig. 10).The ZDC release methods (Fig. 9), on the other hand, directly reflect user preference, indicating increased use of the 'Request Release Time' method than during the introductory period for electronic APREQ negotiation described in Ref. [7].The use of CFR even when semi-automatic or automatic modes were available may indicate some discussion about particular APREQ flights was warranted.Traffic managers resorted to CFR less frequently in recent months.
+D. APREQ ReschedulingTower traffic managers may request a new release time for a previously scheduled APREQ flight if it appears the flight will be unable to comply with its current release time, or if the opportunity arises to meet an earlier time and incur less delay.Of the 26,733 APREQ flights, tower traffic managers renegotiated release times for 6,936 flights (25.9%) and the rescheduling process led to a new release time for 6807 flights (25.5%).Removing release-time-difference outliers beyond [1.5 * interquartile range] yields 6,373 flights with new release times.Table 2 describe release-time differences (final release timeinitial release time) for these flights, so that a positive difference indicates the flight was  rescheduled to a later time.1,935 of these flights (30.4%) were rescheduled to an earlier time (mean= -431.6 s; SD=306.1 s); for those with initial and final release times both negotiated via IDAC, the total delay savings over the study period was 73.8 hrs.Data for comparison with rescheduling using CFR are unavailable.Another possible reason to reschedule an APREQ is to better ensure EDCT compliance.However, on a percentage basis, APREQ flights that were also subject to an EDCT were rescheduled approximately as often as APREQ flights not subject to an EDCT (26.9% vs. 25.8%,respectively).In some circumstances center traffic managers may simply release a flight that is nominally subject to an APREQ restriction.So-called 'free releases' occurred for 269 APREQ flights (1%) during the study period.
+E. APREQ Aircraft LocationsThe ATD-2 surface system records the estimated 'surface state' of flights, which can be used to identify where APREQ flights were during the APREQ negotiation process.Tower traffic managers are expected to request a release time for flights after the pilot calls ready and before the aircraft has initiated the pushback operation (i.e., while the aircraft is still at the gate in the 'SCHEDULED' state).Excluding prescheduled flights that are always at the gate when prescheduling occurs, Fig. 11 shows that tower traffic managers received the majority of initial release times before the flight started taxiing.An apparent trend toward obtaining release times later, during pushback, may actually reflect enhancements made to the ATD-2 system that results in earlier detection of pushback events from surfacesurveillance data.Fig. 12 shows the surface states of APREQ flights upon receipt of renegotiated release times.The majority of release times are rescheduled while flights are in the 'TAXI_OUT' state in the active movement area prior to reaching the runway queue.It is possible that rescheduling of flights in 'TAXI_OUT' or 'IN_QUEUE' states is triggered based on the STBO Client's projected compliance information.The 'IN_QUEUE' state may reflect aircraft that are actually parked out of the main runway queue.Data from the later months in the study period show an increased number of flights had renegotiated release times, in part due to increased rescheduling via IDAC at ZTL.The reduced number of rescheduled APREQ flights in September 2018 warrants further investigation.Table 3 depicts the initial and final surface states for the 6,213 APREQ flights that were not prescheduled, but were later rescheduled.The greatest proportion (24.4%) first had a release time negotiated at the gate ('SCHEDULED') and then renegotiated in the movement area ('TAXI_OUT').13.3% of flights were assigned updated release times in the ramp area, whereas 14% were in the runway queue.11.8% of flights registered a rescheduled release time prior to pushback.The table shows that most flights were scheduled at the gate, but possible compliance issues that warranted rescheduling did not arise until flights attained the 'TAXI_OUT' or 'IN_QUEUE' states.The STBO Client compliance projections are likely to be more accurate by this time.
+F. APREQ Assigned DelaysThe delay assigned to APREQ flights was computed using the last-updated airline expected departure time ('Ltime') for correspondence with Ref. [7], as shown in Eq. ( 1):
+APREQ delay = Final approved release time -Ltime(1)  25,573 APREQ flights (95.7%) have a valid Ltime; removing delay-value outliers beyond [1.5 * inter-quartile range] yields 24,267 values covering 90.8% of all APREQ flights.Fig. 13 shows the resulting APREQ-delay histogram with one-minute bins for release times negotiated with ZDC and ZTL.The ZDC APREQ-delay distribution (N=14,693; mean=23.9mins; SD=8.3 mins) is similar to the ZTL APREQ-delay distribution (N=9,462; mean=22.1 mins; SD=8.3 mins).The slightly higher mean delay for ZDC may reflect the larger number of APREQs through ZDC to the northeast U.S. The overall APREQ-delay distribution has a mean of 23.2 mins (SD=8.4mins), which corresponds closely to the APREQ-delay distribution for the introductory electronic-negotiation period [7].A trend of slightly lower median delays for ZTL APREQ flights than ZDC APREQ flights, with comparable variation, holds when examining the data along several dimensions.APREQ flights that are also subject to EDCT restrictions show slightly higher median APREQ delay (Fig. 14; whisker end-points are at [1.5 * inter-quartile range] from the box edges); this may indicate later release-times are more commonly requested to meet EDCT restrictions.There is no apparent difference in APREQ delay by departure bank or release-time request method, with slightly lower median APREQ delays incurred by ZTL flights.The data also show slightly higher median delays for rescheduled APREQ flights compared non-rescheduled APREQ flights, in accordance with the tendency to reschedule APREQ flights to a later release time.Median assigned delay tends to be lower for the runways not typically used for departures to the respective centers, a possible effect of APREQs assigned during lower-traffic periods when departure-traffic direction is less critical.Flights that received an approved release time prior to taxing also incurred less median APREQ delay.For brevity, plots of these results are not shown.
+G. APREQ ComplianceOverall, 17,854 APREQ flights (66.8 %) were compliant with their assigned departure release times (within two minutes before and one minute after the assigned time).Fig. 15 shows the monthly compliance percentage over the study period.A trend toward improved compliance is evident, with monthly compliance reaching 71.8% in January 2019.To confirm the trend, Fig. 16 shows average compliance computed using a rolling window over 10,000 individual APREQ flights and smoothed by taking every 100 values.Fig. 16 shows a clear trend toward increasing compliance that extends to the end of the study period.Compliance was also examined along various dimensions in a manner similar to APREQ delay, considering the same 24,267 APREQ flights that remain after removing delay outliers.Compliance for ZTL flights is generally slightly higher than for ZDC flights.This trend holds, for example, when examining non-rescheduled versus rescheduled flights; otherwise there is no apparent difference in compliance.Electronic release-time request methods also show a limited positive effect on the release-time-compliance percentage over CFR (Fig. 17), similar to the results in Ref. [7].One case in which the compliance percentage for ZTL flights was lower than that of ZDC flights was for APREQ flights that were also subject to an EDCT restriction (Fig. 18).Median delay values were slightly higher for such flights, as shown in Fig. 14.The compliance percentage was also lower for ZTL flights that used runways not typically assigned to ZTL flights (Fig. 19).Finally, Fig. 20 depicts average APREQ compliance by APREQ delay grouped in five-minute bins (axis labels indicate APREQ delay was less than or equal to the labeled value).Again compliance appears relatively insensitive to the amount of assigned delay, with ZTL enjoying a slight advantage in compliance.Compliance was worst for flights with delays of five minutes or less.Taken together, these findings indicate the main drivers of APREQ compliance lie elsewhere, potentially in the context of flight-specific surface operations.
+H. Approval Response TimesAn important advantage of electronic release-time negotiation is the time savings relative to CFR [7].Using IDAC message data available from 13 February 2018 to the end of the study period, approval response times for electronically negotiated release times were computed as the time difference between a request message and the corresponding approval message for a particular flight.Response times and associated electronic release-time request method were obtained for 12,241 APREQ flights (45.8% of all APREQ flights); for rescheduled flights, the computed response time is that of final renegotiation.The overall median response time was 9 secs.Fig. 21 depicts the response-time distributions for each center and release-time request type (whisker end-points are at [1.5 * inter-quartile range] from the box edges).Median response times are slightly lower for 'Request Release Time' requests for both centers.Median response times are slightly lower for ZTL than for ZDC, with slightly lower variation-another factor that could impact observed compliance.As discussed in Ref. [7], response times are consistently better than CFR response times that might be experienced during busy periods, which can exceed five minutes.
+IV. Conclusions and Further ResearchThis paper documents ATD-2 electronic APREQ negotiation in daily operations at CLT over fourteen months.The analysis indicates that field traffic managers are consistently exercising capabilities provided by the STBO Client and TBFM IDAC to good effect.Electronic departure-approval requests from CLT to ZDC and ZTL have largely supplanted CFR.In addition, compliance is improving, supported in part by the capability to reschedule release times electronically.Detailed examination of assigned APREQ delays suggests that the delay assigned to a flight via a given release time is not obviously affected by bank, restrictions, release-time request method, or other factors.It may therefore depend primarily on the demand at the stream-insertion points used as scheduling points by each center.Rescheduling APREQ flights typically results in slightly higher median delay, but also provides delay savings for a sizable proportion of flights.Renegotiating release times may also contribute to improved compliance by providing more achievable release times.The results also suggest an APREQ flight's bank, the method used to negotiate its release time electronically, and whether it was also subject to an EDCT or had its release time rescheduled do not significantly impact APREQ compliance-nor does the amount of assigned delay.This may indicate a variety of specific contextual factors related to surface traffic movement, pilot response, and situation awareness and skill of CLT Tower controllers also play a significant role.Normal use of a mainly departure-only runway, coupled with the capability to easily renegotiate release times for flights projected to miss their assigned times, may bolster the compliance of ZTL APREQ flightswhich in turn has contributed to improved overall compliance since the introduction of IDAC at ZTL.Overall, these promising findings support future, broader deployments of similar capabilities because no specific systematic factors appear to negatively impact compliance with electronically negotiated release times.The ATD-2 ground system has clearly contributed to streamlining release-time requests and improving APREQ compliance during the operational period examined here.STBO Client features, including projected compliance indications, EDCT compliance windows, and APREQ exclusions, likely provide incremental advantages that are difficult to discern at the aggregate level.Electronic release-time negotiation also provides significant time savings in approving release times for both tower and center traffic managers, consistent with the response-time data in Ref. [7].Additional research is needed to examine the impacts of specific ATD-2 enhancements on APREQ compliance.For example, automatically propagating approved release times to AEFS's flight strips may afford tower controllers advanced notice, and enable them to better formulate plans for managing APREQ flights.Future research will examine additional effects of APREQ compliance on other important IADS metrics (e.g., arrival-time compliance), and apply more sophisticated analyses to determine the contributions of specific ATD-2 system enhancements on compliance improvements.Fig. 11CLT airport surface layout.
+Fig. 2 Fig. 3 STBO23Fig. 2 Non-compliant APREQ departures resulting in excessive vectoring in ZDC airspace (left) versus smooth stream-insertion of compliant APREQ departures (right).
+Fig. 44Fig. 4 Timeline with available slots for selected APREQ aircraft shown in green and unavailable slots shown in red vertically in the middle.Predicted arrivals are shown in gray.
+Fig. 6 Fig. 767Fig. 6 CLT departure banks.
+Fig. 88Fig. 8 APREQ flights by airport configuration.
+Fig. 99Fig. 9 ZDC release-request when automatic or semi-automatic negotiation mode was available.
+Fig. 1111Fig. 11 Surface states of non-prescheduled APREQ flights at initial release-time approval.
+Fig. 1313Fig. 13 APREQ assigned delay computed using Eq.(1) by negotiation center.
+Fig. 1414Fig. 14 APREQ assigned delay by center per restriction category.
+Fig. 1616Fig. 16 Rolling window of average compliance with dates when cumulative numbers were reached.
+Fig. 1919Fig. 19 APREQ compliance by center per runway.Fig. 20 APREQ compliance by center per assigned APREQ delay in five-minute bins.
+Table 1 CLT Departures Per Day.1Mean (Std. Dev.)755.5 (52.3)Min.593Median764Max.865
+Table 2 Rescheduled release time difference (s).2Mean (Std. Dev.) 306.1 (645.0)Minimum-14221 st Quartile-120Median3003 rd Quartile698Maximum2129
+Table 3 Surface-state combinations at initial and final release-time approval.3Surface State on Initial Release-Time Approval (Non-prescheduled APREQ flights)Final Surface State(RescheduledAPREQ Flights)SCHEDULED PUSHBACK RAMP_TAXI_OUT TAXI_OUTIN_QUEUESCHEDULED731 (11.8%)11 (0.2%)4 (0.1%)3 (0.0%)-PUSHBACK367 (5.9%)259 (4.2%)-1 (0.0%)-RAMP_TAXI_OUT826 (13.3%)243 (3.9%)129 (2.1%)--TAXI_OUT1517 (24.4%)485 (7.8%)129 (2.1%)129 (2.1%)1 (0.0%)IN_QUEUE868 (14.0%)345 (5.6%)88 (1.4%)57 (0.9%)20 (0.3%)
+		
+		
+
+			
+AcknowledgmentsThis research was supported by the NASA ATD-2 project, Al Capps, Project Lead.Thanks go to the many dedicated researchers and practitioners who have supported the ATD-2 field demonstration.
+			
+
+			
+
+				
+
+
+	
+		Comparing European ATM master plan and the NextGen implementation plan
+		
+			DavidBatchelor
+		
+		10.1109/icnsurv.2015.7121357
+		
+	
+	
+		2015 Integrated Communication, Navigation and Surveillance Conference (ICNS)
+		
+			IEEE
+			10 April, 2019
+		
+	
+	Federal Aviation Administration, "NextGen Priorities Joint Implementation Plan," URL: https://www.faa.gov/nextgen/media/NG_Priorities_Joint_Implementation_Plan.pdf [retrieved 10 April, 2019].
+
+
+
+	
+		Airspace Technology Demonstration 2 (ATD-2) Phase 1 Concept of Use (ConUse)
+		
+			YJung
+		
+		NASA TM-2018- 29770
+		
+			2018
+		
+	
+	Jung, Y., et al., "Airspace Technology Demonstration 2 (ATD-2) Phase 1 Concept of Use (ConUse)," NASA TM-2018- 29770, 2018.
+
+
+
+	
+		Operational Impact of the Baseline Integrated Arrival, Departure, and Surface System Field Demonstration
+		
+			ShivanjliSharma
+		
+		
+			AlCapps
+		
+		
+			ShawnEngelland
+		
+		
+			YoonJung
+		
+		10.1109/dasc.2018.8569828
+	
+	
+		2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)
+		London
+		
+			IEEE
+			2018
+		
+	
+	Sharma, S., Capps, A., Engelland, S., and Jung, Y., "Operational Impact of the Baseline Integrated Arrival, Departure, and Surface System Field Demonstration," 37th IEEE Digital Avionics Systems Conference, IEEE, London, 2018.
+
+
+
+	
+		Impact of Departure Prediction Uncertainty on Tactical Departure Scheduling System Performance
+		
+			AlanCapps
+		
+		
+			EdwardWalenciak
+		
+		
+			ShawnEngelland
+		
+		10.2514/6.2012-5674
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		Indianapolis
+		
+			American Institute of Aeronautics and Astronautics
+			2012
+		
+	
+	Capps, A., Walenciak, E., and Engelland, S., "Impact of Departure Prediction Uncertainty on Tactical Departure Scheduling System Performance," 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Indianapolis, 2012.
+
+
+
+	
+		
+			SEngelland
+		
+		
+			ACapps
+		
+		
+			KDay
+		
+		
+			MKistler
+		
+		
+			FGaither
+		
+		
+			GJuro
+		
+		NASA TM-2013-216533
+		Precision Departure Release Capability (PDRC) Final Report
+		
+			2013
+		
+	
+	Engelland, S., Capps, A., Day, K., Kistler, M., Gaither, F., and Juro, G., "Precision Departure Release Capability (PDRC) Final Report," NASA TM-2013-216533, 2013.
+
+
+
+	
+		Scheduling and Delivering Aircraft to Departure Fixes in the NY Metroplex with Controller-Managed Spacing Tools
+		
+			EricChevalley
+		
+		
+			BonnyParke
+		
+		
+			JoshKraut
+		
+		
+			NancyBienert
+		
+		
+			FaisalOmar
+		
+		
+			EverettPalmer
+		
+		10.2514/6.2015-2428
+	
+	
+		15th AIAA Aviation Technology, Integration, and Operations Conference
+		Dallas
+		
+			American Institute of Aeronautics and Astronautics
+			2015
+		
+	
+	Chevalley, E., Parke, B., Kraut, J., Bienert, N., and Omar, F., "Scheduling and Delivering Aircraft to Departure Fixes in the NY Metroplex with Controller-Managed Spacing Tools," 15th AIAA Aviation Technology, Integration, and Operations Conference, Dallas, 2015.
+
+
+
+	
+		Evolution of Electronic Approval Request Procedures at Charlotte Douglas International Airport
+		
+			LindsayStevens
+		
+		
+			ToddJCallantine
+		
+		
+			RobertStaudenmeier
+		
+		10.1109/dasc.2018.8569318
+	
+	
+		2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)
+		London
+		
+			IEEE
+			2018
+		
+	
+	Stevens, L., Callantine, T., and Staudenmeier, R., "Evolution of Electronic Approval Request Procedures at Charlotte Douglas International Airport," 37th IEEE Digital Avionics Systems Conference, London, 2018.
+
+
+
+	
+		Aggregate Statistics of National Traffic Management Initiatives
+		
+			JosephRios
+		
+		10.2514/6.2010-9382
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth
+		
+			American Institute of Aeronautics and Astronautics
+			2010
+		
+	
+	Rios, J., "Aggregate Statistics of Traffic Management Initiatives," 10th AIAA Aviation Technology, Integration, and Operations Conference, Fort Worth, 2010.
+
+
+				
+			
+		
+	
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+		
+A. Pushback Start TimeThe purpose of the pushback start measure was to assess the uncertainty of the predicted pushback time.The SDSS component of the PDRC prototype system begins computing surface trajectories and takeoff time predictions as soon as a flight plan is received for a given flight.These surface trajectories begin with at the departure gate position and with an estimated pushback (i.e.OUT) time.Multiple sources of pushback time estimates were available to the PDRC system during the evaluation.These include the filed flight plan time, pushback times received directly from an interface with a major air carrier, and a secondary source of pushback data from a commercial flight data service.The primary focus of this research was on the accuracy of pushback received directly from the air carrier.
+Measurement ApproachThe pushback prediction used in this analysis was recorded immediately prior to the actual pushback event.Truth data used to assess the predictive accuracy of the pushback event was the actual out event data received from a gate docking system at DFW airport.This system uses video surveillance of the gate to detect when the flight has moved one meter, at which point it sends notification of the event.The actual OUT data were supplied by the airline system.Estimated OUT times from the air carrier were compared with actual OUT times from the carrier for all flights from the May 30, 2012 through June 22, 2012.The data used were from air carrier predictions that immediately preceded the actual OUT occurrence, generally within 10 seconds prior to actual OUT event.All flights which did not have an actual OUT or predicted OUT from the airline were removed from the sample.Given the objective was to evaluate the general uncertainty associated with this event and observers were not available to capture details on each OUT event occurrence, the outliers were removed from the sample by removing 1.5 times the interquartile range (IQR).The IQR method of filtering was used due to asymmetric distributions for some departure events in this research.The remaining set of data contained 30,792 departing flights from DFW with air carrier data.
+Uncertainty ResultsThe results of the pushback time measure are illustrated in Fig. 2. The mean for this sample was -52 seconds, indicating that flights pushback earlier than their last predicted OUT time from the carrier.After eliminating outliers, the median for this sample was 0 seconds and the standard deviation was 148 seconds.Fifty-three (53) percent of flights in this sample push back earlier or exactly on time with their estimated pushback time.Eighty-six (86) percent of flights push back within one minute late, and ninety-two (92) percent of flights push back prior to 2 minutes late.For tactical departure scheduling purposes, flights that arrive at the spot early pose no challenge for scheduling into the overhead stream.While this is an encouraging statistic, 2,355 flights in this sample pushed back greater than two minutes later than their predicted time.This is significant because if any of these flights were tactically scheduled prior to pushback, it is unlikely the flights could be expedited to depart within the current-day tactical window of two minutes early through one minute late (-2/+1).A notable observation is that the data received from the airline to report actual OUT time is currently at the resolution of one minute.For error measurement purposes, the actual OUT was compared to the predicted OUT estimate without rounding or truncations of the data.A later version of the airline data feed will contain the actual OUT in seconds level precision.
+B. Pushback DurationThe purpose of the pushback duration measure was to assess the uncertainty associated with the range of the pushback event duration.Currently, PDRC's SDSS component accounts for pushback duration with an adaptable "pushback buffer" value.This value may be tailored to account for gate location and ramp geometry.The pushback duration results are utilized in later sections of this paper to determine the pushback duration prediction error.The pushback duration prediction error is the difference in actual pushback duration from the mean pushback duration.
+Measurement ApproachOften the pushback is thought of as an instantaneous event in time, when in reality it has a duration which is dependent upon a number of factors like jet blast policy for an airport and gate geometry.The pushback event was defined to be the number of seconds from the OUT event discussed in the previous section until the flight begins forward motion under its own power.Observations indicate that nearly all flights at DFW employ the use of a tow tractor, known as a 'tug'.Truth data used to measure pushback duration are from manual observations made by test personnel.Manual observations were collected for 194 flights departing various gates during the months of May through July 2012.The observer of the pushback event had either direct line of site visibility to the flight or live video camera with pan/tilt/zoom control capability.The start and end of the pushback event were recorded at second's level precision.For 138 of the events, the time at which the tug was disconnected was also captured.
+Uncertainty ResultsThe results of the pushback duration measure are depicted in Fig. 3.The sample taken had a mean value of 202 seconds, with a median of 189 seconds.The sample demonstrated a significant amount of variation with a standard deviation of 73 seconds, a minimum pushback time of 77 seconds and a maximum pushback time of greater than 7 minutes.The pushback duration data best fit a lognormal distribution.This lognormal distribution is not surprising given this measurement is of time durations which are all positive and independent of one another.Gate-specific pushback duration variability can occur due to limited space for the tug to push the flight into regions of the ramp taxi area, or areas in which the jet blast from engine start may not be allowed.Due to this geometry, the tug may be required to push the flight back then subsequently pull the flight to a different location prior to disconnecting and aircraft engine start.Significant pushback duration variance existed by air carrier as well with the highest carrier average pushback of 246 seconds and the lowest 148 seconds.
+C. Ramp Taxi DurationThe purpose of the ramp taxi duration measure was to assess the uncertainty associated with the range of ramp taxi time duration.Currently PDRC's SDSS component models ramp movement as a constant-speed taxi from the gate location to the spot.The ramp taxi duration results are utilized in later sections of this paper to determine the ramp taxi prediction error.The ramp taxi prediction error is the difference in actual ramp taxi time versus the predicted ramp taxi time.
+Measurement ApproachThe ramp taxi event was defined as the number of seconds from forward motion in the ramp area until the aircraft reaches the spot.The primary source of truth data used to assess ramp taxi uncertainty was from manual observations by test personnel with visual access to the pushback event mentioned in the previous section through the flight's arrival at the spot.Manual observations were collected for 189 flights at various DFW gates during the months of May through July 2012.The start and end of the pushback event were recorded at seconds level precision.
+Uncertainty ResultsThe results of the ramp taxi measure are depicted in Fig. 4. The mean ramp taxi time for the sample was 85 seconds with a median of 81 seconds.Some variation was noted with a standard deviation of 40 seconds, and a minimum of 5 seconds with a maximum of over 5 minutes.The distance from the ramp taxi start location to the spot was an important consideration.The average ramp taxi speed was computed for each flight by dividing the ramp taxi duration by the ramp taxi distance.The mean and median ramp taxi speed for all flights was 8 knots.A significant amount of ramp taxi speed variation existed within the sample with a standard deviation of 3 kts, a high of 20 kts and a low of 3 kts.The variation was more evident amongst aircraft type than by air carrier, with the lowest average ramp taxi speed of 6.5 kts by the Boeing 737 series and the highest average ramp taxi speed of 8 kts by the McDonnell Douglas MD-80 series.
+D. Spot Crossing DurationThe purpose of the spot crossing duration measure was to measure uncertainty associated with the range of times aircraft were held at the spot prior to entering the airport movement area (AMA).Currently PDRC's SDSS component has the ability to add delay for spot crossing to deconflict with other flights, however, this was not used in the PDRC operational evaluation.Therefore, any time spent waiting at the spot is considered to be prediction error.
+Measurement ApproachThe spot crossing duration was defined as the number of seconds that the flight waited at the airport spot prior to entering the AMA.Manual observations were collected for 190 flights during the months of May through July 2012.
+Uncertainty ResultsThe results of the spot crossing duration times are depicted in Fig. 5. Approximately 81% of flights at DFW did not stop at the spot prior to entering the AMA.This can be seen in Fig. 5 by the large number of aircraft which had 0 to 10 seconds delay.When flights do stop at the spot, there is generally a small time expense to this action and the average wait is 29 seconds.Significant variation exists with a minimum of 0 seconds and max of 100 seconds for this sample.Many of the longer wait times at the spot can be explained by already present transiting flight on the AMA taxiway.Another situation that may explain the non-zero wait time is ground controller delay in contacting the flight and issuing a clearance to enter the AMA.
+E. Airport Movement Area Taxi DurationThe purpose of the AMA taxi duration measure was to assess the uncertainty of the taxi time duration in the FAA-controlled airport movement area.Currently PDRC's SDSS component models AMA movement as a constant-speed taxi from the spot to the runway departure queue following a node-link surface trajectory.AMA taxi prediction error is any error generated by the PDRC system using the current day algorithms and AMA taxi decision trees available for this prediction.
+Measurement ApproachThe AMA taxi time duration is defined as the amount of time from entering the AMA to the point at which the flight enters the departure queue.The AMA taxi time measure intentionally excludes uncertainty associated with the departure queue itself.Truth data used for AMA taxi time was obtained from post-analysis routines of PDRC output.This logic determined the entry into the AMA as well as the time the flight entered the departure queue.The AMA entry and departure queue time were compared to create the actual ramp taxi time for each flight.The PDRC AMA taxi time prediction was obtained on each flight by assuming a constant AMA taxi speed of 17 knots over the distance between the spot and departure queue which is used in SDSS.For each AMA taxi time prediction, the predicted taxi time to the departure queue was compared with the actual.Outliers were eliminated from the data sample using 1.5 times the IQR.The remaining sample of 46,325 flights from June and July 2012 are discussed in the next section.
+Uncertainty ResultsThe results of the airport movement taxi time statistics are depicted in Fig. 6.The overall mean AMA taxi time prediction error is 25 seconds, while the median error is 23 seconds.A positive error indicates the tendency of the PDRC system is to under predict the amount of time it takes for the flight to taxi from the spot to the runway threshold.Under predicting the AMA taxi time is undesirable in tactical departure scheduling because it may allow insufficient time for the local controller to stage the flight in the departure queue and control the flight to meet its coordinated departure time.The absolute AMA taxi error from the PDRC system was 35 seconds with a median absolute error of 29 seconds.The average absolute error is approximately 16% of the size of the flight's total AMA taxi time duration.Variance also existed in the AMA taxi prediction error with a standard deviation of 38 seconds, a minimum of 76 seconds early and a maximum taxi error of 131 seconds late.The variance is most prominent when viewing the data by air carrier and aircraft type.The top carrier and aircraft type combination have an average of 54 seconds taxi average error, while the lowest have an -13 seconds average taxi error.This suggests that the prediction error could be reduced by using different taxi speeds based upon air carrier and aircraft type.
+F. Takeoff Clearance Reaction Time UncertaintyThe purpose of this measure was to assess the uncertainty associated with the range of times of pilot throttle-up response to ATC takeoff clearance.Currently PDRC's SDSS component does not explicitly model this portion of the departure.The takeoff clearance reaction time results are utilized in later sections of this paper to determine the prediction error that would exist assuming the mean clearance reaction time for all flights.
+Measurement ApproachTakeoff clearance reaction time was defined as the time from ATC issuance of the 'Cleared for Takeoff' directive to the point the flight begins its takeoff roll.This time, as well as the takeoff roll duration, was not part of the surface system's prediction.Thus, an estimate was required prior to communicating the predicted wheels OFF time to the downstream decision support system in PDRC.The clearance reaction time measure required undelayed ATC voice clearance instructions as well as direct line of site to the departing flight to determine when it began its takeoff roll.A total of 108 flights were observed from the DFW Center tower during May-July 2012.
+Uncertainty ResultsThe results of the takeoff clearance reaction time measure are depicted in Fig. 7.An interesting observation was that approximately 35% of flights did not stop on the runway threshold but rather continued directly into their takeoff roll.For these flights, the departure clearance was given before the runway hold line or immediately upon entering the runway.These flights are captured in the first bin of the histogram in Fig. 7.The mean and median for the sample taken were both 6 seconds.The data sample also demonstrated some variance with a standard deviation of 5 seconds, a minimum of 0 and a maximum of 25 seconds.Using the mean and median times for the entire sample do not give the best indication of how long it took for the time it took the pilot to react to the departure clearance given this includes flights that did not stop at all.For those flights that did stop on the runway threshold, the mean and median clearance reaction time was 9 seconds.
+G. Takeoff roll durationThe purpose of the takeoff roll measure was to assess uncertainty that exists in the range of times from a flight's start of roll to the time at which the rear wheels were OFF the airport surface.An estimate of this measure was required given the downstream decision support system's ascent model begins at wheels OFF time and location.PDRC's SDSS component uses an adaptable value for takeoff roll duration.Currently, this value is the same for all aircraft types.The takeoff roll prediction error discussed in this paper is the difference in actual takeoff roll versus the mean takeoff roll.
+Measurement ApproachDirect observation of takeoff duration was selected over surface data analysis techniques to ensure the accuracy of the start of roll and rear wheel liftoff.This measure required direct line of site to the departing flight in order to determine when the flight began its takeoff roll and wheels were off of the airport surface.One hundred ninety one (191) flights were observed from DFW center tower during 2011 and 2012 PDRC evaluations.
+Uncertainty ResultsThe results of the takeoff roll duration measure are depicted in Fig. 8.Both the mean and median takeoff time duration for the sample taken was 38 seconds.Some variation was noted with a standard deviation of 7 seconds, a minimum of 18 seconds and a maximum of 55 seconds.Analytical models for calculating the distance for takeoff roll exist in the literature. 5These models generally require knowledge of takeoff weight, prevailing winds on the surface or other variables that are not currently available to the PDRC system in real time.Given this data access limitation, as well as a focus on takeoff duration rather than takeoff roll length, sampling of actual takeoff roll duration was utilized to formulate an average takeoff time duration.However, assuming a significant sample size it may be possible to narrow the variation by use of empirical data.In the sample of data collected for this research, the average takeoff duration for a McDonnell Douglas MD-80 aircraft was 41 second, compared to an average takeoff time for an Embraer ERJ 145 of 32 seconds.
+H. TRACON transit timeThe purpose of the TRACON transit time measure was to assess uncertainty that exists in the transit between the wheels off event and crossing the departure fix on the boundary of TRACON and Center airspace.The following paragraphs describe improvements to TRACON transit time predictions that were incorporated into PDRC's TMA/EDC component and used during the operational evaluation.Currently, departure logic in PDRC's TMA/EDC component predicts that the flight will fly an adaptable number of nautical miles in the direction of departure and then acquire the first departure fix in the departure route.Analysis of the DFW departure data revealed that the first fix was significantly downstream in the aircraft's route of flight.Due to this, the en route transit time predicted by TMA/EDC assumed that the flight would head directly toward this fix instead of capturing the nominal waypoints along the RNAV departure route.Figure 9a illustrates the horizontal profile of the TMA/EDC predictions which are representative of the current operational system logic.In this diagram, DFW is the green dot and DARTZ (red X) is the first fix in TMA's estimated route.The thick, dull gray line is the actual track.The various colored lines which extend from the gray route are the TMA/EDC provided estimated routes at that point in time.In an ideal scenario, these lines would overlay the thick gray route.In order to provide a more accurate route for PDRC evaluation, several potential solutions were analyzed.The solution selected was to create more specific departure routing which includes the expected TRACON departure fixes from the RNAV departure route.The routing assignment in adaptation was linked to the departure runway, which is automatically passed to TMA/EDC from PDRC's SDSS component.Figure 9b provides a graphical view of PDRC predictions of a DFW departure after implementing this solution.As illustrated the predictions and the actual tracks align closely.The previous figures plot the route geometry before and after but do not reflect the impact to the estimate times-ofarrival to the departure fix.To determine this, the predicted and actual transit times were compared for a sample of 109 flights from DFW to IAH. Figure 10 plots the difference between the actual time of flight and the TMA predicted time of flight to the departure point using the routing change illustrated in Fig 9b .The actual transit time was defined as the time between the first radar track and the time when the flight crosses the departure fix.The data in this figure are stratified by departure runway.The points shown in blue diamonds were measures of TMA Estimated Time-of-Arrival (ETA) error to the meter fix before the routing solution was implemented.The same measure was taken for a sample of 53 flights after the routing solution previously mentioned was added to the system.The results are illustrated as green triangles in Fig. 10.North flow departures time prediction error to Runway 35 prior to the routing solution showed a mean error of 176 seconds, while after the solution this was reduced to a mean error to 28 seconds.Runway 36 demonstrated similar improvement with a mean error of 235 seconds prior to the routing solution that was reduced to 62 seconds after the solution.
+Measurement ApproachThis measurement considered the transit between the wheels off event and crossing the departure fix on the boundary of TRACON and Center airspace.The truth data used to assess this uncertainty was the actual OFF time as derived from the surface system and actual departure fix crossing time derived from airborne surveillance from TRACON and Center data sources.TRACON transit time prediction error is any error associated with the PDRC system using the current day algorithms used in the field evaluation.
+Uncertainty ResultsThe improvements mentioned in the previous section were incorporated into PDRC and used during the operational evaluation.Ninety two (92) flights from the PDRC evaluation were analyzed.Figure 11 provides a breakout of the size and frequency of TRACON transit time error.The transit time error is measured in absolute values to prevent aircraft that had negative flight time error (transit time lower than predicted) from biasing the results.For the PDRC scheduled flights during the operational evaluation, the mean absolute error was 25 seconds with a median TRACON transit time error of 21 seconds.Some variation was observed with a standard deviation of 20 seconds, a low error of 1 seconds and high of 122 seconds.The flights with the highest TRACON transit time error were scrutinized.Amongst these flights was an aircraft scheduled to depart on Runway 35L.This runway prediction was supplied by the surface system based upon statically adapted rules.Later, the flight changed to departure from Runway 36R.The runway the flight was scheduled with added approximately 40 seconds of additional transit time.This example highlights the need for obtaining the correct runway assignment prior to the en route scheduling process.
+I. Center Transit TimeThe purpose of the Center transit time measurement was to assess the uncertainty in transit between departure fix crossing and meter point crossing events.
+Measurement ApproachThe departure fix is located on the boundary between TRACON and Center airspace, while the departure meter points are generally located on the neighboring Center boundary.Truth data used to assess this uncertainty were derived departure fix crossing time and meter point crossing times from airborne surveillance.Those flights which had a change to the meter point assignment after scheduling were removed from the sample to eliminate flights which changed intent between the time the flight was scheduled and crossing of the meter point.Center transit time prediction error is any error associated with the PDRC system using the current day algorithms used in the field evaluation.
+Uncertainty ResultsThe results of the Center transit time uncertainty measure are illustrated in Fig. 12.The mean Center transit time error for all PDRC scheduled flights was 49 seconds with a median error of 32 seconds.Note that the mean error is approximately twice as high as the TRACON transit time error despite the fact that the flight distance for these two measures are approximately the same.A significant factor is this error is that the PDRC evaluation used the EDC component of the TMA decision support tool which did not present times to the sector controllers' scopes.Thus, sector controllers made sequencing choices independent of the PDRC guidance which introduces individual sequencing preferences into the uncertainty.Manual observation of PDRC scheduled flights and discussions with Center Traffic Managers also revealed other factors, including significant speed fluctuations in the overhead stream, flights that cut corners off of the nominal route, pop-up flights that were scheduled after the PDRC scheduled flights, and altitude error.In the case of altitude error, the primary challenge was that the TMA/EDC system has no knowledge of the Letter of Agreement between Fort Worth Center and Houston Center in which aircraft are provided to Houston at flight level 290 if Houston is in East flow and flight level 310 if Houston is in West flow.Without the crossing altitude information TMA/EDC is left to speculate that the flight will cross at their filed flight plan altitudes which could have significant differences in wind speed and/or could take some time to maneuver to.
+III. System PerformanceTo determine system performance, PDRC research utilized a combination of quantitative metrics and qualitative feedback from operational personnel and subject matter experts.The focus of this section is on the objective metrics that were used to assess PDRC system performance.
+A. OFF Time ComplianceOne objective of PDRC is to improve upon schedule compliance by reducing uncertainty that has been demonstrated in manual coordination. 3Thus, OFF time compliance is an important system metric for PDRC.
+Measurement ApproachThe approach used in this measurement was to leverage the highest precision measurement source available to evaluate compliance.For the baseline sample, a full year of OFF time compliance data were available from operational TMA/EDC recordings covering more than 400 scheduled flights from October 2010 until November 2011.Flights with strategic times (EDCTs) were removed from the compliance analysis presented here but utilized in other analyses.EDCTs were not counted in the primary compliance measure because they introduced variation due to procedural differences which were not the focus of this research.Briefly, the research team observed individual controllers following different procedures in situations where flights were subject to both a strategic and tactical TMI.The OFF time agreed upon between Center and Tower traffic managers was defined to be the coordinated OFF time, which was compared against the departure time as obtained from the departure message from en route automation.Given that this large sample of data covered a long duration in which unknown circumstances might have been involved without a PDRC observer to report them, the outliers outside of 1.5 times the IQR were removed.Measuring PDRC OFF time compliance was more straightforward than the baseline given firsthand knowledge of every scheduled flight.For example, one PDRC scheduled flight that was subject to an APREQ procedure was later expedited in order to prevent potential hail damage.At the point that verbal direction was given to expedite the flight, the APREQ time was no longer valid.However, no electronic commands were issued for this flight and had the team not been aware of this occurrence then the flight would otherwise have looked non-compliant.Flights which had both a strategic and tactical TMI were captured for analysis in PDRC, but like the baseline set they did not count toward OFF time compliance results.The measure of OFF time compliance for a PDRC flight is the coordinated OFF time versus the actual wheels off time as available in the PDRC.PDRC calculates the actual OFF by utilizing a detected start of takeoff roll and adapted roll duration.A total of 120 flights were scheduled by the PDRC system during the operational evaluation from May 30, 2012 through July 26, 2012.For a flight to count as a PDRC scheduled flight, both the surface and the Center traffic managers had to schedule the flight using the PDRC system and agreed upon scheduling procedure.
+ResultsThe distribution of PDRC OFF time compliance is illustrated in Fig. 13.The mean compliance was 33 seconds with a median of 37 seconds, indicating a slightly later actual OFF time than planned time on average.A fair amount of variance was exhibited in this sample as well, with a standard deviation of 63 seconds, a minimum of 135 seconds early and a maximum of 165 seconds late.The two flights with the highest OFF time error were both due to the flight not having its weight and balance numbers when it arrived at the runway threshold.Table 1 provides a comparison between the baseline OFF time compliance and the PDRC system OFF time compliance.The second column of this table has the PDRC OFF time compliance mean, median and standard deviation values.The baseline OFF time compliance is listed in the third column, and the last column indicates the estimated percentage of PDRC OFF time compliance compared to the baseline compliance.This improvement is characterized as a lower bound estimate due to the fact that outliers were removed from the baseline data set but not the PDRC data set.As previously discussed, outliers were removed given the PDRC team did not observe all flights in the one year sample.As the table indicates, PDRC scheduled flights demonstrated a significant improvement over baseline levels of OFF time compliance.
+B. Hit Slot Performance MeasureThe purpose of the hit slot performance measure was another way to assess how well the system delivered flights to the available airspace, or slot, they were originally scheduling into.
+ApproachThe purpose of the hit slot performance measure was to assess how well the system delivered flights to the available airspace, or slot, they were originally scheduling into.The hit slot measure compares the leading and trailing flight at the time of scheduling with those at the time the flight crosses the meter point in en route airspace.A detailed explanation of this measurement and the baseline hit slot results can be found in prior PDRC research. 1
+ResultsForty-one percent (41%) of PDRC scheduled flights hit the slot they were scheduled into.This represents only a modest improvement over the baseline level of hit slot performance of 39%. 1 The merge of PDRC scheduled flights into the overhead stream was analyzed to determine the primary reason for low hit slot performance.The most common cause for missing the scheduled slot was a change in the overhead stream from the time the flight was scheduled to the time the flight arrived at the meter point.Departure uncertainty from nearby airports as well as airborne flights that short cut their route contributed to the overhead stream uncertainty.
+IV. DiscussionThis section discusses inferences of system performance from individual departure predictive accuracy measures.
+A. Impact of Communication Uncertainty and APREQ Window Size on OFF Time complianceTable 1 of this paper indicated PDRC exhibited a significant improvement over baseline absolute OFF time compliance.The primary reasons for improved OFF time compliance using PDRC is inferred to be reduced communication uncertainty between the Tower and Center traffic managers and use of a PDRC-enabled target time rather than the standard APREQ window.This section provides a summary of information gained during the PDRC evaluation from direct observations and traffic management controller (TMC) interviews.Without the use of PDRC, the coordinated OFF time window for an APREQ is communicated verbally over facility inter-phone from the Center to the Tower.Currently, there is no set national standard for the time window to use in this communication or the phraseology to employ in this procedure.However, this time window is generally accepted as being 3 minutes and is structured to favor flights departing early rather than late.The general rule is to build a time window two minutes ahead (-2) and one minute behind (+1) the flight's desired OFF time.Observations of this verbal exchange and interviews of Center personnel indicate a significant degree of uncertainty in this communication.For example, a time of 17:25:26 Zulu from the TMA/EDC system is truncated and displayed in minutes level granularity to Center personnel as 1725.The Center TMC then verbally communicates the APREQ window which may be communicating a single value of 1725 or any number of variations in APREQ window size, bias and phraseology.After receiving the time window verbally from the Center TMU, the Tower TMC must interpret the information.In some cases, the TMC receiving the information may assume the beginning of the first minute to the end of the last minute given.In the case of the time communicated as 1724 to 1727, this interpretation would be 1724:00 to 1727:59, which would be a 3 minute and 59 second window.Other Tower TMCs indicate that they take the specified window to mean the beginning of the first minute to the beginning of the last minute given.Interviews with Tower personal revealed that another source of communication uncertainty is the location at which the flight is expected to be at this time window.In some cases, the Tower TMC assumed the location that the flight was expected at the negotiated time was when the flight was "tagged up", or the point at which TRACON surveillance was first received for the flight.However, the trajectory from the TMA/EDC system begins at wheels OFF from the airport surface.Observations of "tag up" as compared to actual OFF time indicated this duration has a 26 second mean with 25 second median.To reduce the complexity of this communication, the PDRC system automatically sent the time expected by en route automation at seconds level precision to the surface system.The Tower TMC then used the seconds level precision to communicate a minute's level precision value to the surface local controller.The local controller was asked to try to achieve wheels OFF at the exact minute communicated to the best of their ability rather than using the standard APREQ compliance window described above.
+B. Combined View of Departure ErrorEarlier sections of this document described departure event measurements of uncertainty for departure events analyzed in this research.However, a reasonable question may be, how well does the system perform in the presence of this uncertainty?To answer this question, it was necessary to analyze the level of predictive error associated with each departure event.In most cases, the PDRC prediction for the event was utilized to obtain this measure.In some cases, like in the hypothetical case of scheduling flights from the gate which was not performed during the PDRC evaluation, it was necessary to estimate the level of error using a reasonable approach at the prediction like those discussed in earlier sections of this paper.Figure 14 combines the departure prediction measures described in this research into a single diagram.This figure provides a view of the size and distribution of the current prediction error for each departure event in the presence of current day uncertainty.In the majority of cases, the PDRC prediction for the event is utilized to obtain this measure.In some cases, like in the hypothetical case of scheduling flights from the gate which was not performed during the PDRC evaluation, it was necessary to estimate the level of error using a reasonable approach at the prediction.The mean values of each measure are shown in bold near the red cross.The median value, upper quartile and lower quartile define the boundaries of the box structure in the box plot, and the 'whiskers' extend on both directions of the box to encompass the variance of the distribution without inclusion of the outliers.In the case of pushback start and center transit error, a portion of the box plot whisker cannot be seen because they go beyond the scale of the diagram.The variance in the pushback start error, pushback duration error, AMA taxi error and Center transit error are the largest among all departure events.Of those events, only the AMA taxi and the Center transit error are part of current day tactical departure scheduling.Both the pushback duration and pushback start error would need to be considered when performing tactical scheduling prior to the spot.Even assuming perfect OUT time compliance, the pushback duration event alone experiences variances that are high enough to prevent a flight from being able to make the current day tactical departure window which only allows flights to be one minute late.Additionally, while mean ramp taxi error is low based upon the average ramp taxi speed method utilized, the upper tail stretches nearly 70 seconds.If significant ramp taxi error occurred then this would leave little room for error for any remaining event predictions.However, the error measured in this analysis is absent air carrier efforts to meet a specified time.It is possible that with greater air carrier involvement flights could be expedite to meet an earlier time if required.Spot crossing duration has a positive overall mean of 6 seconds.While this value is low, the maximum spot crossing error was 100 seconds and spot crossing duration is an event that can add to overall system uncertainty in current day tactical departure scheduling.The AMA taxi represents a significant source of uncertainty in today's tactical departure scheduling process.The taxi time error has a number of error components, the most sizable of which are runway prediction error from the spot and predicted taxi speed from the spot.The size of the error of the clearance and the takeoff roll time are negligible in comparison with the other error sources once taken into account.If they were not taken into account however, then on average each flight would have 6 seconds of clearance error plus 38 seconds of roll time error, for a total of 44 seconds error on average.While this seems small, that equates to approximately 5nmi in the overhead stream (assuming 420 kts).TRACON transit time error has been reduced significantly with the use of more detailed TRACON routing in TMA/EDC as well as automatic utilization of the runway assignments passed from the surface system.However, the overall average of the error is positive.That is, the current system is consistently under predicting the transit time from OFF to the departure fix.For tactical scheduling purposes, it is better to over predict rather than under predict TRACON transit time if forced to choose between the two.Over predicting the transit time would allow the flight to be delayed to meet the time rather than accelerated.Center transit time has the largest positive error of any departure event measured.Bearing in mind that all the flights measured were during a volatile period in which an APREQ event was put in place to help manage, a certain degree of variance is not surprising.Additionally, this operational evaluation involved only outbound tactical departure scheduling 3 using the TMA/EDC decision support tool.Currently, the FAA operates TMA/EDC in an open-loop mode.Unlike arrival metering with TMA, TMA/EDC schedule times and sequence information are not displayed on sector controllers radar scopes.Center TMCs use TMA/EDC to manage constrained traffic flows to provide sector controllers with a workable traffic situation.Sector controllers solve the traffic puzzle with no knowledge of the TMA/EDC planned solution.Thus, differences between the TMA/EDC and controller solutions are to be expected.Given the timing associated with widespread deployment of a surface capability that could supply the OFF times required, it is likely better to assume a metering environment in which times are presented to the controllers with +/-30 seconds of error like those demonstrated by the Efficient Descent Advisor (EDA). 6
+C. Extending tactical departure scheduling to the gateThe cumulative departure scheduling error that may occur when scheduling from the gate was estimated by taking 1000 random draws, with replacement, from the error data samples described in previous sections of this paper.The error components of each departure event were accumulated to form the total surface departure error associated with each of the 1000 flights in the sample.If the total departure error was one minute or less, the flight was considered compliant with the surface OFF time.Total departure error that allowed a flight to be available earlier than their scheduled time was considered compliant for this measure given the flight could be delayed by ATC to meet the required time.The cumulative error estimate for PDRC during the operational evaluation was taken by using the takeoff roll time and clearance reaction time that were available during the evaluation, given these values were slightly different than those discussed in this research.As indicated in Table 2, scheduling from the spot using the levels of PDRC accuracy available during the PDRC operational evaluation yielded an estimated 71% departure compliance.That is, 71% of flights had one minute or less total surface error when scheduling from the spot.For comparison purposes, the actual percentage of flights in the PDRC operational evaluation with one minute or less surface error were determined to be 70%.Thus, the theoretical estimates and actual distribution for PDRC OFF compliance are very close.An estimate was taken of the improvement to OFF time compliance that could be achieved from implementing changes described in this research.Specifically, the prediction error associated with using the mean clearance reaction time, mean takeoff roll time and adding a 30 seconds buffer to remove late bias associated with current OFF time compliance.The estimated OFF time compliance when scheduling from the spot with these changes is 92%.OFF time compliance while scheduling from the gate was estimated by using the error components associated with pushback start, pushback duration and ramp taxi.Scheduling from the gate using the levels of PDRC accuracy available during the operational evaluation yielded an estimated 64% compliance.However, scheduling from the gate with the changes described in this research would yield an estimated 73% OFF time compliance.For comparison, OFF time compliance achieved nation-wide with manual scheduling process is approximately 69%. 1 It is important to note that this analysis did not consider active air carrier participation in the tactical departure scheduling process.Currently, the PDRC concept places no requirements on air carriers beyond passively providing gate assignment and pushback estimate information that already resides in air carrier systems.Other concepts such as Spot and Runway Departure Advisor (SARDA), 7,8 Collaborative Departure Queue Management (CDQM), 9 and Surface Collaborative Decision Making (Surface CDM) 10 assume an active air carrier role in departure scheduling to enable surface delays to be absorbed at the gate or in the ramp area.Combining active air carrier participation from these concepts with PDRC's integration between Tower and Center departure scheduling systems may enable the tactical departure scheduling horizon to be extended to the gate with satisfactory compliance.
+D. Removing Late Bias from Departure Fix complianceGiven only modest improvements to hit slot performance over the baseline measure despite PDRC's significant improvement to OFF time compliance, an alternative measure of system performance was developed.The purpose of the departure fix compliance performance measure was to assess how well the system delivered flights to the boundary between TRACON and Center airspace compared to the scheduled time.This measure was useful because it provided an interim point between wheels off and meter point crossing at which the performance of the system could be analyzed.In addition, the majority of airborne vectoring and speed controls occur after the departure fix which allows an objective metric to be obtained from operational data with fewer confounding influences than the hit slot measure.To determine if the departure fix compliance was a reliable performance measure, PDRC OFF time compliance and departure fix crossing errors were analyzed for correlation.The correlation coefficient for these two data sets was 0.937, which indicates a high correlation.This suggests that as OFF time uncertainty decreases, so does departure fix crossing uncertainty.In contrast, the correlation coefficient between PDRC OFF time compliance and meter fix compliance was only 0.16, indicating a low correlation.The departure fix compliance measure utilized in PDRC relied upon the coordinated departure time negotiated between the systems and TMA/EDC's estimate of time to fly to the departure fix.The two values were combined to form the scheduled departure fix crossing time.The scheduled crossing time was then compared against the actual crossing time to determine if it was compliant.The compliance standard used was the same as today's standard of two minutes early through one minute late (written as -120/+60).The results for departure fix compliance for PDRC scheduled flights are that 51% hit the -120/+60 second window.Given significant improvement to OFF time compliance demonstrated with PDRC as well as lower variance in the overall distribution, one might expect higher departure fix compliance than was demonstrated.Analysis of departure fix compliance revealed that the primary reason for lower than expected departure fix compliance is flights were slightly later than planned.This fact is not surprising given that the local controllers using the PDRC times were required to change their procedure to use a single minute time rather than the time window that they were accustomed to.To compensate for this, a slight modification can be made to the times that are communicated via PDRC.This modification can be implemented in the software communication layer between the Center system and the Tower system such that no changes to PDRC scheduling procedures would be required.Analysis of possible buffer values using PDRC scheduled flights indicated departure fix compliance as high as 86% may be possible with this simple software change.
+V. ConclusionsDuring the operational evaluation of Precision Departure Release Capability at DFW, OFF time compliance improved from an average absolute error of 108 seconds to less than 59 seconds.PDRC demonstrated greater predictability than the baseline sample by decreasing OFF time error from a standard deviation of 96 seconds to 40 seconds.Significant improvements to TRACON transit time predictions were achieved by including TRACON-specific routing in the horizontal profile and electronically supplying the airborne system with the departure runway assignment from the airport surface system.Despite these improvements, additional work is needed to reduce TRACON transit time error.Prediction errors associated with the departure events were utilized to estimate the OFF time associated with scheduling tactical departures from the gate.This estimate indicates that OFF time compliance of 73% of flights scheduled from the gate may be possible without requiring active airline involvement, which exceeds baseline tactical departure scheduling OFF time compliance.Figure 1 .1Figure 1.Tactical departure scheduling is affected by the cumulative uncertainty of numerous departure events.
+Figure 2 .2Figure 2. Comparison of airline provided actual OUT with airline provided estimated pushback times.
+Figure 3 .3Figure 3. Histogram showing pushback durations.
+Figure 4 .Figure 5 .45Figure 4. Ramp taxi time duration uncertainty.
+Figure 6 .6Figure 6.Airport movement area taxi prediction error assuming 17 knot taxi speed.
+Figure 7 .Figure 8 .78Figure 7. Scattergram showing duration from takeoff clearance to start of roll.
+Figure 9 .9Figure 9. TMA departure route predictions before and after implementation of an adaptationbased solution.
+Figure 11 .Figure 10 .1110Figure 11.Absolute Error of TRACON Transit Time
+Figure 12 .12Figure 12.Absolute Center transit time error.
+Figure 13 .13Figure 13.PDRC OFF time compliance distribution.
+Figure 14 .14Figure 14.Comparison of prediction error for the various departure events analyzed in the PDRC operational evaluation.
+Table 1 . PDRC absolute OFF time compliance compared with baseline. PDRC absolute OFF time compliance (sec)1Baseline OFF timeOFF time errorcompliance, outliersversus baselineremoved (sec)(lower bound %)
+Table 2 . Estimated OFF time compliance percentage using cumulative departure event error2Downloaded by NASA Ames Research Center on November 29, 2012 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5674Scheduling from theScheduling from theScheduling from theScheduling from thespot using PDRCspot using changesgate using PDRCgate with changesaccuracy duringdescribed in thisaccuracy duringdescribed in thisoperational evaluationresearchoperational evaluationresearch71%92%64%73%
+			Downloaded by NASA Ames Research Center on November 29, 2012 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5674
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+AcknowledgementsThe authors would like to acknowledge the essential support provided by FAA personnel at the Fort Worth Center Traffic Management Unit and Dallas/Fort Worth ATCT.Finally, we wish to thank our colleagues at NTX and NASA Ames whose support was critical to the success of PDRC research objectives.
+			
+
+			
+
+				
+
+
+	
+		Characterization of Tactical Departure Scheduling in the National Airspace System
+		
+			RichardCapps
+		
+		
+			ShawnEngelland
+		
+		10.2514/6.2011-6835
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			Sep. 2011
+			
+		
+	
+	AIAA-2011-6835, 11th
+	Capps, A. and Engelland, S.A., "Characterization of Tactical Departure Scheduling in the National Airspace System," AIAA-2011-6835, 11th American Institute of Aeronautics and Astronautics (AIAA) Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, VA, 20-22 Sep. 2011.
+
+
+
+	
+		Traffic Management Advisor Flow Programs: an Atlanta Case Study
+		
+			ShonGrabbe
+		
+		
+			BanavarSridhar
+		
+		
+			AvijitMukherjee
+		
+		
+			AlexMorando
+		
+		10.2514/6.2011-6533
+	
+	
+		AIAA Guidance, Navigation, and Control Conference
+		Portland, Oregon
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 2011
+			
+		
+	
+	Grabbe, S., "Traffic Management Advisor Flow Programs: an Atlanta Case Study", American Institute of Aeronautics and Astronautics (AIAA) Guidance, Navigation, and Control Conference, Portland, Oregon, 8-11 Aug. 2011.
+
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+		Trajectory-Based Takeoff Time Predictions Applied to Tactical Departure Scheduling: Concept Description, System Design, and Initial Observations
+		
+			ShawnEngelland
+		
+		
+			RichardCapps
+		
+		10.2514/6.2011-6875
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			Sep. 2011
+			
+		
+	
+	AIAA-2011-6875, 11th
+	Engelland, S.A. and Capps, A., "Trajectory-Based Takeoff Time Predictions Applied to Tactical Departure Scheduling: Concept Description, System Design, and Initial Observations," AIAA-2011-6875, 11th American Institute of Aeronautics and Astronautics (AIAA) Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, VA, 20-22 Sep. 2011.
+
+
+
+	
+		Benefit Assessment of Precision Departure Release Capability Concept
+		
+			KeePalopo
+		
+		
+			GanoChatterji
+		
+		
+			Hak-TaeLee
+		
+		10.2514/6.2011-6834
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			Sep. 2011
+			
+		
+	
+	AIAA-2011-6834, 11th
+	Palopo, K., Chatterji, G., and Lee, H., "Benefit Assessment of the Precision Departure Release Capability Concept," AIAA- 2011-6834, 11th American Institute of Aeronautics and Astronautics (AIAA) Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, VA, 20-22 Sep. 2011.
+
+
+
+	
+		Takeoff Performance Monitoring System display options
+		
+			DavidMiddleton
+		
+		
+			RaghavachariSrivatsan
+		
+		
+			LeePerson, Jr.
+		
+		10.2514/6.1992-4138
+		NASA TP- 3403
+	
+	
+		Flight Simulation Technologies Conference
+		
+			American Institute of Aeronautics and Astronautics
+			1994
+		
+	
+	Middleton, D.B., Srivatsan, R., and Person, L.H., Jr., "Flight Test of Takeoff Performance Monitoring System," NASA TP- 3403, 1994.
+
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+	
+		Development and Testing of Automation for Efficient Arrivals in Constrained Airspace
+		
+			RCoppenbarger
+		
+		
+			GDyer
+		
+		
+			MHayashi
+		
+		
+			RLanier
+		
+		
+			LStell
+		
+		
+			DSweet
+		
+	
+	
+		27th International Congress of the Aeronautical Sciences (ICAS)
+		Nice, France
+		
+			Sep. 2010
+			
+		
+	
+	Coppenbarger, R., Dyer, G., Hayashi, M., Lanier, R., Stell, L., Sweet, D., "Development and Testing of Automation for Efficient Arrivals in Constrained Airspace," 27th International Congress of the Aeronautical Sciences (ICAS), Nice, France, 19- 24 Sep. 2010.
+
+
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+	
+		A Concept and Implementation of Optimized Operations of Airport Surface Traffic
+		
+			YoonJung
+		
+		
+			TyHoang
+		
+		
+			JustinMontoya
+		
+		
+			GautamGupta
+		
+		
+			WaqarMalik
+		
+		
+			LeonardTobias
+		
+		10.2514/6.2010-9213
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
+		
+			American Institute of Aeronautics and Astronautics
+			Sep. 2010
+			
+		
+	
+	Jung, Y. C., Hoang, T., Montoya, J., Gupta, G., Malik, W., and Tobias, L., "A Concept and Implementation of Optimized Operations of Airport Surface Traffic," 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Fort Worth, TX, 13-15 Sep. 2010.
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+
+
+	
+		Tower Controllers' Assessment of the Spot and Runway Departure Advisor (SARDA) Concept
+		
+			THoang
+		
+		
+			YJung
+		
+		
+			JHolbrook
+		
+		
+			WMalik
+		
+	
+	
+		9th USA/Europe ATM R&D Seminar (ATM2011)
+		Berlin, Germany
+		
+			June 2011
+			
+		
+	
+	Hoang, T., Jung, Y., Holbrook, J., and Malik, W., "Tower Controllers' Assessment of the Spot and Runway Departure Advisor (SARDA) Concept," 9th USA/Europe ATM R&D Seminar (ATM2011), Berlin, Germany, 14-17 June 2011.
+
+
+
+	
+		Field test results of Collaborative Departure Queue Management
+		
+			ChrisBrinton
+		
+		
+			SteveLent
+		
+		
+			ChrisProvan
+		
+		10.1109/dasc.2010.5655527
+	
+	
+		29th Digital Avionics Systems Conference
+		Salt Lake City, Utah
+		
+			IEEE
+			Oct. 2010
+			
+		
+	
+	Brinton, C., Lent, S., and Provan, C., "Field Test Results of Collaborative Departure Queue Management," 29th Digital Avionics Systems Conference, Salt Lake City, Utah, 3-7 Oct. 2010.
+
+
+
+	
+		An Integrated Collaborative Decision Making and Tactical Advisory Concept for Airport Surface Operations Management
+		
+			GautamGupta
+		
+		
+			WaqarMalik
+		
+		
+			YoonJung
+		
+		10.2514/6.2012-5651
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2012
+		
+	
+	FAA Air Traffic Organization Surface Operations Office
+	FAA Air Traffic Organization Surface Operations Office, "U.S. Airport Surface Collaborative Decision Making (CDM) Concept of Operations (ConOps) in the Near-Term -Application of Survace CDM at United States Airports," 15 June 2012
+
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+				
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+I. IntroductionASA"s current Integrated Arrival/Departure/Surface research portfolio includes integration of surface information with en route departure scheduling.The Precision Departure Release Capability (PDRC) activity is assessing the value of using surface trajectory-based takeoff (OFF) time predictions for departure scheduling.Companion papers 1,2 present a concept overview and results from benefits assessment studies.This paper describes the NAS shortfalls that PDRC technology seeks to address and assesses current PDRC levels of predictive accuracy against the current need.The document begins by describing a nation-wide survey of current tactical departure scheduling operations.Existing system shortfalls are then examined via a discussion of system performance along with the measurement approach and corresponding results.The shortfalls discussion is followed by a description of the current levels of OFF time prediction accuracy that can be obtained in the PDRC system today.The paper concludes with a discussion of sites most likely to benefit from PDRC technology.
+II. Current Day Tactical Departure SchedulingIn order to identify existing shortfalls which may be eliminated with reduced departure prediction uncertainty, it is necessary to have an understanding of the current day tactical departure scheduling process.This section covers the following five topics: 1) Tactical departure scheduling overview, 2) Current Inbound Tactical Departure Scheduling Capability, 3) Current Outbound Tactical Departure Scheduling Capability, and 4) Tactical versus strategic departure scheduling.
+A. Tactical Departure Scheduling OverviewTactical departure scheduling is the process used by ATC to regulate air traffic flow to eliminate local demand/capacity imbalances and satisfy local traffic management initiatives (TMIs).Tactical departure scheduling is not required during normal NAS operations as the airspace into which the flight is being released generally has sufficient capacity to accommodate the departure.However, during periods of high demand or low capacity for the airspace being scheduled into, tactical departure scheduling may be utilized.Tactical departure scheduling in the NAS today can be divided into two distinct tactical scheduling modes, which are outbound scheduling of departures from an airport within the departure Air Route Traffic Control Center (ARTCC, hereafter referred to as "Center") to a remote Center and inbound scheduling of departures into an arrival stream of a Traffic Management Advisor (TMA) metered airport.The inbound and outbound terms are generic labels for tactical departure scheduling functions provided by existing decision support tools (i.e.TMA scheduling, "internal" scheduling, "adjacent" scheduling, "coupled" scheduling, extended metering, etc.)The flight length associated with the tactical timeframe varies somewhat in the literature.The authors chose an upper bound of 90 minutes as the guideline for flight lengths subject to tactical departure scheduling.This flight length was chosen in part based upon information obtained from operational data usage of the decision support tools that support tactical departure scheduling.Figure 1 illustrates the relationship of the Dallas/Fort Worth (DFW) departure airport relative to arrival metering to Houston Intercontinental (IAH) airport.Given that DFW resides within the IAH metering freeze horizon and the limited airspace available to maneuver after departure prior to the outer meter arc, a high level of departure prediction accuracy is required.Later sections provide an estimate as to the level of predictive accuracy that is required.Call For Release (CFR) is a common tactical departure scheduling procedure which requires Air Traffic Control Tower (ATCT) personnel to call the Center Traffic Management Unit (TMU) for a scheduled departure time prior to releasing the aircraft for departure.The CFR procedure is applied to departing aircraft in order to ensure the demand placed on local airspace resources do not exceed the available capacity.In a CFR scenario it may or may not be necessary to delay the aircraft based upon the latest information available on the constrained flow at the time that an aircraft is ready to depart.The improved departure time compliance associated with the CFR procedure provides more accurate schedule predictions than are available via the aircraft"s filed flight plan departure time (also known as Predicted Departure Time or PTIME) or by use of Expect Departure Clearance Times (EDCTs).EDCT times are generated by Traffic Flow Management (TFM) as a part of the strategic departure scheduling system and are not intended for tactical use.Aircraft PTIMEs represent a starting point from which the departure planning process begins but are historically prone to OFF time uncertainty.The required departure compliance window for CFR aircraft varies somewhat by facility.Today, no nationwide guidance exists, but based upon information obtained from traffic managers, generally inter-facility agreements call for flights to depart within a three minute window.This three-minute window is generally structured to allow departure two minutes prior to, or one minute later than, the target coordinated departure time.The idea of allowing the aircraft to depart two minutes early is that it is easier to delay the aircraft to fit into the constrained flow than to accelerate the aircraft to meet its scheduled time.Figure 2 provides an illustration of nationwide departure time compliance comparison between estimation methods available to TMCs during the month of January 2011.January was selected for operational data analysis primarily due to the availability and completeness of the TMA operational data set during this time period.The values reported in Fig. 2 are the average absolute difference between the expected departure time and the actual departure time.The operational TMA data analyzed had information on aircraft PTIME, EDCT times, TMA times and actual departure times which were used for this nationwide departure time compliance analysis.An obvious difference exists in the departure time compliance between PTIME estimates, EDCT controlled times and CFR controlled times with the departure times coming from the CFR process providing the best compliance of the three.Using the CFR process during the month of January, approximately 69.2% of aircraft subject to CFRs in which TMA automation was utilized met the required -2/+1 window.In contrast, if EDCT times were required to meet a -2/+1 window the compliance would have been approximately 20.4 %.Using PTIME compliance this percentage would drop to only 4% of flights that met the -2/+1 window.
+B. Inbound Tactical Departure Scheduling CapabilityAs adjacent center metering has expanded the reach of TMA, the greatest need for departure scheduling capability has been for airports residing in another Center.Analysis of January 2011 operational data shows that 69.3% of all departure scheduling is performed from an origination Center that is different than the destination Center being scheduled into.The expanded scope of TMA usage is a factor to consider in analysis of tactical departure scheduling shortfalls, another factor is the effect that tactical departure scheduling capability has on the balance of delay that is assigned to the airborne stream versus airport surface.In December of 2005 a feature was added to TMA that allowed the TMC to determine whether or not departures should compete directly with active airborne flights.Prior to this feature, TMA always scheduled aircraft into the overhead stream in a manner that the departure had the same priority as airborne aircraft.The intent of this feature was to prevent airborne delays from reaching the point which it made it difficult for controllers to achieve the TMA meter crossing times.However,  the tradeoff associated with limiting the airborne delays is an increase in departure delays.When the TMC chooses to delay the airborne flow, the TMA system will treat the departing aircraft with equal priority as airborne aircraft and assign a delay to unfrozen aircraft in the metered airborne stream if needed.In this situation, TMA may delay both the airborne stream and assign a ground delay to the departing aircraft.Analysis of the current usage based upon data from January 2011 indicates that the large majority (92%) of flights scheduled in TMA took all of their tactical departure delay on the surface.The ability for the TMC to determine whether the aircraft tactical delay should be taken airborne, on the surface, or a combination of the two is complicated by uncertainty in the scheduling process.Analysis of tactical departures scheduled into the arrival TMA system during metering indicates that approximately 21% of all scheduled aircraft experience both a TMA assigned ground delay and TMA assigned airborne delay.To prevent aircraft that are assigned delay on the airport surface from being delayed again once they join the airborne flow, the TMC may "freeze" the aircraft into the airborne flow when scheduling in TMA.If the TMC selects this option when scheduling a tactical departure, the TMA system will freeze the aircraft"s scheduled time of arrival to the meter point thereby preventing any additional delay from being added to the aircraft once it becomes airborne.This feature allows the TMC to ensure the aircraft does not receive unplanned airborne delay; however, if the aircraft does not depart when expected and cannot achieve the time which is frozen into the arrival metering system"s schedule, then the space that was being reserved for this aircraft will go unutilized barring additional action by ATC to prevent this from occurring.Currently, 29% of departing flights that are scheduled into an arrival TMA system are scheduled frozen into the airborne flow: the remaining 71% of aircraft are allowed to adjust their position in the TMA arrival schedule upon first surveillance.An additional shortfall of the current day inbound tactical departure scheduling system occurs when the tactical departure delays become very large.This situation may require Air Traffic Control System Command Center (ATCSCC) involvement.In the large majority of cases the assigned ground and airborne delay are small (i.e. less than 5 minutes 73% of the time in TMA), however, cases do exist in which airborne and/or ground delay is in excess of one hour.In the month of January there were approximately 20 occurrences of TMA assigned ground delays in excess of one hour.The majority of the examples of large TMA assigned ground delay were to either New York Center or Atlanta Center metered airports.In many cases, flights with high TMA-assigned surface delay also received an airborne delay from the TMA system.These examples of high ground delay with airborne delay may lend insight into why into why sites like New York Center and Atlanta Center are top users of the "schedule frozen" option previously discussed.When high tactically-assigned ground delay occurs in the NAS, the ATCSCC may choose to implement an Airspace Flow Program (AFP) to regulate the flow of aircraft into the destination airport with the objective of reducing the TMA-assigned surface delays.The AFP scheduling scenario used for this purpose is unique in that it is designed to work in conjunction with the arrival TMA system; hence it is called a TMA Flow Program (TFP).The objectives of a TFP are to pre-condition the arrival stream such that TMA can utilize available space in the stream for tactical departure scheduling purposes.The boundaries of the TFP are set to be roughly contiguous with the arrival metering system"s freeze horizon and any airport with departures inside of this boundary are exempt from the program.Using a TFP the TFM suite of tools assigns a ground delay to aircraft bound for the metered airport which are located outside of the red circle shown in Fig. 3, while TMA assigns a tactical ground delay (and potentially airborne delay depending on TMC selection) for those aircraft bound to the metered airport located within the red circle.
+C. Outbound Tactical Departure Scheduling CapabilityIn addition to the TMA arrival metering system, the Enroute Departure Capability (EDC) is now part of the tactical departure scheduling decision support tools available to TMC personnel.The EDC system design re-uses a number of common components of the arrival TMA system like its adaptation data structure, route processing algorithms and trajectory generation functions.While many of the core components of TMA have been leveraged to provide EDC capability, there are notable differences between arrival TMA and the EDC system.The EDC system serves a different traffic management objective than the arrival TMA system.EDC"s focus is outbound tactical departures leaving from one of the airports within a Center which are destined to a remote Center facility.In contrast the tactical departure scheduling capability in arrival TMA system is only focused on aircraft that are scheduled into its metered airports.EDC is commonly used to assist in the application of miles in trail restrictions between facilities, especially when the airspace being scheduled into is highly constrained or has multiple miles in trail initiatives to satisfy.An additional use of EDC is to assist in regulating departures into sectors which are experiencing high demand.In contrast, arrival TMA use is primarily motivated by the traffic volume in the arrival streams entering the metered airport rather than sector loading considerations.The TMA EDC system is deployed to all 20 Centers within the NAS.Similar to the nationwide deployment of the arrival TMA system, there is significant variability in how EDC is used from one Center to another.As indicated by the blue portion of the bar chart in Fig. 4, the Center with the most frequent EDC usage is Boston Center, followed by Atlanta Center and Indianapolis Center.The combined usage of these three sites alone is greater than total EDC usage at all other Centers.Although Atlanta Center is the second largest user of EDC, the frequency of Atlanta"s EDC usage is significantly less than that of inbound tactical departure scheduling into Atlanta"s arrival TMA system.Figure 4 illustrates inbound and outbound tactical departure scheduling usage.The total departure delays assigned by Arrival TMA versus EDC follow a similar model with inbound tactical departure scheduling assigning a total of 3,563 hours of surface delay to aircraft in the month of January 2011 versus a total of 480 hours of surface delay assigned by the outbound tactical departure scheduling system (13.5% of inbound).
+D. Tactical Versus Strategic Departure SchedulingWhile a significant amount of literature exists on the strategic departure scheduling process within the NAS which utilizes the Traffic Flow Management (TFM) suite of tools, information on the tactical departure scheduling process is quite limited.The two scheduling processes are distinct from one another and are currently not directly integrated.The strategic and tactical schedules have similar, but different objectives and usage characteristics.A significant difference between tactical and strategic departure scheduling is the scope of the initiative.Strategic departure scheduling is focused on correcting large demand/capacity imbalances that exist in the NAS usually due to convective weather or high demand.This often requires significant delays over an extended period of time which may be assigned hours in advance of the affected aircraft"s departure time.In contrast, tactical departure scheduling focuses on a specific air traffic flow that is subject to a local traffic management initiative (like Miles in Trail or Adjacent Center Metering) and generally introduces small delays to specific aircraft on an as-needed basis.Tactical departure scheduling system delays are approximately 4 minutes per aircraft on average with a median of 1 minute, which is significantly lower than TFM delays with approximately 66 minute average and 52 minute median delays.These statistics are derived from January 2011 operational data.The difference in average delays is likely due to the national scope of TFM which must assign departure delay well in advance of departure, in contrast with tactical departure scheduling which applies delay on an as-needed basis to a single aircraft at a time.Tactical departure schedules are able to consider the latest airspace conditions minutes before takeoff.The frequency of use of tactical departure scheduling versus strategic as measured by the number of aircraft affected for January 2011 also varies significantly as illustrated in Fig. 5.The combined number of departures scheduled using the TMA and EDC tactical decision support tools (labeled "inbound" and "outbound" tactical departures in Fig. 5) was approximately 350% greater than aircraft affected by EDCTs (strategic TFM controlled departures).It is worth noting that inbound tactical departure scheduling (i.e. using arrival TMA) occurred significantly more frequently than outbound tactical departure scheduling (i.e. using EDC).For this analysis, an aircraft was counted as being tactically scheduled only if the aircraft was both scheduled and "accepted" or "frozen" into the TMA Arrival or EDC system.A significant number of aircraft (approximately 18,489 during January, 2011) were initially scheduled in the TMA system but the scheduling process was not finalized by "accepting" or "freezing."
+III. NAS-wide Tactical Departure Scheduling Performance AnalysisIn addition to analyzing the January operational data, operational observations of scheduling performance were evaluated at DFW during the month of July 2011.Data from operational observations were used as a point of reference with which to test the data analysis measurement methodologies that were applied NAS-wide.This section discusses the metrics used for tactical departure scheduling performance and the results obtained in this analysis.Potential benefits due to reduced departure time uncertainty from PDRC can be quantified by the improvement in meeting a slot, reduction of manual intervention to mitigate missed or unattainable slots, and increased flight efficiency due to a reduction in airborne vectoring and speed controls.
+A. 'Hit Slot' MetricA key performance measurement in the tactical departure scheduling process is the efficiency with which available airspace in the constrained flow are being utilized by scheduled departure aircraft.Gaining insight into this measurement is important because it allows an objective means to analyze the utilization of tactical departure scheduling into the constrained overhead stream that may be lost due to departure prediction uncertainty.To obtain an assessment of slot utilization, operational data from the TMA and EDC systems were analyzed.A "hit slot" measurement was created for this analysis.The objective of the "hit slot" measurement is to determine whether or not the tactically scheduled departure joined the constrained flow at the sequence in which it was scheduled into prior to departure.This measurement allows an estimation of the effectiveness of the scheduling process based upon detailed scheduling information available in the operational TMA data.This section discusses details on the estimation approach used for this metric as well as results.Figure 6 provides an illustration of the "hit slot" measurement geometry for DFW to IAH tactical departure scheduling.For the "hit slot" measurement, the leading and trailing aircraft identification, TMA and EDC estimated times of arrival to the meter point (known as Meter point ETAs) and scheduled times of arrival to the meter point (known as Meter point STAs) were collected at the time at which the aircraft was scheduled in the operational TMA and EDC systems.Aircraft sequence and scheduling information were also collected at the point at which the aircraft received its first surveillance hit, and then again when it crossed the meter point location.The leading and trailing aircraft identification were examined to determine if they matched at each point in the aircraft"s flight history from scheduling, to first track, to the actual sequence at crossing.An aircraft was said to hit its scheduled slot if its sequence relative to its leading and trailing aircraft remained when it was scheduled and when it crossed the meter point location.The same "hit slot" sequencing analysis was repeated for each aircraft at the point at which surveillance was first acquired.This analysis measured whether or not the sequence provided by TMA and EDC after processing the first track hit matched the sequence at the actual meter fix crossing.This step was added to allow comparison of the difference in predictive accuracy between pre-departure scheduling versus attaining first surveillance.An important consideration of the "hit slot" measurement is determining the inclusion/exclusion criteria for aircraft to be used in the analysis.Aircraft which were excluded from the analysis included: 1) Aircraft which did not cross the meter point they were scheduled to due to lack of receipt of a crossing message, 2) Aircraft which did not have a record of leading and trailing aircraft at the point of scheduling, first track hit and crossing of the meter point based upon information available to the system at the point in time these events occurred 3) International tactical scheduling from Canada to NAS facilities given lack of departure time information available to TMA 4) Atlanta inbound tactical departure aircraft given the "hybrid metering" scenario that Atlanta uses does not allow display of metered sequence, 5) Aircraft for which a Host departure message was not received 6) For arrival TMA only metered aircraft were included, 7) Only aircraft which the TMC scheduled and "accept" or "froze" were used.To determine the sequence of aircraft at the times of interest mentioned above, the native stream class identification used by TMA and EDC was leveraged.For example, all jets scheduled over meter fix RIICE are a part of the RIICE_JETS TMA stream class.This information is made available in the native TMA data utilized for this analysis, as was the scheduled time of arrival to the meter fix (or meter point for EDC) for each stream class.The logic developed to support the "hit slot" measurement ordered all aircraft by STA from lowest to highest, by stream class.This ordering was of all aircraft which were "scheduled" in the operational TMA or EDC system, which included any tactical departure schedules that had been scheduled at that time.Upon each schedule update the leading and trailing aircraft of every flight was identified assuming one existed.If an aircraft did not have a leading or trailing aircraft in the scheduler, these values were subsequently ignored in the analysis as previously mentioned.Upon occurrences of events of interest the sequence was stored along with the other aircraft metadata for later analysis.The results from the "hit slot" analysis were separated into inbound (arrival TMA) versus outbound (EDC) tactical departure scheduling.A number of the results are represented as percentages due to inclusion/exclusion rules and data integrity checks.While certain aircraft had to be excluded to ensure data quality and that the measurements were on the right set of aircraft, the percentages are expected to hold true for the entire population of tactically scheduled departures in January due to the large sample size used for this analysis (over 22,400 aircraft after applying inclusion/exclusion logic).Table 2 shows a high-level summary of the results from running the "hit slot" measurement on all operational TMA and EDC facilities for the month of January.The "Hit Scheduled Slot %" column represents the percentage of all tactically scheduled aircraft in January 2011 that had the same leading and trailing aircraft sequence when scheduled on the surface as when they crossed the meter point being scheduled to.The "Hit First Surveillance Slot %" provides this information but uses updated sequence obtained from TMA or EDC after the first surveillance is made available.The "% Difference" takes the difference between the two hit slot percentages and then applies that percentage to all aircraft that were tactically scheduled to estimate to total number of aircraft that missed their slot due to departure time prediction uncertainty.Given that this difference provides an estimate of what the TMA and EDC algorithms had for their internal sequence prior to versus after first surveillance, this is believed to be a good estimate of slots that were missed due to departure time prediction uncertainty.Figure 7 provides an illustration of the departure events which collectively add to the uncertainty of tactical departure scheduling process.This analysis captures information from TMA and EDC system predictions that occur when the TMC scheduled the aircraft in operations prior to wheels-off, then compares this estimate to the TMA and EDC predictions immediately after wheels OFF when surveillance is first acquired.By capturing the estimates at these two time periods and comparing their difference, the ascent model portion of the prediction which is common between the two estimates, is isolated from the measurement.While a goal of tactically scheduling an aircraft into a constrained flow is to identify and utilize resources ("slots") before the aircraft departs, the impact to the NAS which occurs when a scheduled slot is not met can vary.Observed cases of missed tactically scheduled departure slots indicate that they can often lead directly to lost capacity, most notably delay caused by the case in which an aircraft is scheduled frozen into an arrival TMA slot but does not meet its expected departure time window.Other observed impacts of missing the departure slot are inefficient flight paths due to required vectoring and/or speed controls (which can lead to excess fuel utilization) as well as increased controller and TMC workload (discussed in later section).According to the hit slot metric data obtained, approximately 1 in 4 aircraft hit their arrival slot in TMA, while more than 1 in 3 hit their slot in the EDC system.The primary reason for the difference is believed to be the size of the slot being scheduled into given that the average stream class separation difference in EDC is much larger than that of TMA.Based upon operational data from January 2011, the average stream class separation for arrival TMA is 8.2 nm, while the average stream class separation in EDC is 23.6 nm.The larger separation in EDC is consistent with intuition given that EDC"s purpose is primarily to ensure MIT separations are met and the required separation being enforced is often quite large.The size of the slot being scheduled into is also believed to be the primary difference in percentage of aircraft that hit their scheduled slot in arrival TMA and EDC after the first track hit.As table 2 indicates there is a significant difference with EDC approximately 18% more aircraft hitting the slot at this point in time versus arrival TMA.The percentage  of aircraft that hit their slot after surveillance suggest that there may be room for improvement in the predictive capabilities of the ascent modeling of TMA and EDC.Future analysis may be warranted to analyze predictive accuracy of the ascent modeling due to aircraft weight, wind error, inaccurate routing, etc. While, on average, aircraft hit their TMA-scheduled slots approximately 26.9% of the time, a fairly significant variation exists by site.The results of the hit slot metric were calculated for all TMA and EDC locations nationwide.The highest site percentage of the "hit slot" measurement of all the arrival TMA systems was 32.9%, while the lowest was 18.5% The highest site percentage of all EDC systems was 52.5%, with the lowest being 22.7%.The site specific variance may warrant additional consideration to determine the primary factors which lead to the variance.Given that the "hit slot" percentage differs on a site by site basis, this suggests that the impact to the NAS may vary by facility as well.
+B. Arrival Metering Workload metricIn addition to missed slots from departure time uncertainty, another shortfall to consider in current day tactical departure scheduling is the workload for the TMC and controllers.During the month of January 2011 approximately 153,426 flights had metering information delivered to sector controllers with the expectation that the controller would delay aircraft as necessary to meet the metered times.Of the metered aircraft, approximately 34,360 (22.4%) were scheduled into the arrival stream using arrival TMA arrival scheduling capability.This represents a statistically significant portion of the overall metered aircraft during January.The large sample of metered flights was analyzed to determine if manual intervention by either the sector controller or TMC during metering was higher for tactically scheduled departures than for flights which were not tactically scheduled.Three measures were utilized for this evaluation, which were the frequency controller swaps, controller resequences and individual aircraft reschedules by the TMC.The following gives a brief explanation of what these measures capture.Sector controller tools associated with metering include two capabilities to control the sequence that TMA associates with arrival aircraft.These capabilities are known as swap and re-sequence.The swap capability allows the controller to identify any two aircraft on their display and exchange their meter point crossing times.This capability is used when the sector controller may disagree with the sequence or times that are being presented to him/her by the TMA system.The tactically scheduled departure aircraft and the flights which were not tactically scheduled were analyzed to determine the frequency of required manual activity.The increased percentage of aircraft that required manual controller or TMC activity during metering suggests that tactical departure scheduling is a factor in increased workload for both sector controllers and TMCs.The highest increase of manual activity observed was the percentage increase of aircraft that undergo a single aircraft re-schedule.This measure showed a 6.1% increase for tactically scheduled departures over those aircraft which were not tactically scheduled.A summary of these results can be seen in Table 3.
+C. Effect of not scheduling a tactical departure into a constrained flowObservations of tactical scheduling performance from DFW into IAH during June and July of 2011 indicate that the benefit of increased departure time prediction accuracy may not be limited to the set of tactically scheduled departures previously discussed.Examples of these potential benefits were observed during PDRC engineering shadow evaluations.A typical example of this was for aircraft departing DFW with a destination of IAH which were not scheduled in the TMA system.In these examples the departing aircraft was sequenced ahead of several other aircraft in the stream class that were in close proximity.The addition of the departing aircraft added a 1 minute delay to the immediate trailing aircraft, which in turn added two minutes of delay to its trailing aircraft, and so on for a total of four aircraft which received airborne delay due to the departing aircraft.Vectoring off of nominal routes was visually observed in a number of these cases.During PDRC observations in July, a number of occurrences were noted in which departures that were not tactically scheduled and coordinated between Center and ATCT personnel resulted in the use of speed controls and/or vectoring to accommodate the departing aircraft.During evaluations the "not scheduling" scenario which leads to this situation was discussed with Center personnel.Comments received indicate that while additional work is needed by sector controllers to accommodate uncoordinated departures, this is not viewed as an issue for sector controllers so long as other sector workload does not rise to a level of saturation that makes handling uncoordinated departure scheduling problematic.This information is consistent with previous research into the effect of "not scheduling" an aircraft into an arrival TMA flow. 3,4However, beyond the sector workload implications is the consideration of flight efficiency which effect fuel consumption.A coordinated departure release may have helped to reduce speed controls and vectoring which may in turn help reduce fuel consumption.
+IV. Surface Departure Prediction AnalysisThe objective of PDRC is to leverage trajectory-based OFF time predictions to improve upon the current-day tactical departure scheduling process.Achieving this objective requires that one have accurate OFF time predictions from the surface system at the point in time which this information is required by the en route scheduling system.This section discusses a method to estimate the minimal required look-ahead time for OFF time predictions to satisfy tactical departure scheduling requirements.Also discussed are surface departure prediction accuracy requirements for present-day operations as well as recommendations for future surface analysis.
+A. Estimation of departure prediction look-ahead time requirement for Tactical Departure SchedulingIn an ideal scenario, highly accurate aircraft wheels OFF times would be available to tactical and strategic planners hours ahead of the point at which the aircraft was ready to depart.In this ideal scenario all planners would be working from the same set of accurate information and making decisions that could be used to address local, regional, or national demand/capacity imbalances.However, highly accurate OFF times hours in advance of departure is not a feasible objective given the amount of pre-departure uncertainty which exists today. 3,4,6,7The cumulative effect of uncertainty from pushback prediction, through ramp taxi, spot transition, air movement area taxi, departure queue management, departure release, take off roll, ascent modeling, and forecast wind errors prior to reaching the meter crossing point provide a large amount of unpredictability.This uncertainty makes the departure planning process quite challenging.While accurate wheels OFF estimates hours in advance may be an unrealistic objective in the NAS, providing accurate OFF time estimates minutes in advance of wheels OFF is an achievable objective which may help reduce or eliminate some of the challenges faced by tactical departure scheduling.An important question to consider for departure prediction accuracy is "how far in advance of departure does the downstream scheduling system need to have accurate OFF time predictions?"In order to estimate the minimal look-ahead time at which accurate OFF time predictions are required for aircraft departing into an arrival metering flow, one should consider the relative positions of the departure airport and the arrival metering freeze horizon.The geometry of the DFW-to-IAH metering scenario is illustrated in Fig. 8. DFW airport lies within the IAH arrival metering freeze horizon and the standard tactical departure scheduling procedure is to accept and freeze the aircraft into the arrival IAH flow to prevent the aircraft from receiving both a ground delay and an airborne delay.Due to this scheduling methodology, any surface or airborne prediction error in tactical departure scheduling to IAH during metering directly impacts the airborne arrival stream.For present-day operations this OFF time prediction is entirely manual.For the DFW to IAH metering scenario, the typical airborne aircraft scheduled into IAH over meter fix RIICE freezes at approximately 30.4 minutes prior to meter fix crossing when IAH traffic is in East flow, which is the predominant configuration used during metering at IAH.The typical flight time from DFW airport to the RIICE meter fix crossing is approximately 27.7 minutes.This means that an aircraft on the DFW surface which is ready to depart will be competing for slots with airborne aircraft whose schedules have been frozen on average for 30.4 -27.7 = 2.7 minutes (162 seconds).If the DFW aircraft are to compete with unfrozen aircraft for a slot into the constrained flow then the tactical scheduling process must occur at least 162 seconds prior to departure.The 162 second figure represents a theoretical minimum for the tactical departure scheduling lead time.Additional time is required for the Center TMU to consider the schedule and communicate the release time to ATCT.Some time is also required for the TMA scheduler to find a slot for the aircraft in its schedule and optimize the overall arrival stream schedule based upon the new information.The time needed for scheduling purposes in addition to the theoretical 162 seconds is being called the "coordination time" in Fig. 8.Operational observations of PDRC at DFW during July 2011 have revealed that the typical departure schedule process is initiated approximately 5 minutes prior to departure during Call For Release situations.According to ATCT and Center personnel this amount of time prior to departure allows for sufficient coordination and meets the minimal need for look-ahead time requirements at DFW.That is not to say that both ATCT and Center don"t want the times earlier, but this was an acceptable timeframe for the manually-coordinated tactical departure scheduling process in place today.Considering site feedback and the 2.7 minute flight time difference which would allow these aircraft to compete with non-frozen aircraft in the IAH metered stream, this allows approximately 2.3 minutes of "coordination time" for the tactical departure scheduling process at DFW.It is believed that this look ahead time estimation process can be used for other airports that have a high demand for tactical departure scheduling to identify the look ahead time at which accuracy departure time predictions are needed.Based upon PDRC field test observations as well as data obtained from FAA evaluation of TMA scheduling from air traffic control towers, 8,9 it is estimated that through automation the "coordination time" taken for the tactical departure scheduling process can be reduced to approximately 30 seconds.Thus, the minimal look ahead time requirement for DFW is 162 + 30 = 192 seconds prior to wheels OFF.
+B. Surface prediction accuracy at required look-ahead time for Tactical Departure SchedulingThe look-ahead time need was based upon relative geometry of the departure airport to the arrival metering freeze horizon plus required coordination time.Look-ahead requirement will likely vary based upon different airport geometry relative to arrival metering freeze horizons, or the airspace geometry associated with EDC flows.Beyond the look-ahead requirement, there remains the question of required departure prediction accuracy at the specified look-ahead time.The departure prediction accuracy requirement may be estimated from observed CFR time compliance in today"s tactical scheduling scenario.If surface automation delivers the same level of accuracy provided today by the manual CFR procedure, then it follows that it should provide similar benefit to the existing system.Any increase in the accuracy of the departure prediction times or increased look-ahead time for the prediction would be potentially beneficial to tactical departure scheduling system performance.An additional observation to consider is that workload associated with the manual CFR procedure may lead to relatively infrequent use.Any automation that may help reduce the workload threshold at which this level of accuracy could be obtained would likely be used more frequently, which would potentially lead to increased benefits.Another factor to consider is that of any uncertainty that is the result of manual entry or miscommunications like those reports in a companion paper. 1 Currently, the manual CFR procedure must deliver OFF times that comply with a -2/+1 minute window.Based upon tactical departure scheduling data for the month of January 2011, this time window is being met approximately 62% of the time by ATCT control of flights to meet their CFR coordinated OFF time.Based upon measurements obtained of the Surface Decision Figure 9. SDSS prediction accuracy at DFW -June 2011.Support System (SDSS) accuracy in June of 2011, SDSS can predict aircraft wheels OFF at the same level of controlled CFR flights at approximately 137 seconds prior to OFF time.That is to say that without any CFR manual coordination required (e.g.closed loop system); SDSS can achieve similar levels of predictive accuracy as departure time compliance being achieved today through the CFR process at 137 seconds prior to departure.To meet the tactical departure scheduling requirements for DFW, this level of accuracy must be extended at least to the point of 162 seconds as mentioned previously including any coordination time required for the tactical departure flight.However, it is not necessarily true that SDSS must provide this level of accuracy out to the five minutes which current DFW procedure provides.This is due to the coordination time required when using automation is expected to be reduced from the time it takes in the current procedure.During the initial evaluation of PDRC the focus was on establishing confidence in the surface and en route scheduling components, not on reducing the time period it takes for tactical departure scheduling to occur.Future evaluations should work to increase the amount of look ahead time that accurate OFF time predictions are available while reducing the amount of coordination time required for the tactical departure scheduling process.Work is currently underway to increase the accuracy of the existing surface management system"s predictive capability for those aircraft which have acquired surface surveillance.In addition to the increasing the system"s predictive accuracy, areas of research that are recommended are: stability of the OFF time estimates which are provided to the downstream scheduler, utilization of departure prediction confidence in tactical departure scheduling, evaluation of tactical scheduling methods which require OFF time estimates in excess of 10 minutes prior to departure and expansion of OFF time estimates to include airports without ASDE-X surveillance capability.
+V. NAS facilities likely to have greatest benefit from PDRC TechnologyGiven knowledge of the current tactical departure scheduling demand at each NAS facility, as well as estimated look ahead time requirements for each facility based upon geometry like that illustrated in Fig. 8, a list of the top NAS facilities which would benefit from PDRC technology was constructed.This survey focused on inbound tactical departure scheduling since 86.5% of tactical departure scheduling ground delay incurred in the NAS today is scheduled in this manner.The estimation methodology begins with sites that have a proven demand for tactical departure scheduling like those listed in Table 1.Only the top 10% users of tactical departure scheduling airport pairs (e.g.KDFW into KIAH) excluding international scheduling were considered.This yielded 81 airports scheduling into 7 different metered airports, each of which tactically scheduled over 130 aircraft during the month of January.The next step was to analyze each departure/arrival airport pair to determine the lookahead time need of each airport, like that illustrated in Fig. 8.In order to include look-ahead time needs that are achievable based upon surface surveillance availability, it was necessary to bound the look-ahead time by the average surface taxi out time.The nationwide average of unimpeded taxi out time of 10.7 minutes was obtained from the FAA"s Aviation System Performance Metrics (ASPM) database.Those airports with greater than 10.7 minutes look-ahead time requirement prior to departure were eliminated from the list, which left 55 airports.The remaining candidate airports were further filtered according to current or planned availability of an ASDE-X surface surveillance system which would allow for trajectory based OFF time estimates to be supplied to the tactical departure scheduler.This remaining list consisted of 26 airports, which were ordered by the delay they incurred in January 2011, as listed in Table 4.The "Scheduling From" column in Table 4 indicates the airport from which tactical departure scheduled aircraft are departing, while the "Scheduled into Metered Airport" indicates the destination of the tactical departure scheduled.At the top of the list are two airports that are not only ASDE-X equipped, but also have a current Surface Decision Support System (SDSS) adapted.In addition, the third and fourth airports on the list are currently being adapted for the SDSS system in support of other research.A notable omission from Table 4 is scheduling from Charlotte to Atlanta.While 426 aircraft were tactically scheduled from Charlotte to Atlanta during the month of January, only 35 of these occurred during an Atlanta metering period.The lack of tactical departure scheduling during metering may be due to the "hybrid metering" design that Atlanta uses in which adjacent centers meters outside of Atlanta Center airspace but the metering advisories are not displayed on Atlanta Center glass.Analysis of site geometry relative to the freeze horizon indicates that the look-ahead time at which accurate departure predictions are needed becomes greater as the distance from the departure airport within the freeze horizon increases.Inbound tactical departure scheduling analysis has demonstrated that the majority of scheduling occurs near the arrival freeze horizon boundary (11.3 minute average flight time to freeze horizon with 11.4 minute standard deviation).Some of the airports being scheduled from to an arrival metering facility lie geographically inside of the freeze horizon, while others lie outside of the freeze horizon.Heavier usage of tactical departure scheduling near the freeze horizon is consistent with intuition as flights which are sufficiently far away from the TMA freeze horizon generally have sufficient time and space in the arrival stream in order to secure a slot prior to the freeze horizon location.As departing airports get closer to or are within the TMA freeze horizon, the scheduling process becomes more dependent upon the departure prediction accuracy as there is less time for a departing aircraft to compete for resources in the overhead stream while the demand for overhead resources generally also becomes greater.In this manner the geometry of a departure airport relevant to the freeze horizon of the arrival TMA system being scheduled into is an important factor to consider.Figure 10 illustrates this relationship which is being referred to as the "Goldilocks Zone" in which achievable levels of departure prediction accuracy can be used for tactical departure scheduling.The following example considers if a departure airport requires 15 minutes flying time within the arrival freeze horizon to an arrival metering facility.To actively compete with non-frozen aircraft which are currently airborne in the arrival stream, the look-ahead time predictions must be accurate enough for TMA at least 15 minutes prior to departure.Any error in the departure prediction estimate scheduled at this point will directly impact the arrival stream efficiency as well as controller workload if the sector controller meter list is rippled due to changes.On the other hand if the departure airport is 60 minutes flying time outside of the freeze horizon, then despite the level of departure prediction accuracy, the aircraft will likely have adequate time to be scheduled into the arrival TMA system.
+VI. ConclusionsAnalysis of operational TMA and EDC data from all current deployed facilities covering over 1,082,000 flights during the month of January 2011 indicates that these tactical departure scheduling capabilities are widely used in the NAS today with over 65,000 scheduled aircraft per month using these methods.Increased utilization of tactical departure scheduling decision support tools has been fueled by expansion of adjacent center metering and nation-wide deployment of the EDC capability.Although tactical departure scheduling with TMA and EDC has become a widely used component in NAS operations today and represents a significant improvement over the previous process which lacked trajectory based ascent modeling, analysis of the current system"s performance indicates that significant room for improvement exists by reducing departure time uncertainty.Based upon operational data analysis described in this paper, 6,792 inbound tactically scheduled aircraft and 1,911 outbound tactically scheduled aircraft in January 2011 NAS wide are estimated to have missed the airspace slot they were scheduled into due to departure time prediction uncertainty.The effect to the NAS of a missed scheduled departure slot often leads directly to lost capacity, most notably in the case in which an aircraft is scheduled frozen into an arrival TMA slot but does not meet its expected departure time window.However, measuring the impact to the NAS of a missed departure slot is not always straightforward as some ability to recover the airspace resources exists, often at the cost of additional TMC or controller workload and/or inefficient flight paths.While the shortfalls of the existing tactical departure scheduling system have become more evident and quantifiable, solutions to these shortfalls are in early stages of maturity relative to other NAS systems.Determining the level of predictive accuracy that trajectory based OFF time predictions must attain for tactical departure scheduling delay reduction benefit is complicated by the lack of surface automation available in operations today and the challenges associated with evaluating a passive OFF time estimation process.This paper proposes metrics and methods to estimate the look ahead time requirement of surface predictions, as well as to identify target airports that are likely candidates for NAS deployment of PDRC technology based upon the departure airport"s geometry relative to areas of high airspace demand like those encountered near time based metering freeze horizons.Indications are that departure prediction accuracy requirements for tactical departure scheduling in the NAS are likely not a single value, but rather a range of values that vary in significant part based upon site specific geometry and airspace demand.Figure 1 .1Figure 1.Inbound tactical scheduling geometry which requires a high level of departure prediction accuracy.
+Figure 2 .2Figure 2. Average Nationwide Departure time compliance for January 2011.
+55
+Figure 3 .3Figure 3. Example of TMA Flow Program into Atlanta.
+Figure 4 .4Figure 4. Tactical Scheduling of Arrival TMA and EDC -Jan 2011.
+Figure 5 .5Figure 5. Departures Scheduled with Decision Support Tool -Jan 2011.
+Figure 6 .6Figure 6."Hit Slot" metric geometry for DFW to IAH Scheduling.
+Figure 7 .7Figure 7. Tactical departure scheduling to the meter point incorporates cumulative uncertainty from a number of departure events.
+Figure 8 .8Method to estimate OFF prediction look ahead time need for DFW aircraft departing into Houston arrival metering.
+Figure 10 .10Figure 10.Inbound tactical departure scheduling 'Goldilocks Zone' relationship between departure airport location and freeze horizon.
+
+3,4Metering Freeze HorizonDeparture AirportOuter Meter ARCMeter FixArrival Airport
+Table 11gives examples and frequency of usage of inbound tactically scheduled aircraft across Center boundaries during the month of January 2011.The "Number of Aircraft Scheduled into remote Center" lists the number of times a TMC from a Center other than the destination Center scheduled aircraft using TMA capability.Note that not all scheduling performed is from an adjacent center, for instance Indianapolis Center schedules into New York Center although the two Centers do not share a boundary.Another unique case occurs when aircraft departing Canadian airspace Call For Release into New York Center airspace.
+Table 1 . Departure Scheduling from remote ARTCC Jan 2011.1Number of AircraftScheduled intoFrom Center Into Centerremote CenterJacksonvilleAtlanta6267WashingtonAtlanta6072BostonNew York3955WashingtonNew York3719IndianapolisAtlanta3081ClevelandNew York3012OaklandAtlanta2951Los AngelesAlbuquerque2243MemphisAtlanta1619CanadaNew York1234IndianapolisNew York469ClevelandAtlanta389Albuquerque Los Angeles384Fort WorthHouston382ChicagoCleveland210Kansas CityChicaco
+Table 2 . 'Hit Slot' measurement results for all operational TMA/EDC facilities during January 2011. System Hit Scheduled Slot % Hit First Surveillance Slot % % Difference Estimated Number of Aircraft that missed their slot due to departure time prediction uncertainty2Arrival TMA26.939.312.56792EDC39.457.117.71911
+Table 3 . Percentage of aircraft which required manual intervention-Jan 2011. Workload Category Not Tactical Departure % Tactical Scheduled Departure % % Difference Approximate # Aircraft subject to increased manual activity Controller Swaps34.46.62.3792Controller Re-sequences4.46.01.7572Single Aircraft Re-schedule5.011.16.12125
+Table 4 . Sites which would benefit from PDRC technology -Jan 2011.4ScheduledScheduled IntoNumber ofFromMetered AirportHoursScheduledAirport CodeScheduling From Airport NameCodeScheduling Into Metered Airport NameDelayAircraftKMCOOrlando InternationalKATLHartsfield -Jackson Atlanta International47.9628KMEMMemphis InternationalKATLHartsfield -Jackson Atlanta International38.0381KATLHartsfield -Jackson Atlanta InternationalKCLTCharlotte/Douglas International32.4426KBOSLogan InternationalKPHLPhiladelphia International28.0385KLASMc Carran InternationalKLAXLos Angeles International18.8381KIADWashington Dulles InternationalKCLTCharlotte/Douglas International17.4263KDTWDetroit Metropolitan Wayne CountyKPHLPhiladelphia International16.1278KSDFLouisville InternationalKATLHartsfield -Jackson Atlanta International15.9230KCLECleveland-Hopkins InternationalKPHLPhiladelphia International15.7203KLAXLos Angeles InternationalKLASMc Carran International15.4318KSFOSan Francisco InternationalKLAXLos Angeles International15.0333KDFWDallas/Fort Worth InternationalKIAHGeorge Bush Intercontinental/Houston13.3168KCVGCincinnati/Northern Kentucky InternationalKCLTCharlotte/Douglas International12.9258KDCARonald Reagan Washington NationalKCLTCharlotte/Douglas International12.0246KBWIBaltimore/Washington InternationalKCLTCharlotte/Douglas International11.4271KPHXPhoenix Sky Harbor InternationalKLASMc Carran International11.1196KCVGCincinnati/Northern Kentucky InternationalKATLHartsfield -Jackson Atlanta International10.8199KSANSan Diego InternationalKPHXPhoenix Sky Harbor International7.3189KSJCNorman Y. Mineta San Jose InternationalKLAXLos Angeles International7.2168KLASMc Carran InternationalKPHXPhoenix Sky Harbor International6.6200KMCOOrlando InternationalKCLTCharlotte/Douglas International6.1250KLAXLos Angeles InternationalKPHXPhoenix Sky Harbor International5.7213KSDFLouisville InternationalKCLTCharlotte/Douglas International5.7190KSNAJohn Wayne-Orange CountyKLASMc Carran International5.5140KSNAJohn Wayne-Orange CountyKPHXPhoenix Sky Harbor International3.6173KSFOSan Francisco InternationalKLASMc Carran International2.6154
+		
+		
+			
+
+				
+
+
+	
+		Trajectory-Based Takeoff Time Predictions Applied to Tactical Departure Scheduling: Concept Description, System Design, and Initial Observations
+		
+			ShawnEngelland
+		
+		
+			RichardCapps
+		
+		10.2514/6.2011-6875
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			September 20-22, 2011
+		
+	
+	submitted to AIAA 11th Aviation Technology
+	Engelland, S., Capps, A, "Trajectory-Based Takeoff Time Predictions Applied to Tactical Departure Scheduling: Concept Description, System Design, and Initial Observations", submitted to AIAA 11th Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, VA., September 20-22, 2011.
+
+
+
+	
+		Benefit Assessment of Precision Departure Release Capability Concept
+		
+			KeePalopo
+		
+		
+			GanoChatterji
+		
+		
+			Hak-TaeLee
+		
+		10.2514/6.2011-6834
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			September 20-22, 2011
+		
+	
+	submitted to AIAA 11th Aviation Technology
+	Palopo,K., Lee, H, Chatterji, G., "Benefit Assessment of the Precision Departure Release Capability Concept", submitted to AIAA 11th Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, VA., September 20-22, 2011.
+
+
+
+	
+		Mitigating the Effect of Demand Uncertainty due to Departures in a National Time-Based Metering System
+		
+			StevenLandry
+		
+		
+			AlvaroVillanueva
+		
+		10.2514/6.2007-7713
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			September 18-20, 2007
+		
+	
+	AIAA's 7th Annual Aviation Technology, Integration, and Operations (ATIO) Technical Forum
+	Landry, S. J., and Villanueva, A., AIAA-2007-7713, "Mitigating the Effect of Demand Uncertainty Due to Departures in a National Time-Based Metering System". AIAA's 7th Annual Aviation Technology, Integration, and Operations (ATIO) Technical Forum, Belfast, Northern Ireland, September 18-20, 2007.
+
+
+
+	
+		Effects of the uncertainty of departures on multi-center traffic management advisor scheduling
+		
+			JThipphavong
+		
+		
+			SJLandry
+		
+		AIAA-2005-7301
+		
+			September 26-28, 2005
+			Arlington, VA
+		
+	
+	AIAA's 5th Annual Aviation Technology, Integration, and Operations (ATIO) Technical Forum
+	Thipphavong, J., and Landry, S. J., "Effects of the uncertainty of departures on multi-center traffic management advisor scheduling.", AIAA-2005-7301, AIAA's 5th Annual Aviation Technology, Integration, and Operations (ATIO) Technical Forum, Arlington, VA., September 26-28, 2005.
+
+
+
+	
+		Traffic Management Advisor Flow Programs: an Atlanta Case Study
+		
+			ShonGrabbe
+		
+		
+			BanavarSridhar
+		
+		
+			AvijitMukherjee
+		
+		
+			AlexMorando
+		
+		10.2514/6.2011-6533
+	
+	
+		AIAA Guidance, Navigation, and Control Conference
+		Portland, Oregon
+		
+			American Institute of Aeronautics and Astronautics
+			August 08-11, 2011
+		
+	
+	Grabbe, S., "Traffic Management Advisor Flow Programs: an Atlanta Case Study", AIAA Guidance, Navigation, and Control Conference, Portland, Oregon, August 08-11, 2011.
+
+
+
+	
+		Multi-Center Traffic Management Advisor: Operational Test Results
+		
+			ToddFarley
+		
+		
+			StevenLandry
+		
+		
+			TyHoang
+		
+		
+			MonicarolNickelson
+		
+		
+			KerryLevin
+		
+		
+			DennisRowe
+		
+		
+			JerryWelch
+		
+		10.2514/6.2005-7300
+		AIAA-2005-7300
+	
+	
+		AIAA 5th ATIO and16th Lighter-Than-Air Sys Tech. and Balloon Systems Conferences
+		Arlington, VA
+		
+			American Institute of Aeronautics and Astronautics
+			September 26-28, 2005
+		
+	
+	Farley, T. C., Landry, S. J., Hoang, T., Nickelson, M., Levin, K. M., Rowe, D., and Welch, J. D., "Multi-Center Traffic Management Advisor: Operational Test Results," AIAA-2005-7300, Proceedings of the 5th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Arlington, VA, September 26-28, 2005.
+
+
+
+	
+		Analysis of En Route Sector Demand Error Sources
+		
+			JimmyKrozel
+		
+		
+			DanRosman
+		
+		
+			ShonGrabbe
+		
+		10.2514/6.2002-5016
+		AIAA-2002-5016
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Monterey, California
+		
+			American Institute of Aeronautics and Astronautics
+			August 5-8, 2002
+		
+	
+	Krozel, J., Rosman, D., Grabbe, S., "Analysis Of En Route Sector Demand Error Sources", AIAA-2002-5016, AIAA Guidance, Navigation, and Control Conference and Exhibit, Monterey, California, August 5-8, 2002.
+
+
+
+	
+		Linking Traffic Management to the Airport Surface: Departure Flow Management and Beyond
+		
+			NDoble
+		
+		
+			JTimmerman
+		
+		
+			TCarniol
+		
+		
+			MKlopfenstein
+		
+		
+			MTanino
+		
+		
+			VSud
+		
+	
+	
+		Eighth USA/Europe Air Traffic Management Research and Development Seminar (ATM2009)
+		Napa, CA
+		
+			29 Jun -2 Jul 2009
+		
+	
+	Doble, N., Timmerman, J., Carniol, T., Klopfenstein, M., Tanino, M., and Sud, V., "Linking Traffic Management to the Airport Surface: Departure Flow Management and Beyond," Eighth USA/Europe Air Traffic Management Research and Development Seminar (ATM2009), Napa, CA, 29 Jun -2 Jul 2009.
+
+
+
+	
+		TMA En Route Departure Capability Jacksonville ARTCC and Orlando ATCT Usage Data
+		
+			SFutato
+		
+		
+			KMcmillan
+		
+		
+			SCallon
+		
+		
+			April 8, 2008
+		
+	
+	Futato, S., McMillan, K., Callon, S., "TMA En Route Departure Capability Jacksonville ARTCC and Orlando ATCT Usage Data", April 8, 2008.
+
+
+				
+			
+		
+	
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+I. Introductionecent NASA research [1][2][3] has focused on improving tactical departure scheduling in scenarios where wellequipped airport Towers interact directly with Center Traffic Management Units (TMUs) to implement departure management initiatives such as Call For Release (CFR).The research presented in this paper is part of an effort to extend tactical departure scheduling improvements to lesser-equipped airports and to address constraints that exist in the terminal environment.The FAA's Next Generation Air Transportation System (NextGen) plans, 4,5 call for the ability to accurately schedule a flight from its departing gate to its arrival gate in advance of its actual gate departure (i.e.gate-to-gate scheduling).Specifically, gate-to-gate scheduling presumes the planning and control of a flight from its departure gate to the runway, to the terminal departure fix, Center departure metering fix, through En Route airspace to the arrival metering fix, runway and finally to the arrival gate.For gate-to-gate scheduling to be effective in the NextGen environment, surface, terminal, Center, and national constraints must all be simultaneously satisfied by the departure scheduling tool.NextGen gate-to-gate scheduling also requires accurate prediction and execution of, trajectory-based operations in the terminal area.Observations at the Dallas/Fort Worth communication requirements all add substantial workload to air traffic control.Thus, a terminal solution that increases workload during these busy periods is unlikely to be accepted by operational personnel.
+B. Terminal Departure SchedulerThe prototype terminal departure scheduler seeks to resolve many of the unique challenges mentioned in the previous section.The following sections discuss the process employed to sequence and schedule each flight.
+SequencingThe terminal departure scheduler gives greater priority to the flights that are ordered earlier.The process the terminal departure scheduler uses to decide what order is used in scheduling is referred to as the sequencing logic.This section briefly describes the sequencing logic which was derived, in large part, from two existing schedulers, the Traffic Management Advisor (TMA) Dynamic Planner (DP) 18 and the Surface Decision Support System (SDSS) 19 surface scheduler.These two schedulers were chosen as a basis for terminal departure scheduling logic because of their relevance to the problem at hand, demonstrated success in operational enviornments and their ability to handle flights at various stages in the departure process.The terminal departure scheduler runs on a user defined periodic rescheduling interval, hereafter referred to as the scheduling cycle.Currently a five second scheduling cycle is used for processing given it matches the frequency of position data updates.For each scheduling cycle, flights are re-sequenced and rescheduled.The sequencing order ensures that flights which are higher priority (see Table 1) from an operational readiness standpoint are scheduled first and that frozen flight times do not change from one iteration to the next.Table 1 describes the categories that are used to sequence flights.This table is listed in priority order from highest to least.Thus all flights that are in the first category (crossed departure fix) are scheduled prior to the flights in the second category (terminally controlled airborne).While some flights may fall into multiple traffic management initiative (TMI) categories, a flight can only belong to one sequencing category.The highest priority sequencing category a flight qualifies for is assigned to it.Thus, a flight with both a terminally controlled frozen OFF time and a CFR will be assigned the higher priority sequencing category associated with terminally controlled frozen flights.Each sequencing category has its own sorting rules.For flights that have already crossed the departure fix, they are sorted by their crossing time.Airborne flights that have yet to cross the departure fix are sorted by their undelayed estimated departure fix crossing time.Surface flights which have a TMI use the controlled OFF time associated with that constraint, while all other surface flights use their undelayed estimated OFF time to determine sequencing order.
+Scheduling Processing LogicRather than accomplishing this with a single monolithic entity, a collection of smaller schedulers are orchestrated by a master scheduler.This design approach was chosen to model industry best practices of loosely coupled, course grained services 20 and to maximize reuse of existing components from prior research.The individual schedulers can be seen at the top of Fig. 1 and are comprised of a pre-scheduler, departure fix scheduler, airport scheduler and a post-scheduler.The terminal departure scheduling process begins by a call to the master scheduler from the main processing logic of the terminal departure system.The frequency of the call to the master scheduler is configured in system properties files.For this research a frequency of five seconds was utilized.Once invoked, the first step the master scheduler performs is initializing a temporary copy of the flight object for the scheduling process.The primary purpose of this activity is to know the starting time for each flight and allow it to be bound by time ranges in later processing if necessary.Given the scheduler must resolve times at multiple locations (airport and fix) a temporary copy of the flight is initialized at both locations.If the flight has a CFR or EDCT time, this time is used to set the latest time the flight can depart.For CFR and EDCT flights, a single minute-level of granularity is used for departure time as opposed to a departure time window.After initializing the flights, the master scheduler calls the pre-scheduler component.The pre-scheduler's primary role is to address flights that have missed their terminally controlled OFF times and thus need to be rescheduled.The pre-scheduler will evaluate all the flights being scheduled to determine if any have missed their coordinated OFF time by the configured number of seconds.If so, the flight will lose its controlled time which will result in the flight having lower sequencing priority as described in the previous section.Once flights have been initialized, they are sorted according to the sequencing categories listed in Table 1.Each flight then undergoes scheduling from earliest to latest in each sequencing category.For each flight, the terminal departure transit time is calculated.For the simulation, this transit time is supplied by a flight time decision tree and terminal departure transit time error described later in this document.For realtime prototype system processing, the terminal departure transit time prediction is provided by the research Traffic Management Advisor (rTMA) system.The rTMA terminal departure transit time prediction includes the effect of winds at crossing altitude.Once the flight time is calculated, it remains constant for the remainder of the scheduling cycle assuming no changes to departure fix have occurred.If the flight is airborne, the remaining terminal departure transit time will be calculated by subtracting the amount of time already spent in transit.Next, the scheduler resolves the departure fix and airport times for each flight.The following steps are taken until both the departure fix time and runway departure time are fully resolved.Fully resolved means that both locations have a time that has no scheduling conflict with other flights and meets all specified traffic management constraints.To accomplish this objective the master scheduler calls the departure fix scheduler which schedules each flight to the appropriate departure fix based upon the OFF time estimate and the terminal departure transit time.Once a departure fix time is obtained, the scheduler then calls the airport scheduler.To resolve the flight's scheduled OFF time, the airport scheduler uses the later of the initial OFF time calculated in an earlier step or the adjusted OFF time derived from the departure fix time.The reason for this is the derived OFF time can be no earlier than the flight can achieve.The adjusted OFF time is calculated by subtracting the terminal departure transit time from the resolved departure fix crossing time.If a CFR or EDCT time exists for this flight, this constraint is taken into account in the airport schedule time.In the current implementation, the SDSS scheduler is used for Dallas Fort Worth (DFW) flights.A simplified model was used for less-equipped airports, which included all D10 airports except DFW.The simplified model for less-equipped airports utilizes fewer site adaptation files than the SDSS scheduler.If the resulting OFF time from the airport schedule time matches the required OFF time to meet the departure fix, scheduling for this flight is complete.If the times do not match, the scheduling process continues again using the updated OFF time as a starting point for scheduling to the departure fix.Once all flights are scheduled in the manner described above, the master scheduler calls the post-scheduler process.The primary purposes of the post scheduler are to determine if a flights should be frozen, store the data and distribute the data to the appropriate processes.The purpose of freezing a flight is to ensure that the controlled departure time that has been communicated to a surface local controller does not change.A flight is frozen if its time to departure is less than the configured freeze horizon value.The results from the scheduling process for each flight are stored in the terminal database.The purpose of the database is to assist in system processing and post operational analysis.The results from the scheduling process are then distributed to the other system processes.In real-time operations, this makes the scheduling results available to Tower personnel.
+III. Terminal Departure Scheduling Simulation CapabilityThe objective of this work is to develop a departure scheduler that can be assessed by air traffic personnel in an operational terminal departure environment.The term departure scheduler refers to a software program that is used by terminal personnel to schedule flights from multiple departure airports within their control which possess varying levels of OFF time precision.The departure scheduler receives real-time flight planning data, surface OFF time estimates and terminal transit time estimates as input and produces a controlled wheels off (OFF) time for each flight which meets all required air traffic constraints on the runway, departure fix, downstream Center metering points and strategic traffic management initiatives.The OFF time provided by the scheduler ensures that minimal separation is maintained at both the runway threshold and departure fix.The OFF times provided to operational personnel are expected to be treated as a controlled OFF time.That is, air traffic personnel will actively control the flight to meet the departure time similar to the process used for EDCT and CFR controlled times.Fast-time simulation was used to better understand terminal departure scheduler performance when subjected to variances in OFF time error, flight time error and varying traffic constraints.The terminal departure scheduler used in this simulation is also expected to execute as the prototype scheduling software that will be used in terminal departure prototype system processing.The terminal departure prototype is a new decision support tool being developed and evaluated in the D10 terminal environment.To achieve the objective of using the same scheduler for both fast-time simulation and prototype processing, an evaluation harness was developed to allow the terminal departure scheduler to be evaluated in multiple modes including; real-time data mode, playback mode and simulation mode.This paper focuses only on the departure scheduling simulation mode.The simulation analysis is executed by running the prototype terminal departure scheduler within the fast-time simulation evaluation harness, as illustrated in Fig. 2. Key inputs such as surface taxi time and terminal transit time undergo perturbation by applying stochastic uncertainty.Once the scheduled OFF time for an aircraft is calculated, then the aircraft's actual OFF time is adjusted by a random variable.The application of error to the terminal transit time is called terminal transit error, whereas the application of this error to surface events is called surface error.This research varies both terminal transit error and surface error to assess the robustness of scheduler design to these variations.The evaluation harness developed for this work provided required components enabling fast time simulation; namely a component to provide input data to the scheduler, a feedback mechanism to model controller response to scheduler output and an error generation component to inject realistic operational error into the simulation.Figure 2 illustrates the terminal departure scheduler evaluation harness, which is briefly described in the following subsections.
+A. InputsThe inputs to the terminal departure simulation consist of flight data, constraints and decision trees for multiple airports in the simulation.The following subsections will briefly discuss the inputs required for this research.
+Flight Data Input FilesThe terminal departure simulation capability includes the ability to generate input files that match the demand and operational criteria specified.The result of this input generation process is an output file of flights that match the given criteria.Some of the choices available when creating simulation input files are the amount of desired departure fix demand per hour, the percentage of departure fix demand from each airport by departure runway, the percentage of flights subject to other traffic constraints (e.g.EDCT or CFRs) and other variables.This research modeled D10 airspace to evaluate terminal constraints, as depicted in Fig. 3.This diagram includes airports contained within the boundaries and the departure fixes located on the borders.The D10 TRACON is centered on DFW and extends outward approximately forty miles in all directions.It contains two major scheduled passenger service airports, DFW and Dallas Love Field (DAL), which are separated by approximately ten miles.Several busy general aviation airports, a regional cargo hub, and a Naval Air Station Joint Reserve Base contribute to the complexity of this TRACON environment.The sixteen departure fixes are arranged in groups of four called departure gates (not to be confused with airport parking gates), which depict their general location relative to the TRACON boundaries.For example, the north gate includes departure fixes LOWGN, BLECO, GRABE, and AKUNA.It is common for restrictions to be imposed on entire gates, without mention of the fixes, so it is important to understand which fixes belong to which gates.A year of operational data from 2013 was analyzed to determine average flight times and variation to each departure fix from each departure airport.
+Input Files Control VariablesPrototype  While simultaneous departure fix constraints are often applied in terminal departure operations, this research found it useful to focus primarily on the effect to system performance with a single departure fix constraint.The scenario used most frequently in this paper was a 10 miles in trail (MIT) constraint over departure fix SOLDO on April 10, 2013.This day was selected primarily because of availability of firsthand observations of operations from D10 TRACON and detailed output data to further analyze the traffic scenario.
+ConstraintsTerminal departure constraint inputs possess substantial flexibility.This flexibility is demonstrated in the use of routing constraints, flow control constraint (e.g.MIT) and creative combinations of both.This section discusses terminal departure constraints, as listed in Table 2, and their handling by the simulation framework.The terminal departure constraint is distinct from Center tactical departure metering constraints (i.e.CFRs).However, the terminal constraint and CFR share many properties.For example, they are both local tactical constraints which require a precise departure time and approval prior to releasing the departure.A key distinction between terminal constraints and CFRs is the domain that implements the restriction.While an underlying reason for a terminal constraint may originate from the Center environment, the entire process is implemented in terminal airspace by terminal personnel.Another distinction between terminal constraints and CFRs is the process used to regulate the departure.Unlike CFRs, terminal constraints typically do not come with a specific departure time window but rather only the expected sequence of departing flights.For this reason, the process is often called departure sequencing by terminal personnel.The departure sequencing process is used instead of specific departure times due to extremely high levels of uncertainty that are present in the terminal departure environment.Table 2 lists commonly used terminal departure constraints.The restrictions modeled in this research are complete departure fix combine, MIT and a speed constraint.The gates referred to in these restrictions are groups of departure fixes.For example using the illustration in Fig. 3, departure fixes NOBLY, TRISS, SOLDO and CLARE all belong to the East departure gate.The terminal departure simulation input files control the type of constraint the simulation provides to the scheduler, the time at which it is injected into the system and the duration of the constraint.
+Decision TreesSimulation input files are used to control the size and distribution of surface and terminal transit error.These input files are called decision trees because they provide branch like options that allow methodical selection of a property based upon one or more decision variables.Surface taxi times and terminal departure transit times are supplied by decision trees.The primary purpose of surface taxi times in simulation is to allow analysis of delay distribution on the airport surface.Terminal transit times are used to simulate realistic flight times from each departure airport to each departure fix.These decision tree distributions are specified by a mean value, to which a Gaussian error distribution is applied.The distributions used in simulation were determined by analysis of operational data, information learned in first-hand observations of operational events and prior research. 1he OFF time and flight time error associated with the terminal departures in the simulation is also governed by decision trees.Error is expressed as a stochastic value with the specific mean and standard deviation.Error values used in this research were obtained through a combination of operational data analysis, direct operational observations and results from prior research. 1Using the decision trees, error can be applied to flights at several locations in the departure process, including the pushback time, surface taxi, departure queue and terminal transit.For this research error was applied to surface taxi, departure queue (as controlled OFF time error) and terminal transit.The distribution of error is assumed to be Gaussian, which is consistent with prior research on tactical departures. 1
+Simulation VariablesThe terminal departure simulation framework uses an input file to control simulation variables.This section discusses frequently modified parameters.The simulation requires a scenario start and stop date and time.While the simulation will be the duration the user specifies, it is typically best to ensure that all flights in the input file have sufficient time to cross the departure fix.While it may be desirable to evaluate a fixed traffic demand period, terminal departure pushes tend to have the highest error near the end.Thus, if comparing two scenarios to one another, the average departure delay values may be misleading if the entire flight demand has not been resolved.Error can be applied on the surface in several areas rather than all in the departure queue.The purpose of this capability is to provide a more realistic model of where delay would occur on the airport surface when terminal delays are encountered.Booleans exist in simulation input to allow error to be applied at pushback, taxi and to the controlled OFF time.If these Boolean values are set to true, the decision tree associated with the surface event is used for the distribution.Error can also be applied to the airborne flight time.If this Boolean is set to true then the flight time error decision tree is used to apply the specified error distribution to departure transit time of flights.A simulation input variable specifies the freeze type.The options are either sequence freeze or schedule freeze.A variable exists to specify the freeze horizon, which is the number of seconds prior to OFF that a flight is frozen by the scheduler.Sequence freeze ensures that this departure scheduler maintains the order of departing flights once the flight reaches its freeze horizon.Schedule freeze requires a flight to meet its departure time within the specified parameters in addition to sequence freeze requirements of maintaining departure order.For more information on sequence and schedule freeze capability, see the freeze section of the results.For schedule freeze a variable must be set that specifies the number of seconds past the controlled OFF time a flight is automatically rescheduled.Lastly, an airport switch penalty variable is used to model the current day behavior when departure control alternates from one airport to another (i.e.DFW, Love Field, Addison, etc.).The switch penalty is only used in current day (baseline) modeling as this delay is believed to be eliminated with reduced coordination uncertainty provided by automation and graphical displays in the Towers.
+B. External Feedback Provided to the Scheduler by the Simulation FrameworkThe simulation framework provides feedback to the terminal departure scheduler in response to its guidance.This response seeks to model the response expected in the operational environment.
+Feedback mechanismThe terminal departure simulation capability ensures minimal separation is enabled at the departure runway and departure fix.The purpose of this logic is to create a realistic environment in which to evaluate the terminal departure scheduler.Minimal departure runway separations for large, well-equipped airports rely upon the separation logic from the SDSS.Each smaller airport surface scheduler has adapted separation that is used for minimal separation.For this research the runway separations for all airports was the same as used by the SDSS system that runs at DFW airport.For departure fix separation, the routing or miles in trail constraint is enforced at the departure fix.For flights that would otherwise have insufficient separation at the departure fix boundary, the simulation places the flight in a controller intervention status.When a flight is placed in controller intervention status it is allowed to achieve the required amount of separation at the departure fix boundary.The amount of time that a flight is in controller intervention status is recorded.The purpose of recording the amount of time a flight is in controller intervention status in the simulation is to allow a method for evaluating controller workload associated with terminal departure scheduling.
+Error generationThe error generation component applies a stochastic error to the time component in question as specified in the appropriate decision tree.Error can be applied to flight pushback time, taxi time, controlled OFF time assignment and terminal transit time.The size and distribution of error is controlled by the decision tree files as previously discussed.
+C. OutputsThe terminal departure simulation environment produces output to both a flat file and a database.Database output is especially useful when executing a large number of Monte Carlo simulations like that performed in this research.The simulation output include details on each flight, simulation run, the error exerted on the flight, expected transit times, actual transit times, delay incurred on surface and airborne and the amount of controller intervention.
+IV. ResultsThe results outlined in this section give insights into the effectiveness of the scheduling algorithm when exposed to a range of air traffic constraints, departure time uncertainty and flight time uncertainty.The primary metrics evaluated are size of departure delay, throughput and controller intervention.
+A. Scheduler Performance under Varying Levels of OFF Time CompliancePrior research on tactical departures 1 indicates that substantial improvements to OFF time compliance can be achieved with surface automation and reduced communication uncertainty.Improved OFF time compliance is expected for terminal departures for the same reasons.In addition, OFF time improvement is expected in the terminal environment due to greater situational awareness of upcoming flight departure times which is not possible in operations today due to the opaqueness of the schedule.This section analyzes terminal departure scheduler performance when exposed to a range of OFF time error.
+SetupTo analyze the effect of OFF time compliance error on system performance, all experimental variables other than the OFF time error were held constant.The constraint used in the system was a 10 MIT restriction over departure fix SOLDO with an expected crossing speed of 350 knots.The demand was 30 flights per hour for a duration of 80 minutes.This created a total of 40 flights that were evaluated in 500 Monte Carlo runs.For each Monte Carlo run, the OFF time error was varied according to the specified Gaussian distribution.The OFF time error was varied from levels expected when terminal departure scheduling automation is available, to estimated levels with no automation, to one standard deviation greater than no automation levels.The OFF time compliance used for terminal automation simulation was a mean of -9 seconds and a standard deviation of 60 seconds for DFW flights.This compliance matches that seen in prior research for DFW when using surface automation. 1 The OFF time compliance used for all other D10 airports was slightly higher given the lack of surface automation available at these airports.For these airports, a mean of 0 seconds and standard deviation of 90 seconds was used.The highest OFF time compliance error was obtained by adjusting the standard deviation of the baseline estimate by a factor of two.
+ResultsAs expected, the results indicate that better OFF time compliance leads to better terminal departure performance.Average ground delay per flight was 11.6 minutes, 16.3 minutes and 22.3 minutes for the automation, no automation and high error cases respectively.The 4.7 minute change in average delay between the automation and no automation case suggest that OFF time compliance is a significant factor in achieving reduced delay.The change in average delay between the no automation and high error case suggests that average delay will continue to increase as OFF time error increases.Figure 4 plots the distribution of ground delay associated with each OFF time error level as a function of time.In this diagram the delay for each error scenario was grouped in 10minute increments and plotted as a function of minutes into the departure push.The distribution is plotted as a line instead of histogram to aid in comparing the distribution amongst the error cases.The distribution of delay over time between the error cases is similar, however, there are two key differences.First, as the OFF time error increases the average amount of delay assigned to flights increase.This is visible in the separation between the lines which builds over time due to a slightly higher slope on higher error cases.Secondly, as the OFF time error increases the duration of the departure push is extended.All plots end with zero delay when the complete demand of 40 flights has been resolved by crossing the departure fix.In this case the duration of the departure push was 37 minutes longer in the high error case than with the automation.Differences in the duration of the departure push for each OFF time compliance error case indicate a difference in departure throughput.To analyze the effect on throughput when varying OFF time error, the maximum departure rate metric was used.The maximum departure rate measures the highest number of flights that crossed the departure fix in a 10-minute window.This throughput measure is robust to changes in demand that can occur throughout the push, as well as push startup and shutdown variations.Figure 5 illustrates the departure rate of each error scenario over time.During the first 20 minutes all three scenarios show increasing departure rate as additional flights are injected into the simulation over time.Once the available capacity is saturated, the throughput difference between the automation levels of OFF time error and other cases becomes more apparent.The highest difference in throughout is 5.4 flights per hour, which is seen in the 80-89 minute window between the automation and high error cases.The automation error case ends first, followed by the no automation error case and last is the highest error case.This suggests that OFF time error has a direct effect on departure throughput.These findings underscore the benefits to terminal departure delay reduction and increased throughput that can be provided by greater OFF time precision from surface automation.
+B. Scheduler Performance under Varying Levels of Flight Time ErrorScheduling a departure in the terminal environment in the NAS today requires two mental calculations by controllers, an OFF time estimate and a flight time estimate.In some cases the controller may not attempt to estimate the flight time but rather wait for the flight to clear a pre-determined airborne location prior to departing a trailing flight.Observation of the terminal scheduling process indicates that different methods may be employed by different personnel.Flight time estimates are important for future automation as well.The terminal scheduler de-conflicts a departure with other flights on the runway and the departure fix.Thus, if the flight time is inaccurate the model upon which flights are being assigned delay can be incorrect.This experiment analyzes the sensitivity of departure scheduler performance to flight time error.
+SetupAll experimental variables other than the flight time error were held constant.The April 10 th , 2013 scenario mentioned in the previous section was utilized, however, for all scenarios the OFF time error remained at expected levels with future automation.The flight time error was varied from a mean error of 0 seconds to a mean error of 25 seconds.The standard deviation of flight time was varied from 15, 30, 60 and 240 seconds.The flight time error level used to estimate
+Auto No AutoNo Auto high error future automation was a mean of 25 seconds with a standard deviation of 30 seconds for DFW.This flight time error was chosen because it matches that seen in prior research. 1In the automation scenario, flight time error from smaller airports was slightly higher due to variance from less frequent demand from departure airports to departure fixes in non-standard terminal constraint situations.Small airport flight time error for future automation is expected to be a mean of 35 seconds and standard deviation of 40 seconds.
+ResultsConsistent with intuition, the simulation results indicate that lower flight time error leads to less controller intervention.As indicated in Table 4, in the lowest flight time error case the percentage of flights that are estimated to require controller intervention of one minute or greater are 22%.As previously discussed a flight is considered in controller intervention status when the simulation evaluation harness determines that inadequate separation will exists at the departure fix boundary.The flight remains in controller intervention status until enough simulation time transpired to achieve the required amount of separation.Controller intervention percentage grows a modest 1% in the automation scenario but increases substantially to 37% of flights in the largest error scenario.In addition to increased need for controller intervention, the duration of the intervention is also longer.In the low flight time error case average controller intervention is 115 seconds, while in the largest flight time error scenario average controller intervention is estimated to be 135 seconds.Perhaps a less obvious effect of increased flight time error is the transitive effect which may lead to ground delay.Given short flight times in the terminal area, unexpected airborne delay can ripple back to departing airports that are scheduling into this environment.As indicated in Table 3, the average ground delay changes by 3.6 minutes from the lowest flight time error scenario to the highest.The maximum effective throughput listed in Table 3 is defined as the highest percentage of the hourly rate achieved during a 10 minute window.This throughput metric is useful to analyze max throughput despite demand variations that can occur due to clumping of demand at the beginning or end of the push.The highest throughput was demonstrated by the low flight time error case in which 88% of its given demand was resolved.The lowest throughput was demonstrated by the highest flight time error case, with a maximum effective throughput of 79%.Given 500 Monte Carlo simulation runs were performed for each scenario, it was possible to compare the shortest and longest length of a departure push.This metric can be used to estimate the best and worst case scenarios from a system performance standpoint.The difference between the longest and shortest departure push was 135 minutes versus 115 minutes for the low and highest flight time error cases.While the variations to departure performance are not as significant as those demonstrated by OFF time compliance, they do suggest a strong correlation between improved flight time prediction and better system performance.It is worth noting that the flight time results discuss in this section all use the east gate.Given D10's predominant use of south flow configuration, terminal transit times departing the north gate are generally longer.Longer terminal transit times often increase flight time variance.Additional analysis would be required to determine the effect airspace geometry has on the metrics measured in this section.
+C. Analyzing the Switching Penalty in Baseline OperationsA less obvious benefit from terminal departure scheduling automation is that associated with loss of throughput due to coordination timing between departing facilities.This time is referred to in this research as switching time, which has an associated switching penalty.Based upon observations of current day terminal departure scheduling, the primary reason that a switching penalty exists today is the inherent opaqueness in the schedule.During terminal constraints, key personnel are often so busy with tasks that they are not able to coordinate with all the required parties in a manner that allows adequate lead time to prepare the next flight in sequence for departure.Inadequate lead time can result in unutilized departure demand.This phenomenon is known as a switching penalty.However, with automation available to all required parties and an indication of the forthcoming flight's departure time, the switching penalty is expected to be removed.This experiment assesses the impact of a switching penalty in baseline operations.Later in this paper, the switching penalty is combined with expected improvements to OFF time and flight time error to estimate benefit of automation over the current day baseline.
+SetupTo analyze the effect of switching time penalty on system performance, all experimental variables other than the switching time penalty variable were held constant.The switching time penalty variable is specified in the simulation as the number of seconds of delay incurred when switching from one departure airport to another.In terminal departure operations today this delay is generally experienced over time while waiting for the tower that just received authorization to depart flights communicate with the pilots and prepares them for departure.While this waiting is occurring, delay at other airports continues to build.The switching penalty is only imposed if the flight is ready to depart.For instance, if a 30 second switching penalty is enforced but a flight is 60 seconds late due to OFF time error compliance, then no penalty is enforced.However if the flight was ready to depart, a 30 second delay would be added to that flight and any other flights that were immediately trailing the flight.When switching from one departure airport to another, the departure controller must first recognize the readiness for this activity by observing the departing flight from the preceding airport.Then the terminal departure controller communicates with the airport departure controller to allow the flight to depart.Finally, the airport departure controller then communicates with the pilot to clear the flight for departure.Based upon estimates from prior research 1 which analyzed response times to controller commands, the entire switching process is estimated to take at a minimum 30 seconds.Thus, the switching penalty values analyzed were 0 seconds, 30 seconds, 60 seconds and 120 seconds.The OFF time error was held constant at future automation levels to minimize the effect of multiple error sources on the switch penalty analysis.
+ResultsAs expected, the results indicate that as the switch penalty increases, so does the average delay and push duration.No switch penalty resulted in a 13 minute delay average, while a 120 second switch penalty resulted in a 15.9 minute delay average.Increased switch penalty also has an effect on throughput.The longest duration departure push occurred in the highest switch penalty case, which was 14 minutes longer than no switch penalty scenario.These results indicate the switching time period encountered in current day terminal operations has a substantial impact on flight delay and throughput.Increased visibility into the departure schedule from automation is expected to reduce or eliminate this shortfall.
+D. Scheduler Performance with Varying Miles in Trail ConstraintsTerminal departure simulation was used to investigate the effect of increasing MIT restrictions on terminal departure performance.This section gives insight into how terminal departure delay grows as MIT increases and what the expected benefit of terminal departure automation is as a function of MIT constraint.
+SetupA MIT constraint over a single departure fix was used for this experiment.The size of the MIT constraint was varied from 10 to 30 miles in trail in 5 mile increments.The demand for all scenarios was 30 flights per hour for a duration of 80 minutes.Two scenarios were analyzed at varying MIT, one representing a current day without automation and the other terminal automation.The no automation scenario used 30 seconds switching penalty, a mean OFF time error of 15 seconds and a standard deviation of 115 seconds.The automation scenario had no switching penalty and used a mean OFF time error of -9 seconds with standard deviation of 60 seconds for DFW, and a mean of 0 seconds with standard deviation of 90 seconds for other airports.
+ResultsResults indicate that MIT has a strong relationship to average ground delay and throughput.As illustrated in Fig. 6, as MIT increases, so does the average ground delay assigned.In all cases evaluated the automation scenario outperformed the no automation case.In terms of percentage improvement over no automation, the greatest benefit is seen at lower MIT values.Specifically, at 10 MIT the automation scenario demonstrates a 35.2% reduction in average departure delay over the no automation case.There are believed to be two reasons for the decreased percentage of delay as a function of increased MIT.First, the portion of delay that is saved due to removal of the airport switching penalty stays the same across all MIT values.As a percentage of the total ground delay this portion is higher in the 10 MIT case than in larger MIT cases.Secondly, as MIT values continue to grow, the demand reaches a point of saturation such that flights with high OFF time compliance error that would not have made their departure time now do.Thus, the benefit of improved OFF time compliance error is proportionally smaller as the delays grow larger.To analyze the effect on throughput when varying MIT, the maximum departure rate metric was used.The maximum departure rate measures the highest number of flights that crossed the departure fix in a 10 minute window.This throughput measure is robust to changes in demand that can occur throughout the push, as well as push startup and shutdown variations.Figure 7 illustrates the departure throughput by MIT.As expected, the departure rate decreases as MIT increases.The largest gains in throughput between the no automation and automation case are seen at the lowest MIT.At 10 MIT a 3.7 flights per hour departure rate improvement is observed by the automation scenario over the no automation scenario.For the traffic levels analyzed in this experiment, that is approximately 16.6% improvement in departure throughput.
+E. Analysis of Schedule versus Sequence Freeze PerformanceA shortfall of the current day manual terminal departure scheduling process is the lack of visibility into the scheduled departure time.Given the short notice flights may receive prior to departure, efficiency is lost due to time required for staging the departure for takeoff.Future terminal departure automation is expected to provide the departure information with sufficient lead time for controllers to prepare the flight for departure.However, when communicating this information it is important that as few changes to the communicated departure time occur as possible.Changes to controlled departure time can increase controller workload and decrease efficiency.Given the expectation that the time will remain unchanged, the process of communicating a controlled time to operational personnel is often referred to as 'freezing' the flight. 18This section discusses the evaluation of two distinct terminal departure freeze methodologies.Observations of D10 terminal departure scheduling indicate that operational personnel adhere to a specified departure sequence, however, they are not required to adhere to a specified departure time.The process of specifying and communicating the order of departing flights that will not change barring a re-plan is called a sequence freeze. 18The process of specifying both the order and departure time for departing flights is referred to as a schedule freeze.In this prototype terminal departure scheduler the user must specify either a sequence or schedule freeze capability.If schedule freeze in enabled and a flight misses its assigned departure time by a specified number of seconds, the terminal scheduler will reschedule the flight.When a flight is rescheduled it will be assigned the next available time which does not impact other frozen flight times.If sequence freeze is enabled, the specified flight order is maintained without regard to departure compliance.This prototype terminal departure scheduler requires the user to specify a freeze horizon window for use in the scheduling process.The freeze horizon window is the time prior to departing the airport that a flight becomes frozen.The freeze horizon must be large enough to allow adequate time for controllers to prepare a flight for departure.However, the challenge with extending this time period too far prior to departure is that uncertainty can be frozen into the schedule which might otherwise be resolved more efficiently later using more accurate departure time information.For the analysis discussed in this section, the freeze horizon value was held constant at 180 seconds.A goal of this analysis is to analyze the tradeoffs of sequence freeze versus schedule freeze capability in terminal departure scheduling.Given substantial uncertainty that exists in terminal departure operations, the overall stability and efficiency of the departure schedule may be compromised if a large percentage of flights have to be re-planned due to missing their scheduled departure time.The balance between greater visibility into the schedule and departure planning stability is analyzed with simulation using realistic assumptions for expected departure time compliance.
+SetupThe variables modified in this experiment were OFF time error and rescheduling time window.OFF time error was varied between the three levels described in earlier sections.The resecheduling time window specifies the number of seconds past the expected OFF time that a flight must be rescheduled.The rescheduling time window value was varied from 30 seconds through 360 seconds.
+ResultsFigure 8 illustrates the effect of OFF time error on the schedule freeze capability.These results were obtained by using a 60 second reschedule time window for the schedule freeze capability.The OFF time error was varied from levels expected with future terminal departure automation, to estimated levels in today's operations without automation, to twice the standard deviation of the no automation estimate.As seen with previous results, average delay incurred by flights generally grows as the departure push continues.Varying the OFF time error has a visible effect on the distribution of delay over time.Consistent with previous results, lower OFF time error yields lower delay.A difference from prior results is that in this case a number of flights were required to be rescheduled due to missing their assigned OFF time by greater than 60 seconds.Out of a 40 flight scenario, the number of flights that required rescheduling were 5, 14 and 16 for the automation, no automation and high error cases respectively.Thus, as OFF time compliance error grows, the number of flights that missed the required departure window also grew.To compare the performance of the sequence freeze capability against the schedule freeze capability in a fair manner, OFF time error was held constant.Since this capability is targeted at future automation, the expected OFF time error associated with that environment were used.Figure 9 illustrates the delay distribution over time of sequence freeze capability against schedule freeze capability at varying rescheduling time windows.The performance of the schedule freeze capability improves as the rescheduling window is raised.The worst performance of all freeze scenarios is the 30 second rescheduling time window.The reason for this is the number of flights that require rescheduling are higher given the low threshold for OFF time compliance performance.The process of rescheduling a flight creates more demand that must be resolved.This in turn takes more time which leads to higher delay.As the rescheduling time window gets larger fewer flights are required to be rescheduled, resulting in improved performance of the schedule freeze capability.As such, the best performance of the schedule freeze capability is seen when the rescheduled window is at 360 seconds.Even at 360 seconds the schedule freeze time window's average delay is slightly larger than that of the sequence freeze average delay.Thus, at the error levels expected in future terminal departure automation, the sequence freeze capability performs better than the schedule freeze capability.The results shown in Fig. 9 are based upon the expected levels of OFF time error with future automation.As Fig. 8 demonstrates, the size of the OFF time error has an effect on the delay distribution of scheduled freeze capability.The assumption on OFF time error made in this research is lesser-equipped airports will have a 50% larger standard deviation of OFF time error than well-equipped airports.If the OFF time error is more disproportionate than assumed given higher OFF time error from lesser-equipped airports, then the schedule freeze may become a more attractive option to ensure lesser-equipped airport delay is not propagated to well-equipped airports.Additional research is needed to study the sensitivity of schedule freeze parameters to varying and disproportionate OFF time error levels.
+V. DiscussionThe simulation results described in previous sections illustrate the cumulative nature of terminal departure delay.Terminal departure delay builds upon itself until the demand is resolved or the constraint is removed.This finding underscores the importance of reducing the duration of the terminal departure restriction to the greatest degree possible.To support this objective the terminal departure solution should aim for simplicity to reduce the amount of time required to set up the constraint and communicate it to all required parties.Equally, if not more important, is the need to ensure that a terminal departure restriction does not remain in place unnecessarily.This suggests the tactical departure scheduling capability would benefit from close integration with future automation geared toward automatic detection of local flow imbalances like the Integrated Departure Route Planner (IDRP). 11esults indicate a direct relationship between OFF time compliance and departure scheduler performance.Improved compliance demonstrates a notable improvement to delay and throughput.High OFF time compliance error may also lead to increased controller workload and airborne fuel utilization.This underscores the importance of leveraging newer technologies like that demonstrated in prior tactical departure scheduling research 1 as well as focused efforts to improve departure compliance at lesser equipped airports.Results also indicate a direct relationship between terminal transit time prediction and departure scheduler performance.In addition to creating higher controller workload and greater fuel utilization, flight time error can result in delay being propagated back to the airport surface.The terminal departure scheduling solution should seek to build upon improvements to predictive accuracy of terminal transit time made in prior work. 1 The departure scheduler used in this evaluation demonstrated robustness to terminal transit prediction error up to twice the levels expected in today's operations.However, at prediction error levels 4 times the variation expected, substantial controller workload and additional ground delay is experienced.These error levels may occur during inclement weather scenarios in which the nominal departure route is blocked.More research is needed to further assess the sensitivity of performance to flight time error and identify approaches to resolve this challenge.Schedule freeze capability allows greater transparency into flight's departure plan than does sequence freeze capability.Additionally, schedule freeze can help ensure that uncertainty at one airport does not impact departing flights at a separate airport.However, implementing a schedule freeze requires a rescheduling methodology for those flights which do not make their controlled departure time.The results of this analysis indicate that, at the OFF time error levels expected with terminal departure automation, the additional demand caused by schedule freeze rescheduling creates higher average delays and longer pushes than sequence freeze.Additional research may be warranted to more fully evaluate freeze options in the terminal departure environment.
+VI. ConclusionsFast time simulation modeling of terminal departure traffic was used to assess performance of a new terminal departure scheduler.The prototype terminal departure scheduler was exposed to a range of air traffic constraints, departure time uncertainty and flight time uncertainty to better understand its sensitivity to these variables.Simulation was used to assess the tradeoffs of sequence and schedule freeze methodologies in the terminal departure environment.Both freeze capabilities were evaluated under a range of possible OFF time errors.Sequence freeze capability demonstrated lower average delay than schedule freeze capability for expected levels of OFF time compliance in future automation.Simulation results of D10 airspace indicate delay reductions of 35 percent over current-day scheduling practices are possible for commonly used terminal departures constraints, as well as an increased departure throughput of 17 percent.This benefit is derived via a combination of improved OFF time compliance, reduced flight time error and removal of the airport switching penalty associated with lack of automation in terminal departure operations today.Results indicate modest decreases to controller workload and airborne fuel utilization are possible.The results of this study were used to establish design considerations for a terminal departure scheduler which will undergo evaluation at NASA's North Texas Research station.The results are also used to inform the concept of operations (ConOps) document being developed on the future terminal departure scheduling process.Figure 1 .1Figure 1.Terminal Departure Scheduler Processing Flow.
+Figure 2 .2Figure 2.An evaluation harness was developed to assess a prototype terminal departure scheduler.
+Figure 3 .3Figure 3. D10 departure airspace was modeled to evaluate terminal constraints.
+Figure 4 .4Figure 4. Lower OFF time error leads to lower average delay.
+Figure 5 .5Figure 5. Departure throughput varied substantially between the lower and higher error scenarios.
+Figure 6 .Figure 7 .67Figure 6.Average Ground Delay Varies by MIT.
+Figure 8 .8Figure 8. Delay distribution of 60 second reschedule time window at various OFF time error levels.
+Figure 9 .9Figure 9.Comparison of delay distribution of sequence freeze and scheduled freeze capability at varying rescheduling time windows.
+Table 1 . Sequencing categories determine the order a flight is scheduled.1Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2020Sequencing Category(in priority order fromAircraftgreatest to least)DescriptionLocationSorted ByThe flight has crossed the departureCenterActual departureCrossed Departure Fixfix.Airbornefix crossing timeUndelayedTerminally ControlledFlights that have a terminal constraintTerminalestimatedAirborneand are airborne.Airbornedeparture fix timeUndelayedFlights that have no terminal constraintTerminalestimatedUncontrolled Airborneand are airborne.Airbornedeparture fix timeTerminally controlled flights have aTerminallyTerminally Controlledfrozen OFF time. This category is theSurfacecontrolled frozenFrozenfocus of this research.ActiveOFF timeFlights that are surface active and haveSurfaceCall For ReleaseCall For Releasea Call for release TMI.ActivetimeFlights that are surface active and haveSurfaceStrategic TMIan EDCT.ActiveEDCT timeTerminally ControlledTerminally controlled flights that areSurfaceEstimatedUnfrozen Surface Activesurface active but not yet frozen.Activeundelayed OFFFlights that are surface active with noSurfaceEstimatedSurface ActiveTMI constaint.Activeundelayed OFFTerminally ControlledTerminally controlled flights that areSurfaceEstimatedSurface Inactivenot yet surface active.Inactiveundelayed OFFFlights that have no terminal constraintSurfaceEstimatedSurface Inactiveand are not surface active.Inactiveundelayed OFF
+Table 2 . Common terminal departure constraints.2Constraint NameConstraintDescriptionTypeComplete DepartureRouteAll traffic that was assigned to the original fix isFix Combinemoved to one or more alternate departure fixes.Limited DepartureRouteA select set of flights bound to a departure fix isFix Combinemoved to one or more alternate departure fixes.Stream basedRouteA departure fix becomes the only location terminalDeparture fixflights bound to the destination in question mayCombinedepart the terminal area.Gate SwapRouteChanges the gate a departure fix traffic is bound toone or more alternate gate(s). This adds therequirement for departing flights to file a new flightplan.Miles in TrailFlowRequires MIT separation enforced at the departurefix. This flow constraint is often enforced with arouting constraint.Speed ConstraintFlowRequires departures to meet a speed restriction,typically until reaching the departure fix.Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2020
+Table 4 . Results of Flight Time Error Variation on Departure Scheduler Performance.4Standard% RequiredControllerAverageMaximum EffectiveDurationMeanDeviationControllerInterventionGroundThroughput per hourLongestScenario NameError (s)Error (s)InterventionDuration (s)Delay (m)(% total demand)Push (m)Low Flight Time Error0152211512.288115Automation (expected)25302311713.884117Automation w/2sigma25602911913.984119Automation w/4sigma252403713515.879135
+			Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2020
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+AcknowledgementsThe authors would like to acknowledge the essential support provided by FAA personnel at Dallas/Fort Worth terminal Radar Approach Control (TRACON) facility, DFW ATCT and DAL ATCT.Finally, we wish to thank our colleagues at NASA/FAA North Texas Research Station (NTX) and NASA Ames whose support was critical to the success of terminal departure research objectives.
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+			MKistler
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+			FGaither
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+		NASA/TM-2013-216533
+		Precision Departure Release Capability (PDRC) Final Report
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+			June 2013
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+	Engelland, S.A., Capps, A., Day, K., Kistler, M., Gaither, F., and Juro, G., "Precision Departure Release Capability (PDRC) Final Report," NASA/TM-2013-216533, June 2013.
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+		Precision Departure Release Capability (PDRC) Concept of Operations
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+			ACapps
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+			KDay
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+		NASA/TM-2013-216534
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+			June 2013
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+	Engelland, S.A., Capps, A., and Day, K., "Precision Departure Release Capability (PDRC) Concept of Operations," NASA/TM-2013-216534, June 2013.
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+
+	
+		Trajectory-Based Takeoff Time Predictions Applied to Tactical Departure Scheduling: Concept Description, System Design, and Initial Observations
+		
+			ShawnEngelland
+		
+		
+			RichardCapps
+		
+		10.2514/6.2011-6875
+		NASA/TM-2013-216531
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		
+			American Institute of Aeronautics and Astronautics
+			June 2013
+		
+	
+	Engelland, S.A., Capps, A., Day, K., Robinson, C., and Null, J.R., "Precision Departure Release Capability (PDRC) Technology Description," NASA/TM-2013-216531, June 2013.
+
+
+
+	
+		NextGen Mid-Term Concept of Operations for the National Airspace System
+		
+			Faa
+		
+		
+			September 2010
+		
+	
+	FAA, "NextGen Mid-Term Concept of Operations for the National Airspace System," version 2.1, September 2010.
+
+
+
+	
+		A functional analysis of integrated arrival, departure and surface (IADS) operations in NextGen
+		
+			MSimons
+		
+		10.1109/dasc.2012.6382982
+		MITRE CAASD MTR110240R1
+	
+	
+		2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC)
+		
+			IEEE
+			January 2012
+		
+	
+	MITRE CAASD MTR110240R1, "A Concept for Integrated Arrival, Departure, and Surface (IADS) Operations for the Mid-Term", January 2012.
+
+
+
+	
+		Design and Evaluation of the terminal Area Precision Scheduling and Spacing System
+		
+			HNSwenson
+		
+		
+			JThipphavong
+		
+		
+			ASadovsky
+		
+		
+			LChen
+		
+		
+			CSullivan
+		
+		
+			LMartin
+		
+		
+			2011
+			Berlin, Germany
+		
+		
+			Ninth USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	Swenson, H. N., Thipphavong, J., Sadovsky, A., Chen, L., Sullivan, C., & Martin, L. (2011). "Design and Evaluation of the terminal Area Precision Scheduling and Spacing System." Ninth USA/Europe Air Traffic Management Research and Development Seminar. Berlin, Germany.
+
+
+
+	
+		Effects of scheduling and spacing tools on controllers' performance and perceptions of their workload
+		
+			LynneMartin
+		
+		
+			HarrySwenson
+		
+		
+			AlexanderSadovsky
+		
+		
+			JaneThipphavong
+		
+		
+			LiangChen
+		
+		
+			AnthonyYSeo
+		
+		10.1109/dasc.2011.6096106
+	
+	
+		2011 IEEE/AIAA 30th Digital Avionics Systems Conference
+		Seattle
+		
+			IEEE
+			2011
+		
+	
+	30 th DASC
+	Martin, L., Swenson, H., Thipphavong, J.,Sadovsky, A., Chen, L., & Seo, Y. (2011). "Effects of Scheduling and Spacing Tools on Controllers' Perceptions of the Their Load and Performance." Digital Avionics Systems Conference (30 th DASC). Seattle.
+
+
+
+	
+		Development of Conflict Free, Unrestricted Climbs for a terminal Area Departure Tool
+		
+			JThipphavong
+		
+		
+			HSwenson
+		
+		
+			PLin
+		
+		
+			ASeo
+		
+		
+			LBagasol
+		
+		
+			YCJung
+		
+		
+			DIsaacson
+		
+		
+			YCIsaacson
+		
+		
+			DR
+		
+	
+	
+		Proc. of the AIAA's Aircraft Technology, Integration, and Operations (ATIO) Forum
+		of the AIAA's Aircraft Technology, Integration, and Operations (ATIO) ForumDenver, Colorado; Los Angeles, CA
+		
+			2011. 17-19 November 2003 10 Jung,. Oct 1-3, 2002
+		
+	
+	AIAA's 3 rd Annual Aviation Technology, Integration and Operations Conference
+	Thipphavong, J., Swenson, H., Lin, P., Seo, A., & Bagasol, L. (2011). "Efficiency Benefits Using the terminal Precision Scheduling and Spacing System." AIAA Aviation Technology Integration and Operations (ATIO). Virginia Beach 9 Jung, Y.C., Isaacson, D. "Development of Conflict Free, Unrestricted Climbs for a terminal Area Departure Tool", AIAA's 3 rd Annual Aviation Technology, Integration and Operations Conference, Denver, Colorado, 17-19 November 2003 10 Jung, Y.C., Isaacson, D.R., "Design Concept and Development Plan of the Expedite Departure Path," Proc. of the AIAA's Aircraft Technology, Integration, and Operations (ATIO) Forum, Oct 1-3, 2002, Los Angeles, CA.
+
+
+
+	
+		12 Federal Aviation Administration Air Traffic Organization Operations Planning Research & Technology Development Office Air Traffic System Concept Development, AJP-66
+		
+			JJWang
+		
+		
+			PaulChang
+		
+		
+			KDatta
+		
+	
+	
+		Prepared for NASA Ames Research Center, Contract No. NAS2-98074
+		
+			August 2003. September 2007
+		
+	
+	Life-Cycle Cost/Benefit Assessment of Expedite Departure Path (EDP). Integrated Arrival/Departure Control Service (Big Airspace) Concept Validation
+	Wang, J.J., Chang, Paul, and Datta, K, "Life-Cycle Cost/Benefit Assessment of Expedite Departure Path (EDP)," Prepared for NASA Ames Research Center, Contract No. NAS2-98074, August 2003. 12 Federal Aviation Administration Air Traffic Organization Operations Planning Research & Technology Development Office Air Traffic System Concept Development, AJP-66, "Integrated Arrival/Departure Control Service (Big Airspace) Concept Validation", September 2007.
+
+
+
+	
+		Benefits Analysis of a Departure Management Prototype for the New York Area
+		
+			JDearmon
+		
+		
+			NTaber
+		
+		
+			HBateman
+		
+		
+			LSong
+		
+		
+			TMasek
+		
+		
+			DGilani
+		
+		
+			2013
+			ATM
+		
+		
+			Tenth USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	DeArmon, J., Taber, N., Bateman, H., Song, L., Masek, T., Gilani, D., "Benefits Analysis of a Departure Management Prototype for the New York Area", Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013).
+
+
+
+	
+		Generic Operational Concept for Pre-departure Runway Sequence Planning and Accurate Take-Off Performance
+		
+			ETuinstra
+		
+		
+			KHaschke
+		
+		
+			09 July 2009
+		
+	
+	Tuinstra, E., Haschke, K., "Generic Operational Concept for Pre-departure Runway Sequence Planning and Accurate Take- Off Performance", 09 July 2009.
+
+
+
+	
+		Characterization of Nationwide TRACON Departure Operations
+		
+			MatthewSKistler
+		
+		
+			AlanCapps
+		
+		
+			ShawnAEngelland
+		
+		10.2514/6.2014-2019
+	
+	
+		14th AIAA Aviation Technology, Integration, and Operations Conference
+		Atlanta, GA
+		
+			American Institute of Aeronautics and Astronautics
+			June 16-20, 2014
+		
+	
+	Kistler, M., Capps, A., Engelland, S., "Characterization of Nationwide TRACON Departure Operations", 14 th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Atlanta, GA, June 16-20, 2014.
+
+
+
+	
+		Measuring Uncertainty in Airspace Demand Predictions for Traffic Flow Management Applications
+		
+			CraigWanke
+		
+		
+			MichaelCallaham
+		
+		
+			DanielGreenbaum
+		
+		
+			AnthonyMasalonis
+		
+		10.2514/6.2003-5708
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, Texas
+		
+			American Institute of Aeronautics and Astronautics
+			August 2003
+		
+	
+	Wanke, C. R., Callaham, M. B., Greenbaum, D. P., and Masalonis, A. J., "Measuring Uncer-tainty in Airspace Demand Predictions for Traffic Flow Management Applications," Proceedings of the AIAA Guidance, Navigation, and Control Con- ference, AIAA, Austin, Texas, August 2003.
+
+
+
+	
+		Analysis of En Route Sector Demand Error Sources
+		
+			JimmyKrozel
+		
+		
+			DanRosman
+		
+		
+			ShonGrabbe
+		
+		10.2514/6.2002-5016
+		AIAA-2002-5016
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Monterey, California
+		
+			American Institute of Aeronautics and Astronautics
+			August 5-8, 2002
+		
+	
+	Krozel, J., Rosman, D., Grabbe, S., "Analysis Of En Route Sector Demand Error Sources", AIAA-2002-5016, AIAA Guidance, Navigation, and Control Conference and Exhibit, Monterey, California, August 5-8, 2002.
+
+
+
+	
+		The dynamic planner: the sequencer, scheduler, and runway allocator for air traffic control automation
+		
+			GWong
+		
+		NASA/TM-2000-209586
+		
+			2000
+		
+	
+	Wong, G., "The dynamic planner: the sequencer, scheduler, and runway allocator for air traffic control automation", NASA/TM-2000-209586 (2000)
+
+
+
+	
+		A probabilistic modeling foundation for airport surface decision support tools
+		
+			ChrisBrinton
+		
+		
+			SteveAtkins
+		
+		10.1109/icnsurv.2009.5172837
+	
+	
+		2009 Integrated Communications, Navigation and Surveillance Conference
+		
+			IEEE
+			May 2009
+			
+		
+	
+	Brinton, C. and S. Atkins, "A Probabilistic Modeling Foundation for Airport Surface Decision Support Tools," 2009 ICNS Conference, 13-15 May 2009.
+
+
+
+	
+		Best practices for designing and implementing decision services, Part 1: An SOA approach to creating reusable decision services
+		
+			JBoyer
+		
+		
+			June 13, 2012
+		
+	
+	Boyer, J.,"Best practices for designing and implementing decision services, Part 1: An SOA approach to creating reusable decision services", June 13, 2012
+
+
+				
+			
+		
+	
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+INTRODUCTIONAs the number of operations at the nation's largest hub airports continues to grow, and as future operational concepts (e.g., NASA's Small Airport Transportation System or SATS concept) allow more aircraft to use smaller, non-hub airports, the need for environmental consideration in shaping the terminal * AIAA Member, Systems Analyst, Aviation R&D † Software Analyst, Aviation R&D ‡ Vice President, Aviation R&D § AIAA Member, Aerospace Engineer area operations is essential to minimize adverse impact to residents in the immediate vicinity of the airport (traditionally taken as those people inside the 65dB DNL contour).Reduction of noise exposure levels around the airport requires intelligent modification of the arrival and departure trajectories.Promising concepts being investigated in this area include low-noise approaches such as the Continuous Descent Approach (in which idle thrust levels are utilized over the majority of the descent) [1], Curved Approaches (which can potentially minimize over-flight of noise-sensitive areas) [2], and Precision Navigation Instrument Departures [2].Similar studies in the U.S. are being conducted at MIT [3].One downside of these approaches at this stage of their development is their tendency to require significantly higher levels of separation as well as improved navigational precision.Similar efficiencyrelated concerns have arisen in the context of regional airspace re-design efforts in the Chicago, Washington, and New York areas.Terminal area air traffic control (ATC) procedures contribute to noise issues (e.g., holding one flow below another flow), but their contributions have been difficult to quantify and even more difficult to remedy.This paper addresses both of these issues.First, we quantify the impact of postulated changes (such as consolidation of base-leg extensions and strict 24-hour adherence to noise abatement procedures) in terminal procedures at four U.S. airports [4].Second, we present an architecture for a Noise Avoidance Planner (NAP) that integrates with the Center-TRACON Automation System (CTAS) for both departures and arrivals to show how noise-awareness can be combined with efficient scheduling and sequencing.
+AIRSPACE DESIGN AND NOISE METRICSNoise exposure is inherently tied to the nature of the flight paths flown over a given population distribution.Thus, assessing the impact of flow American Institute of Aeronautics and Astronautics changes (geometry, traffic mix, runway use, etc.) requires a set of tools for first realizing such changes and then computing the impact of the changes on noise exposure.
+Airspace DesignThe primary tool used to emulate the effects of noise-aware decision support tools (DSTs) on traffic patterns was Metron Aviation's Airspace Design Tool (ADT).This tool provides the capability to import tracks from various sources (ETMS, ARTS, INM, NIRS, etc.), to display them in two and three dimensions, and to manipulate them graphically in 3space.Supporting tools allow manipulation of nonspatial characteristics, such as aircraft types, event times, and runway assignments.
+Noise Impact ModelingThe FAA's Noise Impact Routing System (NIRS) was used to compute noise exposure and to quantify the impact of postulated changes in operational procedures (assumed to be enabled by the availability of noiseaware DSTs).NIRS was chosen because it provides the same noise-calculation capability as the FAA's Integrated Noise Model (INM).It also includes several additional key features that are useful for the comparison of noise effects when aircraft have their flight characteristics influenced by the different DSTs.The two most important of these additional NIRS features are the ability to follow a specified flight profile and the ability to compare noise impacts in graphical and tabular formats for alternative cases (e.g., with or without DSTs).The input data for the noise calculations consists of detailed track and event data.Each track is represented by a sequence of points (latitude, longitude, altitude) defining a flight path into or out of a given airport on a given runway.Associated with each track is a set of events that represent the specific flights that are to operate on this track.Each event contains a description of the aircraft type, the engine/airframe type, the time of the event, and the number of events of this particular type.Noise computation involves tracing aircraft states along the spatial track and calculating the noise impact at each population location ("centroid") due to each flight.For this purpose, a flight path is generated for each track-event combination which models the thrust required for the aircraft to follow the prescribed trajectory.This process utilizes the altitude control codes specified in the track definition in order to "fly" the prescribed track in a fashion consistent with the modeled flight dynamics of each aircraft type.The principal noise metrics are the Sound Energy Level (SEL) and the Day-night Noise Level (DNL).SEL quantifies perceived acoustic energy across events of different intensities and durations, while DNL quantifies total noise exposure due to multiple events at different times of day.In this work, we quantify the effectiveness of postulated modifications to operational procedures in terms of the change in the size of the total population receiving 50dB DNL or greater exposure.This is consistent with increasing pressure on the aviation community to substantially reduce noise exposures below the traditional 65 dB DNL.
+OPERATIONAL IMPACT OF NOISE-AWAREDECISION SUPPORT TOOLS To quantify the potential benefits of adding noiseawareness to terminal area DSTs, we developed a number of techniques for manipulating data describing arrival and departure operations at four U.S. airports -Chicago O'Hare (ORD) and Midway (MDW), Boston Logan (BOS), and San Francisco (SFO) [4].These airports were chosen as a cross-section of the 20 airports originally investigated in Reference [5] on the basis of data availability and the existence of potential noise-mitigation opportunities.Each of these airports provided important insights into the operational and noise-mitigation issues that will be faced by development and deployment of noise-aware DSTs.The source of the operational data used for each airport in this study is given in Table 1.Data representing an annualized average day of traffic was used in each case.The approach used in developing changes to operational procedures was to visually study the arrival and departure traffic flows and identify potential changes in terminal area ATC procedures that might yield noise benefits (see Figure 1).This process included searching for interactions between arriving and departing flows and defining ways to mix these flows apart from the traditional method of procedural separation.The primary source for the noise mitigation opportunities identified in this study was a survey of 20 airports [5] based upon contacts with local noise offices (including Fly Quiet Programs where applicable) and controllers.The result of this assessment was an enumeration of (a) the current operational features which could be improved through design of suitable noise-abatement procedures, and (b) limitations in executing current noise abatement procedures.These limitations include navigational errors, controller instruction errors (e.g., speed/heading directives), and the fact that the majority of such procedures are generally only carried out under low-demand situations (due to increase separation requirements, etc.).Population data for the regions surrounding each airport was extracted from U.S. Census Bureau data for the year 2000.This population data extended well beyond the areas in which noise exposures would change due to effects of DSTs, so all such changes were captured in the noise calculations described below.
+Types of Opportunities SimulatedThe impact of new procedures and improved navigational capabilities was approximated by modifying the original operational data in different ways.Table 2 summarizes the general types of noise mitigation opportunities explored during this study, along with the relevant data characteristic modified in order to simulate the opportunity.The noise mitigation opportunities that were evaluated included "Avoid Dive and Drive", "Direct Climb to Cruise", the construction of additional runways, and the movement of "noisy" aircraft to noise-preferred runways.As can be seen in Table 2, a large number of the simulations involved the modification of track (lat/lon) location.However, mitigation strategies requiring altitude profile modifications, such as "Avoid Dive and Drive" and "Direct Climb-to-Cruise", were also addressed.Each opportunity's evaluation consisted of: assessing the current traffic situation (as described by operational data, aeronautical charts, etc.) and proposing new procedures or capabilities (e.g., greater adherence to noise-preferred trajectories via RNAV type capability).Since nighttime operations are weighted so heavily by the Day-Night Sound Level (DNL) metric, a substantial effort was also made to reduce the spillage of evening-night shoulder events into the night hours and to increase the usage of noise-preferred runways for night operations.Simulation of mitigation opportunities involving wind, speed profiles, and noisepower distribution (NPD) curves were not addressed.Figure 3 shows a similar situation at BOS in which departing aircraft off runway 04R are postulated to have precise enough navigation to enable them to be tightly funneled over a low population area rather than being dispersed over more highly populated regions.In the case of Figure 4, the departure events from runways 19L/R were reassigned to existing tracks departing over the water on runway 10L.
+Results ObtainedAfter modifying the track and event data to simulate the effects of one or more noise-aware DSTsconsistent with the specific elements of the traffic patterns at each airport and the population distributions encountered in the vicinity of these patterns -a noise impact analysis was carried out using NIRS.Noise exposure was computed for the baseline (without DST) and alternative (with DST) cases, and differences between the cases were calculated.NIRS provides the capability to generate noise impact tables, graphs, and maps.The impact table and impact graph provide categorization of population centroids in noise bins of interest, and quantify the "before DST" and "after DST" effects on population centroids within each bin.The impact map provides a graphical depiction of the population centroids whose noise exposure categories are different between the baseline and alternative cases.
+Avoid EarlyTurns American Institute of Aeronautics and Astronautics (1) applying traffic-scaling factors that raised the 2001 traffic levels to those future levels estimated for ORD/MDW, BOS, and SFO and (2) re-calculating noise impacts at all population locations based on the new traffic levels.The chosen net measure of this noise mitigation benefit was the total number of people receiving annual DNL at 50 dB or greater.For our initial analysis, we assumed that the proposed mitigation strategies would be implemented for 100% of the affected flights.Thus, the noise mitigation results represent the impact of 100% compliance to the noise-mitigation traffic patterns.Later, we also assessed the sensitivity of these results to partial implementation of the proposed traffic modifications where possible.This assessment was done in recognition that operational limitations related to safety and capacity might constrain the application of the suggested noise-sensitive procedures.In other words, the postulated mitigation strategies may not be 100% effective.Thus, for each noise mitigation strategy, we enumerated the potential operational limitations that might inhibit their use.We then performed a simple analysis to provide a rough estimate of the impact of partial implementation on noise exposure benefits.As a first approximation, we have chosen to define a measure of DST effectiveness, called the DST Effectiveness Factor, which is meant to capture an estimate of the realizability of a given DST.We define this factor independently for each mitigation opportunity, since operational constraints vary across airports depending on the nature of their traffic flows.This DST Effectiveness Factor is used to scale the noise impact (measured in terms of the net change in the population experiencing noise of 50 dB or higher) to produce a more realistic estimate of the potential noise benefits.In order to compare the effectiveness of different mitigation strategies, we define a measure called the Expected Noise Benefit (ENB), as the product of the percentage decrease in the total population above 50dB DNL and the DST Effectiveness Factor.The ENB gives the total expected noise benefit associated with each mitigation opportunity.We provide an overall rating for each mitigation opportunity on the basis of the ENB to identify those with the highest potential value should the identified strategy be implemented.In this portion of our work, we manipulated tracks solely to quantify noise benefits measured in terms of reduced average exposure via the DNL metric.Estimating the impact that these manipulations would have on operations was beyond the scope of this effort, but every effort was made to perform the manipulations in a manner that would not have significant operational impact.Approaches, and Noisy Aircraft on Preferred Runways) at the estimated levels of DST effectiveness represents a 13% decrease in the population above 50 dB, or over 31,000 people These quantitative results lead to several general conclusions: (1) DSTs can provide substantial benefits at airports that have noise-mitigation opportunities similar to those analyzed in detail here for ORD, MDW, BOS, and SFO; (2) the benefits will probably lie in the range of 10% to 50% of the population exposed at 50 dB DNL or above; and (3) such benefits are likely to be extremely attractive to airports that desire to improve public acceptance of aircraft noise, especially in light of conflicting pressures for decreases in noise impact and increases in capacity.American Institute of Aeronautics and Astronautics
+NOISE AVOIDANCE PLANNERThe aforementioned study of mitigation opportunites consisted of essentially "static" changes to operational procedures.Although these changes showed potential for benefits from a noise perspective, there was no means of judging the impact of such changes on the operational efficiency of the airport.As a means of overcoming this limitation, we describe the initial development of the Noise Avoidance Planner (NAP).NAP, being developed under a Phase II SBIR with NASA Ames Research Center, is a noise-aware version of the CTAS Final Approach Spacing Tool (FAST) and Expedite Departure Path (EDP) DSTs.NAP is intended to operate dynamically in real-time to enable the FAST/EDP scheduling logic ( [6]) to utilize a noise figure-of-merit (FOM) in determining path stretching and speed modifications for resolution of spacing constraints.Currently, FAST and EDP operate on the basis of analysis categories -a set of unique states into which aircraft arriving or departing the terminal area are partitioned for the purpose of route generation, sequencing and conflict resolution.The DFW site adaptation database currently has approximately 520 unique analysis categories.For example, the category DFW_18R_BAMBE_ JET_BEFORE_FEEDER_GATE applies to a jet assigned to DFW Runway 18R while the aircraft is outside of the Bambe arrival metering fix.These analysis categories define the initial route for the aircraft as well as its confliction resolution sets.Associated with each analysis category is also a set of degrees-of-freedom (DOFs) that FAST and EDP utilize to realize the required path stretching and speed control for achieving the desired inter-aircraft spacing.A DOF is defined to have both FAST and SLOW limit values (e.g., the minimum and maximum extent of fanning from a particular waypoint).These FAST and SLOW limit values define the lower and upper bounds for the time required to fly along a portion of the trajectory.These bounds form the basis for spatial constraint resolution.This resolution is time-based in nature.Specifically, combinations of DOFs are sought which provide the necessary amount of delay to properly space leading and trailing aircraft at various segments American Institute of Aeronautics and Astronautics along the trajectory.At present, the FAST/EDP constraint resolution process terminates once a single satisficing solution has been found.The search through the possible space of DOF combinations is currently deterministic in nature and follows a pre-defined recipe (including a fixed set of mixing ratios for the various DOFs).From the perspective of generating noise-preferred advisories with FAST/EDP, however, this search methodology is unacceptable.What is needed is a set of satisficing solutions (those that satisfy all constraints) from which the best noise solution can be selected.Figure 5 illustrates this idea, showing a search through a sequence of points in DOF combination space (A i ) where multiple satisficing solutions (e.g., the green X's) are collected and compared relative to one another using a noise figureof-merit.to enable noise metrics to influence decisions, need to enable CTAS to discover multiple solutions -not just a single satisficing solution.A 1 A 2 A N A 3 A 4
+SET OF EQUALLY GOOD NON-NOISE SOLUTIONS noise scale best (noise + CTAS) solution returned
+Figure 5. Change in FAST/EDP Logic Needed for Noise Avoidance Planning
+ArchitectureAs an initial step toward introducing noise awareness into the FAST/EDP scheduling logic, we focus on the noise sensitivity of the vector (e.g., spatial) DOFs of each analysis category.As such, we define the trajectory space for a given category to be bounded spatially by the FAST and SLOW limits of its vector DOFs, assuming all other DOFs (e.g.speed DOFs) are set to their FAST limits (see Figure 6).This trajectory space defines a time band ranging from ∆ min (which represents no delay from the vector DOF) to ∆ max (which represents the most delay achievable with the vector DOF).In general, each vector DOF will have a different range of possible delay absorption values.We can define the noise exposure for the bounds on the trajectory space by computing the SEL experienced by the population underlying the FAST and SLOW trajectories, respectively.To determine the noise exposure for trajectories between these bounds we have two options.We could choose to linearly interpolate in the noise metric (SEL) space -assuming that the noise exposure level is monotonic between the two end points.Alternatively, we can choose to interpolate in the trajectory space first, and then subsequently compute the noise exposure values for each of the interpolated trajectories.This latter approach does a better job of capturing local fluctuations in noise exposure level due to the distribution of the underlying population.Space for Noise Avoidance Planning At present, the Noise Avoidance Planner consists of two distinct off-line processing steps combined with real-time data handling.The first step stimulates the FAST/EDP scheduling logic to generate the interpolated set of trajectories for each analysis category.The second step then processes these trajectory sets with NIRS to compute a noise sensitivity for each analysis category (and its associated vector DOF).These sensitivities take the form of noise exposure curves as a function of DOF value (i.e., as a function of delay absorption).Note that since the offline steps are solely a function of the site adaptation and population distribution, they only need to be performed once in their entirety.Results of this offline processing are stored in a database for access during run-time.Localized changes to the site adaptation can be accommodated simply by reprocessing any new or revised analysis categories.Changes in population distribution due to new census data will require reprocessing of all analysis categories.The run-time usage of the noise sensitivity data is anticipated to consist of a simple table lookup into the F: FAST limit S: SLOW limit ∆ min ∆ max American Institute of Aeronautics and Astronautics database to return the tradeoff between noise exposure and delay.The noise-aware version of the FAST/EDP scheduling logic will be modified to incorporate this sensitivity data to search for a combination of DOFs which minimizes the noise exposure level whenever possible.For this purpose, we assume noise preferred values of the vector DOFs will be selected and values of the speed DOFs will be chosen to achieve the remaining amount of delay needed for traffic separation.Figure 7 illustrates the basic components of the FAST/EDP architecture used to develop the initial NAP functionality.
+Off-Line Noise ComputationAs shown in Figure 7, the noise sensitivity generation process is driven by a Simulation File containing a set of aircraft radar hits which correspond one-to-one with the set of analysis categories for a particular site adaptation database.Our process for creating the Noise Sensitivity Database leverages off of the existing FAST/EDP flight processing logic.In the current architecture, the Communications Manager (CM) is used to distribute the flight plans to both the Route Analyzer (RA) and Profile Selector (PFS) processes.The CM then distributes the radar hits to the RA which classifies the aircraft into a particular analysis category.The RA uses the category's binary analysis tree (specifying the order in which its N DOFs are to be used in absorbing delay) to define the set of 2 N unique combinations of DOF limits.These combinations correspond, for example, to each of the paths to the right-most leaves of the tree in Figure 6.For each of these combinations of FAST and SLOW DOF values, the Trajectory Synthesizer returns the corresponding 4-dimensional trajectory.From this trajectory, the aircraft's time of arrival to the meter fix and/or runway can be computed.The set of arrival times is then passed to the Profile Selector (PFS) which uses its own TS to define the initial trajectories for all flights.Note that, unlike the RA which is event-driven in nature, the PFS scheduling process is initiated every six seconds.As such, the scheduling process is applied to all flights which accumulate between updates.Since we have established (in the Simulation File) only a single aircraft in each analysis category at any given time, there are nominally no conflicts for the PFS to resolve.Thus, no iteration through the DOF analysis tree is initiated.Since our approach hinges on the exploration of the vector DOFs, we have modified the PFS logic to initiate a pseudo resolution cycle in which we set the amount of delay to be absorbed incrementally between the FAST (zero seconds delay) and SLOW (∆ max ) limits.Each time through this cycle, the TS returns a resolution trajectory which is then recorded to a file.The set of stored trajectories is then post-processed using NIRS to develop the corresponding noise exposure values.In this fashion, we are able to span the trajectory space for each analysis category and define the noise sensitivity curve as a function of the category's vector DOFs.Examples of the exercising of the vector DOFs are given in Figure 8 and Figure 9 for DFW arrivals on 18R over BAMBE (FAN FROM WAYPOINT) and FEVER (BASELEG EXTENSION) respectively.
+ResultsAs an initial demonstration of the value of providing a noise figure-of-merit to FAST/EDP, we present the variation in Sound Energy Level (SEL) exposure over the space of trajectories for the two analysis categories described in the previous section.The variation (for population centroids experiencing greater than 55dB of exposure) is shown in Figure 10.We present the results in terms of the percentage of the total population (within a 30 nautical mile radius of DFW, or 4710027 people) experiencing SEL values greater than 55dB.This figure shows that interpolating linearly (in noise space or SEL value) between the FAST and SLOW limits can provide a rather poor estimate of the actual noise impact for the intermediate trajectories.Instead, by interpolating in DOF space (e.g., geometric trajectory space), we are able to capture the finer detail of the noise exposure/delay tradeoff surface.For example, simple linear interpolation in SEL space for the 18R BAMBE FAN FROM WAYPOINT category would predict that 2 percent of the population would experience SEL greater than 55dB for 100 seconds of delay absorption.Interpolating in trajectory space, however, provides a better estimate of 3.5 percent.Currently we use a simple uniform sampling (in DOF/time) between the two limits (e.g.∆ i = ∆ max /10).In general, one could define more sophisticated sampling schemes (for example an iterative bisecting scheme with a difference threshold) to maximize the capture of the details of the noise exposure surface between the DOF's FAST and SLOW limits.One can also assess the variation in noise exposure for different delay values graphically by examining the noise footprint created by displaying the color-coded SEL values for each population centroid.Figure 11(a)-(c) show the noise footprint created by arrivals into DFW 18R over BAMBE using the FAST route (zero delay), 69 seconds of delay, and 212 seconds of delay, respectively.A considerable shift of the noise footprint can be observed in these figures.In particular, as the aircraft fans closer to the SLOW limit, it is actually at a higher altitude prior to initiating its turn onto the final approach course.Therefore, the noise footprint is reduced for populations away from the final approach course.
+A MERGING OF PHILOSOPHIESThe previous section demonstrated the variability in noise exposure for a single aircraft flying each of the trajectories contained within the space bounded by the vector DOF's FAST and SLOW limits.These sensitivity curves are a first step towards the integration of noise-awareness into the decision support capabilities of ATM tools such as FAST/EDP.Such a capability can allow noise to influence localized routing decisions (e.g., trading path stretching for speed adjustment).Will aggregation of noise-preferred trajectories (on the basis of SEL) over the course of a 24-hour period result in a net reduction in noise exposure (in terms of DNL)?Shaping the noise impact of routings on a given population distribution is a terminal area-wide airspace utilization problem.FAST/EDP, however, are designed as tactical, time-based sequencing tools.Therefore, it seems plausible that if one consistently modifies traffic flow on a given segment in a similar fashion (e.g., locally noise-preferred), one could inadvertently create a new noise problem under that modified flow.This leads one to the consideration of adding a "rolling window" type of noise exposure Base extension Arrivals over FEVER FAST limit SLOW limit American Institute of Aeronautics and Astronautics measure to the run-time FAST/EDP processing.In this manner, statistics regarding the distribution of noise exposure over the affected communities as a whole could be collected and used to either positively or negatively reinforce certain routing decisions in a timevarying manner.The one-to-one correspondence between analysis category and DOF sensitivity we take advantage of at present is enabled by the fact that only a single vector DOF is currently defined (and used for resolution) for each analysis category.If one relaxes that restriction such that multiple vector DOFs are chained together, the resulting bounds of the reachable trajectory space increase in complexity.One then must consider various combinations of limit trajectories and take into account the spatial coupling of multiple vector DOFs.This reiterates the point that the noise exposure for a trajectory is defined by the combination of all segments, not necessarily a single segment.One possible approach to this problem, derived through analogy to graph search algorithms such as A* ( [7]), is to develop a single heuristic estimate of the noise sensitivity downstream of a given decision point for each possible "branch" of the decision tree.This would allow a tool such as NAP to condition current routing decisions based on an estimate of future noise impact.The idea would be to avoid situations in which a locally optimal sequencing decision on one segment leads to a excessively high noise impact a later segment.The current NAP architecture leverages the existing FAST/EDP infrastructure to explore the trajectory space.This was a natural choice given the manner in which FAST/EDP uses this space to resolve spatial conflicts.Another option, however, would be to essentially ignore the existing scheduling logic and instead, simply search for an "optimal" (e.g., in a "global" sense -with minimal procedural constraints) noise trajectory for a given aircraft/engine combination from each meter fix to the runway (or vice versa for departures).This trajectory could be represented as a "cloud" defining the relative sensitivity of noise values in its immediate neighborhood.In other words, any trajectory contained in the cloud would be essentially equal from a noise perspective.The burden would then fall on FAST/EDP to stay within this noise preferred region as much as possible given its sequencing and scheduling constraints.An obvious issue with this approach is if the noise-preferred cloud region does not overlap with the FAST/EDP trajectory space.In this case, the current technique of spanning the FAST/EDP trajectory space in some manner would be applicable.
+CONCLUSIONS AND FUTURE WORKWe have described results obtained from a quantitative study which demonstrated the potential benefits of noise-aware DSTs by simulating modifications to terminal area air traffic control procedures.What this initial benefits study lacked was a means of addressing the throughput and efficiency impacts related to such procedures.We then presented some initial results obtained during the development of a noise-aware version of the FAST/EDP DSTs, called the Noise Avoidance Planner.We focused on vector degrees of freedom to develop their noise sensitivity with respect to delay absorption.These results seem to indicate that there is potential for tools such as FAST (arrivals) and EDP (departures) to factor noise into their sequencing and scheduling decisions.It was pointed out, however, that decisions that provide a noise benefit for certain population centroids can have a corresponding negative impact (e.g., increase in noise exposure) for other locations.Aggregate measures of benefit, potentially over extended time horizons, are thus generally preferred.The definition and evaluation of such metrics are the next steps in the development of the Noise Avoidance Planner.Future research will involve examination of the impact of the vertical degrees of freedom (thrust, altitude profile) and increased emphasis on departure scenarios.Finally, it should be pointed out that noise exposure is a time-varying phenomenon that is a strong function of the terminal area weather conditions, including cloud coverage and winds.For this reason, a spatial trajectory that is noise-preferred on a calm, clear day may not be the same as that required on a windy day with low ceilings.Future research is needed to incorporate actual and forecast winds into the noise prediction process -with a need for real-time evaluation of the trajectory space given winds.Figure 1 .1Figure 1.Identification of potentially noise-sensitive ATC procedural separation at ORD and MDW
+Figure 2 through2Figure 2 through Figure 4 illustrate several of the mitigation opportunities simulated in this manner.
+Figure 2 .2Figure 2. Improved Flight Corridor Adherence for ORD Runway 22L Departures
+Figure 3 .3Figure 3. Increased Flight Corridor Adherence for BOS Runway 04R Departures
+Figure 4 .4Figure 4. SFO Noisy Aircraft Departures (19L/19R) Reassigned to 10L (red = original; cyan = modified)In each of these figures, the original tracks are shown in red, with the corresponding modified tracks indicated in cyan or blue.Figure2highlights a mitigation strategy for ORD which involves tighter adherence to departure corridors specified by the Chicago Fly Quiet Program -in this case, keeping departure tracks over highways for an extended period during the climb out prior to initiating their turns.Figure3shows a similar situation at BOS in which departing aircraft off runway 04R are postulated to have precise enough navigation to enable them to be tightly funneled over a low population area rather than being dispersed over more highly populated regions.In the case of Figure4, the departure events from runways 19L/R were reassigned to existing tracks departing over the water on runway 10L.Results ObtainedAfter modifying the track and event data to simulate the effects of one or more noise-aware DSTsconsistent with the specific elements of the traffic patterns at each airport and the population distributions encountered in the vicinity of these patterns -a noise impact analysis was carried out using NIRS.
+Impacts for future years (2006 and 2011) were obtained from the baseline and modified 2001 data by baseline tracks modified tracks
+Figure 6 .6Figure 6.Defining the Endpoints of the TrajectorySpace for Noise Avoidance Planning At present, the Noise Avoidance Planner consists of two distinct off-line processing steps combined with real-time data handling.The first step stimulates the FAST/EDP scheduling logic to generate the interpolated set of trajectories for each analysis category.The second step then processes these trajectory sets with NIRS to compute a noise sensitivity for each analysis category (and its associated vector DOF).These sensitivities take the form of noise exposure curves as a function of DOF value (i.e., as a function of delay absorption).Note that since the offline steps are solely a function of the site adaptation and population distribution, they only need to be performed once in their entirety.Results of this offline processing are stored in a database for access during run-time.Localized changes to the site adaptation can be accommodated simply by reprocessing any new or revised analysis categories.Changes in population distribution due to new census data will require reprocessing of all analysis categories.
+Figure 7 .7Figure 7. Basic Noise Avoidance Planning Architecture (NAP-specific components yellow)
+Figure 8 .Figure 9 .89Figure 8.The trajectory space spanned by the FAN FROM WAYPOINT DOF for arrivals over BAMBE
+Figure 10 .10Figure 10.The variation of noise SEL with respect to delay absorption for several DOFs
+(a) No delay (FAST) route (b) 69 Seconds of Delay (c) 212 Seconds of Delay Figure 11.Frames (a)-(c) show noise footprints for arrivals into DFW 18R for different delay values.DFW 18R BAMBE ARRIVALS DFW 18R BAMBE ARRIVALS DFW 18R BAMBE ARRIVALS American Institute of Aeronautics and Astronautics
+Table 1 . Summary of Track and Event Data Sources for Quantification Study Study Airport Original Data Source Original Data Format1ORD&Chicago TRACONNIRS tracks and eventsMDWAnalysisProjectforthefivedata for 2000configurationsmostoften used on an annualbasisBOSMassPort 2001 dataPre-INM tracks andand Metron 1997INM tracks/events fordataaverage annual daySFOSFO Noise OfficeINM tracks/events for2001 dataaverage annual day
+Table 2 .2Summary of mitigation opportunitiesNoise MitigationData CharacteristicsOpportunityModifiedNoise-sensitive ATM approachproceduresAvoid dive and driveAltitude profileAvoid base leg extensionTrack locationinto noise sensitive areasSide-step approachesTrack location
+Route tracking (stay in precise route corridor)Follow routes over lowTrack locationpopulation areasAvoid shortcuttingTrack locationRunway/route selectionFanning across regionTrack locationRoute older aircraft to lessTrack location,noise-sensitive runwaysequipment typeGreater usage of noise-Runwaypreferred runwaysassignmentAirport interactions within aTRACONModify existing proceduresTrack location,to consider noisealtitude profilesNighttime operationsExtend procedures to higherEvent timetraffic levelsImprove efficiency so thatEvent timenight time operations can beinitiated on timeNoise-sensitiveATMdeparturesAltitude/speedDirect climb-to-cruiseprofiles American Institute of Aeronautics and Astronautics
+Table 3 .3Categorizing Expected Noise BenefitsCategory Percent Improvement RequiredHigh>=10% reduction in population above 50 dBModeratefrom 2% to 10% decreaseLowless than 2% decreaseThe summary of quantitative results in Table 4indicates that Expected Noise Benefits vary across abroad range, from less than 0.1% to over 20%. This isdue to the enormously varied traffic patterns,mitigation-opportunity characteristics, and populationdistributions across the airports studied. In particular:• At ORD and MDW, 2 of the 7 mitigationopportunities studied were rated high or moderateimpact. Achievement of the 2 high and moderatemitigation objectives at ORD and MDW (PreferredFlight Track Conformance and Direct Climb toCruise) at the estimated levels of DSTeffectiveness represents a 15% decrease in thepopulation above 50 dB, or over 460,000 people.• At BOS, all 4 of the mitigation opportunitiesstudied rated high or moderate impact.Achievement of the 4 high and moderatemitigation objectives at BOS (Arrival andDeparture Corridor Adherence, Noisy Aircraft onPreferred Runways, and Night Operations onPreferred Runways) at the estimated levels of DSTeffectiveness represents a 47% decrease in thepopulation above 50 dB, or over 105,000 people• At SFO, 4 of the 7 mitigation opportunities studiedrate high or moderate impact. Achievement of the4 moderate mitigation objectives at SFO (Shorelineand Dumbarton Departures, Quiet Bridge
+Table 4 .4Summary of Noise Mitigation Effectiveness Including DST EfficiencyNet Population Noise Impact(100% effective)
+		
+		
+
+			
+ACKNOWLEDGEMENTSThis research was funded by the Advanced Air Transportation Technologies (AATT) Project at NASA Ames Research Center under Research Task Order 61 of the Air Traffic Management System Development and Integration (ATMSDI) contract and a Phase II SBIR through NASA Ames Research Center.
+			
+
+			
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+				
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+	
+		Optimization of noise abatement arrival trajectories
+		
+			HGVisser
+		
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+			RA AWijnen
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+		10.2514/6.2001-4222
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Montreal, Canada
+		
+			American Institute of Aeronautics and Astronautics
+			August 2001
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+	Visser, H.G., Wijnen, R.A.A., "Optimization of Noise Abatement Arrival Trajectories," Proc. of the AIAA GNC Conference, Montreal, Canada, August 2001.
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+		Research into new noise abatement procedures for the 21st century
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+			LouisJErkelens
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+		10.2514/6.2000-4474
+	
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+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Denver, Colorado
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+			American Institute of Aeronautics and Astronautics
+			August 2000
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+	Erkelens, L.J., "Research into New Noise Abatement Procedures for the 21 st Century", Proc. of the AIAA GNC Conference, Denver, Colorado, August 2000.
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+		Quantification of Noise Benefits for ATM Decision Support Tools, Task 2: Quantification of Benefits Results for
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+		A concurrent sequencing and deconfliction algorithm for terminal area air traffic control
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+		Denver, Colorado
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+			American Institute of Aeronautics and Astronautics
+			August 2000
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+	Robinson, J.E. III, and D.R. Isaacson, "A Concurrent Sequencing and Deconfliction Algorithm for Terminal Area Air Traffic Control", Proc. of the AIAA GNC Conference, Denver, Colorado, 14-17 August 2000.
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+IntroductionThe continued growth of air traffic within the United States, combined with the use of Òhub and spokeÓ operations by air carriers, has led t o increased congestion and delays in the terminal airspace surrounding the nationÕs busier airports.The problem of congestion is exacerbated at hub airports, where air carriers schedule large numbers of flights to arrive and depart within a short time period.These arriving and departing groups of aircraft are commonly referred to as banks, and the simultaneous arrival of several banks of aircraft can easily strain the capacity of an airport.In order to ensure that the safe capacity of the terminal area is not exceeded, ATM often places restrictions on arriving flights transitioning from en route airspace to terminal airspace.The constraint of arrival traffic is commonly referred to as arrival flow management, and includes techniques such as metering, vectoring, and the imposition of miles-in-trail restrictions.These constraints are enacted without regard for the relative priority which airlines may be placing on individual flights, based on factors such as crew criticality, passenger connectivity, critical turnaround times, gate availability, on-time performance, fuel status, or runway preference [1].To air carriers, ÒhubbingÓ makes good economic and competitive sense [2].At the same time, however, hubbing operations often lead to overcapacity periods and precipitate delays which can directly impact the economic efficiency of an air carrierÕs flight operations.Air traffic control automation tools are used in arrival flow management to assist controllers in efficiently matching traffic demand and airport capacity while minimizing delays.These tools use sequencing and scheduling algorithms t o automatically plan the most efficient landing order and landing times for arriving aircraft [3].NASA and the Federal Aviation Administration (FAA) have designed and developed a suite of software decision support tools (DSTs) to improve the efficiency of high-density airspace [4].Collectively known as the Center-TRACON Automation System (CTAS), operational evaluation of these DSTs has shown them to be effective in improving airport throughput and reducing delays while maintaining controller workload at a reasonable level [4].One of these tools, the Traffic Management Advisor (TMA), is currently being used at the Fort Worth Air Route Traffic Control Center (Center) to perform arrival flow management of traffic into the Dallas/Fort Worth airport (DFW).The TMA is a time-based planning tool that assists Traffic Management Coordinators (TMCs) and Center controllers in efficiently balancing arrival demand with airport capacity [5].The primary algorithm in the TMA is a real-time scheduler which generates efficient landing sequences and landing times for arrivals within about 200 n.mi.from touchdown [6].Aircraft are scheduled so that they arrive in a firstcome-first-served (FCFS) order based on an estimated time of arrival (ETA) at the runway.While FCFS scheduling establishes a fair order based on estimated times of arrival, it does not take into account individual airline priorities among incoming flights.The development of new arrival flow management techniques which consider priorities expressed by air carriers will likely reduce the economic impact of ATM restrictions on the airlines.This will in the future lead to increased airline economic efficiency by allowing airlines to have greater control over their individual arrival banks of aircraft.As part of its Collaborative Arrival Planning (CAP) research and development program, NASA-Ames is exploring the possibility of allowing airlines to express relative arrival priorities to ATM through the development of new CTAS sequencing and scheduling algorithms which take into account airline arrival preferences.An earlier study focused on the feasibility of scheduling Òdelay exchangesÓ among pairs of individual arrival aircraft as a means of accommodating an airline request for an earlier arrival [7].
+Priority SchedulingSuccessful airline operations today require increasingly complex airline schedules.The interconnection of the schedules of major airlines with their subsidiary carriers and code-sharing partners adds to this complexity.As a result of increasing scheduling complexities and interdependencies, achieving a specific order within a bank of arrival aircraft has become of greater importance to the smooth and efficient operation of many airlines.Even a small group of aircraft belonging to a single airline may be interconnected in a fairly complex manner, with passengers and cargo from multiple flights feeding one large connecting flight or vice-versa.For example, an arriving bank of aircraft may include a large jet which is primarily delivering passengers to a number of smaller turboprop aircraft arriving in the same bank.This same large jet in turn, may be taking on passengers from other jet aircraft in the bank to deliver passengers/cargo to their final destinations.Passenger connectivity is only one of many factors which influence an airlineÕs schedule.Consideration must also be given to factors such as the availability of gates, and ground equipment and personnel to service aircraft and transfer passengers and cargo between flights.Even in the simple example just cited, the efficient operation of these flights will depend strongly on maintaining the integrity of the airline schedule by meeting the planned times of arrival and hence the desired order of arrival.For most airlines, the schedule which is determined internally by the airline to satisfy its business and economic objectives is an ÒidealÓ schedule.This schedule is ideal in the sense that the everyday realities of operating an airline and interacting with the various elements of the National Airspace System (NAS) largely preclude this ideal schedule from ever being achieved.Because of the uncertainties throughout both the airline (equipment breakdowns, maintenance problems, personnel shortages) and the NAS (weather, ground delays, ATM restrictions), aircraft often arrive in the terminal airspace in an order which does not match the ideal order of the airline schedule.Current arrival flow management using FCFS sequencing and scheduling algorithms will likely result in aircraft arriving at the runways in an order which does not match the preferred arrival order.The ability to specify the preferred arrival order within the userÕs own arrival bank is useful for maximizing bank integrity and minimizing bank time (i.e., exchange of passengers/cargo, and aircraft servicing) [8].Earlier studies have shown that scheduling aircraft according to an FCFS sequence based on estimated time of arrival at the runway produces a schedule which is considered to be both fair to air carriers and efficient in terms of minimizing delays which must be absorbed [3].These studies also have shown that the resulting scheduled arrival sequence at the runway will, for the most part, match the FCFS sequence which is input to the scheduling algorithm.Because the scheduling algorithm attempts to preserve the input sequence, specifying a preferred sequence will result in a schedule which closely approximates the preferred arrival order.The concept of Òpriority schedulingÓ is then defined as the scheduling of a bank of arrival traffic according to a preferred order of arrival.The focus of the present study is t o determine the feasibility of scheduling a bank of arrival aircraft using a preferred sequence instead of an FCFS sequence based on ETA at the runway.
+It is importantto distinguish between ÒschedulingÓ or ÒscheduleÓ in the context of airline operations, and ÒschedulingÓ or ÒscheduleÓ in the context of air traffic control automation.The former refers to the daily scheduled times of departure and arrival which an airline determines for all of its flights, while the latter refers to the process of automatically choosing (a) the order or sequence in which the aircraft should land or cross a particular fix, and (b) the time that each aircraft in the sequence should pass over a specified fix [6].
+Fast-time SimulationA fast-time simulation originally developed for statistical evaluation of CTAS sequencing and scheduling algorithms has been modified for use in this investigation [9].In contrast to real-time simulation or field tests, which would require on the order of ninety minutes to examine a single traffic rush period, the fast-time simulation allows examination of large numbers of statistically similar rush periods in a matter of minutes.For each simulated traffic situation, the deviation of a designated bankÕs scheduled arrival order from the preferred arrival order can be determined.The impact of priority scheduling on delays is also determined by comparing delays for priority scheduling and FCFS scheduling.Because this simulation does not provide any information about the controller workload required to meet the calculated schedule, the output of the simulation is used only to determine the effectiveness of priority scheduling and its impact on scheduled delays.The fast-time simulation is comprised of three major components: an airport model, a statistical model of the arrival traffic flow, and the scheduler.
+Airport ModelThe arrival airspace at DFW is divided into Center and Terminal Radar Approach Control (TRACON) regions, with the TRACON encompassing the airspace within approximately 40 n.mi. of the airport.Arrival traffic is merged at four waypoints on the Center-TRACON boundary which correspond to the four primary arrival directions.These waypoints are referred to as feeder gates because during heavy traffic periods traffic is funnelled through these gates as a means of controlling or metering the flow rate into the terminal area [6].Traffic flowing to each gate is separated into two independent streams which are vertically separated by 2,000 feet at the feeder gate.This allows jet and turboprop aircraft, which have significantly different airspeed ranges, t o cross the feeder gates independently and avoid conflicts due to overtakes near the gates.The airport is modeled according to the landing practices at DFW with four feeder gates and three runways available for landing.The runways are considered to be independent so that no stagger requirements are necessary for scheduling.The airport model is comprised of the minimum flight times from each feeder gate to all landing runways for each independent stream.These TRACON transition times were obtained from an analysis using the minimum flight times measured for several traffic samples [10].The TRACON transition times vary with feeder gate, aircraft type, runway assignment, and airport configuration.The airport model contains transition times for both airport configurations at DFW: Ònorth flowÓ with arrival traffic arriving/departing in a northerly direction, and Òsouth flowÓ with traffic arriving/departing in a southerly direction.It should be noted that since the data used in this simulation were collected, a fourth arrival runway has been added at DFW.However, the three-runway model and traffic data are sufficient for purposes of this investigation.
+Traffic ModelThe traffic model is based on actual traffic data recorded during six rush periods at DFW.Although the traffic data were recorded over a span of several months, the mix of aircraft type remained nearly constant for each traffic sample.The data were recorded during the Ònoon balloon,Ó a daily arrival rush lasting approximately ninety minutes.The noon balloon was chosen as the basis for the traffic model because during this arrival rush demand exceeds airport capacity and air traffic managers impose time-based metering restrictions through CTAS sequencing and scheduling algorithms.Data recorded during the six rush periods include the aircraft type, aircraft identification, arrival stream, and the estimated time of arrival at the feeder gate (ETA FG ).The average of these estimated times of arrival for the six rushes is taken as the nominal ETA FG .Errors in aircraft time of arrival in Center airspace are modeled by adding an approximately Gaussian distribution to the nominal estimated time of arrival at the feeder gate.The maximum range of the variation in the ETA FG is specified as an input to the simulation and is referred to as the Center arrival error.
+Bank DefinitionAlthough an actual arrival bank of aircraft for an airline may consist of between 30 and 50 aircraft, in this study it is assumed that a bank is comprised of a single group of up to 20 aircraft belonging t o one airline and its subsidiary carrier.With a majority of the flights in the traffic model belonging to American Airlines (AAL) and American Eagle (EGF), these flights are used t o form arrival banks.The bank is not a contiguous set of aircraft because aircraft belonging to other airlines are interspersed among the bank aircraft, as would be the case in a real traffic situation.The bank of aircraft is defined by specifying the first member of the bank, and the number of aircraft belonging to the bank.For the purposes of this simulation, we assume that the preferred order of arrival at the runway equals the order of arrival based on the minimum ETA at the runway with no Center arrival error.Each of the bank aircraft is assigned a priority ranking which is simply equal t o the preferred order of arrival for the aircraft within the bank.The minimum estimated time of arrival at the runway (ETA RWY ) is calculated by adding the TRACON transition times for each of the three runways to the nominal ETA FG , and selecting the minimum of the three resulting values.This ETA RWY represents the earliest possible time of arrival for an aircraft provided that the aircraft could fly to the runway with no delay.For example, consider the list of aircraft shown in Table 1, which represents a portion of a single arrival rush where AAL1150 has been designated as the lead aircraft in the bank, and the number of aircraft in the bank has been specified as five.The number in the first column represents the sequence number or position of the aircraft within the arrival rush when the aircraft are time-ordered according to increasing ETA RWY .Each arrival rush or traffic sample consists of 108 aircraft, and in the example in Table 1 the aircraft belonging t o the defined bank range from the 57th aircraft t o the 65th aircraft in the arrival rush (AAL1554).The resulting bank aircraft are denoted by bold text for purposes of illustration.This example shows that aircraft belonging to other airlines are interspersed among the arrival aircraft which comprise the bank.The second column is the aircraft identifier and the third column is each aircraftÕs corresponding minimum ETA RWY .The fourth column shows the priority ranking which is assigned to each of the aircraft belonging to the bank based on this preferred order of arrival.
+Table 1 Bank definition and preferred arrival orderThe actual order of arrival for aircraft in a traffic rush period is generated by adding the Center arrival error to the nominal ETA FG .The Center arrival error represents the uncertainties in the NAS which cause the same flight to arrive in Center airspace at different times on different days.Because the minimum ETA RWY is calculated by adding a TRACON transition time to the ETA FG , the minimum ETA RWY will also vary.As a result, when the aircraft are ordered according t o increasing ETA RWY , the actual order for the bank aircraft will differ from the preferred arrival order.In addition, the number of aircraft interspersed among the arrival bank may vary because the variation in arrival time is modeled for all aircraft in the traffic rush, not only those belonging to the specified bank.Table 2 shows the resulting estimated arrival order for the specified bank when a Center arrival error having a range of up to +/-5 minutes is added to the traffic sample.
+FCFS SchedulingThe FCFS scheduler is intended to approximate the sequencing and scheduling algorithms presently used in CTAS at the Ft.Worth Center.A detailed description of the actual scheduling algorithm can be found in [6].Aircraft are sequenced and scheduled to be first-come-first-served at both the feeder gates and runways while meeting feeder gate and runway threshold separation constraints.Because scheduling is done in time rather than distance, the prescribed minimum separation criteria are translated into minimum time separations at both the feeder gates and the runway threshold.For aircraft crossing the feeder gate, the minimum in-trail separation requirement for aircraft is 5 n.mi., which is translated to a 60second time separation for purposes of this simulation.The separation criteria at the runway threshold are a function of both aircraft weight class and landing order as determined by the FAAÕs wake vortex safety rules.Airport acceptance rate (AAR) is taken into consideration by limiting the number of aircraft which are allowed to enter the TRACON in sliding ten minute intervals, and the scheduler balances flights between runways t o minimize overall delay.The FCFS sequence is established by time-ordering arrival aircraft according to increasing ETA RWY .Beginning with the first aircraft in the sequence, each aircraft is tentatively scheduled to each of the three runways, while ensuring that the prescribed minimum time separation between aircraft at the runway thresholds is met for each subsequent aircraft.The runway which results in the earliest scheduled time of arrival for the aircraft at the runway (STA RWY ) is then chosen as the landing runway .Scheduling to the runway automatically provides the correct amount of traffic to load the runways equally when traffic is heavy (runway balancing), and directs aircraft t o the closest available runway.The scheduled time of arrival at the feeder gate (STA FG ) is determined by subtracting the sum of the TRACON transition time and any TRACON delay from the previously calculated STA RWY .Finally, if STA FG Õs for two flights are less than the required 60 seconds apart, the scheduled times will be altered to meet the required separation at the feeder gate.Table 3 shows the resulting order of arrival when the aircraft are scheduled according to an FCFS sequence.The priority ranking of each bank aircraft is shown in parenthesis following the aircraft identifier.The second and third columns in the table show the FCFS sequence which is input t o the scheduler, with the aircraft time-ordered according to increasing ETA RWY .The fourth and fifth columns are the resulting schedule, with aircraft time-ordered according to increasing STA RWY .Note that the resulting scheduled order of arrival at the runway does not precisely match the FCFS sequence based on ETA RWY which is input t o the scheduler.Because the schedule must meet intrail separation criteria at both the feeder gate and the runway threshold, and the separation criteria at the runway threshold are a function of aircraft weight class and landing order, the FCFS sequence may not be preserved at the runway.Among the aircraft belonging to the designated bank, flights AAL1934 and AAL1428 have shifted positions from the sequence which is input to the scheduler (as have aircraft DAL431 and AAL410, which do not belong to the designated bank).In this case, the position shift has resulted in a scheduled sequence which does more closely match the ideal or desired order of arrival than does the input FCFS sequence based on ETA RWY .However, it is purely fortuitous that the resulting schedule more closely matches the preferred order, and depending on the magnitude of the Center arrival error, the scheduled order may actually deviate further from the preferred order.
+Priority SchedulingThe priority scheduling algorithm is identical t o the FCFS algorithm with one exception: instead of time-ordering the aircraft according to increasing ETA RWY prior to scheduling, the arrival aircraft belonging to the designated bank are ordered according to their priority ranking, which establishes the bank aircraft in the preferred arrival order.It is important to note that only the aircraft belonging to the bank are reordered according to their priority ranking, and that other aircraft in the traffic sample are still sequenced in an FCFS order based on ETA RWY .By reordering only the bank aircraft and scheduling the remaining aircraft according to an FCFS sequence, the impact of the reordering on scheduling efficiency is minimized.Table 4 shows the resulting order of arrival when the bank aircraft are scheduled according to the preferred sequence of arrival.The second and third columns show the priority sequence which is input to the scheduler, with the bank aircraft ordered according to their priority ranking, and the remaining aircraft timeordered according to increasing ETA RWY .The fourth and fifth columns show the resulting schedule time-ordered according to STA RWY .As was the case with FCFS scheduling, the resulting order of arrival does not match the sequence which was input to the scheduler because the schedule must meet separation criteria at the runway threshold which are a function of aircraft weight class and landing order.Although the resulting scheduled bank order does not precisely match the preferred order, it does indeed match more closely the preferred bank order than does the FCFS schedule shown in Table 3.
+Order DeviationTo quantify the effectiveness of the priority scheduling method we need a measure of how closely the scheduled order of arrival for a designated bank matches the preferred arrival order.We first define a position shift (PS) for an aircraft as the difference between the aircraft position in the preferred bank order and the sequence number in the scheduled bank order.
+PS N N PREFERRED SCHEDULED
+= -where N is the sequence number of the aircraft within the bank Table 5 illustrates the calculation of the PS for each of the aircraft in the bank defined in Table 1.The position shift of each aircraft is calculated for both FCFS scheduling (Table 3) and priority scheduling (Table 4).Note that a positive PS indicates that an aircraft is scheduled ahead of its preferred position in the bank, and a negative position shift indicates that an aircraft is scheduled behind its preferred position in the bank.For example, the sequence number of flight EGF628 in the preferred order of arrival is 2 while its sequence number in the FCFS schedule is 5 and its sequence number in the priority schedule is 3.This results in a PS of -3 for the FCFS schedule and -1 for the priority schedule and reflects the fact that EGF628 is scheduled 3 slots behind its preferred position in the bank using FCFS scheduling, and 1 slot behind the preferred position using priority scheduling.Because we are interested in how closely the overall bank order matches the preferred order, we want a single measure which will indicate the deviation from the preferred order for a bank of any length.We then define the order deviation (OD) for a bank as the algebraic sum of the absolute value of the PS for each aircraft in the bank divided by the number of aircraft in the bank.
+OD PS= ∑ | | # of bank aircraft
+# of bank aircraftIt can be seen from this definition that if the OD for a bank of aircraft equals zero, then the scheduled bank order is the same as the preferred bank order.More importantly, the larger the value of the OD, the further the scheduled bank order deviates from the preferred order.This will allow us to easily compare the relative effectiveness of the FCFS and priority scheduling methods in producing the preferred order of arrival.The order deviations for each scheduling method using the example in Table 5 are calculated below.Because the priority scheduling scheme results in the designated bank arriving in an order which more closely matches the preferred arrival order, the OD for the priority scheduled bank is smaller than that for the FCFS scheduled bank.ODFCFS = + -+ + + = | | | | | | | | | | . 0 3 1 1 1 5 1 2 ODPRIORITY = + -+ + + = | | | | | | | | | | . 0 1 1 0 0 5 0 4In order to investigate the statistical performance of the two scheduling methods, a large number of traffic samples are generated for a specified bank.To compare the effectiveness of FCFS scheduling and priority scheduling for a large number of traffic samples, we define the average OD as the sum of the ODÕs for each traffic sample divided by the number of traffic samples.
+OD
+Simulation Inputs/OutputsInputs to the fast-time simulation include the aircraft identifier of the lead aircraft in the bank, the size of the bank, the number of traffic samples, the range in Center arrival error, the airport configuration, and airport acceptance rate.In order to determine the statistical performance of the FCFS algorithm and the priority algorithm, 500 traffic samples are generated for each designated bank.Each traffic sample is comprised of 108 jet and turboprop aircraft, 72 of which are AAL or EGF flights.In this simulation the modeled airport configuration is south flow for DFW.Because the traffic model is limited to a single arrival rush period, and because of the manner in which a bank is defined, banks cannot be formed at or near the end of the arrival rush period.For example, if the bank length is specified as 20, and the designated lead aircraft is the 100th aircraft in the arrival rush, no bank will be formed because there are not enough aircraft following the lead aircraft to form a bank.Although we attempt to form banks across the entire range of the traffic rush period, this cannot be done for the reasons just outlined.The output of the fast-time simulation includes the average OD as well as histograms of the position shifts for each bank of aircraft.Total delays and histograms of individual delays for all aircraft in the traffic rush are generated as well.Results can then be compared for the FCFS scheduling algorithm and the priority scheduling algorithm.
+Results and DiscussionThe primary measure of success of the priority scheduling algorithm is the closeness of the match between the scheduled order of arrival and the preferred order of arrival.Figure 1 is a plot of the average order deviation for a bank size of 20, a range in Center arrival errors of +/-5 minutes, and an AAR of 96 aircraft/hour.For a designated bank whose lead aircraft has a nominal ETA FG given on the x-axis, a corresponding pair of ordinates shows the average OD for the bank using FCFS scheduling and priority scheduling.Figure 1 confirms that the priority scheduling algorithm significantly reduces the average OD from that of the FCFS scheduling algorithm.Note however, that while the OD for each bank is less using the priority scheduling algorithm, the OD is still non-zero for each bank.In other words, while the resulting bank order using priority scheduling matches much more closely the preferred order than does the FCFS order, the scheduled bank order does not precisely match the preferred order.Because the schedule must meet in-trail separation criteria at the runway threshold, and the separation criteria are a function of both weight class and landing order, the preferred order of arrival may not be preserved at the runway.Figure 1 shows the resulting OD for banks of aircraft beginning at different points in the arrival rush.The average order deviation for the FCFS algorithm first increases and then decreases as the ETA FG of the lead aircraft in the bank increases.The change in average OD for the FCFS schedule is due to changing traffic density and mixture in the arrival rush.As the traffic density increases (estimated times of arrival are more closely spaced), a given arrival error will cause larger position shifts within a bank and thus larger order deviations.By the same token, the traffic mix impacts the order deviation because if non-AAL/EGF flights are interspersed among the bank aircraft, the aircraft comprising the bank will be spaced farther apart.Then, for a given arrival error, the OD for the bank will be smaller because the aircraft are not as closely spaced.The average OD for the priority scheduling algorithm also varies with traffic density and mixture and is most effective in a region where some non-AAL/EGF aircraft are interspersed among the bank aircraft.The effects of AAR, bank size, and Center arrival error on the success of the priority scheduling algorithm are also examined.For the sake of brevity, no plots are shown but important results are summarized here.Results show that for a given Center arrival error and bank size, the priority OD tends to decrease with decreasing AAR, meaning that the priority scheduling algorithm is more effective for a more restrictive AAR.This is actually a characteristic of both the priority scheduler and the FCFS scheduler, and it can be shown that for a lower AAR, either scheduler is better able to preserve the order in which the aircraft are scheduled.Lowering the AAR effectively reduces the airport capacity (because demand remains constant), requiring that the scheduled times of arrival (STAÕs) be spaced farther apart.Because the STAÕs must be spaced farther apart, differences in crossing times or separation criteria are less likely to cause the resulting order to deviate from the order in which the aircraft are scheduled.Therefore the resulting schedule for either algorithm will more closely match the sequence in which the aircraft are scheduled.Results also show that increasing the size of the bank of aircraft does not significantly impact the effectiveness of the scheduling algorithm.However, increasing the magnitude of the Center arrival error for a given bank size and AAR does lead to a decrease in the effectiveness of the priority scheduling algorithm.For purposes of illustration, a histogram of the position shifts for a bank of aircraft led by AAL535 is shown in Figure 2.This histogram, along with the OD values labeled in Figure 1, demonstrate the relationship between average OD and the closeness of the match between the scheduled bank order and the preferred arrival order.Priority scheduling reduces the spread of the position shifts for the designated bank of aircraft.In this case, aircraft belonging to the designated bank are scheduled in the preferred position (position shift = 0) approximately 60% of the time using priority scheduling.Using FCFS scheduling, bank aircraft are scheduled in the preferred position only about 25% of the time.The increase in the number of aircraft scheduled in the preferred position leads to a decrease in average OD for the bank.Because this simulation does not provide any information about the controller workload required to meet the priority schedule, the output of the simulation is used only to investigate the feasibility of the priority scheduling method in terms of scheduling efficiency.However, it can be reasonably assumed that an increase in scheduled delays greater than a certain amount would be unacceptable to air traffic controllers because of the likely adverse effect on controller workload.Similarly, airlines would likely find an increase in scheduled delays which exceeds a certain threshold to be unacceptable from the standpoint of increased costs.While the amount of delay increase acceptable controllers and airlines would have to be determined before a priority scheduling method could be considered practicable, the present simulation provides initial insight into the impact of priority scheduling on scheduling efficiency.This can be measured as the change in average delay per aircraft when priority scheduling is used instead of FCFS scheduling.q q q q q q q q q q q q q q q q q q q q q q q q q For each designated arrival bank whose order deviation is shown in Figure 1, a corresponding pair of points in Figure 3 shows the change in average delay for the AAL/EGF aircraft in the arrival rush, and for the non-AAL/EGF (ÒOthersÓ) aircraft in the arrival rush.Figure 3 shows that the change in delays due to priority scheduling varies with the position of the bank in the arrival rush, and that the greatest delay increase occurs for a bank which starts near the beginning of the arrival rush.This is attributable to the changing traffic density and traffic mixture in the arrival rush, and to the fact that all aircraft following the bank lead aircraft may be impacted by the reordering of the bank aircraft before scheduling.Because a larger number of aircraft may be impacted by the reordering, the aggregate increase in delays will be greater for a bank which begins earlier in the arrival rush.The average delay increase then diminishes as the ETA FG of the lead bank aircraft increases, and priority scheduling in some instances results in a slight decrease in average delay per aircraft.In these instances the priority schedule is actually more efficient than the FCFS schedule.The priority scheduling algorithm has the smallest impact on scheduling efficiency in regions where arrivals are not closely spaced and banks have non-AAL/EGF flights interspersed among the bank aircraft.Although a scheduling method which takes into account user preferences would ideally have no impact on scheduling efficiency when compared with FCFS scheduling, Figure 3 shows that for certain traffic conditions, the priority scheduling method results in little or no decrease in scheduling efficiency.v v v v v v v v v v v v v v v v v v vv v v vv v 0 0.5Any type of scheme which allows the introduction of user preferences into the arrival flow management process must ultimately be fair to all air carriers.In light of this, we are particularly interested in determining whether the priority scheduling of flights belonging to one airline disproportionately impacts the scheduled delays of aircraft belonging to other airlines.Examination of the delay increases for AAL/EGF flights in Figure 3 shows that for most of the banks, the delay increase for AAL/EGF flights in the arrival rush is greater than the delay increase for the non-AAL/EGF aircraft.By reordering only the aircraft belonging to the designated bank and scheduling all other aircraft according to an FCFS sequence, the impact of reordering on aircraft belonging to other airlines is minimized.This strategy also minimizes the impact of the reordering on scheduling efficiency, and in some instances results in improved efficiency by decreasing scheduled delays.The effects of AAR, bank size, and Center arrival error on the change in scheduled delays are also examined.For a given bank size and Center arrival error, when priority scheduling is used instead of FCFS scheduling, the change in average delay per aircraft tends to increase as AAR is increased.Results are similar to those seen in Figure 3 with the greatest change in delay occurring for banks which begin early in the arrival rush, and the change in delays decreasing for banks which are positioned later in the arrival rush.Increasing the magnitude of the Center arrival error for a given bank size and AAR substantially increases the change in delays for banks of aircraft arriving early in the rush period, while not significantly impacting the change in delay for banks arriving later in the traffic period.Finally, results show that the change in delays due to priority scheduling is largely unaffected by an increase or decrease in the size of the arrival bank.q q q q q q q q q q q q q q q q q q q q q q q q q v vv v v v v v v v v v v v v v v v v v v v v v v
+Concluding RemarksThis paper introduces the concept of priority scheduling as a means of taking into consideration airline arrival preferences in sequencing and scheduling algorithms for air traffic control automation.Priority scheduling is defined as a method of scheduling a bank of arrival aircraft according to a preferred arrival order instead of according to an FCFS order based on estimated time of arrival at the runway.A fast-time simulation originally developed for statistical evaluation of CTAS sequencing and scheduling algorithms has been modified for use in this investigation.Because this simulation does not provide any information about the controller workload required to meet the priority schedule, the output of the simulation is used only t o investigate the feasibility of the priority scheduling method in terms of scheduling efficiency and how closely the bankÕs scheduled arrival order matches the preferred arrival order.Results show that for the simulated traffic conditions, the priority scheduling algorithm results in a scheduled bank order which closely matches the preferred order.Results also show that when compared with FCFS scheduling, priority scheduling will, for certain traffic conditions, substantially reduce deviations from the preferred bank order while causing little or no decrease in scheduling efficiency.Figure 2 Figure 323Figure 1 Average order deviation
+Figure 33Figure 3 Change in average delays per aircraft when priority scheduling is used instead of FCFS scheduling
+Table 2 Actual arrival order2
+Table 4 Priority Sequence and resulting schedule4SequencePreferredFCFSPositionPriorityPositionNumberOrderScheduleShift forScheduleShift forinFCFSPriorityBankScheduleSchedule1AAL1150AAL11500AAL115002EGF628AAL1934-3AAL1934-13AAL1934AAL14281EGF62814AAL1428AAL15541AAL142805AAL1554EGF6281AAL15540
+Table 5 Calculation of position shift for a bank of aircraft5
+		
+		
+			
+FCFS SequenceResulting			
+			
+
+				
+
+
+	
+		ÒAirline Arrival Prioritization,Ó NAS Status Information Subgroup Memo
+		
+			ALacher
+		
+		
+			DBenfield
+		
+		
+		
+			May 19, 1997
+		
+	
+	Lacher, A., and Benfield D., ÒAirline Arrival Prioritization,Ó NAS Status Information Subgroup Memo, www.metsci.com/faa/cdm/nassi.html, May 19, 1997.
+
+
+
+	
+		Quarterly Update October - December 2015
+		
+			LBond
+		
+		10.1163/2210-7975_hrd-9806-2016016
+	
+	
+		Ó Journal of ATC
+		
+			
+			October -December 1997
+			Brill
+		
+	
+	Bond, L., ÒGlobal Positioning Sense II: An Update,Ó Journal of ATC, October - December 1997, pp. 51 -55.
+
+
+
+	
+		Initial Characterization of the 30 kW Miniature Arc Jet (mARC II) at NASA Ames Research Center
+		
+			FNeuman
+		
+		
+			HErzberger
+		
+		10.2514/6.2020-3108.vid
+	
+	
+		ÒAnalysis of Delay Reducing and Fuel Saving Sequencing and Spacing Algorithms for Arrival Traffic,Ó NASA TM 103880
+		
+			American Institute of Aeronautics and Astronautics (AIAA)
+			October 1991
+		
+	
+	Neuman, F. and Erzberger H., ÒAnalysis of Delay Reducing and Fuel Saving Sequencing and Spacing Algorithms for Arrival Traffic,Ó NASA TM 103880, October 1991, NASA Ames Research Center.
+
+
+
+	
+		
+			HErzberger
+		
+		
+			TJDavis
+		
+		
+			SMGreen
+		
+		ÒDesign of Center-TRACON Automation System,Ó Proceedings of the AGARD Guidance and Control Panel 56th Symposium on Machine Intelligence in Air Traffic Management
+		Berlin, Germany
+		
+			1993
+			
+		
+	
+	Erzberger, H., Davis, T. J., and Green, S. M., ÒDesign of Center-TRACON Automation System,Ó Proceedings of the AGARD Guidance and Control Panel 56th Symposium on Machine Intelligence in Air Traffic Management, Berlin, Germany, 1993, pp. 52-1 -52-14.
+
+
+
+	
+		ÒDesign and Operational Evaluation of the Traffic Management Advisor at the Fort Worth Air Route Traffic Control Center,Ó 1st USA
+		
+			HNSwenson
+		
+	
+	
+		Europe Air Traffic Management Research and Development Seminar
+		
+			June 17-19, 1997
+			Saclay, France
+		
+	
+	Swenson, H. N., et al., ÒDesign and Operational Evaluation of the Traffic Management Advisor at the Fort Worth Air Route Traffic Control Center,Ó 1st USA/Europe Air Traffic Management Research and Development Seminar, Saclay, France, June 17-19, 1997.
+
+
+
+	
+		ÒDesign Principles and Algorithms for Automated Air Traffic Managment,Ó AGARD Lecture Series No. 200, Knowledge-Based Functions in Aerospace Systems
+		
+			HErzberger
+		
+		
+			November 1995
+			San Francisco
+		
+	
+	Erzberger, H., ÒDesign Principles and Algorithms for Automated Air Traffic Managment,Ó AGARD Lecture Series No. 200, Knowledge-Based Functions in Aerospace Systems, San Francisco, November 1995.
+
+
+
+	
+		Delay exchanges in arriving sequencing and scheduling
+		
+			GregoryCarr
+		
+		
+			HeinzErzberger
+		
+		
+			FrankNeuman
+		
+		10.2514/6.1998-4478
+	
+	
+		Guidance, Navigation, and Control Conference and Exhibit
+		Boston, MA
+		
+			American Institute of Aeronautics and Astronautics
+			August 10-12, 1998
+		
+	
+	Carr, G. C., Erzberger, H., Neuman, F., ÒDelay Exchanges in Arrival Sequencing and Scheduling,Ó AIAA Guidance, Navigation, and Control Conference, Boston, MA, August 10-12, 1998.
+
+
+
+	
+		Enabling user preferences through data exchange
+		
+			StevenGreen
+		
+		
+			TsuyoshiGoka
+		
+		
+			DavidWilliams
+		
+		
+			StevenGreen
+		
+		
+			TsuyoshiGoka
+		
+		
+			DavidWilliams
+		
+		10.2514/6.1997-3682
+	
+	
+		Guidance, Navigation, and Control Conference
+		New Orleans, LA
+		
+			American Institute of Aeronautics and Astronautics
+			August 10-12, 1997
+		
+	
+	Green, S. M., Goka, T., Williams, D. H., ÒEnabling User Preferences Through Data Exchange,Ó AIAA Guidance, Navigation, and Control Conference, New Orleans, LA, August 10-12, 1997.
+
+
+
+	
+		ÒFast-Time Statistical Evaluation of Sequencing and Scheduling Algorithms for Multiple Runways
+		
+			FNeuman
+		
+		
+			HErzberger
+		
+		
+			MSchueller
+		
+		
+	
+	Ó to be published as a NASA technical memorandum
+	Neuman, F, Erzberger, H., Schueller, M., ÒFast-Time Statistical Evaluation of Sequencing and Scheduling Algorithms for Multiple Runways,Ó to be published as a NASA technical memorandum.
+
+
+
+	
+		Benefits analysis of terminal-area air traffic automation at the Dallas/Fort Worth International Airport
+		
+			MarkBallin
+		
+		
+			HeinzErzberger
+		
+		10.2514/6.1996-3723
+	
+	
+		Guidance, Navigation, and Control Conference
+		
+			American Institute of Aeronautics and Astronautics
+			July 1996
+		
+	
+	Ballin, M, and Erzberger, H., ÒAn Analysis of Landing Rates and Separations at the Dallas/Fort Worth International Airport,Ó NASA TM-110397, July 1996.
+
+
+				
+			
+		
+	
diff --git a/file118.txt b/file118.txt
new file mode 100644
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--- /dev/null
+++ b/file118.txt
@@ -0,0 +1,310 @@
+
+	
+	
+		
+Nomenclature
+I. IntroductionNE of the largest challenges faced by the Air Traffic Management (ATM) community is the integration of new tools and concepts into the existing airspace system.These new concepts often need to be integrated with legacy systems.In the past few years much of the global ATM research community has proposed advanced systems based on Trajectory-Based Operations (TBO) 1 .The concept of TBO uses four-dimensional aircraft trajectories as the base information for managing safety and capacity.Both the US and European advanced ATM programs call for the sharing of the trajectory data between decision support tools for successful operations.However, the actual implementation of sharing trajectory information presents many challenges.Many advanced tools and concepts define functional and accuracy requirements for the trajectory predictor to meet their specific needs.These requirements can often be inconsistent or even conflicting across different systems.Two integration case studies, involving three systems, are discussed.These cases will focus specifically on the trajectory prediction functionality, a common feature for all three systems but very different in requirements and implementation.The first case study examines the issues with integration of the Efficient Descent Advisor (EDA), a tool which provides advisory conflict-free trajectories to meet a scheduled time, with the conflict detection/resolution functions of ERAM.These systems were developed completely independently.The integration of these tools is complicated by the fact that they perform similar functions but are driven by different requirements.The difference in the resulting trajectories can lead to conflicting advisories.The second case describes the issues with integrating the existing operational scheduler, the Traffic Management Advisor (TMA) with the EDA, which generates speed and altitude advisories to meet the TMA scheduled times.Both tools were originally developed by NASA from the same code baseline.It was anticipated that the integration of the tools would be simplified by their common source.However, recent efforts at NASA to integrate new concepts into the Operational TMA baseline, which has diverged significantly from the NASA research baseline, have shown that this is not the case.Difficulties have arisen due to the divergence of code and requirements development of the two tools.
+II. HistoryThere have been many studies about the synchronization of trajectory predictions between airborne avionics systems and ground-based systems. 2,3These studies show that performance of the ground-based trajectory predictors can be improved by the reception of aircraft state and intent data.Of less consideration has been the integration of different ground-based decision support tools that perform overlapping but not identical functions.This paper will examine issues with the operational integration of three such tools, ERAM, Efficient Descent Advisor (EDA) and Traffic Management Advisor.ERAM is the system that replaces the En-Route HOST computer.In this paper, the primary ERAM function of concern is the detection of potential conflicts and maintenance separation between aircraft in the en-route airspace 4 .TMA is a legacy system deployed and maintained by the Federal Aviation Administration (FAA) 5.6 .The purpose of TMA is to reduce congestion in the terminal airspace during high traffic periods.Predictions are made of when the aircraft will transition from the en-route to the terminal airspace.These predictions are used to create a sequence for the aircraft to enter the terminal area using a first-come, first-serve algorithm.Controllers use time of crossing into the terminal airspace to ensure appropriate spacing between arrivals.If necessary, aircraft are assigned some amount of delay in order to cross at their assigned time, which the controller may absorb by issuing speed changes, temporary altitudes, or flight path modifications.EDA was designed by NASA as an enhancement to the TMA system 7,8 .EDA provides the controller with advisories on how to meet the crossing time assigned by TMA.These advisories specify trajectories that meet the assigned time while minimizing fuel usage.The integration of the TMA and EDA systems would seem to be fairly straightforward as both systems originated from NASA and were built on a common software baseline.The TMA software was delivered to the FAA over 15 years ago.Initially, an effort was undertaken to synchronize the two code baselines so enhancements and code corrections could be shared.This became burdensome as some of the fixes and requirements made to support the operational system were incompatible with changes or functionalities for the research baseline and vice versa.The decision was made to abandon joint development resulting in the divergence of the operational and research baselines.During the period of divergence many of the prototypical algorithms were removed from the code baseline in order to reduce code complexity and minimize risk of unintended behavior.The research prototype meanwhile underwent several efforts of refactoring in order to better support EDA and other topics under investigation.At this point, these must really be considered to be two separate systems.Although the two code bases started with the same aircraft models and equations of motion, the outcome of these years of divergence is that the two code bases have very significant differences in the parsing of constraints and intent.Consequently, the trajectory requests still share a common data structure, but the resulting trajectories may look significantly different.This becomes problematic for the integration of EDA and TMA if the TMA system provides a scheduled time that is unattainable by EDA's methods of calculation.Lockheed Martin is the contractor tasked by the FAA to enable EDA in the ERAM architecture.As the current custodians of the software, they have been enhancing the functionality of the ERAM predictor to improve performance in the transition airspace and to introduce the EDA concepts into their legacy system.However, there are significant differences in aircraft modeling and the equations of motion used, as the two systems were developed independently.The complexity of integration of these two systems in not unexpected.
+III. IssuesThe problems faced in integrating EDA into the ERAM/TMA environment are fairly common in the development of large software systems.Many of the issues that arise when running these systems in an integrated environment is that while the tools have overlapping functions, several underlying requirements are incompatible.ERAM detects conflicts based on its best estimation of the trajectory the airplane will fly, using all available intent information and historical profiles modified by observed flight performance in the adapted airspace.In contrast, EDA and TMA base their advisories on fuel-optimal profiles that the airline would prefer to fly and more detailed models of aircraft performance.These tools use airline or aircraft manufacturer data to select speeds to descend at the latest possible point and still make the crossing restriction at the transition point into the terminal airspace.This can lead to very different shapes and flight times between ERAM and EDA/TMA trajectories.Another significant issue in the maintenance of large-scale legacy software systems is ensuring that the validation process adequately tests that the performance meets the specified requirements for a project, particularly when those requirements may evolve over time.The primary metric for validation of TMA was a time accuracy metric.However, analysis of software change requests shows correction of errors for large jumps in the estimation of the time of arrival as a flight progresses toward the meter fix as opposed to the accuracy of the original ETA.These jumps would cause issues in the scheduling algorithm and also reduce controller confidence in the predicted times.The testing procedures were not updated to reflect the addition of this "stability requirement."This stability requirement could complicate integration of new EDA functionality as some of these modifications modify or suppress changes to the trajectory shape or default speeds, which are degrees of freedom used by EDA to build advisory trajectories.The software architecture can be a hindrance to integrating new concepts into legacy systems.For ERAM, altitude restrictions are critical intent information that must be met as part of the generated profiles.Altitude restrictions are locations where aircraft must comply with a specific altitude for procedural separation and workload balance.These altitude restrictions have been implemented in the software in such a way that they cannot be ignored during a trajectory prediction.Conversely, EDA seeks to avoid the fuel penalty of the aircraft leveling off at the altitude restriction by providing advisories for an uninterrupted descent profile that meets the scheduled time.The trajectories generated do not account for the altitude restrictions in any condition.The EDA-generated trajectory may cause ERAM to detect a conflict, as the aircraft is no longer flying to the intent that ERAM expects.EDA advisories are vetted for conflicts but due to the different models used by the two systems, may not find the same results.At best, these inconsistencies could lead to unnecessary iteration and less than optimal descent profiles.These differences in software architecture will complicate merging the two algorithms.Ideally, the capabilities should all be functions of compatible requirements if not the same trajectory predictor, but how to integrate and validate the different algorithms so that the functionality and accuracy are sufficient for all applications is difficult.One of the difficulties in integration is that the boundary between the functionality of the trajectory predictor and that of the "client" application is drawn in different places. 10The Action Plan 16 group, founded by an initiative of the joint FAA/Eurocontrol Cooperative R&D Committee, developed a Common Trajectory Predictor (TP) structure to capture the essential components of a trajectory predictor 9 .A major point of contention in the development of this structure was the determination of which components were intrinsic to the trajectory predictor and which were functions of the decision support tool.Often, for convenience or improved performance, decision support tool functions are embedded in the trajectory prediction software.This can make integration into a single baseline difficult.The TP functions may expect specific data to be processed prior to being called, or the decision support tool or system may rely on the TP to perform certain functions specific and unique to its own need.One example of this would be in the parsing of the flight plan route provided by the aircraft.This route is comprised of a series of navigation fixes and airways, which must be decomposed into physical locations.For ERAM, this function is grouped into the trajectory prediction capability.For TMA and EDA, the decision support tool parses the route, as it may modify the fixes as part of its process in analyzing or advising the controller on the best ATC instructions to issue.EDA uses the lateral path as a degree of freedom for meeting a TMA secheduled arrival time, while TMA in some areas may modify the proscribed jet route in order to support requirements for getting traffic to the meter fix earlier (essentially to mimimize back-up during congestion).Vivona et al. 10 proposes separating these functionalities using the TP boundary rule in development of the software architecture.The TP Boundary Rule is "…if the capability directly supports a key function of the client application, then the capability is considered a client application capability and outside the scope of the TP."However, refactoring two large legacy systems, while necessary, would be time consuming and expensive.In 2010, a comparison of the functional requirements that drove the development of the trajectory predictors for ERAM Release 1 and EDA/TMA was conducted by NASA and Lockheed Martin 11 .This study compared the tools for the aircraft behavior modeled and the mathematical assumptions that were used to calculate the trajectory based on those behaviors.It was critical to break down the comparison into these two factors as the former determines which profiles could be processed and the latter accounts for the differences in profiles.As illustrated in the paper and Table 1, both EDA and ERAM handle similar types of altitude constraints.
+IV. Case StudiesEDA • • • • ERAM r1 • • •It is in the handling of the constraints where the difference emerges (Table 2).ERAMr1 uses an empirically based model of aircraft speed and vertical rate as the best approximation of speed in an environment where the speeds are unknown.The empirical model is based on analysis of thousands of trajectory histories at a particular site, where speed and altitude profiles are averaged over many operational conditions.EDA, conversely, uses a speed schedule of mach and CAS as these are the values that would be modified as advisories to meet a time, along with a high fidelity model of the aircraft performance for that specific type of aircraft and a nominal model of expected pilot behavior, assuming no intermediate restrictions from controllers prior to the TMA meter fix.ERAM handles additional functionality of waypoint-defined "AT or BELOW" constraints, where the constraint is a limit rather than an exact target state.These could be of great use to EDA but would require additional integration efforts to minimize possible negative effects such as ETA jumping when iterating to meet a TMA advised time.
+B. Integration of EDA and TMAThe change in the operational TMA system that would have a great impact on the EDA system is the modification of nominal speed profile used for the calculation of descents.The nominal speed value was originally selected based on typical airline and aircraft manufacturers' preferences.However, in response to an FAA requirement to increase the number of aircraft at the beginning of each arrival rush (known as "front loading" to increase pressure on the runways), the operational TMA system was modified to increase the indicated nominal descent speed.Doing so produced the desired earlier scheduled times of arrival, but had several potential unintended consequences.First, these speed changes can degrade the accuracy of the trajectory prediction made by TMA.For example, the nominal speed for A320 aircraft flying to LAX used by the TMA system is given as 310 kts.Fig. 1 shows estimated calibrated airspeeds flown by aircraft in descent into LAX.TMA and EDA model the descent of a jet such as the A320 using a speed profile of a constant mach speed segment (used for acceleratingto a descent speed higher than the cruise speed, if necessary) followed by a constant CAS segment to the arrival fix.This constant CAS segment can be viewed in Fig. 1 as the approximately vertical values from 25,000 to 10,000 ft.As can be seen, for this day the tendency for aircraft of this type was to fly in the constant CAS portion in the range of 250-300 kts.Using the higher values as a nominal descent speed can also increase the risk that the software will be unable to calculate the trajectory that can meet all the aircraft constraints.In both these cases, the FAA has validated that these risks do not have a significant effect on the performance of TMA.These effects would have more impact on the EDA systsm.Both the trajectory accuracy and failure issues could cause the iteration algorithm used by EDA to perform suboptimally.Most significantly, the increase of speeds removes a degree of freedom from EDA as there would be less ability to use faster speed to meet scheduled times.
+V. ConclusionIntegration of new concepts into legacy systems will always be challenging.Even systems that were created from a common software baseline may not be trivial to recombine after a significant period of isolated development.The trajectory prediction functions in EDA, ERAM and TMA systems have all been developed separately to meet the needs of their specific system.The trajectory predictors are composed of many optimizations to meet their project's requirements that should not be lost in the process of integration.The issues with integration of the trajectory predictors could be addressed by longer-term consideration of future requirements and how they would be implemented.These do add complexity to the code baseline.Uncertainty in the acceptance of the new capability also discourages retention of the additional functionality.Since many of the tools are deployed and maintained by different organizations and contractors during different timeframes, it is challenging to find and maintain commonality between the tool's trajectory predictors.Still, many of these advanced capabilities can be maintained with low risk to the performance of the initially implemented systems and save considerable money and development time in the future.Both case studies illustrate the need for clear, consistant and cross-comparible TP requirements, developed at as early a stage as possible.It would be difficult to build a single trajectory predictor which could meet the requirements of all systems.If the requirements were written in a consistant format, this could drive the code implementation to find common structures and minimize architectural differences.Similarly, consistant application of the TP boundary rule would enable more common TP structure between systems, allowing the TPs to function interchangeably.A.Integration of ERAM and EDA Downloaded by NASA AMES RESEARCH CENTER on August 29, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-443
+Figure 1 .1Figure 1.Calibrated airspeeds for Airbus 320 aircraft flying into LAX over a 24-hour period, 2011.
+Table 1 : Vertical Constraints Handled TP Cruise Altitude1Departure/Arrival SpeedInterimLimitTransition Alt (altimeterAltitudeAltitudesetting)
+Table 2 : Vertical Speed Models TP Descending Segments2Level Segments
+			Downloaded by NASA AMES RESEARCH CENTER on August 29, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-443
+		
+		
+
+			
+AcknowledgmentsThe author would like to thank Robert Vivona and Gabriele Enea of the Engility Corporation for the analyses they have conducted on the Operational TMA baseline.
+			
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+		Air-ground trajectory synchronization &#x2014; Metrics and simulation results
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+			DavidS KChan
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+			GlenWBrooksby
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+			JoachimHochwarth
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+			SergioTorres
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+		Seattle, Washington
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+	Chan, D.S.K., Brooksby, G.W., Hochwarth, J., Klooster, J., and Torres, S., "Air-Ground Trajectory Synchronization --Case Studies and Metrics", 30th Digital Avionics Systems Conference, Seattle, Washington, 16-20 October 2011
+
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+		Trajectory Synchronization between air and ground trajectory predictors
+		
+			SergioTorres
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+			JoelKKlooster
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+			LilingRen
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+			MauricioCastillo-Effen
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+		10.1109/dasc.2011.6095978
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+		2011 IEEE/AIAA 30th Digital Avionics Systems Conference
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+	Torres, S., Klooster K. J., Ren, L., and Castillo-Effen, M., "Trajectory Synchronization between Air and Ground Trajectory Predictors", 30th Digital Avionics Systems Conference, Seattle, Washington, 16-20 October
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+		Evaluation of Prototype Enhancements to the En Route Automation Modernization's Conflict Probe
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+		En route Descent Advisor concept for arrival metering
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+			StevenGreen
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+			RobertVivona
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+		10.2514/6.2001-4114
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+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Montreal, Canada
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+			American Institute of Aeronautics and Astronautics
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+	Green, S. M., and Vivona, R. A., "En route Descent Advisor Concept for Arrival Metering," AIAA-2001-4114, AIAA Guidance, Navigation, and Control Conference, Montreal, Canada, Aug. 2001.
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+		Design and Development of the En Route Descent Advisor (EDA) for Conflict-Free Arrival Metering
+		
+			RichardCoppenbarger
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+			RichardLanier
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+			DougSweet
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+			SusanDorsky
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+		10.2514/6.2004-4875
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+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Providence, RI
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+			American Institute of Aeronautics and Astronautics
+			Aug. 2004
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+	Coppenbarger, R. A., Lanier, R., Sweet, D., and Dorsky, S., "Design and Development of the En Route Descent Advisor (EDA) for Conflict-Free Arrival Metering," AIAA-2004-4875, AIAA Guidance, Navigation, and Control Conference, Providence, RI, 16-19 Aug. 2004.
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+		The Traffic Management Advisor
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+	Swenson, H. N., Hoang, T., Engelland, S., Vincent, D., Sanders, T., Sanford, B., and Heere, K., "Design and Operational Evaluation of the Traffic Management Advisor at the Fort Worth Air Route Traffic Control Center," 1st USA/Europe Air Traffic Management R&D Seminar, Saclay, France, June 1997.
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+	FAA/Eurocontrol Action Plan 16 Common Trajectory Prediction Capability: Generic Trajectory Predictor Structure Available for public download from http://acy.tc.faa.gov/cpat/tjm//.
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+		Abstraction Techniques for Capturing and Comparing Trajectory Predictor Capabilities and Requirements
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+	Vivona, R. A., Cate, K. T., and Green, S. M., "Abstraction Techniques for Capturing and Comparing Trajectory Predictor Capabilities and Requirements," AIAA Guidance, Navigation, and Control Conference and Exhibit, Honolulu, HI, 18-21 Aug. 2008. 11
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+		Comparison of Aircraft Trajectory Predictor Capabilities and Impacts on Automation Interoperability
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+			StevenGreen
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+IntroductionThere is a need to integrate the proposed increase of new entrants and their diverse missions into the National Airspace System (NAS), manage the corresponding expected increase in traditional operations, and enable enhanced collaboration between users and the Federal Aviation Administration (FAA) 1 .Some new entrant operations (e.g., large Unmanned Aerial Systems (UAS) and commercial space) have been active in the airspace for several years but more recently there is a significant movement to enable the large scale use of small electric Vertical Take-Off and Landing aircraft (eVTOL) for rapid passenger movement in an urban environment 2 .The US airspace is already managing, on average, approximately 26,000 scheduled traditional commercial operations per day 3 and their delays cost the US approximately $26.6B in 2017. 4The inclusion of new entrant operations will likely increase costs due to delays as more users access the airspace.To address these issues, NASA's Aeronautics Research Mission Directorate (ARMD) has defined a "pivot" to address these transportation challenges and recently approved the Air Traffic Management -eXploration (ATM-X) project to achieve the goals of equitable access to the airspace for all users, vehicles, and missions while also improving current operations.
+II.Project Description ATM-X is one of four projects in NASA's Airspace Operations and Safety Program (AOSP) within ARMD.The ATM-X technical goals, to be realized over two phases, are to explore alternate methods to incorporate new technologies into the NAS using a software "service-oriented paradigm" based on Unmanned Aerial System (UAS) Traffic Management (UTM) principles. 5UTM principles allow for system and management flexibility whenever possible and imposing structure when required and includes seamless airspace access for all users, scalability for new demand and users, collaboration through digital information exchange, resilience to disruptions and uncertainty, and increased availability and use of services.Individual software services allow greater flexibility for airspace management support and potential for faster modernization, as the new algorithms are not forced to be intertwined with larger, centralized software systems.Breaking out algorithms, such as airborne weather routing, into a modular software service allows the algorithms to be developed and updated by third-parties with less modification to the larger system using a software service-oriented paradigm.This service-oriented paradigm is currently being developed and used by the UTM project to manage small UAS operations; an example of a field-tested capability for ATM-X research and a model for managing flight vehicles in field demonstrations.With a large set of challenges to enable access by the new entrant market, as well as to continue to improve traditional operations, Phase 1 of ATM-X (FY18-FY20) will focus on two use-cases supported by four sub-projects.The "Urban Air Mobility (UAM) Operations" use-case will address passenger-carrying eVTOL urban operations by performing research to understand how to develop verifiably-safe and secure airspace and vertiport management technologies to enable missions at a user-specified tempo in low altitude, controlled airspace.The second use-case, "Northeast Region Operations", will focus on improved collaboration, dynamic airspace access and planning, and integrated scheduling in the US Northeast Region for traditional NAS users.The following lists the objectives of the four ATM-X sub-projects that will address those two use-cases; the subprojects are described in more detail later in the document:
+ATM-X Subproject DescriptionsDuring Phase 1 research, the project will develop concepts and prototype technologies for improved data exchange and digital negotiation to enable traditional operators' desire for improved collaboration, throughput, flexibility and predictability and be extensible to allow new entrants' desire to access and operate in controlled airspace.The guiding principles of Phase 1 are "Explore, Build and Learn" to address these activities.There will be research in both UAM and traditional operations with appropriate prototype development and evaluations to learn what research and prototypes will be carried forward into Phase 2. To leverage the research of other projects, ATM-X will collaborate with NASA's AOSP System Wide Safety (SWS) Project to ensure the development of safe, secure, and efficient operations in both use-cases.The second Phase for ATM-X (approximately from FY21-FY26) will build towards longer-term goals, and address challenges and activities informed by Phase 1 results and evaluations.Phase 2 will mature the technologies and the service-oriented ATM system for a possible field demonstration of capabilities for routine access for a wide range of vehicles and missions.The relationship between the ATM-X sub-projects is illustrated in Error!Reference source not found.. Passenger carrying UAM operations are addressed in the Initial Urban Air Mobility Operations Integration sub-project and traditional operations in the Northeast Region are addressed by the Increasing Diverse Operations and Integrated Demand Management sub-projects.The arrows in this figure show representative work areas of these sub-projects that will be integrated and tested utilizing the Testbed.The Testbed will provide a realistic and flexible environment, allowing advanced concepts and the study of interaction across components, and will be critical in the development of a service-oriented ATM architecture.
+III. Development ApproachThe ATM-X project will conduct research towards the service-oriented paradigm by developing prototypes and focus on research capabilities that will significantly improve the safety, security, efficiency, and reliability of the future NAS.To address the safety aspects of the technologies, ATM-X will leverage the work in other AOSP activities including verification and validation of software systems in SWS and NASA's cybersecurity efforts.In addition to the research focus of Phase 1, ATM-X will develop prototype services and a reference implementation of a service-oriented architecture design for technical evaluations that leverages recent work by the UTM project.Additionally, there will be opportunities to identify technologies for more mature development in Phase 2 and evaluate technologies collaboratively in this phase.To guide the development, the key principles of ATM-X are presented below, derived from the UTM principles described earlier.These ATM-X principles are traceable in the research and resultant technologies.• Seamless access to the airspace for both on-demand and scheduled operations;• Scalability to match future air traffic demand where and when it occurs;• System and management flexibility whenever possible and imposing structure when required;• Collaboration between all participants through secure, integrated information sharing; • Resilience to uncertainty, degradation and disruptions; and • Availability of user and third-party services.The two use-cases are:• UAM Operations: An extension to current passenger carrying helicopter operations, but with a higher tempo and a denser network, using radically different vehicles being proposed by a wide range of new and existing aviation companies.This use-case is part of the larger UAM concept.NASA defines UAM as a safe and efficient system for air passenger and cargo transportation within an urban area.It is inclusive of small package delivery and other urban UAS services and supports a mix of onboard/ground-piloted and increasingly autonomous operations 6 .These passenger carrying vehicles would conduct high-frequency, short-distance flights between fixed locations, through dense urban centers and can also provide shortdistance movement of goods.Most vehicles will be electrically powered and carry two to six passengers, or equivalent cargo, on flights of 10-70 miles.A mature vision of these operations will generally be on-demand with limited planning and may not necessarily follow pre-approved routes.However, near-term implementations of these operations may employ schedules using fixed routes and some routine short-cuts.• Northeast Region Operations: Research focused initially on enhancing the efficiency and robustness of traditional airspace operations in the complex, highly-controlled airspace in the northeast of the U.S. For years, the FAA and NASA have focused on developing, testing, and deploying new capabilities in less complex operational airspaces to enhance efficiency with reduced risk.However, these new capabilities have not migrated to this more complex area due to a mix of technological and procedural barriers.This research will address setting the stage for seamlessly and equitably integrating new vehicle types (increasingly diverse operations), such as large UAS, autonomous freighters, supersonic aircraft, and UAM into these operations.Through the RTCA NextGen Advisory Committee (NAC) 7 , industry has identified this region as a critical choke-point.The ATM-X effort supports FAA technology development activities and priorities to improve traditional operations in the same geographic area. 8,9ile the two use cases focus on different types of airspace users, both use cases will ultimately address setting the stage for seamlessly and equitably integrating newer non-traditional vehicle types (increasingly diverse operations), such as large UAS, autonomous freighters, and UAM vehicles into these operations.
+IV.Sub-project Descriptions Figure 1 depicted the four sub-projects in ATM-X.The UAM sub-project focuses on the emergent users for a representative future operation, and will conduct research, develop and evaluate technologies, and explore architectures for airspace and vertiport management to enable safe UAM missions at a user-specified tempo.The first use case will serve as a concept focus for this research.IDM and IDO focuses on the needs of traditional airspace users along the lines of the second use case.IDM will develop a concept that utilizes state-of-the-art capacity, demand, and weather forecasts in a coordinated fashion across different traffic flow management capabilities to better manage demand/capacity imbalance under adverse weather conditions.IDO will build on IDM progress, along with evaluating how NASA's Airspace Technology Demonstration (ATD) capabilities and others can be applied, to evaluate digital negotiation using trajectory-based technologies in the Northeast Region.The research will investigate improved user collaboration to enable traditional operators' desire for flexibility and predictability.IDO will explore how to transition the existing system to a future service-based architecture.Testbed will develop a capability to simply and easily connect high-fidelity human-in-the-loop (HITL) and automation-in-the-loop simulations and tests.Testbed will support the evaluation of trajectory-based automation and electronic negotiation, collaborative decision making, and connected service-based technologies.Testbed will also provide connectivity between operational FAA, conceptual NASA and other proposed systems.
+A. Initial Urban Air Mobility Operations Sub-ProjectNASA has conducted research addressing a wide range of air traffic management challenges for traditional aircraft since the 1980s.The concepts, technologies, and procedures developed through these efforts have benefited the flying public and the aviation community in the form of more efficient and predictable operations.Recently, NASA has increased focus on developing technologies and standards for new types of aircraft and missions being pursued by the broader aviation community.One notable example is NASA's ongoing effort as part of RTCA Special Committee-228 to develop minimum operational performance standards (MOPS) for detect and avoid (DAA) capabilities for large UAS.As part of this endeavor, NASA helped develop standards for separation, alerting, guidance, surveillance, and displays for DAA systems that the FAA will use to develop technical standards and regulations.NASA has also developed and demonstrated UTM capabilities for low-altitude small UAS (sUAS) operations.More recently, there has been an increased interest in UAM.Significant industry investments have been made toward developing an ecosystem for UAM that includes manufacturers of eVTOL aircraft and builders of vertiports and other infrastructure on the ground.To conduct UAM operations in a safe, secure, and efficient way in the presence of existing airspace users, tools and methods for airspace integration are needed.Many of the technologies and procedures that have been developed to integrate new entrants, such as large and small UAS into the airspace with existing traditional aviation could be applicable to UAM operations and will be leveraged in this sub-project.The Initial UAM Operations Integration sub-project is focused on the low altitude (e.g., below about 2,000 ft) airspace integration aspect of UAM, both to enable early entrants in the airspace and to identify, develop, and evaluate the services, procedures, and tools necessary to support high-demand, mature operations.This sub-project will collaborate closely with other NASA projects to address safety and security issues.Other aspects of UAM, such as vehicle development, battery technology development, ground infrastructure construction, and legal considerations are also important but will be addressed by other NASA projects and external organizations.This sub-project will work with an ARMD level UAM coordination team and support their activities with partners.Phase 1 research will focus on airspace management and safe, secure, and efficient operations into, out of, and within an urban area.The project will support evaluations and field demonstrations of a wide range of technologies, requirements, and procedures, including but not limited to:• Safe mission planning and operations • Noise constraint management • Secure data exchange system architecture • Communications, navigation, and surveillance • Separation assurance • Dynamic scheduling, sequencing, and spacing • Congestion management • Interoperability with other vehicles -large UAS, sUAS, and traditional aircraft operations In addition, a collaborative concept of operations in which the roles, responsibilities, and interactions of pilots, vehicle automation systems, air traffic control, existing airspace users, safety and security systems, and airspace management automation systems will also be defined.The concept of operations serves as a framework for NASA research and development efforts, lab simulations, and live flight demonstrations planned in this first phase.It also provides a benchmark that the UAM community can use to determine technology development priorities, requirements for infrastructure improvements, and achievable airspace capacities.Table 1 shows some of the UAM airspace management technologies that will be evaluated with partners in Phase 1. FY2018 will provide an initial capability that will be matured through evaluations in subsequent years.
+FY2018Initial weather and obstacle aware separation, scheduling, sequencing, and spacing algorithms FY2019Safe mission planning and operations, weather and obstacle-aware software services and operational procedures FY2020 Separation and scheduling, sequencing, and spacing software services Table 1 Representative UAM activities for Fiscal Year (FY) 2018 -2020
+B. Increasing Diverse Operations (IDO) Sub-ProjectNASA has nearly three decades of experience working with the FAA and airspace users in conducting research, technologies and concepts to improve traditional user operations.This sub-project leverages prior developments by NASA and the FAA to continue to push the state-of-the-art in airspace management.To achieve long-term goals, IDO will define an overarching concept/architecture of a service-based system and develop supporting service technologies to enable safe and secure increasingly diverse operations in dense, controlled airspace.These diverse operations will span a range of vehicle performance and design, mission type, and equipage levels.New entrants (such as supersonic, space launch and re-entry, high-altitude long endurance, UAS, and UAM vehicles) are expected to introduce the greatest diversity to operations as their demand for entering controlled airspace increases.However, it is just as important to ensure that the future airspace systems continue to accommodate traditional users while providing appropriate levels of safety and security.During Phase 1, IDO will define a service-based airspace system that improves efficiency and predictability for traditional users but also prepares the system for increased diversity from new entrants.IDO will evaluate the applicability of NASA's ATD capabilities and others that are built on principles of information sharing and time-based scheduling.These capabilities can be integrated or coordinated to evaluate improvements to gate-to-gate operations incorporating user priorities of traditional airspace users.NASA's ATD project encompasses a collection of critical technology development and demonstration activities that addresses near-term, domain trajectory-based operations and provides benefits to traditional air transportation system stakeholders. 10IDO will develop a concept of operations and accompanying system architecture that evaluates the integration of ATD and other capabilities for a future service-oriented airspace system.This concept of operations will be evaluated in a HITL simulation using Northeast Region scenarios.The scenarios will be developed with FAA and airline partners where recent discussions have included dynamic scheduling of metroplex operations in the New York area and integration of arrival scheduling with pre-departure scheduling and en-route routing.This sub-project seeks to improve operations by exploring the enhancement of software airspace management services described by the concept of operations.For example, existing time-based scheduling services may be enhanced to subscribe to a repository of flight trajectories and constraints to increase coordination.Controller tools may be enhanced to provide better awareness of FAA and user coordinated strategic flight trajectories and ensure that control actions dynamically respond to and reinforce, rather than compromise, the strategic plan.A digital negotiation service may be developed to standardize and speed up routine trajectory negotiations currently performed manually and often in an inconsistent manner.Table 2 shows representative IDO activities and a series of evaluations based on a partner informed Northeast Region scenario.The culminating event is a collaborative high-fidelity simulation utilizing the Testbed.
+FY2018Northeast Region simulation scenario identification and definition FY2019 Development of prototype services and procedures for evaluation FY2020High fidelity simulations in Testbed with external partners to evaluate services Table 2 Representative IDO activities for FY (Fiscal Year) 2018 -2020
+C. Integrated Demand Management (IDM) Sub-ProjectIDM was previously a subproject in the recently-completed Shadow Mode Assessment using Realistic Technologies (SMART-NAS) for Safe Trajectory Based Operations 11 project and continues in ATM-X.This sub-project explores operational integration of near-to mid-term NextGen traffic management capabilities to improve NAS performance when the capacity of critical airspace resources is inadequate to meet demand using current systems.Because of the interactions between multiple flights across multiple airspace constraints, successful trajectory-based operations involve coordinated traffic flow management of constraints, as well as trajectories, to provide a continuous, robust solution for a collection of flights.However, in today's air traffic operations, a number of different, uncoordinated and locally-focused systems are involved in managing these interactions, resulting in inefficiencies in flight trajectories as well as inadequate demand / capacity balance.In order to alleviate this problem in the near-to mid-term NextGen timeframe, IDM leverages two main traffic flow management capabilities in the National Airspace System, namely the FAA's Traffic Flow Management System (TFMS) and Time Based Flow Management (TBFM).In the IDM concept, TFMS tools are used to pre-condition traffic into the more tactical TBFM system, enabling TBFM to better manage delivery to the capacity-constrained destination.IDM is focused on coordinating the management strategies employed by the TFMS.TFMS strategically manages aircraft at the origin airports when the demand is expected to exceed capacity at their destination.TBFM tactically manages demand of the actual flow of aircraft near the destination airport.Currently, TFMS and TBFM work largely independently causing additional delay and inefficiencies in operations.The IDM concept also provides a framework to take advantage of many of the past and current NASA ATM research, which developed powerful, integrated operations / tools for managing trajectory constraints, leveraging existing systems, and adding new automation tools / methods where needed.These solutions are complementary, with each focused on a specific portion of the complete flight trajectory.They represent crucial building blocks towards a gateto-gate management solution.IDM provides an integrated solution across the domains to enable improved system performance.The IDM team conducted HITL evaluations of its concepts focused on arrivals to the Newark Liberty Airport (EWR) 12 .Results show the concept achieved the target throughput while minimizing the expected cost associated with overall delays in arrival traffic.Future work based on these results will evaluate the concept for the LaGuardia (LGA) airport with combined EWR and LGA operations and multiple constraints.These evaluations will comprise a combination of fast-time and HITL simulations.As a result of this research and testing, a description of the IDM concept and procedures plus tool specifications will be delivered to the FAA.Table 3 shows representative IDM activities.This shows a series of increasingly complex simulations to evaluate the evolving IDM concept for multiple airports and constraints.
+FY2018Initial report on IDM concept, procedures and tools requirements based on HITL results and stakeholders' feedback FY2019Conduct HITL experiment to evaluate IDM concept in convective weather for two or more airports FY2020Conduct HITL to evaluate final IDM concept for multiple constraints / airports Table 3 Representative IDM activities for FY (Fiscal Year) 2018 -2020
+D. Testbed Sub-ProjectThe Testbed was formerly known as the SMART-NAS Testbed in the SMART-NAS for Safe Trajectory Based Operations Project. 13Testbed will be capable of integrating FAA operational systems with prototype technologies or services and allow new concepts to be evaluated in a realistic environment.The Testbed's role is as an accelerator of concepts and technology development, with a use-case-driven development approach to support NASA research as well as collaborations.The Testbed development was motivated by a survey of simulation capabilities that highlighted significant needs and limitations of existing in simulation systems.These limitations include:• Tools and systems are rarely integrated across different ATM domains During Phase 1, the Testbed and other NASA teams will develop core capabilities, such as:• Back-end, Big-Data analytics tools to generate realistic simulation scenarios using NASA's ATM Sherlock data warehouse 15 • Cloud technologies to securely, reliably, and cost-effectively connect distributed, NASA and non-NASA, real and simulated NAS infrastructure and flight assets • Low-maintenance mechanisms to integrate a wide array of simulation assets without customized one-toone solutions • Ability to create an on-demand, shadow-mode NAS, high-fidelity evaluation from live traffic, weather data, and airspace information • Ability to evaluate collaborative flight management concepts and technologies over an entire flight profile • In-time system-wide safety analytics and key performance indicators to visualize simulation and operational performance • Ability to assess the impact of new vehicle designs on NAS operations The Testbed core capabilities will be developed through a series of two software builds.Build 1 follows the prototype development begun under the SMART-NAS project and focuses on the development of the primary scenario building, cloud-based connectivity and component communications for use by internal NASA stakeholders.Build 2 matures these technologies and develops the required interfaces to connect external stakeholders, thus supporting the Testbed objectives.Towards the end of Phase 1, ATM-X will provide a plan that includes community involvement in both the development, support and distribution of Testbed.Representative Phase 1 work is shown in Table 4.
+FY2018Establish connections to NASA ATM tools and initial connections to partner systems FY2019 Support NASA and partner activities and system evaluations using a service-oriented system FY2020 Expanded activities and system evaluations using improved connectivity, scalability, and usability to external stakeholder systems Table 4 Representative Testbed activities for FY (Fiscal Year) 2018 -2020 V. Summary The ATM-X project is a two phased project to integrate new, diverse entrants into the NAS, while also leveraging NASA's prior ATM achievements that continue to improve traditional airspace operations.In the first phase, ATM-X will collaboratively develop ATM services and evaluate them in HITL simulations and field activities to inform the work for Phase 2. The evaluations are based on and in support of two partner defined use-cases for UAM and traditional Northeast Region operations and will adhere to principles of seamless access, and scalable, flexible, resilient, available, and collaborative operations.Phase 2 will further improve upon the relevant research and services identified and developed in Phase 1 for a possible demonstration of diverse operations.To accomplish Phase 1, ATM-X is structured with four sub-projects: UAM sub-project, IDM, IDO and Testbed.The UAM sub-project will conduct research, develop and evaluate technologies, and explore architectures for airspace and vertiport management to enable safe UAM missions at user-specified tempo.IDM and IDO initially focuses on the needs of traditional airspace users.IDM will develop a concept that utilizes state-of-the-art capacity, demand, and weather forecasts in a coordinated fashion across different traffic flow management capabilities to better manage demand/capacity imbalance under adverse weather conditions.IDO will build on IDM progress, along with evaluating how NASA's ATD capabilities and others can be applied, to evaluate digital negotiation using trajectory-based capabilities in the Northeast Region with improved user collaboration to enable operators' desire for flexibility and predictability.Testbed will continue developing a capability to simply and easily connect high-fidelity HITL and automation-in-theloop simulations, supporting the evaluation of electronic trajectory negotiation, collaborative decision making, and connected trajectory-based technologies.Testbed will also provide connectivity between operational FAA, conceptual NASA and other proposed systems.Fig. 11Fig. 1 Relationship of the Four Sub-Projects in Phase 1.
+Table 11Initial Urban Air Mobility Operations IntegrationConduct research and develop technologies for(UAM sub-project)airspace and vertiport management towards enablingUAM missions at user-specified tempoIncreasing Diverse Operations (IDO)Evaluate digitally-negotiated trajectory-basedcapabilities research that includes leveraging NASA'sAirspace Technology Demonstration technologies inthe Northeast Region with improved user collaborationto enable traditional operators' desire for flexibility andpredictabilityIntegrated Demand Management (IDM)Develop Integrated Demand Management conceptusing coordinated traffic flow management capabilitiesto better manage demand/capacity imbalance duringadverse weatherTestbedDevelop a capability to simply and easily connect high-fidelity simulations to support NASA and communityresearch
+•NASA, the FAA and ATM Industry lack an integrated development, test, and evaluation platform for enabling collaboration across disparate government and industry-developed ATM systems • Simulation preparation and execution is time-consuming, resource intensive, error-prone and limited by the capabilities of individual facilities • Simulations are traditionally comprised of individual brick-and-mortar labs or co-located facilities • Live flight assets and enterprise level hardware and software are not accessible for many efforts seeking to advance ATM technology • Many future concepts are untestable with current systems as documented in the National Research Council Autonomy Report, 2014 14
+		
+		
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+		NAS data release policy: Challenges &amp; opportunities
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+I. Introductionhe amount of fuel consumed is an important metric for benefit assessment of air traffic management concepts being considered for improving throughput, increasing capacity and reducing delays.It is also an important metric for environmental impact because for each kilogram of fuel consumed, three kilograms of carbon dioxide, a greenhouse gas, is generated.The main motivations for developing the fuel estimation method is to establish a baseline for the current operations and based on it determine benefits of the proposed four-dimensional trajectory management concepts in terms of fuel usage.Alternative procedures for efficient descent, terminal area scheduling and spacing, and departure release can also be evaluated based on fuel consumption.Four prior related publications on the subject of fuel estimation are cited here as Refs.1-4.References 1 and 2 are focused on departure and arrival fuel consumption below 10,000 feet altitude.Reference 1 compares the International Civil Aviation Organization (ICAO) time-in-mode method based fuel consumption with the actual fuel consumption reported in Flight Data Recorders.Fuel flow-rate patterns were found to be quite different than the ICAO model estimates due to airline climb/descent procedures.This suggests that a fuel consumption model should include aircraft state information such as airspeed.Reference 2 presents thrust specific fuel consumption models for climb and descent.Model parameters are adjusted to fit the aircraft manufacturer data.Thrust specific fuel consumption is multiplied with thrust to determine fuel consumption.Their method assumes nominal climb/descent profiles; it does not consider airline and air traffic control specific operational procedures.Furthermore, the method is only applicable to lower altitudes.Reference 3 describes a closed-form takeoff weight estimation method developed using the constant-altitude-cruise range equation and aircraft design principles.It needs flight-plan data and aircraft performance model to estimate the takeoff weight of the aircraft.The amount of fuel needed for climb, cruise and descent phases of flight and the maximum load factor are computed as a part of the procedure.The method described in this paper differs from Ref. 3 in that it uses the actual flight track data and does not require a model for climb and descent; thus, it is more data driven than model based.Reference 4 describes a fuel estimation procedure using actual trajectory of aircraft, and Base of Aircraft Data (BADA) drag and fuel-flow models.Their procedure is close to the method described here.The main difference is that the fuel estimation procedure is derived from nonlinear equations of motion with point-mass assumptions as opposed to approximations adopted in Ref. 4. Additional contribution of the present work is estimation of aircraft and wind states.Main contribution of this paper is development of the fuel estimation procedure from basic principles without simplifications.The procedure was validated against flight test data provided by the Federal Aviation Administration.A takeoff weight estimation procedure is developed for estimating fuel usage and establishing fuel usage uncertainty bounds when the takeoff weight of the aircraft is unknown.Finally, the adequacy of using position data acquired by air traffic control radar systems for fuel estimation is examined.Results show that in spite of bias, noise and data drop issues, position data could be conditioned for obtaining decent fuel estimates.Section II describes the fuel estimation procedure.The BADA fuel-flow model is discussed in Section III.The equations of motion are given in Section IV.This section also lists an expression for thrust in terms of drag, and aircraft and wind states.Section V provides the BADA drag model.Expressions for lift and bank angle estimation are listed in Section VI.Aircraft state estimation is described in Section VII.Results are discussed in Section VIII.The paper is concluded in Section IX.
+II. Fuel Burn Estimation ProcedureTo determine the amount of fuel consumed, altitude, airspeed and thrust have to be estimated.Altitude is obtained from the trajectory.Airspeed is estimated using a sequence of latitude ( ! ), longitude (! ) and altitude ( h ) reports as a function of time that define the fourdimensional trajectory and wind velocity.Computation of thrust requires an estimate of drag, which depends on lift.Lift depends on estimated aircraft and wind states, and weight.Once lift is determined, the lift induced drag coefficient can be computed.Drag is a function of airspeed, air density, and the drag coefficient, which depends on the aerodynamic configuration of the aircraft.Thrust is determined using estimates of aircraft and wind states, drag and weight.Fuel-flow rate is then obtained using altitude, airspeed and thrust estimates.Weight of the aircraft at a point in time is obtained by subtracting the amount of fuel consumed up to that time from the initial weight (takeoff weight).Fig. 1 shows the steps of the fuel burn estimation procedure.
+III. BADA Fuel Consumption ModelThe BADA fuel consumption model is described for nominal and idle thrust conditions.The nominal fuel-flow rate for jets and turboprops is determined by the product of the thrust specific fuel consumption and thrust, T .Thrust specific fuel consumption for jets is modeled as a linear function of airspeed, V , and for turboprops as a quadratic function of airspeed.Fuel-flow rate is independent of airspeed and thrust for aircraft with piston engines.A generalized expression for the nominal fuel-flow rate for these three different aircraft types can be written in the following form 5 :( )T V f V f f f f nom 2 3 2 1 0 ! + + = (1)where the coefficients in Eq. ( 1) are given in terms of the BADA coefficients 1. Units of the BADA coefficients are provided in the Appendix for completeness.Fuel-flow rate is in kg/s with airspeed in knots and thrust in Newtons.1 f C and 2 f C in TableThe minimum fuel-flow rate for idle thrust is modeled as a linear function of altitude, h , for jet and turboprop engine types and as a constant for piston engine.This model is described by the following equation:h f f f 5 4 min ! = (2)American Institute of Aeronautics and Astronautics 3 Altitude is in feet.The coefficients are again defined in terms of BADA coefficients 2. Units of these BADA coefficients are also listed in the Appendix.Fuel-flow rate coefficients for a jet, a turboprop and a piston aircraft are listed in Table 6 of the Appendix to give the reader a feel for the contribution of these coefficients to fuel-flow rate in Eqs. ( 1) and (2).3 f C and 4 f C in TableThe nominal and the minimum fuel-flow rate models can be combined into a single expression, ( )nom fcr f C f f min, max = (3)The fraction of fuel-flow rate during the cruise phase is fcr C .Its numerical value is 1 during the other flight phases.Equations (1) through (3) show that to estimate fuel-flow rate for jets and turboprops, altitude, airspeed, thrust and the phase of flight (in cruise or not) needs to be known.Altitude is directly available from position reports; airspeed, thrust and the phase of flight have to be estimated.Airspeed can be estimated using the reported position and wind data.Equations of motion, which are discussed in the next section, have to be used for thrust estimation.The amount of fuel consumed can be determined by integrating the fuel-flow rate as! = f t f dt f m 0 (4)
+Engine Type4 f 5 f Jet 3 60 1 f C ! " # $ % & ! ! " # $ $ % & ! " # $ % & 4 3 60 1 f f C C Turboprop 3 60 1 f C ! " # $ % & ! ! " # $ $ % & ! " # $ % & 4 3 60 1 f f C C Piston 3 60 1 f C ! " # $ % & 0 Table 1.Nominal fuel-flow rate model coefficients.
+Engine Type0 f 1 f 2 f 3 f Jet 0 1 4 10 6 1 f C ! " # $ % & ' ! ! " # $ $ % & ! " # $ % & ' 2 1 4 10 6 1 f f C C 0 Turboprop 0 0 1 7 10 6 1 f C ! " # $ % & ' ! ! " # $ $ % & ! " # $ % & ' 2 1 7 10 6 1 f f C C Piston 1 60 1 f C ! " # $ % & 0 0 0 Figure 2. Velocity triangle.where f t is the flight time.
+IV. Equations of MotionThe motion of aircraft, modeled as a point mass, is often described by the following three equations (see Ref.
+6):gn V h R ) ( 1 + = ! & (5) ge V h R ! " cos ) ( 1 + = & (6) and h V h = & (7)! is the latitude, ! is the longitude, h is the geometric altitude and R is the mean radius of the Earth.gn V and ge V are the north and east components of the ground-relative aircraft velocity.h V is the climb or descent rate depending on whether it is positive or negative.The horizontal velocity of the aircraft with respect to the ground is the resultant of the horizontal components of the airmass-relative velocity of the aircraft and the wind velocity.This relationship is shown in Fig. 2, where The magnitude of the airmass-relative acceleration resulting from the thrust, drag, lift and gravitational forces on the aircraft modeled as a point mass is! ! " ! " ! # sin cos sin cos cos sin cos h e n W W W g m D T V & & & & $ $ $ $ $ = (8)where V is airmass-relative speed (true airspeed), T is thrust, D is drag, ! is angle-of-attack, m is mass, g is acceleration due to gravity and ! is flight path angle.Note that! cos V V s =(9)The kinetic equations for airmass-relative heading angle and flight path angle areµ µ # " cos cos cos sin cos sin sin sin V W V W mV L T e n & & & $ + + = (10) and V W V W V W V g mV L T h e n ! ! " ! " ! µ µ # ! cos sin sin sin cos cos cos cos sin & & & & $ + + $ + = (11)µ is bank angle and L is lift.Equations ( 9), ( 10) and ( 11) are derived assuming flat Earth, constant gravitational acceleration and slowly changing mass.The altitude rate, Eq. ( 7), can be written in terms of the airspeed, flight path angle and the vertical component of the wind velocity ash h W V V h + = = ! sin & (12)Since wind varies both with position and time, the time derivative of the north, east and up components of the wind velocity can be determined ash and e n i h h W W W t W W i i i i i , ; = ! ! + ! ! + ! ! + ! ! " " # # (13)Observe that !& , !& and h & are defined in Eqs. ( 5) through (7).Assuming the angle of attack to be zero in Eq. ( 8), ( ) ( )! " ! # $ ! % ! & ' ( ( ) * + + , -. . + + . + + + = 2 1 sin cos V W h W W V W h W g V m D T h e n h h / /(14)This expressions shows that thrust estimate depends on drag, mass, altitude rate, airspeed, rate of change of airspeed, wind terms and the airmass-relative heading angle.Dropping the wind terms, the following simplified expression is obtained:V h mg V m D T & & + + = (15)It is now easy to see that the thrust required for balancing the right hand side of Eq. ( 15) during deceleration and descent can be less than the minimum thrust generated by the engines due to errors in the drag model and aircraft weight.A minimum thrust model is required in these instances.It can be constructed by equating Eq. ( 1) to Eq. ( 2) assuming the BADA fuel model to be consistent.Thus for jets and turboprops, the minimum thrust is obtained as,2 3 2 1 5 0 4 min V f V f f h f f f T ! + ! ! = (16)Note that Eq. ( 16) cannot be used for piston engines because nominal, minimum and cruise fuel-flow are specified to be a constants for piston engines in BADA.
+V. Drag ModelAerodynamic drag force is obtained as the product of the drag coefficient, D C , and the dynamic pressure asS V C D D 2 2 1 ! = (17)where ! is the density of air and S is the wing reference area.D C is given as the sum of the zero-lift drag coefficient, 0 D C , and the induced drag coefficient, which is a quadratic function of the lift coefficient, L C .Thus,2 2 0 L D D D C C C C + = (18) 0 D C and 2 DC are functions of aerodynamic configuration of the aircraft.BADA coefficients associated with the aerodynamic configuration are listed in Table 3. Traditionally drag coefficients are given as a function of Mach number and Reynolds number.BADA models these values as constants; it does not take Mach and Reynolds number effects into account.Note that the additional termLDG D C ! , 0represents drag rise due to deployment of the landing gear.During the approach and landing configurations, drag coefficients are adjusted for flap setting.One of difficulties of drag computation is determining the aerodynamic configuration.BADA specifies conditions based on stall speeds and maximum altitude thresholds that have to be met based on airspeed and altitude to determine the aerodynamic configuration.The only remaining parameter that needs to be specified for drag computation is L C , which can be obtained using the definition of the lift force asS V L C L 2 2 ! = (19)The lift force needed is related to heading angle and flight path angle rates as shown in Eqs.(10) and (11), therefore assumptions have to be made about the trajectory being followed.This is discussed in the next section.
+VI. Trajectory AssumptionsTo stay on course in a wind field, the aircraft has to crab such that the across-track component of the wind is cancelled.The airmass-relative heading angle needed to stay on the path specified by the course angle g ! is obtained from the two relations based on Fig. 2: Table 3. Drag coefficients as a function of aerodynamic configuration.
+Aerodynamic Configuration0 D C 2 D C Takeoff TO D C , 0 TO D C , 2 Initial Climb IC D C , 0 IC D C , 2 Clean CR D C , 0 CR D C , 2 Approach AP D C , 0 AP D C , 2 Landing LDG D LD D C C ! + , 0 , 0 LD D C , gn n s V W V = + ! cos (20) and ge e s V W V = + ! sin (21) as ! ! " # $ $ % & ' ' = ' n gn e ge W V W V 1 tan ( (22)Resultant magnitude of the horizontal component of the airmass-relative aircraft velocity is2 2 ) ( ) ( e ge n gn s W V W V V ! + ! = (23)Combining Eq. ( 23) with Eqs. ( 9) and ( 12), the following expression for true airspeed is obtained in terms of aircraft and wind velocity states:2 2 2 ) ( ) ( ) ( h e ge n gn W h W V W V V ! + ! + ! = &(24)Lift force, which is needed for L C computation in Eq. ( 19), and the bank angle can now be computed using Eqs.(W V W V W V W V W V W V ! + ! ! ! ! ! ! " (25)Substituting !& from Eq. (25) in Eq. (10) and using the relations in Eqs. ( 9), (20), ( 21) and (23),ge gn S V P V P L L & & 2 1 sin + = = µ (26) with 2 2 1 ) ( ) ( ) ( e ge n gn e ge W V W V W V m P ! + ! ! ! = (27) and 2 2 2 ) ( ) ( ) ( e ge n gn n gn W V W V W V m P ! + ! ! = (28)To evaluate the other component of the lift force vector, the first step consists of differentiating Eq. ( 9) or Eq. ( 12) to get !& .Differentiating Eq. ( 12), American Institute of Aeronautics and Astronautics8 ! ! ! cos sin V V W h " " = (29)Substituting in Eq. (11) and using Eqs.( 9), ( 12), ( 20) and ( 21W h W V W V W V W V W W V m P ! + ! + ! ! + ! ! = & (31) 2 2 2 2 2 4 ) ( ) ( ) ( ) ( ) ( ) ( h e ge n gn e ge n gn h e ge W h W V W V W V W V W W V m P ! + ! + ! ! + ! ! = & (32) 2 2 2 2 2 5 ) ( ) ( ) ( ) ( ) ( h e ge n gn e ge n gn W h W V W V W V W V m P ! + ! + ! ! + ! = & (33) 2 2 2 2 2 6 ) ( ) ( ) ( ) ( ) ( ) ( h e ge n gn e ge n gn n gn W h W V W V W V W V W V m P ! + ! + ! ! + ! ! ! = &(34)and2 2 2 2 2 7 ) ( ) ( ) ( ) ( ) ( ) ( h e ge n gn e ge n gn e ge W h W V W V W V W V W V m P ! + ! + ! ! + ! ! ! = & (35)Lift force and the bank angle can be determined using Eq. ( 26) and (30);2 2 C S L L L + = (36) ! ! " # $ $ % & = ' C S L L 1 tan µ (37)L C can now be obtained using Eq. ( 19) with lift force determined using Eq. (36).Drag force can be determined using Eqs.( 18) and (17).Finally, thrust can be computed using Eq. ( 14).Note that the airspeed and airmass-relative acceleration in Eq. ( 14) can be replaced by ground-relative terms using Eqs.(20), (21), ( 23) and (24) as described in the Appendix.The fuel-flow rate is determined using Eq.(3) via Eqs.( 1) and (2), and the fuel consumed is obtained using Eq.(4).
+VII. State EstimationThe procedure for estimating aircraft states, which are needed in the steps described in the previous sections is outlined in this section.Observations for estimation of aircraft states are given as a temporal sequence of latitudes, longitudes and altitudes that constitutes the four-dimensional trajectory of the aircraft.Given this sequence of observations, a state estimator such as a Kalman Filter can be designed using the state equations, Eqs. ( 5) - (7).Alternatively, filters and smoothers can be used for state estimation as described in Ref. 7.Figure 3 shows a Proportional-Integral-Derivative (PID) controller based estimator design for the altitude channel.The objective is to estimate the altitude rate and vertical acceleration from altitude time history.Estimated values are denoted by the superscript "^."The controller assumes a double integrator model of aircraft with altitude rate and altitude as measurements for feedback.The commanded acceleration results in altered altitude rate and altitude.The proportional gain, P K , integral gain, I K , and the derivative gain, D K , of the controller can be chosen by placing the poles of the closed-loop system in the left half of the s-plane.Optimal gains can be chosen by equating the coefficients of the characteristic polynomial to the Butterworth or integral of time multiplied by the absolute value of error (ITAE) polynomials listed in Ref. 8.A discrete version of the estimator in Fig. 3 is obtained by approximating the first and second derivatives of altitude using Taylor Series approximation about time-step k , which is separated from the next time-step 1 + k by time t ! ,as follows. .Once altitude is estimated using Eq. ( 40), the altitude rate and vertical acceleration can be estimated using Eqs.(38) and (39).One could estimate the altitude rate and acceleration by using the observed altitudes instead of the estimated altitudes.This however, would result in noisy estimates because noise in the altitude measurement would be amplified by the differencing process.Reducing noise in the altitude measurement by using the PID filter prior to The estimator implemented by Eqs.(38) through ( 41) is also used for the latitude and longitude channels with latitude and longitude measurements.These independent estimators provide estimates of angles, angular rates and angular accelerations:t k h k h k h ! " + = + ) ( ) 1 ( ) 1 ( & (38) and 2 ) 1 ( ) ( 2 ) 1 ( ) 1 ( ˆt k h k h k h k h ! " + " + = + & & (39) Thus, ) 1 ( ) ( ) 1 ( ) ( ) 2 ( ) 1 ( ) ( ) 1 ( ˆ3 2 2 3 2 t K t K t K k e t k h k h t K k h t K t K! ˆ, ! & ˆ, ! & & ˆ, !ˆ, ! & ˆ and ! & & ˆ.The horizontal components of the aircraft velocity vector can now be estimated using Eqs.( 5) and (6).Finally, the acceleration terms can be estimated using derivatives of Eqs. ( 5) and ( 6) as follows:    large errors occurred at takeoff and just after landing due to sudden change in velocity.! ! ) ( ˆh h R V gn + + = (42) and ! " ! " " !To compute the airmass-relative aircraft velocity terms and the wind terms needed for lift, drag, thrust and fuel-flow computations, horizontal components of the wind velocity were obtained from the 4/17/2009 hourly RUC data.Vertical component of the wind was assumed to be zero.The spatial and temporal partial derivatives of the ground-relative wind velocity are computed using finite-differences (see Eq. 38 for example) along the FDR reported trajectory.Latitude, longitude and altitude rates obtained via the state estimators are used with the partial derivatives to obtain the total derivatives via Eq.(13).Horizontal components of wind velocity and aircraft state estimates were used to estimate true airspeed using Eq.(24).This true airspeed was converted to calibrated airspeed using the standard atmosphere model.Pressure and temperature values derived from RUC data can also be used for this conversion.Figure 8 shows the time histories of the estimated calibrated airspeed and the indicated airspeed from the FDR. Figure 9 shows the difference between the estimated calibrated airspeed and the indicated airspeed derived from the FDR.Mean and standard deviation of the errors are -3.3 knots and 8.6 knots, and the extremal values are -64.2knots and 32.0 knots.As in Fig. 7, large errors occurred at takeoff and upon landing.Results presented in the figures above illustrate aircraft and wind state estimation accuracy.The next set of figures demonstrates the accuracy of fuel burn estimation.Estimated aircraft states, wind states and weight were used to estimate lift using Eq.(36).Takeoff weight of the aircraft was specified to be 39,362 kg (86,778 lb), which was the actual takeoff weight of the FAA aircraft employed for the flight test.Subsequently, the estimated amount of fuel burnt was subtracted from the takeoff weight to estimate weight as a function of time.17) was then used with the estimated aircraft states, wind states and weight to estimate thrust using Eqs.( 14) and ( 16).V & was set to zero for cruise.Thrust estimates were found to be noisy, so they were smoothed using the procedure described in Section VII.The smoothed estimates were then used in Eq. ( 1) to estimate the fuel-flow rate.
+Fuel Estimation ValidationEstimated fuel-flow rate and fuel-flow rate from the FDR during the climb phase are shown in Fig. 10.Notice the low fuel-flow rate in Fig. 10 and the flat altitude in Fig. 5 for the first 10 minutes; they correspond to taxi on the ground.During the cruise phase are shown in Fig. 11.Fuel-flow rates for the descent phase are given in Fig. 12. Observe the big spike in fuel-flow rate from FDR after the 312 minute mark; it is most likely due to increased thrust accompanied with thrust reverser deployment for speed reduction to taxi speed.Based on this observation, the negative estimated thrust is considered to be positive thrust with thrust reverser deployed after landing.The resulting fuel-flow rate value was estimated to be 0.38 kg/s compared with 0.72 kg/s reported in FDR data.For reference, aircraft altitude is about 14,770 feet at the 301 minute mark, 8,000 feet at the 305 minute mark and zero at the 312 minute mark.The aircraft needs to be below 8,000 feet for approach configuration and at or below 3,000 feet for landing configuration.As expected, thrust and fuel-flow rate were found to be strongly correlated.Finally, Fig. 13 shows the estimate of the amount of fuel consumed and the FDR reported values as a function of time.It is difficult to assess the error from this figure therefore Fig. 14 is provided to show the relative error.Relative error drops below 20% seven minutes into flight when the aircraft is at about 11,000 feet altitude.Mean and standard deviation of the fuel burn error were found to be -1.4% and 10.6%.Extremal values were determined to be -79.4% and 36.1%.values occur prior to takeoff.The total amount of fuel consumed during the flight was estimated to be 8,099 kg, which is only two kilograms more than the FDR value.To get this close match, the Bombardier RJ-900 Regional Jet model fuelflow coefficients were multiplied by a factor of 0.853.This value was obtained by trial and error.The fuel consumed estimate is lowered to 8,072 kg with a factor 0.85.The error with respect to the FDR value of 25 kg is reasonable based on the observed uncertainty of 50 pounds (23 kg) in the FDR fuel consumed data.The other meaningful measure is the mean of the relative error shown in Fig. 14.Considering relative error beyond 100 minutes, the mean value is 0.86%; thus, it is fair to assume that the error in the estimated amount of fuel consumed is within 1% of the actual amount of fuel consumed in the flight test.This result validates the fuel estimation procedure described in the paper.
+Model SimplificationTo determine if the simpler model used in Ref. 4 is adequate for fuel estimation, lift was set equal to weight in all phases of flight and thrust was modeled using Eqs.( 15) and ( 16).This means that the wind terms in Eq. ( 14) were dropped.As in the complete model, described in the previous section, V & was set to zero for cruise.Other than replacing the lift and thrust models with simpler models, all the steps described for obtaining the results in the previous section were followed.Results obtained on the flight test data matched the results in the previous section.Differences between these two sets of results were found to be negligible.Mean and the standard deviation of the fuel burn error were found to be -1.35% and 10.65% compared to -1.42% and 10.62% for the data in Fig. 14.Based on these results, the simpler lift and thrust models are adequate for fuel burn estimation.
+Takeoff Weight Estimation and UncertaintyThe results described in the previous section were based on the actual takeoff weight of the test aircraft.In most instances, the actual takeoff weight of the aircraft will not be known therefore, a procedure is needed for estimating the takeoff weight of the aircraft.One possibility is to use the procedure described in Ref. 3. This technique uses the constant-altitude range equation and aircraft design principles for estimating the takeoff weight.The method also needs distance to the top-of-climb point and the weight at this location for given cruise-altitudes and cruise-speeds.This information is derived by simulating the climb trajectory according to BADA aircraft performance and procedure models.The second possibility is to assume an initial weight without fuel and then estimate the amount of fuel needed for flying from the airport of origin to the airport of destination.Since the amount of fuel needed has to be carried onboard the aircraft, a bit more fuel is consumed for carrying this additional weight.A few iterations of adding the fuel needed to the takeoff weight and computing the fuel needed for the flight should yield a good estimate of the takeoff weight.This approach has been employed for generating the results discussed below.The main benefit is less dependence on BADA and more reliance on actual trajectory data.To start the iterations, the takeoff weight was set to the maximum zero-fuel weight of 25,401 kg (56,000 lb) based on manufacturer data (see Ref. 10).Maximum zero-fuel weight includes the structural weight of the aircraft, crew, maximum payload and everything other than the fuel.The zero-fuel weight can be adjusted to a lower value based on assumed load-factor.For example, a load-factor of 0.8 means that payload is assumed to be 80% of the maximum payload.Although the actual zero-fuel weight of the test aircraft 23,509 kg (51,828 lb) was known, it was not used because this would not be known for a typical flight.The assumption is that the aircraft was carrying the full payload.One would need to reduce the payload if the takeoff weight exceeds the maximum takeoff weight or when the destination cannot be reached with maximum fuel.A flowchart in Ref. 3 describes these conditions.Next, the simplified lift and thrust models of the previous section were used with the fuel estimation procedure to determine the amount of fuel needed for the flight and the average fuel burn rate during cruise.The fuel needed was 6,777 kg (14,941 lb) and the average fuel burn rate was 17.8 kg/min (39.2 lb/min).The average burn rate was used to determine the amount of reserve fuel.Federal Aviation Regulations require domestic flights conducted under Instrument Flight Rules to have enough fuel to fly to the first airport of intended landing, then fly to an alternative airport (if conditions require an alternative airport), then for 45 minutes thereafter at normal cruising speed.The weight of the reserve fuel was determined to be 1,599 kg (3,526 lb) assuming 90 minutes of additional flight time.The initial takeoff weight of 25,401 kg was augmented with 6,777 kg of fuel needed for the flight and 1,599 kg of reserve fuel for the next iteration.This process was repeated for the subsequent iterations.Takeoff weight is shown as a function of iterations in Fig. 15.Observe that there is very little change in the takeoff weight estimate after the fourth iteration.After 10 iterations, takeoff weight estimated value is 34,812 kg (76,748 lb).The actual takeoff weight of the Global 5000 was 39,362 kg (86,778 lb), which is 11.6% more than the estimated takeoff weight.The main reason for the difference is that 7,756 kg (17,099 lb) of extra fuel was carried during the flight test.This example shows that accurate takeoff weight estimation is difficult.If the aircraft has greater range capability, it can carry more fuel than that required for the flight.Airlines sometime ferry fuel depending on where fuel is cheapest to purchase.Since the amount of fuel burned is a function of the takeoff weight, any uncertainty in takeoff weight translates into uncertainty in the estimate of fuel burned.To explore this aspect a bit more, the fuel estimation procedure was repeated with maximum takeoff weight of 41,957 kg (92,500 lb) specified by the manufacturer (see Ref. 10).The results are summarized in Table 4.The first row of Table 4 shows results for takeoff weight estimated using the iterative procedure.The second row shows results obtained using the actual flight test weight.The third row shows results with maximum takeoff weight.Second column shows the difference of the takeoff weight with respect to the actual takeoff weight.The third column shows a single value derived from flight test data.Estimated fuel consumption corresponding to the takeoff weights is shown in column four.Column five lists the fuel consumption error with respect to the measured fuel consumption.The second and the fifth columns show that takeoff weight errors translate to fuel consumption errors.The smallest and the largest fuel consumption values in column four represent fuel estimation uncertainty bounds.Analysis to this sort can be beneficial in determining the bounds of environmental impact related to fuel consumption.
+Fuel Estimation Using ASDI DataThe ultimate objective of this paper is to enable fuel estimation using data from air traffic data sources.The primary sources of trajectory data are the Host Computers in the Air Route Traffic Control Centers (ARTCC).These data are provided at a 12-second interval.Trajectory data from Host Computers are consolidated and provided at a one-minute interval as Airline Situation Display to Industry (ASDI) data.Fuel estimation results obtained with the trajectory of the flight test aircraft derived from 4/17/2009 ASDI data are discussed in this section.Track data corresponding to the FAA's Global 5000 aircraft tail number were extracted and compared with position data recorded by the FDR.The first ASDI track data was 3.9 nautical-miles away from the airport, based on the first FDR position data, and at an altitude of 3,100 feet.Similarly, the last ASDI track data was 3.8 nauticalmiles away from the airport, based on the last FDR position data, and at an altitude of 793 feet.In addition, the following ASDI position data quality issues were also identified.While the mean, median and mode temporal separation between two successive track positions were 60 seconds, the maximum and minimum separation were 184 seconds and 30 seconds.The standard deviation was 8 seconds.These observations confirm that ASDI and Host Computer track data are not totally synchronous and periodically suffer from data drops.To determine the accuracy of reported position, FDR position data were first interpolated using spline interpolation and sub-sampled at the ASDI track times.Great circle distances between the resulting FDR position data and the corresponding ASDI position data were then determined.The mean and the standard deviation of the errors were determined to be 1.23 and 0.62 nautical-miles.Minimum and maximum were 0.13 and 3.05 nautical-miles.The main point is that position data acquired by radar is affected by bias, noise and quantization errors.To deal with data quality issues, outliers were removed from ASDI position data; then data were interpolated with a spline fit and sub-sampled at a 60-second interval.The resulting track data were then input to the fuel estimation procedure.The initial weight was set to the actual takeoff weight of 39,362 kg (86,778 lb) minus 408 kg (900 lb), where 408 kg of fuel was burnt to reach the altitude of 3,076 feet based on FDR data.There is a difference of 24 feet in the FDR and ASDI reported altitudes at the first position in ASDI data.The difference in the two altitudes at the last location is 245 feet with FDR altitude of 548 feet and ASDI altitude of 793 feet.The amount of fuel consumed was estimated to be 7,741 kg (17,066 lb) compared to the actual fuel consumption of 7,688 kg (16,949 lb).The estimation error is 0.7% with respect to the FDR reported fuel consumption.Next, the iterative weight estimation procedure was initiated with the maximum zero-fuel weight of 25,401 kg (56,000 lb).After 10 iterations the initial weight of aircraft was estimated to be 34,616 kg (76,315 lb) and the fuel consumed was estimated to be 7,276 kg (16,041 lb).This represents an error of -5.4% with respect to the FDR value of 7,688 kg.Although, the error is more, the iterative weight computation procedure is preferred because initial weight data will not be available as it was for the flight test.The other aspect is that if Host Computer data are used, only a part of the trajectory will be available.Starting with zero-fuel weight at the starting location accounts for fuel burned to fly up to that location to some extent.Estimation results can be expected to improve with Host Computer data because of faster update interval of 12-seconds.
+IX. ConclusionsThis paper described a procedure for estimating fuel consumption based on actual trajectory, and drag and fuelflow models.The method consists of estimating aircraft and wind states and using them to determine lift, drag, thrust and fuel-flow.Fuel consumption estimates generated for a Bombardier Global 5000 flight from Atlantic City to Los Angeles were compared with the Flight Data Recorder values, obtained during a flight test conducted by the Federal Aviation Administration, to validate the method.Results show that fuel usage can be estimated within 1% of the actual value when the takeoff weight is known.The procedure was simplified by setting lift equal to weight and by removing the wind terms from thrust.This simplification did not degrade fuel estimation accuracy.A procedure for estimating takeoff weight was then introduced.Starting with an initial estimate of takeoff weight, this procedure used reserve fuel requirements to iteratively improve the takeoff weight and fuel estimates.The method was found to converge within five iterations.It was shown that fuel usage uncertainty bounds can be determined by varying the takeoff weight.Finally, the adequacy of using trajectory data obtained by air traffic control systems was examined.Trajectory data for the Atlantic City to Los Angeles flight obtained from Airline Situation Display to Industry data were used for estimating fuel usage.Although these data suffered from bias, noise, asynchronous update, and data drops, it was possible to condition the data for obtaining reasonable fuel estimates.Fuel usage could be estimated within 5.4% of the actual value using Airline Situation Display to Industry data with simplified models and the iterative takeoff weight estimation method.W h W V W V W h W h W V W V W V W V V ! + ! + ! ! ! + ! ! + ! !2W V W V W V W W V W W W ! + ! ! + ! = + & & & &" " (A-3)V is given in terms of ground-relative quantities in Eq. (24).Thrust can therefore be computed in terms of ground- relative terms.Units of BADA fuel-flow coefficients are given in Table 5.Fuel-flow rate coefficients for a jet, a turboprop and piston engine types are given in Table 6.Table 6.Fuel-flow rate coefficients for a jet, a turboprop and a piston aircraft.
+Aircraft Type ManufacturerFigure 1 .1Figure 1.Fuel burn estimation procedure.
+Ware the magnitudes of the horizontal components of the ground-relative aircraft velocity and the wind velocity, and s V is the magnitude of the horizontal component of the airmass-relative aircraft velocity.g ! is the heading angle of the ground-relative aircraft velocity with respect to the local north direction.! and w ! are the heading angles of the airmass-relative aircraft velocity and ground-relative wind velocity also with respect to the local north direction.n W , e W and h W as the north, east and up components of the wind velocity vector.
+Figure 3 .3Figure 3. Altitude estimator.
+Figure 4 .4Figure 4. Actual latitude/longitude position history.
+Figure 5 .5Figure 5. Actual altitude time history.
+Figure 7 .7Figure 7. Groundspeed estimation error time history.
+Figure 6 .6Figure 6.FDR reported groundspeed time history.
+Figure 8 .8Figure 8.Time histories of estimated calibrated airspeed and indicated airspeed from FDR.
+Figure 9 .9Figure 9.Estimated calibrated airspeed error time history.
+Figure 11 .11Figure 11.Estimated and FDR fuel-flow rate during cruise.
+Figure 10 .10Figure 10.Estimated and FDR fuel-flow rate during climb.
+Figure 13 .13Figure 13.Estimated and FDR reported fuel consumption time history.
+Figure 12 .12Figure 12.Estimated and FDR fuel-flow rate during descent.
+Figure 14 .14Figure 14.Percentage error with respect to actual fuel usage.
+Figure 15 .15Figure 15.Convergence of takeoff weight estimates.
+Table 2 .2Minimum fuel-flow rate model coefficients.
+Table 4 .4Takeoff weight and fuel consumption uncertainty.Takeoff% WeightMeasured FuelEstimated Fuel% ErrorWeightErrorConsumptionConsumption34,812 kg-11.67,610 kg-6.0139,362 kg08,097 kg8,101 kg0.0541,957 kg6.68,407 kg3.83
+Table 5 .5Units of BADA fuel-flow rate coefficients.Engine TypeCf1Cf2Cf3Cf4CfcrJetkg/(min*kN)knotskg/minfeetdimensionlessTurbopropkg/(min*kN*knot) knotskg/minfeetdimensionlessPistonkg/min--kg/min--dimensionlessCf1Cf2Cf3Cf4CfcrCRJ-900Bombardier0.61472369.758.2151355,9101EMB-120 BrasiliaEmbraer4.5662664.156.555943,0481PA-28-161 CherokeePiper0.44515--0.30872--0.87274
+			& & & &
+			& & & & & &
+			& & & & &
+			& & & & &
+			& & & & & & & &
+			& & & & & &
+			& & & & & &
+			& & & & & & & & & & (A-2)The airmass-relative heading terms can be replaced using Eqs.(20), (21) and (23) as follows.
+		
+		
+
+			
+AcknowledgementsThe author thanks Confesor Santiago of NASA Ames Research Center (he was formerly at Federal Aviation Administration) and Mike Paglione of Federal Aviation Administration, and Robert Oaks of General Dynamics Information Technology for providing the flight test data and taking the time to answer questions.
+			
+
+			
+With the aircraft states computed using the altitude and the latitude/longitude estimators, and the wind related estimates obtained using Eq. ( 13), lift, drag, thrust and fuel-flow rate can be computed.The north and east components of the wind velocity vector can be obtained as a function of time, latitude, longitude and altitude from the Rapid Update Cycle (RUC) data, which are provided by the National Oceanic and Atmospheric Administration (NOAA).The vertical wind velocity can be computed by post processing RUC data using the relation described on page 480 of Ref. 9. h W is small relative to n W and e W ; it can be assumed to be zero.
+VIII. ResultsThis section is organized into five subsections.The first subsection on validation describes the flight test conditions and compares the estimated states with the actual states from the Flight Data Recorder (FDR).Fuel estimation accuracy is examined for the climb, cruise and descent phases of the flight in the second subsection.The next subsection discusses model simplification and its effect on fuel usage estimate.A procedure for takeoff weight estimation that starts by setting the takeoff weight to the maximum zero-fuel weight and then iteratively improves the estimate by adding reserve fuel and fuel consumed is discussed in the fourth subsection.Suitability of using radar-based position data for fuel estimation is explored in the fifth subsection.
+Aircraft State Estimation ValidationTo validate the fuel estimation procedure described in Fig. 1, FDR data from an actual flight of the Federal Aviation Administration (FAA) owned Bombardier Global 5000 aircraft from Atlantic City International airport (ACY) in New Jersey to Los Angeles International airport (LAX) in California on 4/17/2009 were used.These data were sampled at 4 second intervals.The dry weight of the aircraft was 23,509 kg (51,828 lb) and the initial fuel weight was 15,853 kg (34,950 lb).The total amount of fuel burnt during the flight test was 8,097 kg (17,850 lb).The FDR provided latitude/longitude position history is shown in Fig. 4 and the altitude time history is shown in Fig. 5.The temporal sequence of latitude, longitude and altitude derived from the FDR data were input to the latitude, longitude and altitude estimators, that were discussed in the previous section, to estimate latitude, longitude and altitude rates.Components of the groundrelative aircraft velocity computed via Eqs.( 5) and (6) with these rates were then used to estimate the groundspeed.FDR reported groundspeed is shown in Fig. 6 and the error of the estimated groundspeed with respect to it is shown as a function of flight time in Fig. 7. Groundspeed estimation error was found to have a mean of -0.5 knots, standard deviation of 3.6 knots and extremal values of -62.7 knots and 36.1 knots.These
+AppendixThe airmass-relative terms in the thrust equation, ( ) ( )			
+			
+
+				
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+
+	
+		Analysis of Departure and Arrival Profiles Using Real-Time Aircraft Data
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+		Closed-Form Takeoff Weight Estimation Model for Air Transportation Simulation
+		
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+		Fort Worth, TX
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+	AIAA 2010-8164
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+		Prototype Implementation and Concept Validation of a 4-D Trajectory Fuel Burn Model Application
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+	Oaks, R. D., and Paglione, M., "Prototype Implementation and Concept Validation of a 4-D Trajectory Fuel Burn Model Application," AIAA 2010-8164, Proc. AIAA Guidance, Navigation, and Control Conference, Toronto, Ontario, Canada, August 2-5, 2010.
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+Introductionhe motivation for developing an aircraft performance model of a multirotor Urban Air Mobility (UAM) aircraft is to enable generation of trajectories, which are needed for concept evaluation studies and in decision support tools.One of the challenges facing the future Air Traffic Management (ATM) system is integrating new entrants like UAM, Unmanned Aerial Systems (UAS), supersonic aircraft and space-launch vehicles in the U. S. National Airspace System (NAS) with legacy commercial subsonic and General Aviation (GA), while accommodating the business and operational objectives of all stakeholders.The ability to simulate UAM trajectories is essential because they will be used both by the flight operator and the ATM service provider for planning and control purposes.For example, the flight operator will use generated trajectories to determine route of flight, energy consumption, cruise speed, cruise altitude and time of arrival considering forecast winds aloft and alternative landing sites (airports and vertiports) that can be reached from locations along the route for creating flight-plan alternatives.The service provider will employ trajectories for separation assurance and traffic flow management.Separation assurance requires predicting conflicts, generating resolution advisories and evaluating them.Traffic flow management requires forecasting traffic demand at constrained resources-airspace and landing sites-and for determining flow control initiatives such as ground-hold, metering and scheduling needed for preventing the available capacity from being exceeded.In the past several years, different designs have been proposed for electric vertical takeoff and landing (eVTOL) aircraft by the industry.Designs by Ehang (https://www.ehang.com/ehangaav/),Lilium (https://www.lilium.com/),Joby (https://www.jobyaviation.com/),Kitty Hawk (https://kittyhawk.aero/),Boeing-Aurora (https://www.aurora.aero/)and Airbus-Acubed (https://acubed.airbus.com/)have included rotary wing aircraft with multiple rotors, fixed-wing multi-engine tiltrotor, fixed-wing tiltrotor with rear push-propellers, fixed-wing aircraft with multiple lift fans (upward pointing fixed propellers) and rear push-propellers, and multi-engine tiltwing.This paper presents a model of the rotary wing aircraft with multiple rotors and employs it for trajectory synthesis.A trajectory can be generated by driving the mathematical model of the aircraft dynamics with a control system to follow the desired path.The output of the feedback control system for eliminating the difference between the desired state and the estimated state of the aircraft is fed as input to the model of the aircraft dynamics for temporal evolution of the state and outputs.This approach is adequate for traffic simulation and prediction needed for ATM applications.The other related applications of interest to the aviation community are estimator and control system design for estimating the state and controlling the motion of the physical aircraft, respectively.Both these applications require a mathematical model of the aircraft dynamics.Several six-degree-of-freedom models with different levels of complexity that include both the rotational and translational dynamics of a quadrotor aircraft, and controllers for them, are available in Ref. 1 through 5.The control problem has been solved in several different ways.Reference 1 employs nonlinear programming to generate the trajectory that minimizes the total impulse while staying outside a geographical region and subjecting the observer to less than or equal to the specified sound pressure level.Reference 2 uses an adaptive backstepping procedure for horizontal and vertical path tracking and attitude control.Reference 3 uses piecewise Proportional-Integral (PI) control in the along-track direction and Proportional-Integral-Derivative (PID) control in the cross-track direction for path tracking, which generates attitude reference commands.The attitude control law using feedback linearization and acceleration feedback is then used to track the attitude reference commands to orient the vehicle's thrust for generating the acceleration needed for tracking the desired path.Reference 5 linearizes the dynamics and control about an operating point to develop a Linear-Quadratic-Regulator (LQR) for flight control.The main difficulty with including rotational dynamics for trajectory generation, as in Refs. 1 through 5, is that the moment of inertia tensor needs to be known.With multirotor UAM still being designed and being developed, these data are unavailable.Furthermore, aircraft manufacturers are hesitant to share data they consider proprietary and not required for the operator to fly the aircraft.In addition, an attitude controller for trajectory generation for ATM applications is unnecessary because the angular rates and the resulting attitude are not observable from position data obtained from air traffic control radar systems or from position data determined onboard the aircraft and broadcast to the ground.Only six states-latitude, longitude, altitude, speed, heading and climb/descent rate-are observable from surveillance data; therefore, they are the ones employed for ATM decision support.For example, Time-Based Flow Management (TBFM), which is used for scheduling arrivals to major U. S. airports, uses a trajectory synthesis procedure based on a point-mass model that only considers the translational dynamics of the aircraft to predict estimated times of arrival at the metering locations (see Ref. 6).Systems for simulating air traffic such as NASA's Future ATM Concepts Evaluation Tool (FACET) (see Ref. 7), Airspace Concept Evaluation System (ACES) (see Ref. 8) and ATM-X Testbed use Eurocontrol's Base-of-Aircraft-Data (BADA) (see Ref. 9) aircraft performance model for generating the flight trajectory without considering rotational dynamics.Due to these reasons, this paper only models the translational dynamics of multirotor aircraft and derives the controls using the states and the state derivatives, where the state derivatives are obtained using Proportional and Proportional-Derivative (PD) controllers.The Proportional controller is used for tracking the airspeed and the PD controller is used for tracking the horizontal path.The vertical profile is specified.It is shown that this model driven by the non-linear controller developed in this paper can generate trajectories for UAM mission requirements outlined in Ref. 10.The rest of the paper is organized as follows.Section II provides a brief description of a conceptual aircraft model described in Ref. 11.The equations of motion are discussed in Section III.Computation of control variables-thrust, thrust vector angle and bank angle-is provided in Section IV.Prescription of heading and flight-path angles for following horizontal and vertical paths in the presence of wind is examined in Section V. Section VI describes the trajectory generation process and presents results for a flight scenario.The paper is concluded in Section VII.
+II. Multirotor Aircraft ModelParameters needed for generating the trajectory using the aircraft performance model presented in this paper are obtained from the battery-powered electric quadcopter concept vehicle described in Ref. 11.To size and analyze the aircraft designs, Ref. 11 used the NASA Design and Analysis of Rotorcraft (NDARC) tool.The NDARC tool provides models for rotors and lifting surfaces, engines and motors, and energy storage and conversion for requirements and technology trades for aircraft design.The quadcopter concept vehicle employs motors, shafts and gearboxes to power the four rotors.The aircraft performance model in this paper does not consider power topology of the quadcopter for modeling the power consumption and thrust generation by individual rotors; the total thrust and power consumption are modeled.The quadcopter aircraft parameters are listed in Table 1.
+III. Equations of MotionThe motion of aircraft, modeled as a point-mass, is described by the following three equations (see Ref. 12):gn V h R ) ( 1 + =   (1) ge V h R   cos ) ( 1 + =  (2) and h V h =  (3)where  is the latitude,  is the longitude, h is the geometric altitude and R is the mean radius of the Earth.The magnitude of the airmass-relative acceleration resulting from the thrust, drag, and gravitational forces on the multirotor aircraft modeled as a point-mass, defined in Fig. 2, is cos sinTD Vg m   - =- (4)where V is the airmass-relative speed (true airspeed), T is the thrust, D is the drag,  is the thrust vector angle with respect to the airmass-relative velocity, m is the mass, g is the acceleration due to gravity and  is the airmass- relative flight-path angle.Note that Eq. ( 4) assumes the wind acceleration to be zero.s V is defined in terms of true airspeed and flight-path angle as cos V V s =(5)The north, east and vertical components of the ground-relative aircraft velocity in terms of the true airspeed, flightpath angle, heading angle and wind terms are Using Eq. ( 6) and ( 7), groundspeed is obtained as2 2 ) sin cos ( ) cos cos ( e n g W V W V V + + + =     (9)The state equations for airmass-relative heading angle and flight-path angle assuming wind acceleration to be zero can be written as follows:sin sin cosT mV    = (10)and sin cos cosTg mV V     =- (11)where  is the airmass-relative bank angle.Equations ( 4), ( 10) and ( 11) are derived assuming flat Earth, constant gravitational acceleration and slowly changing mass.Mass is constant for battery-powered electric aircraft.To summarize, the state equations for the point-mass model of a multirotor aircraft are given by Eqs.(1) through (3), Eq. ( 4) and Eqs.(10) and (11), where the states are: ,  , h ,V ,  and  .The controls are: T ,  and  , which can be determined as shown in the next section.
+IV. Control ComputationLet the commanded airspeed, heading angle and flight-path angle be denoted by  4), ( 10) and ( 11), the following equations can be written for computing the control variables.cos sinc c c T mV D mg  = + +(12)sin sin cosc c c c T mV     =(13)and sin cos cosc c c c T mV mg     =+(14)Given that three equations are available for three unknowns, the unknowns can be determined as follows.Dividing Eq. ( 13) by Eq. ( 14), the commanded bank angle can be determined as Squaring and adding Eq. ( 13) and ( 14), taking the square root and dividing by Eq. ( 12), the thrust vector angle can be determined as22 1 ( cos ) ( cos ) tan sin cc c c m V V g mV D mg       -   + +  =  ++  (16)and finally, squaring and adding Eqs. ( 12) through ( 14) and taking the square root of the result,c pv c V K V V =- (18)where pv K is the proportional gain and V is the estimated airspeed based on measured airspeed.Similarly, a proportional-derivative controller could be employed for generating commands for following the commanded heading angle.With p K  as the proportional gain and d K  as the derivative gain of the heading controller, and  and  as the estimated heading angle and the heading-angle rate, respectively, ()c p c d KK      = --(19)In this study, the vertical profile is defined by specifying c  in the different phases of flight; it is assumed that the commanded flight-path angle is instantly achieved.Thus,  , the estimated flight-path angle, is set equal to c  .Furthermore, the commanded flight-path angle rate is set to zero,0 c  =(20)The commanded angular acceleration in Eq. ( 19) is integrated forward in time to determine the commanded headingangle rate for use in Eqs.(15) through (17).Note that limits can be placed on the bank angle and acceleration commands generated by Eq. (15) and Eq. ( 18), respectively, to accommodate passenger ride quality requirements.Application of proportional control, especially in Eq. ( 19), can be a bit tricky for large angular differences because of the two ways of turning from one direction to the other-clockwise and counterclockwise, one of which results in a smaller angular rotation; therefore, the smaller angular difference and the direction of the turn needs to be determined.It is straightforward to specify the airspeed in different phases of flight provided enough thrust can be generated for overcoming the drag, gravitational and wind forces for accelerating the aircraft.Specification of heading and flightpath angle is based on the horizontal and vertical paths to be followed in the presence of wind.This is discussed in the next section.
+V. Reference Command GenerationTo stay on course in a wind field, the aircraft has to crab such that the across-track component of the wind is cancelled.This assumes that the aircraft has enough speed to counter the wind.The airmass-relative heading angle needed to stay on the path specified by the course angle g  is obtained from the two relations based on Fig. 1:g g n V W V    cos cos cos = + (21) and g g e V W V    sin sin cos = + (22) as         - + = -      cos cos sin sin 1 V W W g e g n g (23)from the trigonometric identity resulting from the difference of Eq. ( 21) multiplied by sin g  and Eq. ( 22) multiplied by cos g  .To compute the desired airmass-relative heading angle using Eq. ( 23), g  has to be specified using a navigation procedure like great-circle navigation to follow the desired route (horizontal path) of flight.The closedloop great-circle navigation law is given in the following form in Ref. 13:      - - - = - ) cos( sin cos cos sin cos ) sin( tan 1           c c c c c g (24)where the current position of the aircraft is ) , (
+ and the commanded position is ) , ( c c   for heading to the next waypoint along the route or directly to the destination.Airmass-relative heading angle command can now be computed using estimated values of latitude and longitude- and  -in Eq. ( 24), and airspeed and flight-path angle in Eq. (23).Thus, 1 sin cos sin cosn g e g cg WW V    - -  =+  (25)This commanded heading angle value is used in Eq. ( 19).Like the heading angle that needs to be specified for following the horizontal path, flight-path angle needs to be specified for following the vertical path.For example, to maintain level flight in cruise, Eq. ( 8) prescribes the flightpath angle to be1 sin h c W V  - =-  (26)which means that a vertical rate has to be generated to counter vertical wind.The commanded airmass-relative flightpath angle can also be computed for following a climb/descent path prescribed via ground-relative flight-path angle.The ground-relative flight-path angle can be specified as1 tan c g GC hh d  - - =   (27)where c h is the commanded altitude, h is the estimated (current) altitude, and the great-circle distanceGC d is   1 cos sin sin cos( ) cos cos GC c c c dR       - = + -(28)The numerator and denominator in Eq. ( 27) are altitude-to-go and distance-to-go, respectively.Using Eqs. ( 8) and ( 9), the relation between  and g  is 22 sin tan ( cos cos ) ( cos sin )h g ne VW V W V W       + = + + + (29)Because the commanded heading angle is available from Eq. ( 25), commanded airspeed c V is specified and g  is available from Eq. ( 27),  -and equivalently c  -is the only unknown in Eq. ( 29).If the vertical component of the wind is ignored, Eq. ( 29) reduces to a quadratic, which can be easily solved to determine  computed via Eq. ( 29 is assumed to be instantly achieved; it is used directly in Eqs. ( 15) through (17) for computing the controls.For vertical descent to the airport/vertiport surface-final descent,c c h n e V h W W W = - + +(34)The commanded descent rate c h in Eqs. ( 33) and (34) needs to be specified for landing the aircraft and reducing the descent rate to zero (or close to zero) on touchdown.Starting with a descent rate of h at altitude h , the deceleration required for reducing the descent rate to zero after traversal of distance h is obtained using the kinematic relation between acceleration, initial and final speeds and distance as follows.( )2 2 c h h h =(35)The feedback control law given by Eq. ( 35) assumes positive descent rateh has a negative value-and positive h .Because the deceleration required depends on the initial descent rate according to Eq. ( 35), the initial descent rate needs to be such that the deceleration does not exceed the specified deceleration limit.If the starting altitude for vertical descent for landing is li h and the deceleration limit is lim a , the initial descent rate according to Eq. ( 35) needs to belim 2 li li h a h =- (36)This completes the description of the variables that need to be specified for computing the controls in all phases of flight.The trajectory can be determined using either the thrust, thrust vector angle and bank angle-directly as controlsor that resulting from models of thrust, thrust vector angle and bank angle dynamics-driven by the commanded controls-in the equations of motion.The trajectory generation process and an example of the generated trajectory along with the time histories of the control variables are discussed in the next section.
+VI. Trajectory GenerationThe complete procedure for trajectory generation is summarized in Fig. 3.The process is initiated by reading the flight plan, which provides basic information about the type of aircraft, equipage, origin, destination, route of flight, cruise altitude and cruise speed.Flight plans also specify alternative airports and provide additional information required for operating in the U. S. airspace.The flight procedure (also called airline procedure) describes how the aircraft is to be flown.For example, it might stipulate that after takeoff from the vertiport, the aircraft will climb vertically to 50 feet altitude maintaining a climb rate of 500 feet/minute.It will then climb to the cruise altitude of 2,000 feet while maintaining a 10-degree climb angle and a speed of 60 knots.On reaching the cruise altitude, the aircraft will accelerate to the cruise speed of 98 knots and maintain it, and so on.The next step is to read the initial conditions like the location of the flight, altitude and speed for example.After reading the required data, the recursive part of the process is begun by determining the mode of flight, where the modes of flight include: on ground, takeoff and initial climb, climb, cruise, initial descent, approach and final descent to landing.These modes are determined as a function of altitude, speed and location of the flight with respect to reference locations such as the origin and destination of flight.Based on the mode of flight, reference commands are generated as shown in Section V using the measured and estimated states, and the desired states needed for tracking the path and speed profiles.The reference commands and the estimated states are then used to determine the controls as discussed in Section IV.The computed controls are input to the equations of motion, described in Section III, and integrated forward in time to determine the true states.Because the true states of the aircraft are unavailable, a combination of sensors such as Global Positioning System (GPS), accelerometers and gyros are needed along with state estimators running on onboard computers to estimate them.The estimated states are used for onboard control computations for controlling the motion of the physical aircraft.In the ATM systems, observable states are estimated using the aircraft position data acquired by surveillance with radar and transponderbased systems and received from aircraft equipped with an Automatic Dependent Surveillance system.Depending on the degree of realism desired, the "Estimate states" step in Fig. 3 can be as simple as (a) setting the estimated states to the true states, or it can be a bit more complex as (b) adding noise and bias to the true states according to estimation error distributions, or it can be as realistic as (c) modeling the surveillance sensors with their sources of errors and using an Extended Kalman Filter for state estimation.The estimated and the true states are stored for further analysis.The estimated states are then used for determining the mode of flight in the next recursive step.The recursive process is terminated when the mode of flight transitions to "on ground" at the destination.The trajectory consisting of the temporal history of the positionlatitude, longitude and altitude-is output in the final step of the trajectory generation process outlined in Fig. 3.The procedure summarized in Fig. 3 was utilized for computing the trajectory of the six-passenger multirotor aircraft with takeoff mass of 2,940 kg (see Table 1 for other parameters) flying from Palo Alto Airport (PAO), California to San Martin Airport (E16), California.The two airports are 34 nautical miles apart.PAO is located at 37.46 degrees latitude and -122.11degrees longitude, and E16 is located at 37.08 degrees latitude and -121.60 degrees longitude.The simulated horizontal trajectory is shown in Fig. 4 and the vertical trajectory is shown in Fig. 5.The first four minutes and the last five minutes of flight-path angle time history are shown in Figs.6a and6b, respectively.Figure 6a shows the aircraft climbs vertically (90 degrees), climbs at a 10-degree flight-path angle and then levels off for cruise.Figure 6b shows the aircraft initially descends at a -10-degree ground-relative flight-path angle (see Eq. ( 27)) after the end of cruise.The aircraft then reduces speed to the final descent speed (according to Eqs. (34) and (36)) and descends at a steeper flight-path angle (about -30 degrees) to reduce the descent speed to zero on touchdown   7a and7b show the first four minutes and the last five minutes of the thrust time history.Observe the initial variation in thrust prior to one minute in Fig. 7a.It is due to the bank angle required for reorienting the heading from the initial heading to that required for countering the crosswind component of the wind and heading towards the destination.This figure also shows the increase in thrust between two and three minutes for accelerating from 60 knots to 98 knots in cruise.Figure 7b shows an increase in thrust between 25 and 26 minutes to slowdown from 98 knots to 60 knots, and between 28 and 29 minutes to slowdown from 60 knots to the final descent speed.Figures 8a and8b show the corresponding first four minutes and the last five minutes of the thrust vector angle w.r.t.horizontal (sum of  and  ; see Fig. 2) time history.Figure 8a shows the thrust vector angle during initial climb, climb and cruise.Observe the thrust vector angle exceeds 90 degrees between 25 and 26 minutes and between 28 and 29 minutes in Fig. 8b.Angle greater than 90 degrees is for pointing the thrust in the opposite direction of motion to slowdown the aircraft.Figures 9a and9b show the early and the later parts of the bank    angle time history.The initial bank angle is for turning the aircraft from its initial heading to that required for countering the crosswind and flying towards the destination.Note the correspondence between the bank angle in Fig.     9a and the thrust prior to one minute in Fig. 7a.Figures 10 and 11 show the airspeed and groundspeed time histories, respectively.Observe in Figs. 10 and 11 that in the approach segment prior to final vertical descent, the airspeed is over 20 knots while the groundspeed is less than three knots against a headwind of 20 knots.The groundspeed is zero during final descent; airspeed is reduced to zero on touchdown.
+VII. ConclusionsThe results in this paper demonstrate that the performance of the point-mass model of the multirotor electric vertical takeoff and landing aircraft driven by the controllers developed in this paper is suitable for generating trajectories for meeting urban air mobility mission requirements.The ability to generate these trajectories will enable concept evaluation and development of decision support tools for accommodating urban air mobility operations in the air traffic management system.Vare the north and east components of the ground-relative aircraft velocity.h V is the ground-relative climb or descent rate depending on whether it is positive or negative.The horizontal velocity of the aircraft with respect to the ground is the resultant of the horizontal components of the airmass-relative aircraft velocity and the ground-relative wind velocity.This relationship is shown in Fig.1, where g V and s W are the magnitudes of the horizontal components of the ground-relative aircraft velocity and the wind velocity, and s V is the magnitude of the horizontal component of the airmass-relative aircraft velocity.g  is the heading angle of the ground-relative aircraft velocity with respect to the local north direction. and w  are the heading angles of the airmass-relative aircraft velocity and ground-relative wind velocity, respectively, also with respect to the local north direction.north, east and up components of the ground-relative wind velocity vector.
+Figure 1 .1Figure 1.Velocity triangle.
+, respectively.Let the commanded thrust, thrust vector angle and the bank angle be denoted by c T , c  and c  , respectively.Based on the relationships in Eqs. (
+Figure 2 .2Figure 2. Forces on the multirotor aircraft.
+Drag-D -in Eqs.(16) and (17) is a function of V and density of air, which is determined based on estimated altitude.The state variables in Eqs.(15) through (17) are estimated quantities.needed in the above equations can be determined by defining control laws for controlling the corresponding state variables.Cruise control for the airspeed to follow the commanded airspeed leads to ()
+c.Keeping the vertical component of wind results in a quartic equation.Observe that without the wind terms, Eq. (29) simplifies to cg
+gVhas to be maintained at -90 degrees.= means that both the north and east components of the ground-relative aircraft velocity must be zero.Setting says the airmass-relative heading angle should be either in the direction of the horizontal component of the wind velocity or opposite to it.See Fig.1for a pictorial depiction of the horizontal component of wind velocity.Flying into the wind-opposite direction to the horizontal component of the wind velocity-enables zero groundspeed to be achieved by applying the horizontal component of the airmass-relative velocity in the forward direction.Combining Eq. (32) with Eqs.(8) and (3),
+Figure 3 .3Figure 3. Trajectory generation process.
+Figure 4 .Figure 5 .45Figure 4. Horizontal trajectory.Figure 5. Vertical trajectory.
+Figure 7b .7bFigure 7b.Last five minutes of the thrust time history.
+Figure 7a .7aFigure 7a.First four minutes of the thrust time history.
+Figure 6a .6aFigure 6a.First four minutes of the flight-path angle time history.
+Figure 6b .6bFigure 6b.Last five minutes of the flight-path angle time history.
+Figure 10 .10Figure 10.Airspeed time history.Figure 11.Groundspeed time history.
+Figure 9a .9aFigure 9a.First four minutes of the bank angle time history.
+Figure 9b .9bFigure 9b.Last five minutes of the bank angle time history.
+Figure 8a .8aFigure 8a.First four minutes of thrust vector angle time history.
+Figure 8b .8bFigure 8b.Last five minutes of the thrust vector angle time history.
+Table 1 . Quadcopter eVTOL conceptual model parameters.1ParameterValueStructural Mass (excluding the battery)1,684 kgMass of Single Passenger91 kgMaximum Number of Occupants (includes the pilot)6 personsBattery Mass710 kgMaximum Mass (with six occupants)2,940 kgUseful Battery Capacity (80% Battery Capacity)295,778 watt hoursMaximum Deliverable Power501,110 wattsDrag Coefficient1.1984 dimensionlessReference Area1 square meter
+		
+		
+			
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+				
+
+
+	
+		Multidisciplinary Optimization of Urban-Air-Mobility Class Aircraft Trajectories with Acoustic Constraints
+		
+			RobertDFalck
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+			DanielIngraham
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+			EliotAretskin-Hariton
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+		10.2514/6.2018-4985
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+		2018 AIAA/IEEE Electric Aircraft Technologies Symposium
+		Cincinnati, OH
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+			American Institute of Aeronautics and Astronautics
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+		Modeling and Adaptive Flight Control for Quadrotor Trajectory Tracking
+		
+			HakimBouadi
+		
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+			FMora-Camino
+		
+		10.2514/1.c034477
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+		Journal of Aircraft
+		Journal of Aircraft
+		0021-8669
+		1533-3868
+		
+			55
+			2
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+			March-April, 2018
+			American Institute of Aeronautics and Astronautics (AIAA)
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+		10.2514/6.2008-7410
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+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, HI
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+			American Institute of Aeronautics and Astronautics
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+		Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment
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+			ClaireTomlin
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+		10.2514/6.2007-6461
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+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Hilton Head, SC
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+			American Institute of Aeronautics and Astronautics
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+	Hoffmann, G. M., Huang, H., Waslander, S. L., and Tomlin, C. J., "Quadrotor Flight Dynamics and Control: Theory and Experiment," Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, Hilton Head, SC, August 20-23, 2007.
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+		Dynamics and Optimal Control of Quadrotor Platform
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+			MarianBlachuta
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+		10.2514/6.2012-4915
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+		AIAA Guidance, Navigation, and Control Conference
+		Minneapolis, MN
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+			American Institute of Aeronautics and Astronautics
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+		NASA TM-2000-209586, National Aeronautics and Space Administration
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+	Wong, G. L., "The Dynamic Planner: The Sequencer, Scheduler, and Runway Allocator for Air Traffic Control Automation," NASA TM-2000-209586, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, CA 94035-1000, April 2000, URL: http://www.aviationsystemsdivision.arc.nasa.gov/publications/full_list_by_author.shtml [cited: 11/20/2019]
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+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
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+			BanavarSridhar
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+		
+			ShonRGrabbe
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+			GanoBChatterji
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+			KapilSSheth
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+		10.2514/atcq.9.1.1
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+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
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+			9
+			1
+			
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
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+	Bilimoria, K., Sridhar, B., Chatterji, G., Sheth, K., and Grabbe, S., "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, Vol. 9, No. 1, pp. 1-20, 2001, URL: http://www.aviationsystemsdivision.arc.nasa.gov/publications/full_list_by_author.shtml [cited: 11/20/2019] 8
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+		Build 4 of the Airspace Concept Evaluation System
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+			KarlinRoth
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+			VikramManikonda
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+			SharleneRoney
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+			GeorgeHunter
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+		
+			AlexHuang
+		
+		
+			GeorgeCouluris
+		
+		10.2514/6.2006-6110
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+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Keystone, Colorado
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 21-24, 2006
+		
+	
+	Meyn, L., Windhorst, R., Roth, K., Drei, D. V., Kubat, G., Manikonda, V., Roney, S., Hunter, G., Huang, A., and Couluris, G., "Build 4 of the Airspace Concept Evaluation System," Proc. AIAA Modeling and Simulation Technologies Conference and Exhibit, Keystone, Colorado, Aug. 21-24, 2006.
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+		Sensitivity Analysis of Aviation Environmental Impacts for the Base of Aircraft Data (BADA) Family 4
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+		Eurocontrol Experimental Centre
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+			American Institute of Aeronautics and Astronautics (AIAA)
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+		Proc. American Helicopter Society International 74th Annual Forum & Technology Display
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+	Patterson, M. D., Antcliff, K. R., and Kohlman, L. W., "A Proposed Approach to Studying Urban Air Mobility Missions Including and Initial Exploration of Mission Requirements," Proc. American Helicopter Society International 74th Annual Forum & Technology Display, Phoenix, AZ, May 14-17, 2018.
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+		VTOL Urban Air Mobility Concept Vehicles for Technology Development
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+			ChristopherSilva
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+			WayneRJohnson
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+		
+			EduardoSolis
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+			MichaelDPatterson
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+		
+			KevinRAntcliff
+		
+		10.2514/6.2018-3847
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+	
+		2018 Aviation Technology, Integration, and Operations Conference
+		Atlanta, GA
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+			American Institute of Aeronautics and Astronautics
+			June 25-29, 2018
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+	Silva, C., Johnson, W., Antcliff, K. R., and Patterson, M. D., "VTOL Urban Air Mobility Concept Vehicles for Technology Development," Proc. AIAA Aviation Technology, Integration, and Operations Conference, Atlanta, GA, June 25-29, 2018.
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+		Fuel Burn Estimation Using Real Track Data
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+			GanoBChatterji
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+		10.2514/6.2011-6881
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+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			September 20-22, 2011
+		
+	
+	AIAA 2011-6881
+	Chatterji, G. B., "Fuel Burn Estimation Using Real Track Data," AIAA 2011-6881, Proc. AIAA Aviation Technology, Integration, and Operations Conference, Virginia Beach, VA, September 20-22, 2011.
+
+
+
+	
+		En-route flight trajectory prediction for conflict avoidance and traffic management
+		
+			GBChatterji
+		
+		
+			BSridhar
+		
+		
+			KDBilimoria
+		
+		10.2514/6.1996-3766
+	
+	
+		Guidance, Navigation, and Control Conference
+		San Diego, CA
+		
+			American Institute of Aeronautics and Astronautics
+			July 29-31, 1996
+		
+	
+	Chatterji, G. B., Sridhar, B., and Bilimoria, K. D., "En-route Flight Trajectory Prediction for Conflict Avoidance and Traffic Management," AIAA 96-3766, Proc. AIAA Guidance, Navigation, and Control Conference, San Diego, CA, July 29-31, 1996.
+
+
+				
+			
+		
+	
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+I. Introductionirspace sectors have evolved over decades to assist the human controller organize flights for safe and efficient operations through the airspace.Unfortunately, the resulting sector design's inflexibility makes it difficult for it to adapt to changing weather and traffic conditions.With limited means for redistributing capacity in the airspace, traffic flow management techniques, such as delaying aircraft on the ground, are employed to reduce traffic in the affected airspace.Since this leads to delays, reconfiguring the airspace to dynamically adjust its capacity to where and when it is most needed has been proposed as an alternative. 1][4][5][6][7][8][9] This paper examines whether airspace partitions created with several days of data are robust, where robust means that they can be used on other similar days, and it examines the benefit of using different partitions at different times of the day.The focus of earlier airspace partitioning research was on partitions generated with at most one-day of traffic data; the issue of whether the partitions could be used with traffic data from other days was not of concern.The benefit, measured by reduced sector-hours, of using different configurations generated by combining sectors and by combining altitude slices has been examined in Refs. 10 and 11, respectively.This same metric has been used here to show the tradeoff between reduced sector-hours and the number of times the partitions are changed in a day.The Mixed Integer Linear Programming (MILP) method described in Ref. 9 is used with traffic data from ten high-volume low-delay days to design sectors in Fort Worth, Cleveland and Los Angeles centers.These centers were chosen because they are located in different regions of the U. S. and experience very different traffic patterns.A comparison of peak traffic-counts in the sectors for traffic from 57 days including the ten days used in the design shows that the sector configurations in these centers are robust.Results show that sector configurations created with two-hour traffic data can be used for duration of six to twelve hours without exceeding the peak traffic-count requirement.Most of the sector-hour reduction is obtained by using one sector configuration during the daytime hours and one during the nighttime hours compared to using a single configuration for the entire day.Further reduction is achieved if three sector configurations are used during the day.Section II describes the actual air traffic dataset consisting of 57 high-volume low-delay days, out of which, ten days are used for creating the sector configurations.The entire dataset is used for evaluating the sector configurations.Section III discusses the MILP method, and Section IV describes the robust sectorization and validation method for creating sectors.Validation results are discussed in Section V. Tradeoff between sectorhours and the number of configuration changes is discussed in Section VI, and the paper is concluded in Section VII.
+II. Air Traffic DatasetThe analysis and results discussed in this article are based on air traffic data from high-volume low-delay days.High-volume traffic is usually associated with weekdays.Delays are low on days on which the flights are relatively unaffected by weather and congestion caused rerouting and ground holds.Most aircraft stay on their filed route of flight and are on-time with respect to their schedule.To identify such days, delay data for all the days in 2007 were obtained from the Federal Aviation Administration's Air Traffic Operations Network (OPSNET) database.The days were then categorized based on total domestic departure-counts and total time delay in minutes using the multiple-metric K-Means classification method described in Ref. 12. Days were separated into nine groups based on the combination of traffic-volume ("low-volume," "medium-volume," "high-volume") and delay ("lowdelay," "medium-delay," and "high-delay").Figure 1 shows the scatter plot of the 57 days that were selected for this study.Ten of the 57 days, marked in circles, were used for designing the sectors.We will refer to these days as the training set.The ones marked with triangles are the remaining 47 days that were used for evaluating the robustness of these sectors, referred to as test days.Figure 2 shows the average, upper and lower bounds of the number of aircraft in the Fort Worth Center airspace as a function of time for the ten training days.The numbers of training and test days for each day of the week are listed in Table 1.Aircraft position data from the ten training days for each two-hour time period were used in the MILP sector design method, described in Ref. 9, to create 12 sector configurations spanning the 24-hour time period.The MILP sector design method is briefly discussed next.
+III. Mixed Integer Linear Programming MethodThe MILP method discussed in this section assumes a hexagonal tessellation of the airspace.Such a tessellation with tiles marked with numbers uniquely identifying them is shown in Fig. 3. Tiles marked with the letter " s " are special tiles called "seed" tiles.The setup phase of the algorithm counts the total number of aircraft located within the tile along with the total number of aircraft that cross each of the six sides of the tile for the duration of interest.The direction of tile boundary crossing is ignored.The seed tiles are also selected at this point.The optimization process clusters the hexagonal tiles together to form sectors by using a connection variable that represents a directional link between two tiles.This variable contains not only the identity of the linked tiles, but also the accumulated sum of aircraft counts of every tile upstream of that link.This accumulation of aircraft counts   is terminated at the "sink" tile, which is selected during the optimization process from among the pre-determined seed tiles.In this way, the value of the final link going into the sink tile, plus the sink tile's aircraft count equals the total aircraft count of that cluster of tiles.Figure 3 shows a notional solution of the optimization in which the directions of the links between adjacent tiles, all the way to the sink tile 1 are marked with arrows.All the tiles that contribute links to a particular sink tile, s ˆ, are said to belong to one region (sector) of airspace.Note that sink tile does not refer to an actual destination of aircraft.Rather it is a mathematical construct to aid in the formulation of the optimization problem.The solution phase of the algorithm is implemented by six basic constraints and an optimization function.The first constraint ensures that the link variable captures the accumulated number of aircraft upstream in the contiguous cluster of tiles.This is basically a conservation of aircraft constraint between a tile's incoming links, the one outbound link, and its own contribution of aircraft counts.The second constraint predicates that the total number of aircraft captured by the sum of incoming links to a sink tile, plus its own contribution of aircraft counts is constrained to be within 5% of the average number of aircraft, where the average number of aircraft is the sum of the number of aircraft in all tiles for the duration of interest divided by the desired number of sectors.This constraint leads to the creation of sectors with nearly equal numbers of aircraft.The third constraint asserts that the number of sink tiles should equal the desired number of sectors.The fourth constraint establishes that all non-sink tiles (including seed tiles that do not end up becoming sinks) have a single outbound link to an adjacent tile.The fifth constraint specifies that there is no outbound link from a sink tile.Finally, one of the most compelling reasons for using this method of tile clustering is that tile contiguity can be enforced by only allowing links to be formed between adjacent cells.This is the sixth constraint.In practice, this constraint can be implicitly enforced in the data structure utilized by the other constraints.The optimization function consists of the sum of the weighted outbound link values from each tile to its adjacent tiles.The weights are given by the boundary crossing counts computed during the setup phase.These weights are used to ensure that the link directions resulting from minimization of the optimization function are aligned along the major flows seen in the air traffic data.Other details of the MILP method are given in Ref. 9. A notional solution of the optimization shown in Fig. 3 can be viewed as a Directed Acyclic Graph (DAG) rooted at the sink tile 1 in Fig. 4. The graph is directed because the outbound links are defined from a tile to its adjacent tile; it is acyclic because single outbound-links (no backward links between adjacent tiles), conservation of aircraft, and single sink tile per sector prevent the formation of loops.Once the tiles are associated with sectors, a boundary smoothing algorithm described in Ref. 9 is used for generating the final sector boundaries.
+IV. Robust Sectorization and ValidationThis section describes the method for designing sectors using several days of air traffic data, selecting few sector configurations for the 24-hour period, and validating the design.The design is validated by playing back the test traffic data through the designed sectors and determining that the design criteria are not violated.The method is summarized in a block diagram in Fig. 5.The examples and results presented in this and subsequent sections are based on high-altitude traffic, which is above 24,000 feet altitude.6.The minimum and maximum numbers of aircraft during this interval were 23 and 81.This example shows that the traffic-counts in a sector can be unacceptably high when airspace is partitioned into few sectors.To ensure that the traffic-counts stay below a specified threshold in most instances, the airspace needs to be partitioned into more sectors.This is the motivation for step 508 that increases m by two.The previous steps are repeated to create histograms of the type in Fig. 6.    7. The last value of the graph in Fig. 7 is 2346, which is the total number of traffic-count samples in Fig. 6.Based on the last value, the 99.9 percentile value is 2344.The traffic-count corresponding to 2344 is 78 aircraft.This location is marked by an ' !' in Fig. 7.The central idea here is that if the Fort Worth Center airspace were to be partitioned into two sectors during the 6 p.m. to 8 p.m. CST time-interval, the probability is 99.9 percent that the traffic-count would be at or below 78 aircraft in a sector.Lower percentile values can be chosen to remove outlier traffic-count values.The process of computing the cumulative frequency and selecting a traffic-count value corresponding to the specified percentile is repeated for each of the nine sector configurations.The values obtained for nine sector configurations for the first two-hour time period (6 p.m. to 8 p.m. CST) are shown in Fig. 8.The number of sectors needed for ensuring 99.9% probability of traffic-counts staying below a specified traffic-count threshold can be obtained from the data presented in this figure .For example, at least 12 sectors would be needed if a threshold of 20 aircraft were chosen.This example shows that given a percentile value and a design threshold, a sector configuration can be chosen for the time-interval of interest.Step 510 checks if a sector configuration has been selected for the last two-hour interval.If not, t is incremented by two-hours in step 511, and a new timeinterval is determined in step 502.The entire process discussed thus far is repeated for this new time-interval.The result is a selection of 12 sector configurations, one for each two-hour time-interval, in step 509. Figure 9 shows a bar chart of the number of sectors in the configurations selected in the Fort Worth Center.Observe that the number of sectors correlates to the traffic-count shown in Fig. 2.In step 512, two or three sector configurations are chosen from the available 12.This selection is accomplished by organizing the configurations into a few groups and then identifying one representative configuration for each group.The K-Means algorithm discussed in Ref. 12 is used to organize the configurations into groups based on the number of sectors.For example, sector configurations for the first, second, and tenth two-hour time periods shown in Fig. 9 are placed in the first group, 3 rd through 6 th are placed in the second group and the remaining are placed in the third group, when three groups are desired.Based on these three groups, the sector configuration for the first two-hour period (6 p.m. to 8 p.m. CST) is selected for the duration of the first four-hours from 6 p.m. to 10 p.m. CST.Similarly, the sector configuration of six sectors for the 4 a.m. to 6 a.m.time-interval is applied for the eight-hour period spanning the 10 p.m. to 6 a.m.interval.Finally, the third configuration of 16 sectors for the 10 a.m. to 12 p.m. time interval is selected for the twelve-hour period from 6 a.m. to 6 p.m. CST.Note that the sector configuration for the tenth two-hour time period (12 p.m. to 2 p.m. CST) is a member of the first group since it has twelve sectors, but it lies between two members of the third group (10 a.m. to 12 p.m. and 2 p.m. to 4 p.m. configurations).Regardless, the representative member of the third group is used to cover this interval.Selected sector configurations and durations of their application for the Cleveland, Los Angeles and Fort Worth Centers are summarized in Table 2.Once representative sectors are selected in step 512, histograms of the type given in Fig. 6 are created for them in step 514 using aircraft position data from training set and test set days derived from step 513.In step 515 the    cumulative frequency values are computed based on the histograms provided by step 514 (see Fig. 7).These values are then used for determining traffic-counts corresponding to the percentile value (for example, 99.9) used in the design.The sector design is validated by determining if this traffic-count is above or below the specified threshold capacity value (for example, 20 aircraft) used in design.
+V. Validation ResultsResults of validation using three sector configurations of the Fort Worth, Cleveland and Los Angeles centers listed in Table 2 are described in this section.The three sector configurations of the Fort Worth Center are shown in Figs.10-12.Traffic data from the ten training and 47 test set days were played back through these configurations for the time durations noted in Table 2 to compute traffic-counts in the sectors.Histograms were then created with these traffic samples.Cumulative frequency values were computed using these histograms, and 99.9 percentile traffic-counts were determined.Figure 13 shows the histogram of 162,204 traffic-count samples for the Sector Configuration I shown in Fig. 10.The maximum number of aircraft in a sector was found to be 28 aircraft.The 99.9 percentile traffic-count was found to be 20 aircraft; it is marked by the vertical line in Fig. 13.Observe that the value of 20 aircraft is same as the design threshold value in Fig. 8, therefore the sector configuration in Fig. 10 can be applied for the 6 p.m. to 10 p.m. CST duration.This example shows that a sector configuration developed with traffic data from a smaller timeinterval can be applied to a larger time-interval without violating the design criteria.Figures 14 and15 show histograms derived from traffic data from the 57 days and the sector configurations II and III shown in Figs.11 and12.Total numbers of traffic-count samples were 163,158 and 653,632, and the peak traffic-counts in a sector were 42 and 31 aircraft for these two sector configurations, respectively.The 99.9 percentile traffic-counts were determined to be 21 and 18 as shown in Figs. 14 and15.Although the 99.9 percentile traffic-count value of 21 for Configuration II was found to be one above the design value, instances of traffic-count of 21 were found to be small with 99.87 percentile value of 20 aircraft.Given that the traffic-counts in most instances are at or below the design value, Configuration II and III can be used for the desired eight-hour and twelve-hour periods.In situations where the traffic-count is forecasted to be much higher than what the sector was designed for, traffic flow management techniques can be used to moderate the demand.The validation results given here suggest that this would be required infrequently.Validation results for Cleveland, Los Angeles and Fort Worth centers are summarized in Table 3.The last row of
+VI. Sector Configuration Change FrequencyResults presented in the previous section indicate that a single sector configuration used during the busy part of the day can be used for the entire day without exceeding the traffic-count limits.These configurations have the most number of sectors compared to other configurations designed for lower traffic-volume.For example, Configuration III shown in Fig. 12 has 16 sectors compared to Configuration II shown in Fig. 11 that has six sectors.Given that each sector requires resources in terms of equipment and air traffic controllers, it is desirable to have as few sectors as possible for handling the expected traffic.Thus, from a resource utilization perspective, sector configurations should be changed as frequently as possible.Although sector configuration change is permitted in the current air traffic control environment, it is difficult to do so frequently because of safety issues of transitioning from one configuration to the next.Change during a busy period is workload intensive because aircraft have to be handed over to neighboring sectors. 11If done in an uncoordinated manner, aircraft would be within the geometric confines of one sector while being controlled by another sector.Configuration change is difficult even if it is timed with a shift change when a new controller assumes separation responsibility for the sector.Regulations require the sector controller to ensure that the incoming controller has complete situational awareness prior to transfer.This is difficult to achieve if the sector configuration changes upon transfer.Due to these practical impediments, sector configuration change should be considered only when there is a significant benefit.The number of sector-hours has been proposed as a benefit metric for comparing different sector configurations in Refs. 10 and 11.It is obtained by summing the product of the number of sectors in each time-interval with the time-interval duration in hours.Following this definition, 256 sector-hours are obtained for the sector configuration change strategy in Fig. 9 with 12 sector configurations.If Configuration III (16 sectors) were used in the Fort Worth Center for the entire day, 384 sector-hours would be spent.The ratio of the sector-hours between a single sector configuration and 12 sector configurations changed once every two-hours is therefore 1.5; sector-hours can be reduced by 50%.Several different configuration change schedules for the Fort Worth Center are provided in Table 4.The numbers of sectors for the two-hour periods are shown in the table.The first row indicates that the same configuration is used throughout the day.The last row of the table contains the same information as the bar chart in Fig. 9; it shows that sector configurations are changed 11 times: 16 to 12, 12 to 10, 10 to 6, 6 to 2, 2 to 4, 4 to 6, 6 to 14, 14 to 16, 16 to 12, 12 to 14, and 14 to 16.Similar schedules were also created for Cleveland and Los Angeles Centers, and sector-hours were computed for each schedule.384 sector-hours were obtained in the Cleveland Center with 16 sectors used for the entire day; 324 sector-hours were obtained with 16 sectors from 5:00 a.m. to 11:00 p.m. and 6 sectors from 11:00 p.m. to 5:00 a.m.EST.For three configuration changes with 14 sectors during 7:00 p.m. to 11:00 p.m., 6 sectors during 11:00 p.m. to 5:00 a.m., and 16 sectors during 5:00 a.m. to 7:00 p.m. EST, 316 sector-hours were obtained.These sector-hours are lower than 435 sector-hours for the current high-altitude operations in the Cleveland Center reported in Ref. 10. On an average 22 sectors are used for daytime (6:00 a.m. to 11:00 p.m. EST) operations and 11 sectors are used for nighttime operations (11:00 p.m. to 6:00 a.m.EST) in the Cleveland Center.Lower sector-hours were obtained in The results summarized in Fig. 16 show that two configuration changes are needed for reducing the sectorhours from about 50% to 19% in Fort Worth Center, 23% in Cleveland Center and 26% in Los Angeles Center above the minimum sector-hours achievable with the 12 two-hour sectorizations.These results suggest that the current practice in most centers of using one configuration for the daytime hours and one for the nighttime hours is a reasonable one.Sector-hours are further reduced to 20% in Cleveland and Los Angeles Centers and 13% in Fort Worth Center with three configuration changes.If four configuration changes are allowed, the sector-hours are at most 15% above that achieved with the two-hour sectorizations.In summary, results presented in Table 3 and in Fig. 16 advocate both, from safety (99.9 percentile peak traffic-count) and resource utilization (sector-hours) perspectives, that two to three sectors configurations are adequate for a good-weather day.Significant reduction in sector-hours is obtained by using Configuration III during daytime hours and Configuration II during nighttime hours in the three centers.Further reduction is obtained if Configuration I is used during the times listed in Table 2.Although sector-hours can be reduced even more by changing sector configurations according to Fig. 16, the frequency of change should be guided by practical considerations, especially during busy traffic periods.
+VII. ConclusionsA robust sectorization and validation method for partitioning airspace into sectors based on several days of air traffic data was described in the paper.Traffic data from ten days out of a set of 57 high-volume low-delay days in 2007 were used for designing sectors in the Cleveland, Fort Worth and Los Angeles center airspace for each twohour period of the day using the method.Of the twelve sector configurations for each day, three were chosen to span the 24-hour time period.Traffic data from the entire dataset were played back though the three selected sector configurations, and histograms of traffic-counts were computed.These distributions show that the probability of traffic-counts exceeding the threshold value used in the sector design is less than one percent.Examples demonstrate that sector configurations created using two-hour time-interval traffic data from several days can be applied over much longer time-intervals from six-hour to 12-hour durations without violating the design criteria.Sector-hours were computed for several sector configuration change schedules to establish a tradeoff with respect to the number of configuration changes during the day.It was determined that most of the benefit as measured by sector-hours is derived by using two configurations, one during daytime hours and one during the nighttime hours.Further benefit is obtained by using one additional configuration.
+VIII. ReferencesFigure 1 .1Figure 1.High-volume low-delay days.
+Figure 2 .2Figure 2. Upper and lower bounds.
+Figure 3 .3Figure 3. Tessellation of the airspace.Figure 4. Directed Acyclic Graph resulting from optimization.
+Figure 4 .4Figure 3. Tessellation of the airspace.Figure 4. Directed Acyclic Graph resulting from optimization.
+Step 507 transfers control to Step 509 once the nine histograms are obtained with airspace partitioned into two through 18 sectors.The sector selection step 509 is used to select a sector configuration with the appropriate number of sectors for the chosen T .The sector cumulative frequency is computed for each of the nine histograms by summing the frequency along the traffic-count bins.The cumulative frequency graph for the histogram in Fig.6is shown in Fig.
+Figure 5 .5Figure 5. Robust sectorization and validation method.
+Figure 6 .6Figure 6.Histogram of traffic-counts during the two-hour period with Fort Worth Center airspace partitioned into two sectors.
+Figure 7 .7Figure 7. Cumulative frequency of traffic-counts during the two-hour period with Fort Worth Center airspace partitioned into two sectors.
+Figure 8 .8Figure 8. 99.9 percentile traffic-counts during the two-hour period with nine different configurations of Fort Worth Center airspace.
+Figure 9 .9Figure 9. Selected sector configurations of the Fort Worth Center airspace for the twohour time-intervals.
+Figure 10 .10Figure 10.Fort Worth Sector Configuration I based on 6 p.m. to 8 p.m. CST traffic data from training set days.
+Figure 11 .11Figure 11.Fort Worth Sector Configuration II based on 4 a.m. to 6 a.m.CST traffic data from training set days.
+Figure 15 .15Figure 15.Histogram of traffic-counts from 6 a.m. to 6 p.m. CST with Fort Worth Center Configuration III.
+Figure 12 .12Figure 12.Fort Worth Sector Configuration III based on 10 a.m. to 12 p.m. CST traffic data from training set days.
+Figure 13 .13Figure 13.Histogram of traffic-counts from 6 p.m. to 10 p.m. CST with Fort Worth Center Configuration I.
+Table 1 . Numbers of training and test days corresponding to days of the week. Day of week Training days Test days1Monday25Tuesday315Wednesday114Thursday19Friday34Total10
+Table 2 .2Selected sector configurations for Cleveland, Fort Worth and Los Angeles centers.CenterFigure 14.Histogram of traffic-counts from 10 p.m. to 6 a.m.CST with Fort Worth Center Configuration II.
+Table 3 .399.9 percentile traffic-counts in the chosen sector configurations for Cleveland, Fort Worth and Los Angeles centers.table lists 99.9 percentile traffic-counts obtained with Sector Configuration III used for the entire day.Cleveland Center and Los Angeles Center results, like the Fort Worth Center results, suggest that sector configurations developed with traffic data from several days over smaller time-intervals can be used over larger time-intervals on similar days of traffic without peak traffic-counts significantly exceeding the design threshold.ConfigurationCenterCleveland Fort Worth Los AngelesI182017II222113III181818III all day1817
+Table 4 .4Sector configuration change schedules for Fort Worth Center.Number of ChangesChange ScheduleSector-hours02
+		
+		
+			
+
+				
+
+
+	
+		Initial Concepts for Dynamic Airspace Configuration
+		
+			ParimalKopardekar
+		
+		
+			KarlBilimoria
+		
+		
+			BanavarSridhar
+		
+		10.2514/6.2007-7763
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			September 18-20, 2007
+		
+	
+	Kopardekar, P., Bilimoria, K., and Sridhar, B., "Initial Concepts for Dynamic Airspace Configuration," Proc. 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Belfast, Northern Ireland, September 18-20, 2007.
+
+
+
+	
+		Temporal and Spatial Distribution of Airspace Complexity for Air Traffic Controller Workload-Based Sectorization
+		
+			ArashYousefi
+		
+		
+			GeorgeDonohue
+		
+		10.2514/6.2004-6455
+	
+	
+		AIAA 4th Aviation Technology, Integration and Operations (ATIO) Forum
+		Forum, Chicago, IL
+		
+			American Institute of Aeronautics and Astronautics
+			September 20-22, 2004
+		
+	
+	Yousefi, A., and Donohue, G. L., "Temporal and Spatial Distribution of Airspace Complexity for Air Traffic Controller Workload-Based Sectorization," AIAA 2004-6455, Proc. 4 th AIAA Aviation Technology, Integration and Operations (ATIO) Forum, Chicago, IL, September 20-22, 2004.
+
+
+
+	
+		An Efficient Method for Airspace Analysis and Partitioning Based on Equalized Traffic Mass
+		
+			AKlein
+		
+	
+	
+		Proc. 6 th FAA and Eurocontrol ATM Conference
+		6 th FAA and Eurocontrol ATM ConferenceBaltimore, MD
+		
+			June 2005
+		
+	
+	AIAA 2004-6455
+	Klein, A., "An Efficient Method for Airspace Analysis and Partitioning Based on Equalized Traffic Mass," AIAA 2004- 6455, Proc. 6 th FAA and Eurocontrol ATM Conference, Baltimore, MD, June 2005.
+
+
+
+	
+		Genetic algorithms for partitioning air space
+		
+			DDelahaye
+		
+		
+			J-MAlliot
+		
+		
+			MSchoenauer
+		
+		
+			J-LFarges
+		
+		10.1109/caia.1994.323662
+	
+	
+		Proceedings of the Tenth Conference on Artificial Intelligence for Applications
+		the Tenth Conference on Artificial Intelligence for ApplicationsSan Antonio, TX
+		
+			IEEE
+			March 1-4, 1994
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+	Delahaye, D., Alliot, J-M., Schoenauer, M., and Farges J-L., "Genetic Algorithms for Partitioning Air Space," Proc. 10 th IEEE Conference on Artificial Intelligence for Applications, San Antonio, TX, pp. 291-297, March 1-4, 1994.
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+		Dynamic Airspace Configuration Management Based on Computational Geometry Techniques
+		
+			JoeMitchell
+		
+		
+			GirishkumarSabhnani
+		
+		
+			RobertHoffman
+		
+		
+			JimmyKrozel
+		
+		
+			ArashYousefi
+		
+		10.2514/6.2008-7225
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, Hawaii
+		
+			American Institute of Aeronautics and Astronautics
+			August 18-21, 2008
+		
+	
+	Mitchell, J. S. B., Sabhnani, G., Krozel, J., Hoffman, R., and Yousefi, A., "Dynamic Airspace Configuration Management Based on Computational Geometry Techniques," Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, Honolulu, Hawaii, August 18-21, 2008.
+
+
+
+	
+		Airspace sectorization with constraints
+		
+			HuyTrandac
+		
+		
+			PhilippeBaptiste
+		
+		
+			VuDuong
+		
+		10.1051/ro:2005005
+	
+	
+		RAIRO - Operations Research
+		RAIRO-Oper. Res.
+		0399-0559
+		1290-3868
+		
+			39
+			2
+			
+			June 23-27, 2003
+			EDP Sciences
+			Budapest, Hungary
+		
+	
+	Trandac, H., Baptiste, P., and Duong, V., "Optimized Sectorization of Airspace with Constraints," Proc. 5 th Eurocontrol/FAA ATM R&D Seminar, Budapest, Hungary, June 23-27, 2003.
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+	
+		Airspace Sector Redesign Based on Voronoi Diagrams
+		
+			MinXue
+		
+		10.2514/6.2008-7223
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, Hawaii
+		
+			American Institute of Aeronautics and Astronautics
+			August 18-21, 2008
+		
+	
+	Xue, M., "Airspace Sector Redesign Based on Voronoi Diagrams," Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, Honolulu, Hawaii, August 18-21, 2008.
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+		A Weighted-Graph Approach for Dynamic Airspace Configuration
+		
+			StephaneMartinez
+		
+		
+			GanoChatterji
+		
+		
+			DengfengSun
+		
+		
+			AlexandreBayen
+		
+		10.2514/6.2007-6448
+		AIAA 2007-6448
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			August 20-23, 2007
+		
+	
+	Martinez, S. A., Chatterji, G. B., Sun, D., and Bayen, A. M., "A Weighted-Graph Approach for Dynamic Airspace Configuration," Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, Hilton Head, SC, AIAA 2007-6448, August 20-23, 2007.
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+	
+		Analysis of an optimal sector design method
+		
+			MichaelDrew
+		
+		10.1109/dasc.2008.4702801
+	
+	
+		2008 IEEE/AIAA 27th Digital Avionics Systems Conference
+		St. Paul, MN
+		
+			IEEE
+			October 2008
+		
+	
+	Drew, M., "Analysis of an Optimal Sector Design Method," Proc. 27 th Digital Avionics Systems Conference, St. Paul, MN, October 2008.
+
+
+
+	
+		Algorithms for Combining Airspace Sectors
+		
+			MichaelBloem
+		
+		
+			ParimalKopardekar
+		
+		
+			PramodGupta
+		
+		10.2514/atcq.17.3.245
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			17
+			3
+			
+			2009
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Bloem, M., Gupta, P., and Kopardekar, P., "Algorithms for Combining Airspace Sectors," Air Traffic Control Quarterly, Vol. 17, No. 3, 2009, pp. 245-268.
+
+
+
+	
+		Flight Level-based Dynamic Airspace Configuration
+		
+			KennethLeiden
+		
+		
+			StevenPeters
+		
+		
+			StaceyQuesada
+		
+		10.2514/6.2009-7104
+		AIAA 2009-7104
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			September 21-23, 2009
+		
+	
+	Leiden, K., Peters, S., and Quesada, S., "Flight Level-based Dynamic Airspace Configuration," Proc.9 th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Hilton Head, SC, AIAA 2009-7104, September 21-23, 2009.
+
+
+
+	
+		Characterization of Days Based on Analysis of National Airspace System Performance Metrics
+		
+			GanoChatterji
+		
+		
+			BassamMusaffar
+		
+		10.2514/6.2007-6449
+		AIAA 2007-6449
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			August 20-23, 2007
+		
+	
+	Chatterji, G. B., and Musaffar, B., "Characterization of Days Based on Analysis of National Airspace System Performance Metrics," Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, Hilton Head, SC, AIAA 2007-6449, August 20-23, 2007.
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+	
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+I. Introductionhis paper is motivated by the need for selecting days with particular air traffic flow patterns and operational characteristics, as encapsulated in the performance metrics, for validating simulation models and evaluating next generation air traffic system concepts.Evaluation of system-wide impacts in terms of cost and benefits with one or two days of data, or with several days of data with similar traffic conditions, is of limited utility.Such evaluations therefore, have to be conducted with a set containing days with distinct characteristics.In order to balance the effort required against the quality of results achieved for these types of simulations and evaluations, a small set of days that covers all the possible traffic conditions is desirable.The multiple-metric classification method proposed in this paper makes it possible to create such a set of days.
+TPrior effort on the selection of days for validating simulation models is described in Refs. 1 and 2. Reference 1 contains a detailed description of the data and the procedure used, while Ref. 2 is a summary of the same.The approach consisted of using the K-Means algorithm, first proposed in Ref. 3, to partition the set of days into six significant groups, each with at least 2% of the days, and one outlier group for days that could not be assigned to the six significant groups.Each group was separated from others in terms of a single Euclidean distance metric composed of the eight chosen metrics.Based on analysis of the metrics associated with each of the six significant groups, they concluded that Ground Delay Program (GDP) minutes and total operations count, a measure of trafficvolume, were the primary determinants of group membership.Threshold values were computed for these two metrics and used within a decision-tree for labeling a given day as a typical day characterized by one of the six groups.The main limitation of the method is that the Euclidean distance metric, constructed by adding quadratic terms corresponding to metrics with different scales and units, partitions days in the transformed domain of the combined metrics.This obscures the relation of the groups to the individual metrics.Thus, grouping with a finer level of granularity cannot be achieved with this method.The method proposed in this paper overcomes the limitations of the previous approach by creating groups based on each metric individually using the K-Means algorithm.Each day is then tagged with a composite ID consisting of IDs of the groups it belongs to based on different metrics.For example, if a day is a member of Group 1 based on Metric 1, a member of Group 1 based on Metric 2, and a member of Group 3 based on Metric 3, it is tagged with the composite group ID of (1,1,3), where the first index indicates grouping based on Metric 1, the second based on Metric 2 and the third based on Metric 3.All days with the same tag are then placed in one group.A salient feature of the proposed algorithm is that the linguistic description of the group labels based on each metric is retained in the composite label of the final grouping.For example, if groups 1, 2 and 3 mean "low," "medium" and "high," respectively, the composite label (1,1,3) means "low" based on the first metric, "low" based on the second metric and "high" based on the third metric.Thus, the fidelity of partitions of individual metrics is retained in the final grouping.The rest of the paper is organized as follows.Major trends observed in the 517 days of NAS delay data are described in Section II.Total time delay in minutes is used as a distance metric within the K-Means algorithm to partition the set of 517 days into ten groups in Section III.Convergence characteristics of the K-Means algorithm and summary statistics of the ten groups are provided in this section.A multiple-metric classification technique that builds on the single-metric classification technique is then developed in Section IV.Two examples of grouping of days are provided in Section IV to highlight the salient features of the algorithm.Conclusions are discussed in Section V.
+II. National Airspace System Delay and Traffic-Volume CharacteristicsTo keep track of operational efficiency of the air traffic system, the Federal Aviation Administration (FAA) and the Bureau of Transportation Statistics (BTS) keep records of a multitude of metrics including delay, number of operations, conditions at airports, and traffic management initiatives in databases.Several of the frequently used databases are: Aviation System Performance Metrics (ASPM), Air Traffic Control System Command Center (ATCSCC) Logs, BTS data, Enhanced Traffic Management System (ETMS) and OPSNET.Detailed descriptions of the contents of these databases are available in Refs. 1 and4.All the analysis and the results in this paper are based on OPSNET data, which are available via http://www.apo.data.faa.gov.OPSNET data only include delays of fifteen minutes or more experienced by Instrument Flight Rule (IFR) flights that are reported by the FAA facilities.These data do not include delays caused by mechanical or other aircraft operator problems.Speed reductions and pilot initiated deviations around weather are also not reported.Taxi times spent under non-FAA facilities, for example under company/airport ramp towers, are not included in delay reports. 5ASPM also provides delay data that are computed based on the Out-Off-On-In (OOOI) data provided by nine commercial and cargo carriers, which can also be utilized for analyzing days via the methods discussed in this paper.Although the trends in ASPM and OPSNET data are similar, the two databases contain very different types of data that make comparisons between them difficult.They are both useful depending on the analysis desired.OPSNET delay data are provided in a tabular form; numbers of aircraft delayed are reported by category, by class and by cause.Delays by category consist of numbers of aircraft that were subject to departure delays, arrival delays, enroute delays and traffic management system (TMS) delays.The distinction between the enroute and TMS delays is discussed later in this section.Delays by class consist of numbers of air carrier, air taxi, general aviation and military aircraft that were delayed.Delays by cause consist of numbers of aircraft that were delayed due to weather, terminal volume, center volume, equipment limitations, runway issues and "other" issues.International delays are included in the "other" category.In addition to these, average time delay in minutes and total time delay in minutes are included in the table.Seventeen variables of OPSNET national delay data for two days are summarized in Table 1.This table shows that the numbers of aircraft delayed by category (departure + arrival + enroute + TMS) add up to the total number of aircraft delayed.Similarly, the numbers of aircraft delayed by class and by cause also add up to the total number of aircraft delayed during the day of operation.Observe that the average delay is obtained by dividing the total time delay in minutes by the total number of delayed aircraft.There are three significant trends that are easily seen in Table 1.First, the sum of departure and TMS delayed flights account for most of the delayed flights.Second, most aircraft are delayed due to weather.Third, as expected, total time delays are proportional to total numbers of aircraft delayed.To understand NAS delay characteristics, OPSNET delay data covering a period of 517 days (17 months) spanning the period from January 1, 2003 through May 31, 2004 were analyzed.Figure 1 shows a scatter plot of the percentages of aircraft delayed due to weather as a function of days.The mean percentage of aircraft delayed due to weather was found to be 66% and the standard deviation was found to be 21% for this dataset.Additional statistical characteristics are summarized in Table 2.These results show that the number of aircraft delayed due to weather represents a large fraction of the number of aircraft that experience delay in the NAS, a finding consistent with Ref. 6, which states that weather is responsible for approximately 70% of NAS delays.The data shown earlier in Fig. 1 was reorganized as a function of total number of aircraft that experienced delay.These transformed data are shown in Fig. 2. Figure 2 shows that on days when a large number of aircraft are delayed, weather is the dominant cause of delays.Percentages of aircraft delayed due to weather are widely scattered when fewer aircraft are delayed, which indicates that factors other than weather are also responsible for delays on those days.Figure 3 shows the number of aircraft delayed due to weather versus the total number of aircraft delayed.Viewing the sample points with respect to the diagonal line across the figure, it is clear that a high degree of correlation exists between the number of aircraft delayed due to weather and the total number of aircraft delayed in the NAS.Assuming both the number of aircraft delayed due to weather and the total number of aircraft delayed are random variables, the correlation coefficient was computationally determined to be 0.95.Correlation between the number of aircraft delayed due to weather and the total time delay in minutes due to all reportable causes (see the last row of Table 1 for an example) was found to be 0.94.The causes of delay other than weather were also studied.Their statistics are summarized in Table 3 along with those of weather delays.Correlation coefficients 1 ρ in Table 3 are defined with respect to the total number of aircraft delayed, and correlation coefficients 2 ρ are defined with respect to the total accrued time delay in minutes.As evident from the correlation coefficient value of 0.21 in this table, the number of aircraft delayed due to volume has a weak linear correlation with the total number of aircraft delayed in the NAS.Correlation is even lower, 0.11, with respect to the total time delay in minutes.Similarly, the value of the correlation coefficient between the number of aircraft delayed due to equipment, runway and other non-US facilities, and the total number  To determine relative contributions of delays attributed to departure, enroute and arrival phases of flights, and to TMS restrictions, percentages of aircraft delays by category were calculated for the 517-day dataset.It was determined that on an average, on any given day, 47% of the aircraft that are delayed in the NAS, are delayed during departure, 1% during enroute and 14% during arrival phases of flight.The average percentage of aircraft delayed due to TMS was 39%.Analysis of the data showed that, on average, departure delayed flights and TMS delayed flights roughly account for 86% of the flights that are delayed.Additional statistics that characterize these delays are summarized in Table 4.Note that the values of the correlation coefficients 1 ρ and 2 ρ listed in the table are with respect to the total number of aircraft delayed and the total accrued time delay in minutes, respectively.Since traffic management initiatives such as GDP and GS are applied to aircraft while they are on the ground, and rerouting and holding while they are airborne, TMS delays include both ground and airborne delay components.To separate TMS delay into ground delay and airborne delay components, analysis of GDP and GS data, that are also available via OPSNET, is needed.Like data in Table 1, these data are also provided in a tabular format with the following items: 1) date, 2) number of aircraft delayed, 3) total delay in minutes, and 4) average time delay in minutes.For example, GDP and GS data for two days, 10 April 2004   1.Ground delay and airborne delay components can be computed using NAS delay data (see delays by category in Table 1) and the GDP and GS delays (see Table 5) as follows.Let, ,, and be the numbers of aircraft delayed during departure, due to GDP, and due to GS, respectively.The total number of aircraft delayed on the ground is thend n GDP n GS n GS GDP d G n n n n + + = (1)Number of aircraft delayed during the airborne phase can be obtained after subtracting GDP and GS components from TMS delays as, ) (  6 show that on average 74% of the aircraft that experience delay are delayed on the ground, compared to an average of 26% that are delayed while airborne.The last row of Table 6 shows that on some days NAS conditions are unusual in that a large percentage of delayed aircraft experience airborne    In addition to the NAS delay metrics discussed in this section, past studies such as Ref. 1, 2 and 7 have used metrics of traffic volume to select days for analysis.There is consensus in the literature that the "traffic volume" and the delay taken together characterize NAS operations, therefore traffic volume metrics are discussed next.OPSNET database includes Towers: Summary Report, Instrument Operations: Summary Report and Centers: Summary of Domestic Operations Report that contain traffic volume data.These three reports count traffic from different perspectives.One is unable to separate departure counts from arrival counts in the Towers: Summary Report and in the Instrument Operations: Summary Report.A departure at one facility is counted as a departure at that facility and as an arrival at a different facility.Since departures and arrivals are counted together twice in these reports, the total number of operations excluding the overflight operations have to be halved to estimate the number of departures or the number of arrivals.The Centers: Summary of Domestic Operations Report directly provides a count of the number of departures from airports within the ARTCCs.Since departure count eventually drives the overflight count and the arrival count, it represents the traffic demand.Due to this reason, departure count from the Centers: Summary of Domestic Count Report has been used in this paper.Table 7 lists the departure counts and the overflight counts for the two days.Departure counts excluding military flights for the two days obtained by summing the air carrier, air taxi and general aviation departures are 31,959 and 42,062.GS GDP TMS e a A n n n n n n - - + + = (2)A histogram of the total domestic departure counts for the 517 days of data is shown in Fig. 5.The minimum and the maximum numbers of departures were found to be 25,677 on 11/27/2003 (Thanksgiving holiday) and 51,399 on 5/27/2004 (Thursday).Observe that the histogram is bimodal which indicates that days can be classified into two categories -low departure count day and high departure count day.Reference 1 noted similar observations and offered evidence that the bimodal distribution is primarily due to the weekday versus weekend traffic levels.This section described several delay and traffic volume metrics that are available in OPSNET data.Summary statistics described in the tables and the patterns observed in the figures suggest that these metrics can be used for distinguishing one day of NAS operations from another day of NAS operations.To illustrate the use of a metric for classifying days of operations, total time delay in minutes, in Eq. ( 3), is used as a distance metric within the K-Means method in the next section.T n
+III. Single-Metric ClassificationThe motivation for assigning or labeling days into groups with associated properties, such as mean delay values, is to aid selection of prototype days for analysis.For example, a few days from a group of days with large delays and from a group of days with small delays can be selected for system-wide studies using the National Aeronautics and Space Administration's air traffic simulation, concept evaluation, and decision support tools such as the Airspace Concept Evaluation System (ACES), the Center TRACON Automation System (CTAS) and the Future ATM Concepts Evaluation Tool (FACET). 8-11 All classification processes use metrics, or features, of the data to partition it into groups.A popular classification method, known as the K-Means method, partitions data such that the means associated with the groups are as widely separated as possible. 3The method labels the data elements based on their closeness to the group American Institute of Aeronautics and Astronautics means for reducing the group variance.The K-Means algorithm consists of two steps: 1) the initialization step, and 2) the iterative step.Data elements that are far apart from each other are chosen as the initial means of the groups during the initialization step.Each element is then assigned to the group that it is closest to, based on its distance with respect to the group's initial mean value.Group means are then recomputed based on the elements assigned.Each element is then reassigned to its closest group based on its distance with respect to the recomputed mean values.This process of computation of the means and reassignment of elements to groups is continued in subsequent iterative steps.Convergence is achieved when the numerical values of the group means do not change with reassignment of the elements.Iterations are halted once convergence is achieved.To further clarify the initialization and the iterative steps of the K-Means algorithm, consider a vector with the following elements [0, 0.5, 0.8, 1.2, 5, 7,12,15,20,25].If two groups are desired, the elements with values closer to 0 are assigned to the first group and the elements with values closer to 25 are assigned to the second group.Thus, elements one through seven are assigned to Group One and elements eight through ten are assigned to the Group Two in the initialization step.With this assignment of the elements to the groups, average values of the first group and the second group are 3.79 and 20 and the standard deviation values are 4.47 and 5. Reassignment of the elements based on the recomputed means results in the first six elements being assigned to Group One and the last four elements being assigned to Group Two in the first iterative step.Group means are recomputed in the next iterative step.These means are 2.42 and 18 and the standard deviations are 2.87 and 5.72.The next iterative step results in the same means and the standard deviations as those in the prior step; final grouping is therefore achieved in the previous step.For this example, the K-Means algorithm partitions the data into two groups in three steps.If three groups are desired for the above example, a value from the vector that is far away from both 0 and 25 needs to be selected as the initial value for the third group.Observe that this value is 12 since its minimum distance to 0 (12 units) and 25 (13 units) is a maximum compared to the minimum distances of other elements to 0 and 25.Other values in the vector are less than 12 units with respect to either 0 or 25.Once these initial group means are chosen, the subsequent iterative steps are the same as those described in the previous paragraph.It should be noted that good initial conditions are needed because the K-Means algorithm is sensitive to initial conditions.The K-Means algorithm was used for classifying 517 days into ten groups using total time delay in minutes as the discriminating metric.The choice of ten groups was arbitrary.The algorithm converged in thirteen iterations.Its convergence characteristics are shown in Figure 6.Properties of the ten groups, based on total time delay statistics of the elements assigned to the groups, are summarized in Table 8.The second column of the table shows the number of days in the group, with the group ID given in the first column.Columns three and four show the average delay and the standard deviation of the delays in minutes.Columns five and six show the minimum and maximum delays in minutes of the days belonging to the particular groups.The data in this table  7. Note that the extent of the abscissa is limited to the range of the delay data.Sixteen days (seven days in Group 8, seven days in Group 9 and two days in Group 10) that experienced large delays are listed in Table 9.Of the 517 days grouped by the K-Means algorithm, the least delay of 1,686 minutes occurred on 1/11/2003 (a Saturday) and most delay of 186,313 minutes occurred on 5/13/2004 (a Thursday).
+Table 9. Days with large delays.Results presented in this section demonstrate the use of the K-Means algorithm for partitioning a set of days into groups organized in order of a single metric like total time delays.The next section describes a labeling technique that enables use of the single-metric K-Means classification technique for achieving classification based on multiple metrics.
+IV. Multiple-Metric ClassificationMotivation for multiple-metric classification stems from the desire for finer levels of partitioning.For example, a group with large mean delay contains days when aircraft were delayed due to weather and also days when aircraft were delayed due to runway conditions.In order to discern which ones were affected by weather and which ones were affected by runway conditions, one would need metrics such as numbers of aircraft delayed due to weather and due to runway conditions, in addition to total delays.Fidality Classification based on multiple metrics has been traditionally accomplished by weighing and combining several metrics into a single metric, and then using it in a K-Means algorithm.For example, if day ' ' was characterized by metrics:, , …, and if day 'q f 1 , q u 2 , q u f q u ,r ' was similarly characterized by , , …, , a weighted quadratic function of the form1 , r u 2 , r u f r u , 2 , , 1 , ) ( l r l q f l l r q u u w d - = ∑ ≤ ≤ (4)can be defined as the distance metric between days and q r .Note that through are the weights corresponding to the different metrics.Interpretation of the distance metric, Eq. ( 4), for grouping days with the K-Means algorithm is straightforward with as the mean of measure l in groupm k n j p u w d l j l k f l l j k ≤ ≤ ≤ ≤ - = ∑ ≤ ≤ 1 ; 1 ; ) ( 2 , , 1, where is the number of metrics, m is the number of days and is the number of groups.f nAlthough the distance metric, Eq. ( 5), enables transformation of a multiple-metric classification problem into a single-metric classification problem, its deficiencies are noteworthy.Limitations stem from the fact that metrics have different scales and units, and that only their combined contribution is available in the distance metric; classification is insensitive to individual contribution.For example, consider the two metrics in Table 1: 1) number of aircraft affected by departure delays and 2) total time delay.Units of the two metrics are quite different, number of aircraft and minutes.The scales are also different by an order of magnitude; 257 aircraft impacted by departure delays versus 12,616 minutes of total time delay on 10 April 2004.To compensate for these differences, the associated weights have to be scaled correctly, and their units have to be chosen appropriately to enable summation of quantities with disparate units.References 1 and 2 suggest that the inverse of the statistical variance of the metric should be used to weigh its contribution.Even with this scaling, a meaning cannot be ascribed to the grouping in the native domain of the metrics.In order to overcome the limitations of the weighted quadratic distance function used in the prior approach of Refs. 1 and 2, a multiple metric classification technique that treats each metric independently of others in an dimensional metric space is proposed.Since each metric is treated independently, the single metric K-Means algorithm described earlier in Section III can be used for assigning days to groups based solely on each metric.IDs of these groups are then coordinates in the -dimensional metric space.For the sake of discussion, consider the problem of classifying days into four groups using two metrics.Using the K-Means algorithm twice, days are first assigned to four groups based on Metric One, and then to four groups based on Metric Two.The resulting sixteen possible groups are labeled using two indices as follows: (1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (2, 4), (3, 1), (3, 2), (3, 3), (3,4), (4, 1), (4, 2), (4, 3) and (4,4).The first index denotes group ID based on Metric One and the second index denotes group ID based on Metric Two.Thus, a day which is assigned to the second group based on Metric One and to the first group based on Metric Two is a member of group (2, 1).Since a unique group is labeled using two indices in this two metric classification problem, the combined group IDs are coordinates in a twodimensional metric space.f f Generalization of the technique to metrics such that days are classified into groups using metric results inf l n l ∏ ≤ ≤ = f l l g n n 1 (6)number of possible groups.Equation (6) shows that if the same number of groups is desired for each metric, the number of possible groups is given in terms of the power of .For example if groups are desired with each metric, the number of possible groups is .Thus, it is seen that the growth in the number of groups is explosive with increasing number of metrics.Should one conclude that the growth is unbounded based on this observation, or is there an upper bound on the number of groups?The answer is provided by the following.If each day is classified into its own group, one would have the same number of groups as the number of days; hence, number of days is the upper bound.This fact also implies that if is the number of days and , there are at least number of empty groups, groups without any members.Removing these empty groups, the number of possible groups, , is given asf n f n m m n g > m n g - g n ∏ ≤ ≤ = f l l g n m n 1 ), min( (7) where each .m n l ≤ Is it possible that several of the groups counted in Eq. ( 7) are empty?One can demonstrate this to be true by constructing the following examples.Consider the problem of classifying ten days into two sets of five groups using American Institute of Aeronautics and Astronautics two metrics.Following the nomenclature of Eq. ( 7), 2 = f for the two metrics, 5 1 = n using Metric One, 5 2 = n using Metric Two, and for the ten days.Substituting these numerical values in Eq. ( 7) it is seen that .Assume that the first two days are assigned to Group 1, the third and the fourth to Group 2, the fifth and the sixth to Group 3, the seventh and the eighth to Group 4, and the final two to Group 5 based on Metric One, and also based on Metric Two.In this scenario, groups with members are (1, 1), (2, 2), (3,3), (4,4) and (5, 5).All other groups labeled with two different indices, such as (1, 2), (1, 3) and (5, 1), are empty.7).If the first five days are assigned to Group 1 and the next five to Group 2 based on both the metrics, groups (1, 1) and (2, 2) are non-empty while groups (1, 2) and (2, 1) are empty.These two examples clearly show that it is always possible to have empty groups.An aspect of multiple-metric classification that has not been discussed so far is the semantics associated with the group IDs.Without a linguistic meaning, it is difficult to interpret what do group IDs such as (1, 1) and (1, 2) mean.One of the ways of attributing a meaning to the indices is to order them according to the increasing values of the group means.For example if total time delay in minutes was the metric being considered, the index with the least value would correspond to the group with the minimum mean total time delay while the index with the highest value would correspond to the group with the maximum mean total time delay.From an implementation perspective, once classification into specified number of groups is accomplished with the K-Means algorithm using a single metric, and group means are computed based on the metric values of the assigned members, the group means are sorted in an increasing order.Indices of the groups are then re-labeled to reflect the sorted order.This procedure is repeated for each metric to obtain the complete set of indices required for labeling the groups.Three metrics-1) total domestic departure counts, 2) number of aircraft delayed on the ground, and 3) number of aircraft delayed in the air were used as the basis for classification in the multiple-metric K-Means algorithm described in this paper.Recollect that the total domestic departure counts were obtained from the Centers: Summary of Domestic Counts Report discussed in Section II.Numbers of aircraft delayed on the ground and delayed in the air were computed using Eqs.( 1) and (2).Days were initially organized into three groups using the single-metric K-Means algorithm.When number of aircraft delayed in the air was used as a metric, 393 days were assigned to Group 1, 128 to Group 2 and one to Group 3. The mean and the standard deviation values derived from the number of aircraft delayed in the airborne phase metric of the assigned members for these groups are listed in Table 10.The sole member of Group 3, 7/15/2005, had excessive amount of airborne delay of 1891 minutes.1661 aircraft were delayed on the ground on this day.Since this day is an outlier, it has the effect of increasing the standard deviation of the other groups.Due to this reason, it was removed from the dataset.American Institute of Aeronautics and Astronautics The analysis was repeated with the remaining 521 days.The resulting grouping showed that 6/5/2005 became the sole member of Group 3 with 970 aircraft delayed in the airborne phase and 753 aircraft delayed on the ground.Table 11 summarizes these results.Comparing Table 10 to Table 11, it is seen that the removal of 7/15/2005 data lowers the standard deviation values of the groups.Since 6/5/2005 is an outlier day, it was also removed from the dataset.Classification based on number of aircraft delayed in the airborne phase for the remaining 520 days are summarized in Table 12.Observe that the standard deviation values decrease further for groups 1 and 2. It increases for Group 3. The mean values decrease for all three groups.Days belonging to Group 1 can be thought of as days with low number of aircraft delayed while airborne; in Group 2 as days with medium number of aircraft delayed and the ones in Group 3 as days with large number of aircraft delayed.Similar categorization based on number of aircraft delayed on the ground partitions the days in Groups 1 through 3, whose statistics are summarized in Table 13.Results obtained using total domestic departure counts are provided in Table 14.Note that the days were classified into two groups based on the bimodal distribution seen in Fig. 5.Comparing Tables 12 and13, it is seen that the trends are similar with the largest number of days assigned to groups with lower mean and lower standard deviation values.The trends are different in Table 14.More days are assigned to Group 2 with higher mean departure counts.Given that three groups were created using two metrics and two groups using one metric, the total number of possible composite groups, determined using Eq. ( 7), is 18.The range of IDs for these groups is (1, 1, 1) to (2,3,3).With the first index being the group number associated with the total domestic departure counts metric, the second index being the group number associated with the number of aircraft delayed on the ground metric, and the third index being the group number associated with the number of aircraft delayed during the airborne phase metric, each day in the set of 520 days has a three index group ID associated with it.Many of the salient features of multiple-metric classification algorithm are apparent from data in Table 15.A majority of days, 94 and 89, are assigned to Group (1, 1, 1) and Group (2, 1, 1).These groups represent days on which few aircraft were delayed.The difference between them is that Group (1, 1, 1) represents low-volume days while Group (2, 1, 1) represents high-volume days.Table 15 shows that a large number of days were assigned to groups (2, 2, 1) and (2, 2, 2) that represent high-volume days on which many aircraft were delayed on the ground.There are several groups with few days assigned to them; five groups had three or less than three days assigned to them.Table 16 lists the corresponding dates.Member days belonging to small groups can be considered to be special.Days in Group 3 with Group ID (1, 1, 3) experienced relatively low total departure counts, fewer aircraft affected by ground delay, and higher number of aircraft affected by airborne delay.Two members of Group 6 had more aircraft delayed on the ground compared to members of Group 3. Three member days of Group 7 had many more aircraft delayed on the ground and few during the airborne phase.The sole member of Group 9 had many aircraft delayed while on ground and while in the air.The member days of Group 12 experienced high departure counts, relatively few aircraft delayed on the ground, and a large number of aircraft delayed during the airborne phase.Another example of multiple-metric classification using total domestic departure counts and delays by cause: 1) weather, 2) volume, 3) equipment, runway and other, as metrics for partitioning 522 days into groups is summarized in Table 17.Numbers of aircraft delayed due to terminal volume and due to center volume (see "Delays by Cause" in Table 1) were combined to obtain the number of aircraft delayed due to volume.Similarly, numbers of aircraft delayed due equipment, runway and other issues were added together for obtaining the number of aircraft impacted due to these causes.As in the previous example, days were categorized into groups with the K-Means algorithm using each of these four metrics one at a time.Observe that in this example, days are partitioned into 37 groups out of 54 possible groups.
+American Institute of Aeronautics and AstronauticsAmerican Institute of Aeronautics and Astronautics The results of the two examples considered here show that 1) days can be classified into the specified number of groups based on each individual metric, 2) the individual metric group labels can be used for creating multiplemetric group labels, and 3) linguistic description of the individual metric grouping is retained in the composite group label.These examples also illustrate that the multiple-metric classification method does not require that the number of groups be the same based on each metric for creating composite group IDs.In the first example, days were organized in two groups using the total number of departure counts metric and in three groups using the number of aircraft delayed on the ground and the number of aircraft delayed in the air.This technique of maintaining different numbers of groups along different axes of the metric space is in contrast with the method described in Ref. 1 and 2 that only partitions data along the single distance metric.Results demonstrate that the multiple-metric classification method generates groups with several members and also groups with few members; thus, identifying both nominal and off-nominal days.By selecting a typical day from each group, and then using traffic data corresponding to those days, enough data diversity can be assured for validation of simulations and for Monte Carlo type of benefits analysis of novel air traffic management concepts.Resulting benefits metrics can be weighed with number of members in the group that each day is associated with for estimating overall benefits.
+V. ConclusionsConsistent with other studies, analysis of 517 days of National Airspace System (NAS) delay data, which were obtained from the Federal Aviation Administration's Air Traffic Operations Network (OPSNET) database, showed that weather is the predominant causal factor for delays; equipment and runway conditions, and traffic-volume are the other major causal factors.It was also determined the departure and traffic management system delays account for about 86% of the aircraft that are delayed.Ground Delay Program and Ground Stop delay data, also obtained from OPSNET, were combined with the NAS delay data to obtain the number of aircraft delayed on the ground and in the air.The results obtained indicate that on an average 74% of the delayed aircraft are delayed on the ground while only 24% are delayed in the air.
+American Institute of Aeronautics and AstronauticsThe daily total time delay in minutes was used as a discriminating metric within the K-Means algorithm for partitioning 517 days into ten groups.Mean time delay values associated with the resulting groups, computed using time delay values of the member days, arranged in increasing order were found to be approximately equidistant from the preceding and succeeding mean values.Differences between the standard deviation values associated the groups were also found to be small.Most of the days were assigned to groups with smaller mean time delays.Days with large delays were also identified by the algorithm.A multiple-metric algorithm was synthesized with the single-metric K-Means algorithm at its core.The technique consists of creating groups using each metric individually as a distance metric within the single-metric K-Means algorithm.Member days are labeled with the group numbers associated with the metrics.Final grouping is achieved by assigning days with a common label to the same group, such that groups are labeled by the same number of indices as the number of metrics.The multiple-metric algorithm was applied to the problem of organizing the 522 days into groups using a) total domestic departure counts, b) number of aircraft delayed on the ground and c) number of aircraft delayed in the air as the three metrics.Two days that were found to be outliers were removed and the remaining 520 days were classified into 18 groups.Six of the 18 groups had six or fewer days as members.Although these groups represent unusual days, their inclusion in a set of days that represents diverse air traffic conditions is essential for evaluating concepts and validating simulations.The other 12 groups had 14 or more days as members.Another example of multiple-metric classification of 522 days into groups with a) total domestic departure counts, b) weather delays, c) volume delays and d) equipment, runway and other delays as the chosen metrics was presented.In this instance, 37 groups out of 54 possible groups had member days.Of the 37 groups, 20 groups had five or fewer days as members.Comparing the results obtained via the two examples, it was seen that different sets of days can be created and certain unusual days can be identified based on the choice of metrics.The two examples serve as illustrations of the ability of the multiple-metric algorithm to create datasets, consisting of days classified into groups, with enough data diversity for concept evaluation and simulation validation.Figure 2 .2Figure 2. Percentage of aircraft delayed due to weather as a function of total number of aircraft delayed.
+Figure 3 .3Figure 3. Proportion of number of aircraft delayed due to weather to the total number of aircraft delayed in the NAS.
+the numbers of aircraft delayed in arrival phase, in enroute phase, and due to TMS, number of aircraft delayed in the NAS.Results obtained using Eqs.(1) through (3) with 517 days of OPSNET data are shown in Fig. 4; n and values are plotted against values.This figure shows that when more aircraft are delayed, a significantly higher number of them are delayed on the ground compared to in the air.Statistical trends summarized in Table
+Figure 4 .4Figure 4. Proportion of number of aircraft delayed on the ground and in the air to the total number of aircraft delayed in the NAS.
+delay.Of the 665 aircraft that were delayed on 2/22/2003, 513 aircraft (77%) were delayed during their airborne phase of flight.The airborne and ground delay values for this day are marked with a large 'X' and a large 'O' in Fig.4.
+Figure 5 .5Figure 5. Histogram of 517 days of total domestic departure counts.
+Figure 6 .6Figure 6.Convergence characteristics of the K-Means algorithm as it partitions the 517 days into ten groups.
+can be constructed to show that empty groups are possible even when .
+Table 1 . OPSNET NAS delay summary data.1Data Variable4/10/20044/13/2004Delays by CategoryTotal # of Aircraft3912,312Departure257651Arrival101391Enroute012TMS331,258Delays by ClassAir Carrier3381,769Air Taxi26474General Aviation2769Military00Delays by CauseWeather2352,049Terminal Volume5927Center Volume4113Equipment126Runway3024Other25173Time DelayAverage Time (min.)32.2753.51Total Time (min.)12,616123,709American Institute of Aeronautics and Astronautics
+Table 2 . Statistical characteristics of percentages of aircraft delayed due to weather.2CharacteristicAircraft delayed by weatherMean66%Standard deviation21%Minimum5%Maximum98%Median70%
+Figure 1. Percentage of aircraft delayed due to weather.American Institute of Aeronautics and Astronautics of aircraft delayed in the NAS was found to be 0.27.It was found to be 0.16 with respect to the total time delay in minutes.In the hierarchy of prime causal factors for delays, equipment and runway conditions follow weather.Results presented in this section suggest that delay metrics that encapsulate weather characteristics are likely to be useful in the classification of days.Delays attributed to weather, volume, andequipment, runway and other causes are realized viadeparture, arrival, enroute and TMS restrictions.Departure delays incur by holding aircraft at thegate, on the taxiway, short of the runway, and on therunway. Arrival delays accrue when aircraft aredelayed in the arrival Center's airspace or inTerminal Radar Approach Control airspace due torestrictions at arrival airports. Enroute delays occurwhen aircraft are held as a result of initiativesimposed by a facility for traffic managementreasons such as volume regulation, frequencyoutage and weather. The other major category ofdelays in the OPSNET data is TMS delays, whichresult from national or local Center (coordinatedwith Air Traffic Control System Command Center)traffic flow management initiatives such as GroundDelay Programs, local Ground Stops (GS),Departure Sequencing Programs, Enroute SpacingPrograms, Arrival Sequencing Programs, airborneholding, rerouting, Miles-in-Trail, Minutes-in-Trailand Fuel Advisory.
+Table 3 . Summary of weather, volume, and equipment, runway and other delay characteristics.3CharacteristicWeatherEquip.,VolumeRunway& OtherMean66%20%14%Standard21%16%11%deviation 1 ρ0.950.270.210.940.160.112 ρ
+Table 4 . Summary of departure, enroute, arrival and TMS delay characteristics.4Characteristic Departure TMS Arrival EnrouteMean47%39%14%1%Standard17%17%8%1%deviation 1 ρ0.820.860.550.450.730.860.510.492 ρAmerican Institute of Aeronautics and Astronautics and 13 April 2004, are shown in Table5.NAS delay data for the same two days were previously itemized in Table
+Table 6 . Summary of aircraft delayed on ground versus aircraft delayed in the air.6CharacteristicDelayed onDelayed in AirGroundMean74%26%Median76%24%Standard Deviation11%11%Minimum23%7%Maximum93%77%
+Table 5 . OPSNET GS and GDP delay data.5Data Variable4/10/20044/13/2004Ground Stops# of Aircraft Delayed398Minutes of Delay2927,079Average Delay97.3372.23Ground Delay Program# of Aircraft Delayed61,044Minutes of Delay24485,166Average Delay40.6681.57Total Delays Due to GS and GDP# of Aircraft Delayed91,142Minutes of Delay53692,245Average Delay59.5580.77American Institute of Aeronautics and Astronautics
+Table 7 . Centers: Summary of Domestic Operations Report.7Data Variable4/10/20044/13/2004DeparturesAir Carrier17,12220,231Air Taxi9,13212,831General Aviation5,7059,000OverflightsAir Carrier23,02123,753Air Taxi3,5564,318General Aviation2,7634,763
+Table 8 . Summary of properties of the ten groups.8American Institute of Aeronautics and Astronautics show that there are fewer days in groups associated with large delays.For example, group number ten consists of only two days compared to group number one with 145 days.Observe that the mean values associated with the groups are approximately equally spaced and that the standard deviation values are fairly close to each other.Standard deviation values can be expected to increase with fewer groups.Probability density functions corresponding to Gaussian distributions with the group means and standard deviations listed in Table8are shown in FigureGroupNumberMeanStandardMinimumMaximumIDof DaysDelayDeviationDelayDelay(min.)(min.)(min.)(min.)114511,3024,3101,68618,834212626,6264,48919,10734,62839243,0265,09534,94752,11345761,2395,19852,56270,01153880,0905,42171,18690,464632 102,8205,94494,023112,031711 124,6523,612119,692131,17287 141,6335,854133,884148,34197 163,5015,887156,717172,211102 183,4264,083180,539186,313
+Table 10 . Three groups based on number of aircraft delayed in the airborne phase.10To illustrate the utility of the multiple-metric classification technique, an example of classifying 522 days, which included the 517 days discussed previously and five special days used in earlier studies, into groups is presented next.These five special days are 5/17/2002, 4/17/2005, 4/21/2005, 6/5/2005 and 7/15/2005.5/17/2002 is the ACES baseline day.The other four days were used earlier in Ref. 7. They were categorized as a low-volume low-weather day, high-volume low-weather day, low-volume high-weather day and high-volume high-weather day, respectively in Ref. 7.GroupNumberMeanStandardMinimumMaximumNumberof DaysNumberDeviationNumberNumberDelayedDelayedDelayed13931244972182128314100222970311891018911891
+Table 13 . Three groups based on number of aircraft delayed on the ground.13GroupNumberMeanStandardMinimumMaximumNumberof DaysNumberDeviationNumberNumberDelayedDelayedDelayed12493791604864621859131736511,2933861,6893281,3092,778
+Table 12 . Groups based on number of aircraft delayed in the airborne phase (excluding 7/15/2005 and 6/5/2005).12GroupNumberMeanStandardMinimumMaximumNumberof DaysNumberDeviationNumberNumberDelayedDelayedDelayed129110336715821742143815929535538271304604
+Table 11 . Three groups based on number of aircraft delayed in the airborne phase (excluding 7/15/2005).11GroupNumberMeanStandardMinimumMaximumNumberof DaysNumberDeviationNumberNumberDelayedDelayedDelayed1386123477213213430481214604319700970970
+Table 14 . Two groups based on total domestic departure counts.14GroupNumberMeanStandardMinimumMaximumNumberof DaysDelayDeviationDelayDelay(min.)(min.)(min.)(min.)116834,7922,92025,67740,528235246,3352,24340,59651,759
+Table 15 . Final grouping with three-metric classification.15Organizing the resultingtriple index group IDsusing a "dictionary sort" algorithm, each unique group and its members areGroup NumberGroup ID Number of Days1 μμ2μ3σ1σ2σ3determined. The values of1 (1, 1, 1)94 33,97031083 2,718 15837the metrics of the members2 (1, 1, 2)14 33,698444 224 3,179 14941are determination of minimum, then used for3 (1, 1, 3) 4 (1, 2, 1)2 34,650 27 36,060373 348 6,588 120 880 110 2,565 15241 33maximum,meanand5 (1, 2, 2)19 36,816951 221 1,916 15937standard deviation values associated with the groups.6 (1, 2, 3) 7 (1, 3, 1)2 36,583 3 35,219 1,713 131 4,418 301 951 390 4,185 30595 8Results of this process for8 (1, 3, 2)6 37,067 1,586 213 2,296 20430thethree-metric9 (1, 3, 3)1 36,485 1,462 355000classification being discussed here, are problem, outlined in Table 15. Group means for the three metrics are listed in the columns labeled 1 μ , 2 μ and 3 μ ; standard deviation values are listed in the columns labeled as 1 σ , 2 σ and 3 σ .10 (2, 1, 1) 11 (2, 1, 2) 12 (2, 1, 3) 13 (2, 2, 1) 14 (2, 2, 2) 15 (2, 2, 3) 16 (2, 3, 1) 17 (2, 3, 2) 18 (2, 3, 3)89 45,495 47 46,195 3 43,944 60 47,330 50 46,393 27 45,934 18 46,686 1,614 116 2,190 326 383 110 2,200 151 483 204 2,008 117 505 447 2,494 148 898 113 1,961 188 911 205 2,383 156 953 359 2,201 196 38 47,027 1,728 231 1,947 380 20 46,539 1,721 408 2,537 26131 33 52 31 36 58 31 41 81
+Table 16 . Days in small groups.16GroupGroup ID DateNumber3(1, 1, 3)2/22/20033(1, 1, 3)5/16/20046(1, 2, 3)6/14/20036(1, 2, 3)3/28/20047(1, 3, 1)9/14/20037(1, 3, 1)5/23/20047(1, 3, 1)5/30/20049(1, 3, 3)1/4/200412(2, 1, 3)5/2/200312(2, 1, 3)5/5/200312(2, 1, 3)9/30/2003
+Table 17 . Final grouping obtained using departure counts and delays by cause metrics.17Group NumberGroup IDNumber of Days1 μμ2μ3μ4σ1σ2σ3σ41 (1, 1, 1, 1)86 33,6482684972 2,932 16036442 (1, 1, 1, 2)22 35,24335760 252 2,172 17439643 (1, 1, 1, 3)2 33,69115479 498175 1685844 (1, 1, 2, 1)7 35,796355 21289 1,339 15961345 (1, 1, 2, 2)3 37,448402 198 259794 25128616 (1, 1, 2, 3)2 36,120352 229 4958894020 1447 (1, 1, 3, 1)1 35,484296 401 17300008 (1, 1, 3, 2)1 39,454394 541 23800009 (1, 2, 1, 1)25 35,7709635090 2,920 209354610 (1, 2, 1, 2)7 36,85496080 238 2,653 302386811 (1, 2, 1, 3)2 37,02196273 587378 38530 17012 (1, 2, 2, 1)2 37,118960 291 105 3,430 35312813 (1, 2, 2, 2)2 38,540741 306 24965941 100 16414 (1, 2, 3, 2)1 36,485898 710 209000015 (1, 3, 1, 1)4 37,143 1,56740 148 3,049 132322516 (1, 3, 2, 1)2 37,814 1,867 177 1295802711117 (2, 1, 1, 1)78 45,50832580 104 1,774 171263618 (2, 1, 1, 2)21 45,74033085 234 2,349 173214719 (2, 1, 1, 3)8 45,69037091 458 1,609 210248320 (2, 1, 2, 1)29 46,356337 19498 2,204 181463621 (2, 1, 2, 2)21 47,496319 215 274 2,293 129516522 (2, 1, 2, 3)7 47,610314 195 491 1,867 148365823 (2, 1, 3, 1)2 45,004411 412 147 6,233 270331324 (2, 1, 3, 2)2 50,358192 382 265 1,98185181125 (2, 1, 3, 3)1 48,983407 551 540000026 (2, 2, 1, 1)64 46,0909408096 2,318 217303927 (2, 2, 1, 2)27 46,39297796 250 2,481 220284828 (2, 2, 1, 3)3 47,117 1,172 106 555 2,916 33124 29329 (2, 2, 2, 1)19 46,817898 20596 1,910 216513830 (2, 2, 2, 2)17 47,587 1,021 205 262 2,533 241535831 (2, 2, 2, 3)3 47,360765 424 407 4,315 142 337 11332 (2, 2, 3, 3)2 45,890744 429 434 1,87064207133 (2, 3, 1, 1)26 46,221 1,9406680 1,646 341304234 (2, 3, 1, 2)13 47,142 1,89788 252 2,020 279266135 (2, 3, 1, 3)2 45,527 1,943 109 410 3,341 5868336 (2, 3, 2, 1)5 47,467 1,735 209 120482 227343737 (2, 3, 2, 2)3 49,992 2.055 160 280 1,271 9151770
+Table 1818lists the group membership of holidays and special days -ACES baseline day, Joint Planning and Development Office (JPDO) baseline day and days studied in Ref. 7. Group IDs fromTable 15 are listed under Group ID 1 heading and from Table 17 under Group ID 2 heading.Results in this table show that the domestic departure counts are generally lower on holidays.Group ID 1 (2, 2, 2) indicates that the ACES baseline day has high departure counts, moderate number of aircraft delayed on the ground, and moderate number of aircraft delayed in the air; Group ID 2 (2, 2, 1, 1) indicates high departure counts, moderate number of aircraft delayed due to weather, low number of aircraft delayed due to volume and low number of aircraft delayed due to equipment, runway and other issues.The two group IDs for the JPDO baseline day indicate high departure counts, moderate number of aircraft delayed on ground, low number of aircraft delayed in the air, low number of aircraft delayed due to weather, moderate number of aircraft delayed due to volume and high number of aircraft delayed due to equipment, runway and other conditions.Ref. 7 considered 4/17/2005 to be a low departure count, low-delay due to weather day.The group IDs in Table 18 label this day as a high departure count, low-delay due to weather day.The departure count of 40,653 on this day is at the lower end of the high departure count group can be inferred from the statistics given in
+Table 18 . Classification of holidays and special days.18Number DateSignificanceDay of WeekGroup ID 1Group ID 215/17/2002 ACES Baseline DayFriday(2, 2, 2)(2, 2, 1, 1)21/1/2003 New Year's DayWednesday(1, 1, 1)(1, 1, 1, 1)31/20/2003 Martin Luther King DayMonday(1, 1, 1)(1, 1, 1, 1)42/17/2003 President's DayMonday(1, 1, 1)(1, 1, 1, 1)55/26/2003 Memorial DayMonday(1, 1, 1)(1, 1, 1, 1)67/4/2003 Independence DayFriday(1, 1, 1)(1, 1, 1, 1)79/1/2003 Labor DayMonday(1, 2, 1)(1, 2, 1, 1)8 10/13/2003 Columbus DayMonday(2, 1, 1)(2, 1, 2, 1)9 11/11/2003 Veterans DayTuesday(2, 2, 1)(2, 1, 2, 1)10 11/25/2003 Two Days Before ThanksgivingTuesday(2, 3, 1)(2, 1, 3, 3)11 11/27/2003 Thanksgiving DayThursday(1, 1, 2)(1, 1, 1, 1)12 12/25/2003 Christmas DayThursday(1, 1, 1)(1, 1, 1, 1)131/1/2004 New Year's DayThursday(1, 1, 1)(1, 1, 1, 1)141/19/2004 Martin Luther King DayMonday(2, 1, 1)(2, 1, 2, 2)152/16/2004 President's DayMonday(2, 2, 2)(2, 2, 2, 2)162/19/2004 JPDO Baseline DayThursday(2, 2, 1)(2, 1, 2, 3)175/31/2004 Memorial DayMonday(1, 3, 2)(1, 3, 2, 1)184/17/2005 Ref. 7 L/L DaySunday(2, 1, 1)(2, 1, 1, 2)194/21/2005 Ref. 7 H/L DayThursday(2, 1, 2)(2, 1, 3, 2)206/5/2005 Ref. 7 L/H DaySundayNot included (1, 3, 1, 1)217/15/2005 Ref. 7 H/H DayFridayNot included (2, 3, 2, 2)
+Table 14 .14Classification of 4/21/2005 as high departure count, low-delay due to weather day is in agreement with Ref. 7 except that many aircraft were delayed due to volume on this day.The results in Table 18 for 6/5/2005 and 7/15/2005 are in agreement with Ref. 7. Both these days experienced an inordinate amount of airborne and ground delays due to weather.A number of aircraft were also delayed due to volume, and equipment, runway and other issues on 7/15/2005.
+		
+		
+			
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+				
+
+
+	
+		Aggregate Statistics of the National Airspace System
+		
+			JimmyKrozel
+		
+		
+			BobHoffman
+		
+		
+			StevePenny
+		
+		
+			TarynButler
+		
+		10.2514/6.2003-5710
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Herndon, VA
+		
+			American Institute of Aeronautics and Astronautics
+			20170. October 2002
+		
+	
+	Suite 200
+	Krozel, J., Hoffman, J., Penny, S., and Butler, T., "Selection of Datasets for NAS-Wide Simulation Validations," Metron Aviation, Inc., 131 Elden St., Suite 200, Herndon, VA 20170, October 2002.
+
+
+
+	
+		A Cluster Analysis to Classify Days in the National Airspace System
+		
+			BobHoffman
+		
+		
+			JimmyKrozel
+		
+		
+			StevePenny
+		
+		
+			AnindyaRoy
+		
+		
+			KarlinRoth
+		
+		10.2514/6.2003-5711
+		AIAA-2003-5711
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, TX
+		
+			American Institute of Aeronautics and Astronautics
+			August 11-14, 2003
+		
+	
+	Hoffman, B., Krozel, J., Roy, A.., and Roth, K., "A Cluster Analysis to Classify Days in the National Airspace System," AIAA-2003-5711, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, TX, August 11-14, 2003.
+
+
+
+	
+		Some Methods for Classification and Analysis of Multivariate Observations
+		
+			JBMacqueen
+		
+	
+	
+		Proceedings of the 5 th Berkeley Symposium on Mathematical Statistics and Probability
+		the 5 th Berkeley Symposium on Mathematical Statistics and ProbabilityBerkeley
+		
+			University of California Press
+			1967
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+	
+	MacQueen, J. B., "Some Methods for Classification and Analysis of Multivariate Observations," Proceedings of the 5 th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, 1967, pp. 281-297.
+
+
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+	
+		Aggregate Statistics of the National Airspace System
+		
+			JimmyKrozel
+		
+		
+			BobHoffman
+		
+		
+			StevePenny
+		
+		
+			TarynButler
+		
+		10.2514/6.2003-5710
+		AIAA-2003- 5710
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, TX
+		
+			American Institute of Aeronautics and Astronautics
+			August 11-14, 2003. October 1, 2004
+			5
+		
+	
+	Aggregate Statistics of the National Airspace System
+	Krozel, J., Hoffman, B., Penny, S., and Butler, T., "Aggregate Statistics of the National Airspace System," AIAA-2003- 5710, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, TX, August 11-14, 2003. 5 Federal Aviation Administration, "Order 7210.55C: Operational Data Reporting Requirements," U. S. Department of Transportation, October 1, 2004.
+
+
+
+	
+		The Future National Airspace System: Design Requirements Imposed by Weather Constraints
+		
+			JimmyKrozel
+		
+		
+			BrianCapozzi
+		
+		
+			TonyAndre
+		
+		
+			PhilSmith
+		
+		10.2514/6.2003-5769
+		AIAA-2003-5769
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, TX
+		
+			American Institute of Aeronautics and Astronautics
+			August 11-14, 2003
+		
+	
+	Krozel, J., Capozzi, B., Andre, A. D., and Smith, P., "The Future National Airspace System: Design Requirements Imposed By Weather Constraints," AIAA-2003-5769, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, TX, August 11-14, 2003.
+
+
+
+	
+		Validating the Airspace Concept Evaluation System for Different Weather Days
+		
+			ShannonZelinski
+		
+		
+			LarryMeyn
+		
+		10.2514/6.2006-6115
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Keystone, CO
+		
+			American Institute of Aeronautics and Astronautics
+			August 21-24, 2006
+		
+	
+	AIAA 2006-6115
+	Zelinski, S., and Meyn, L., "validating The Airspace Concept Evaluation System For Different Weather Days," AIAA 2006-6115, Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, Keystone, CO, August 21-24, 2006.
+
+
+
+	
+		Build 4 of the Airspace Concept Evaluation System
+		
+			LMeyn
+		
+	
+	
+		Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit
+		AIAA Modeling and Simulation Technologies Conference and ExhibitKeystone, Colorado
+		
+			August 21-24, 2006
+		
+	
+	AIAA-2006-6110
+	Meyn, L., et al, "Build 4 of the Airspace Concept Evaluation System," AIAA-2006-6110, Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, Keystone, Colorado, August 21-24, 2006.
+
+
+
+	
+		Design of Center-TRACON Automation System
+		
+			HErzberger
+		
+		
+			TJDavis
+		
+		
+			SGreen
+		
+	
+	
+		AGARD Conference Proceedings 538: Guidance and Control Symposium on Machine Intelligence in Air Traffic Management
+		Berlin, Germany
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+	Erzberger, H., Davis, T. J., and Green, S., "Design of Center-TRACON Automation System," AGARD Conference Proceedings 538: Guidance and Control Symposium on Machine Intelligence in Air Traffic Management, Berlin, Germany, May 11-14, 1993.
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+		Data-Centric Air Traffic Management Decision Support Tool Model
+		
+			JamesMurphy
+		
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+			RonaldReisman
+		
+		
+			RobSavoye
+		
+		10.2514/6.2006-7830
+		AIAA-2006-7830
+	
+	
+		6th AIAA Aviation Technology, Integration and Operations Conference (ATIO)
+		Wichita, Kansas
+		
+			American Institute of Aeronautics and Astronautics
+			September 25-27, 2006
+		
+	
+	Murphy, J. R., Reisman, R., and Savoye, R., "A Data-Centric Air Traffic Management Decision Support Tool Model," AIAA-2006-7830, Proceedings of 6 th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Wichita, Kansas, September 25-27, 2006.
+
+
+
+	
+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
+		
+			BanavarSridhar
+		
+		
+			ShonRGrabbe
+		
+		
+			GanoBChatterji
+		
+		
+			KapilSSheth
+		
+		10.2514/atcq.9.1.1
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			9
+			1
+			
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Bilimoria, K. D., Sridhar, B., Chatterji, G. B., Sheth, K. S., and Grabbe, S. R., "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, Vol. 9, No. 1, 2001, pp. 1-20. American Institute of Aeronautics and Astronautics
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+Introductionhis paper describes the automated scenario generation process recently developed and implemented in the Air Traffic Management (ATM) Testbed (ATMTB).The ATMTB was formerly known as the Shadow Mode Assessment with Realistic Technologies (SMART) for the National Airspace System (NAS) Testbed (SMART-NAS Testbed (SNTB)).Motivation for the development of this testbed at the National Aeronautics and Space Administration (NASA) is to enable benefit, impact, safety and cost assessments for accelerating the deployment of Concept and Technologies (C&T) in the NAS.Today, C&T introduction into the NAS takes decades.The primary reason for this is an inability to assess the operational impact of the interaction between the proposed C&T and operationally deployed systems in terms of NAS-wide safety, traffic flow efficiency, roles and workload of controllers and traffic managers, and impact on fleet operations.Transition of C&T to operations requires mathematical modeling and simulation, Human-in-the-Loop (HITL) testing and shadow-mode evaluation driven by operational data.Cautious, slow and incremental steps are typically taken towards deployment because of limitations in each of these steps.This includes HITLs limited to a few scenarios, pilots and controllers, and the inability to inject decisions derived from a shadow-mode system into the operations for impact and benefit assessment.Whereas interaction with the operational system during testing and stages of deployment is not permissible due to safety concerns, it is possible to create a simulation environment that closely mimics the NAS using the same operational systems/hardware for enabling such assessments.Driven by this objective, ATMTB is developing infrastructure to enable mathematical modeling, HITLs and testing with operational systems in a simulated environment.The primary motivation for automated scenario generation for HITL simulations is the difficulty of creating scenarios manually.For example, traffic scenarios for the Multi-Aircraft Control System (MACS) 1 , used frequently for HITL-based air traffic concept evaluations at NASA, are generated manually by first creating an initial scenario (seed-scenario) by selecting flight-plans from recorded air traffic data and then modifying it by repeatedly running it in MACS until the characteristics desired for meeting the objectives of the HITL test are achieved.This process is time consuming.Even creating a seed-scenario that results in successful MACS simulation is tedious because of missing and erroneous data.Because of these difficulties, researchers typically base their experimental evaluations on only a few days of data.The evaluation of a concept or technology's system-wide impacts in terms of cost and benefits with one or two days of data is of limited utility.Therefore, the second motivation for automated scenario generation is that these evaluations should instead be conducted with many days of data with distinct/desired characteristics, given the availability of archived data.In the past several years, because of the decreasing cost of storage, large volumes of aviation related data have been collected by several organizations including NASA and the Federal Aviation Administration (FAA).NASA has recently invested in cleaning up and improving the consistency of the archived data.The scenario generation capability has been significantly enhanced this year to download these data files directly from the storage location and generalized to create surface traffic scenarios for Airspace Target Generator (ATG) and flight scenarios for Airspace and Traffic Operations Simulation (ATOS) 2 in addition to scenarios for MACS.This capability has been used to generate MACS scenarios for Dynamic Routes for Arrivals in Weather (DRAW) 3 and Integrated Demand Management 4 HITLs, and ATG scenarios for Airspace Technology Demonstration (ATD-2) 5 .It is currently being used to generate MACS scenarios for Instrument Flight Rules (IFR) traffic, Visual Flight Rules (VFR) traffic and the expected Urban Air Mobility (UAM) traffic for HITL-based evaluations under the ATM-eXploration (ATM-X) project 6 to enable future UAM vehicles to operate in the NAS.The rest of the paper is organized as follows.Because the examples and the results in this paper are focused on MACS traffic scenarios for HITL-based investigations of operational feasibility of the Integrated Demand Management (IDM) concept, the IDM concept is briefly described in Section II.This discussion also highlights some of the difficulties associated with creating scenarios that represent realistic conditions.The manual scenario generation process is outlined in Section III.The automated scenario generation process is discussed in Section IV.Validation of the seed-scenario, comparison of the seed-scenario with the HITL-scenario, and comparison of the HITL-scenario input with the MACS simulation output are described in Section V.The seed-scenario was created using the automated scenario generation process whereas the HITL-scenario was created by manually altering the seed-scenario.Finally, the main findings are summarized in the Section VI.
+II. Integrated Demand Management HITL SetupIntegrated Demand Management (IDM) 7 is a Trajectory Based Operations (TBO) concept to collaboratively organize aircraft trajectories into well-managed flows that match traffic demand to the available capacity.The concept leverages FAA and NASA pre-departure, enroute and arrival technologies to achieve this objective.IDM uses Traffic Flow Management System (TFMS) tools to precondition traffic into the airspace domain of the Time-Based Flow Management (TBFM) system.If it was possible to predict future capacity and flight times accurately, the preconditioned traffic would arrive at the metering locations as intended; TBFM would only impose small delays required for meeting the runway spacing constraint.Unfortunately, incorrect capacity forecast, delayed departure from the airport, wind and weather introduce uncertainty to the arrival time forecast, which disrupts the schedule and sequence intended by preconditioning.TBFM then has to impose additional delays to adjust the schedule for complying with the capacity constraints at the meter fix and runway.Given that the uncertainty is higher and the cost of delay is lower when the aircraft are on the ground compared to when they are airborne and close to the TBFM freeze-horizon boundary, a proper balance between TFMS and TBFM delays is needed for reducing fuel consumption (by delaying as little as possible while airborne), maintaining the airline schedule and fully utilizing the available airport capacity.Several HITL and Automation-In-The-Loop experiments have been completed to investigate the operational feasibility of the IDM concept under realistic conditions.The testbed is currently being enhanced to support fasttime Monte-Carlo simulations for IDM concept evaluations. 8These experiments typically have the structure presented in Fig. 1.MACS simulates air traffic data based on the input traffic and weather/wind scenario files; it also provides a high-fidelity air traffic control simulation environment for controller and pilot interactions.In conjunction with MACS, an emulation of the Collaborative Trajectory Options Program (CTOP), called nCTOP (NASA CTOP), was constructed to perform the key functions of the TFMS version with CTOP capability.The nCTOP and MACS Planner Station blocks shown in Fig. 1 represent emulation of the TFMS with CTOP used at the Air Traffic Control System Command Center.Key functions of nCTOP includes setting capacity constraints at an FCA, automatically assigning delay and allocating trajectories to the pre-departures to balance the predicted arrival traffic demand at the FCA according to its capacity limit.Inputs therefore include the capacity scenario being simulated, and the schedule and Trajectory Options Sets (TOS) likely to be submitted by flight operators.Expect Departure Clearance Times (EDCT) and TOS allocation are output to all MACS stations through the MACS simulation manager.MACS stations for each pilot and controller communicate with all other MACS stations in the simulation, updating aircraft positions.A research version of the FAA's operational TBFM version 4.2.3 with NASA modifications is used to simulate the generation of arrival timelines; controllers are able to reschedule internal departures to fit into the overhead stream based on calculated Scheduled Times of Arrival (STA) at the metering locations such as meter fixes and runway threshold.The experimental setup in Fig. 1 illustrates an example of a concept that requires multiple scenarios; IDM requires capacity scenarios, scheduled traffic scenarios, likely airline TOS, detailed MACS traffic scenarios, and weather scenarios, including convective weather and wind.In addition, the scenarios derived from traffic and weather often require significant modification to meet the desired characteristics for the experiment, which in some instances can reduce experiment realism.For example, in the experiments described in Ref. 9, the baseline traffic scenario derived from recorded traffic from a single day -July 22, 2014 was modified based on feedback from subject matter experts to have the most representative characteristics of the nominal operations into Newark Liberty International (KEWR) during a clear weather day.This five-hour scenario included a total of 66 aircraft.Experiments were ultimately run investigating two wind severity levels-mild and heavy wind, and two traffic demand profiles with different distributions.However, in reality, under such wind conditions, airlines might have filed flight-plans differently compared to the ones in the traffic scenario.Availability of a technique for identification of days with the appropriate clear weather, wind conditions and traffic demand profile would have provided increased realism, as well as reduced the time required to generate the scenario.Future experiments will add significant complexity as convective weather is introduced at different locations 10 , making the generation of realistic scenarios even more challenging.Examples of such scenarios are the use of coded departure routes because of predicted convective weather activity downstream of the TBFM freeze-horizon, and the use of tactical rerouting (e.g., common tactical routes) due to unpredicted convective weather blocking an arrival gate.The IDM example discussed in this section illustrates some of the challenges for automated scenario generation.At the present time, only generation of seed traffic scenarios that run in MACS, ATOS and ATG are being considered.These seed-scenarios would have to be modified based on subject matter expert feedback and to meet additional experiment requirements not reflected in the seed-scenarios.Discussions in the following sections are limited to traffic scenario generation for MACS simulations.
+III. Manual Scenario Generation ProcessThe manual MACS scenario generation process consists of the nine steps summarized below.1) Identify the desired scenario characteristics based on the experimental objectives.a) Determine the general characteristics that serve the purpose of the study.b) Talk to the Subject Matter Experts (SME) to augment and refine the desired characteristics.2) Search for the day and the time-period.a) Select the days with the desired weather conditions.b) Check Aviation System Performance Metrics (ASPM) data for those days, selected in the previous step, and see how the traffic demand evolved for the desired runway configurations.If the runway configurations in the ASPM data do not match the desired runway configurations, a relatively easy scenario editing step is employed in Step 5 to specify the routes to the desired runways instead.c) Choose the time-period based on the desired scenario characteristics.3) Download the Center-TRACON Automation System (CTAS) data for the selected day from the storage location.TRACON is acronym for Terminal Radar Approach Control.4) Convert the downloaded CTAS data into the MACS scenario format using the TCSim Route Analyzer/Constructor (TRAC).5) Modify the scenario if needed by Step 2b and look for any obvious errors in the scenario editor.6) Play the scenario in TRAC and make a determination of its suitability for MACS simulation based on traffic evolution.7) Run an open-loop MACS simulation with the generated-scenario for the time-period, chosen in Step 2c, and analyze the resulting MACS outputs to determine the extent to which the simulation meets the scenario requirements.8) Augment the analyzed-scenario with additional data for meeting the remaining scenario requirements that could not be met in the earlier steps.This step might consist of adding flights, for example, from different flows, regions, hours and days to increase traffic volume.9) Repeat Steps 5 through 8 until all the scenario requirements are met.Step 1 of the manual scenario generation process will stay the same for the automated scenario generation process because the automated scenario generation process will also have to output a scenario in accordance to the desired scenario characteristics.In Step 2, researchers use a guess-and-try technique by first picking a few days that they guess might meet the scenario characteristics identified in Step 1 and then examining the ASPM data for those days.An exhaustive search of such days in a year for example would be difficult to accomplish following the current manual process.It might be feasible to automate this step by enabling search based on surface, enroute and terminal traffic and weather metrics from multiple sources organized in databases and in groups, where the groups could be based on unsupervised/supervised classification techniques employing big-data technologies with data driven metrics/metadata derived from NASA's ATM-data-warehouse.It could be designed to support complex queries such as "find all days in 2016 that are like 01/20/2016" and "find days in 2015 with severe weather within 300 miles from Newark airport and Ground Delay Program (GDP) in Chicago."This might become a significant capability in the future for accelerating concept evaluation and acceptance because it will provide a large set of scenarios representing different operational conditions instead of the few manually-created scenarios for concept evaluation and acceptance testing.Steps 3 and 4 of the manual scenario generation process are quick and accomplished using computer programs.Researchers have reported that the initial MACS scenario file output by TRAC tool from Step 4 requires a lot of manual data entry in Step 5 due to missing and erroneous data.Researchers often resort to looking at old scenario files and talk to SMEs to determine reasonable values to enter missing and to replace erroneous data.Also, the route from the entry location to the runway has to be created by manually copying the filed flight-plan into a column in the scenario file and then modifying them.The automated scenario generation process in the ATMTB creates this route as a sequence of waypoints from the entry point, location of which is derived from track-data, to the closest point ahead of the entry point on the filed flight-plan followed by the waypoints in flight-plan till the end of the Standard Terminal Arrival Route (STAR) and then waypoints along the approach procedure to the designated runway.Approach procedures are defined in the MACS adaptation data.Steps 6 and 7 will eventually be replaced by the verification step of the automated scenario generation process.Whereas it is difficult to completely automate Step 8, it might be possible to automate it partially by creating scenarios for different days and conditions, and then judiciously combining them with the seed-scenario to create a scenario that meets the needs of the experiment.
+IV. ATMTB Automated Scenario Generation ProcessATMTB infrastructure at its present stage of development can be described in terms of the following elements-(1) web-based frontend and backend, (2) Simulation Architect, (3) publish-subscribe messaging middleware, (4) Component Library, (5) simulation management, and (6) scenario generation.The web-based frontend and backend enable the user to interact with the ATMTB for tasks such as composing a simulation, running a simulation and retrieving output data.The Simulation Architect application launched from the web frontend provides a graphical user interface for enabling the user to drag-and-drop and connect predefined (user defined and testbed native) blocks for composing a simulation/scenario generation task.The Simulation Architect writes a set of instructions for simulation management based on block properties such as the component (executable) associated with a particular block, and the links between the blocks.Links specify the input and output relationships between the blocks, which defines the publisher and subscriber relationships in the simulation.Management of the distributed simulation is accomplished by Execution and Component Managers.Execution Manager interprets the instructions provided by the Simulation Architect to instruct the Component Managers to download components from the Component Library to the designated computers and to start them up.Once started, components interact with each other by publishing messages and subscribing to messages delivered using the messaging middleware.Unlike the other five elements, which are testbed infrastructure elements, the scenario generation capability is an application that runs on the testbed.The scenario generation capability was initially developed for creating traffic scenarios for MACS simulations.The automated scenario generation process in ATMTB is initiated by dragging and dropping blocks, specifying the block properties and linking the blocks graphically using the Simulation Architect.The Simulation Architect view for composing MACS scenario generation is shown in Fig. 2. The blocks labeled-Data Loader, Data Filters, and MACS Scenario Builder are parts of the scenario generation program.The preliminary step of scenario generation consists of the user picking a day (date) and specifying it as a property of the MACS Scenario Builder block for the scenario generation program to download the traffic data file from the storage location and read the associated traffic data during runtime.The type of traffic file to be downloaded is specified by selecting the appropriate Data Loader block; Fig. 2 shows the setup for loading ATAC (a particular format) data.The properties specified in Data Filter bocks and the "and"/"or" relations specified by chaining Data Filter bocks in the simulation builder provides instructions for the scenario generation program for reducing (down-selecting) the input traffic data.For example, three data filters can be chained together in series to tell the program to first select arrivals to Newark Liberty International (KEWR) based on the Arrival Airport property of the first filter block, then select aircraft landing on Runway 22L based on the Landing Runway property of the second filter block, and finally select aircraft landing between 17:00 UTC 6/6/2016 and 5:00 UTC 6/7/2016 based on Event Time property of the third filter block.Inclusion of the MACS Scenario Builder block tells the scenario generator to build a scenario for MACS simulation.Other blocks with inputs to the MACS Scenario Builder block instruct the scenario generation program to use the filtered data, aircraft performance models, adaptation data, wind data and initial conditions.The links between the blocks specify the data flow.The output of the Simulation Architect is a set of instructions for the Execution Manager that includes a configuration file for the generation component.The Execution Manager instructs the Component Manager to download the scenario generation program executable from the Component Library to a particular machine and to start it.The Component Manager also provides the configuration file, created by the Simulation Architect and provided by the Execution Manager, to the scenario generation program for generating the scenario.MACS scenarios are generated by processing recorded air traffic data archived in the ATM-data-warehouse in three different types of files-Reduced Record (RD), Event Data (EV) and Integrated Flight Format (IFF).RD files contain a single record for each flight, where the record contains information such as the reference time, unique key, aircraft ID, aircraft type, beacon code, airline, origin (airport or Fix-Radial-Distance (FRD)), takeoff/landing runway, destination airport, top-of-climb/top-of-descent time, runway threshold arrival/departure time, flight-plan (including route) data, and sector/center transition list.EV files contain multiple records for events related to each flight such as reference time, unique key, aircraft ID, aircraft type, event time, event type, object class, old name and new name.MACS scenario generation currently processes takeoff/landing and crossing events, which includes sector, center and TRACON crossings.Object class, old name and new name provide additional information related to the event.For example, complete information for a takeoff from San Francisco (SFO) airport would be available in the EV file as event type-takeoff, object class-airport, old name-"?"(not needed for takeoff event) and new name-SFO.Similarly, a center crossing event for a flight leaving Oakland Center (ZOA) and entering Los Angeles Center (ZLA) would be available as event type-crossing, object class-center, old name-ZOA and new name-ZLA.IFF files contain multiple records for each flight, where the records contain all flight-plans including amended flight-plans and track-data.Data associated with these records include reference time, unique key, aircraft ID, aircraft type, message type (for example, filed flight-plan and amended flight-plan), origin (airport or FRD), destination airport and filed altitude.Data contained in the RD and EV records are especially useful for filtering the traffic data for building the scenarios.The IFF data are useful for augmenting the traffic data derived from RD and EV records.Three types of filters are currently available.RD String Filters are used for selecting records from RD files by matching specified strings to those in the records.Supported filters include Aircraft Type, Airline, Arrival/Departure Airport, Aircraft ID, Center, Sector, and Landing/Takeoff Runway.Filter and List of Strings are properties of the RD String Filter block; the user selects the desired filter from the list of filters and provides a list of strings appropriate for the selected filter.For example, airport code such as KEWR is a string that is compatible with the Arrival/Departure Airport filter.Similarly, 22L is an appropriate string in the list of strings with the Landing Runway filter option.The RD Airport Proximity filter is used for selecting flights to/from airports either inside or outside the specified region by processing RD records.The user sets up the filter by selecting from a list of options related to the properties and inputting the values needed by the properties.Supported properties include Filter, Reference Location, Reference Distance and Airports Included.Options associated with the Filter property are Departure Airport and Arrival Airport; the Reference Location property expects an airport code like KEWR; the Reference Distance property expects distance in nautical miles; the Airports Included property expects values such as all inside, all outside and a list of specified airports codes like KEWR.Finally, Event Time Filter uses EV records to select flights.The Event Time Filter block has Event Type, Minimum Value, Maximum Value and Include/Exclude properties.Examples of Event Type are Landing, Takeoff, Top-of-Climb and Top-of-Descent.Minimum and Maximum Values are day (yearmonth-date) and UTC time (hour-minute-second)).The Include/Exclude property option specifies whether the flight events within the specified time interval are to be included or excluded.In addition to the selection of data specified using filter blocks on the Simulation Architect, Entry Track Method, Entry State Method, Aircraft Performance Model, Airspace Adaptation Database and Atmosphere Model have to be specified as shown in Fig. 2. Figure 3 shows the various inputs that have to be specified for MACS scenario generation and the choices associated with them.Three options for the Entry Track Method relevant to MACS scenario generation are: Distance, Start Time and Top-of-Descent.Target Airport ID and Distance from the Airport are the two parameters of the Distance block.Starting locations of the selected flights are chosen to be inside/outside the circular region defined by these two parameters.Start Time block enables the user to input the desired time past the simulation start time for selecting the starting position.For example, if the desired time is 30 minutes, the position of the flight at or just after when the simulation time is 30 minutes past the simulation start time would be chosen as the starting position.The Top-of-Descent block allows the user to specify a time with respect to top-of-
+V. Validation and Comparison of Automatically Generated and Manually Refined ScenariosThe discussion and the results in this section pertain to the seed-scenario, HITL-scenario and the MACS simulation output; Fig. 5 summarizes the procedure for creating them.The ATAC data are used by the automated scenario generation procedure, described in the previous section, to create the seed-scenario.This seed-scenario is then manually refined to create the HITL-scenario.Finally, traffic is simulated using MACS with HITL-scenario as input.Two sets of results are presented below.The first set compares the seed-scenario with the HITL-scenario, Blocks 3 and 5, and the second set compares the HITLscenario with the MACS simulation output, Blocks 5 and 7 in Fig. 5.The seed-scenario for MACS simulation of arrival traffic to KEWR spanning six-hours starting at 17:00 UTC was created by processing June 6, 2016 RD, EV and IFF files archived in the ATM-data-warehouse.The seedscenario has 299 flights with 274 landing on Runway 22L, six on 22R and one on 29.Arrival runway could not be determined for the remaining 18 aircraft.Two types of analysis were done to characterize the seed-scenario.The first type consisted of determining the number of flights associated with the same parameter value such as call-sign and beacon-code.Figure 6 and7 show the number of flights associated with the same callsign and aircraft-type, respectively.Table 1 summarizes these results for different parameters.For example, of the 290 unique call-signs, 9 call-signs were associated with more than one flight; Fig. 6 shows that each of the nine callsigns were associated with two aircraft.Of the 35 different aircraft types in the seed-scenario, 24 (see the second row of Table 1) were associated with several aircraft as shown in Fig. 7. Similarly, one destination airport, KEWR, was associated with every flight.Of the four landing runways-22L, 22R, 29 and "not-set", one aircraft landed on 29, 274 on 22L, six on 22R and 18 did not have an assigned runway (not-set category).Thus, one flight was  The two types of analysis proved to be very useful for determining errors in the scenario.For example, the entry point IAS histogram in Fig. 8 shows that the scenario generation program determined the IAS of an aircraft to be 712 knots.The Mach number for the passenger aircraft associated with this flight was determined to be 1.7, which is wrong.Whereas checks were built into the scenario generation program, the checks are not always successful because of data quality issues.In this particular instance, several successive actual track-data reports used for determining the entry state were erroneous.Figure 8 also shows that 78 aircraft had the correct entry point IAS of zero because they were on the ground at the simulation start time.The cruise altitude histogram showed that seven flights had a cruise altitude of zero, which is incorrect.Results suggest that these types of analyses should be included as an extension to the automated scenario generation process to remove flights with improper parameters from the seed-scenario.In addition to detecting data quality issues, an important aspect of validation is determining the reasonableness of the scenario.For example, it is not desirable for several flights to have the same call-sign in the HITL-scenario.There are two possible ways of addressing this issue.One is to create new call-   number column of the MACS scenario file, where Mach number is determined using BADA model speeds if the cruise altitude is above the Mach transition altitude.This implies that the researcher should run the MACS simulation with wind data.If the researcher uses the file without wind data, MACS would simulate flights with unrealistic groundspeed.Analyses for generating the results for the paper suggest that if realistic landing rate is desired in the scenario and the researcher wishes to run the scenario without wind data for example, average cruise groundspeed should be output in the cruise speed/Mach column of the scenario file.Figure 9 shows the actual landing rate at KEWR, and the predicted landing rate using Eq. ( 1) with average cruise groundspeed.Landing rate is determined as the number of flights in the hourly window, continuously shifted temporally at a fiveminute interval.The figure suggests that MACS scenario with average cruise groundspeed would result in a scenario that would reasonably replicate the actual landing rate.The two types of analyses done for the seed-scenario were repeated for the HITL-scenario to determine the differences between them.The manually refined HITLscenario that was used for the IDM HITL in March 2018 was created by the researcher by selecting flights from the seed-scenario and altering some of the values such as cruise speeds and entry time to achieve the desired landing rate.To have the demand exceed arrival capacity of 40 aircraft/hour, entry times of flights in the seed-scenario were altered to squeeze six-hours of arrival traffic into five-hours for creating the HITL-scenario.The HITLscenario has 191 flights, a subset of flights in the seedscenario, with all landing on Runway 22L.Other than three flights, all the flights in the HITL-scenario are in the seed-scenario.All flights from the seed-scenario within a 40 nautical-mile circular region around KEWR were not selected for the HITLscenario; some flights were rejected if their entry time was less than 30 minutes past 17:00 UTC.Flights were also removed in an attempt to maintain the ratio of the number of internal flights to the total number of flights in the HITL-scenario to the 23% seen in the seed-scenario, where the internal flights are those that originated within the 400 nautical-mile circular region surrounding KEWR.The ratio of the internal to the total flights in the HITL-scenario was found to be 30%.Results summarized for the seed-scenario in Table 1 are provided for the HITLscenario in Table 2.This table shows that the flights in the HITL-scenario had a unique call-sign, and that they landed on the same runway (Runway 22L).The ratios of "Once to Unique" and "Repeated to Unique" in Table 1 and 2 expressed as percentage are shown side-by-side in Table 3.This table shows that most ratios seen in the seed-scenario are maintained in the HITL-scenario except for the entry point sector-ID.Compared   to seed-scenario with 70 entry point sector-IDs, the HITL-scenario had three sector-IDs: ZDC-01, ZOB-01 and ZBW-01, which were assigned to 65, 74 and 52 flights, respectively.Tailoring of the HITL-scenario to achieve the objective of higher traffic demand with respect to airport arrival rate of 40 aircraft per hour, which was realized by squeezing six-hours of traffic into five-hours, is apparent in Fig. 10. Figure 10 shows the actual and the predicted landing rate graphs for the HITL-scenario.The actual landing rate graph is based on the actual landing time of 191 aircraft in the HITL-scenario whereas the predicted landing rate graph is based on Eq. (1).Comparing Figs. 9 and 10 it is seen that several flights arriving during the early part of the scenario were removed from the seedscenario to create a gradually increasing traffic demand in the HITL-scenario.The increase in traffic demand achieved in the HITLscenario can also be achieved by an algorithm as follows.Let the desired arrival rate be n aircraft/hour.The desired temporal separation, t  , between successive aircraft is then 60/ n minutes.Thus, ( 1) ( )LL t i t i t    (2)where () L ti is the landing time of the leading aircraft and ( 1)L ti  is the landing time of the following aircraft.Solution of the recursion Eq. ( 2) is ( ) (1) ( 1)LL t i t i t    (3)where (1) L t is the landing time of the first aircraft and 1 i  .Combining Eq. ( 2) with (1), the entry times can be determined as,. () ( ) (1) ( 1) () R EL Avg li t i t i t Vi     (4)The final step of the validation process is comparison of the MACS simulated traffic with that intended by the scenario.Figure 11 shows the comparison of the predicted landing rate with the MACS simulated traffic landing rate Figure 10.HITL-scenario KEWR landing rate.using the HITL-scenario.Analysis showed that the predicted landing rate graph is sensitive to the cruise speed.As expected, faster cruise speeds shift the graph to the left and slower to the right along the abscissa.The difference between the two graphs seen in Fig. 11 is due conversion of Mach to cruise speed (true airspeed) and the aircraft performance models employed in MACS.requires true airspeed to be specified below Mach transition altitude and Mach above it.Mach numbers specified in the HITL-scenario were converted to true airspeed using standard atmosphere for predicting the landing rate.Using June 6, 2016 RUC data for this conversion could have resulted in a slightly different outcome.An additional source of error is that 18 aircraft in the MACS simulation came close to landing but did not actually land, they continued flying past the runway.To create a substantial scenario validation capability, the analyses described in the paper will need to be extended.One such example is the ability to determine the deviation of the MACS simulated track-data with respect to the flight-plan specified in the input scenario data.This could help identify errors in the flight-plan, missing waypoints in the MACS adaptation database, and MACS trajectory modeling errors.
+VI. ConclusionsThe automated scenario generation process recently developed and implemented in the Air Traffic Management Testbed being developed at the NASA Ames Research Center was described.The earlier manual scenario generation process for generating Multi-Aircraft Control System scenarios for use in the Human-in-the-Loop experiments was described to motivate automated scenario generation.Two scenarios were analyzed: (1) the seed-scenario generated using the automated scenario generation method and (2) the Human-in-the-Loop-scenario created by a researcher starting from the seed-scenario.Results summarized in tables show that many of the characteristics seen in the seed-scenario are preserved in the Human-in-the-Loop-scenario.Two types of analyses were described for comparing the seed and the Human-in-the-Loop scenarios.The first type analyzed duplicate parameters associated with flights such as call-sign, beacon-code and entry point sector-ID; the second type examined the distributions of route length, cruise speed, cruise altitude, actual landing time, predicted landing time, entry time, and entry point speed and altitude.Results obtained suggest these analyses are useful for determining data quality issues and for eliminating flights with unreasonable parameter values from the seed-scenario.Landing rate based on Multi-Aircraft Control System simulated traffic using the Human-in-the-Loop-scenario were compared with the expected landing rate based on the route length and average cruise speed of flights in the Humanin-the-Loop-scenario. Causes for the differences seen in the landing rates were identified.Close examination of the Human-in-the-Loop-scenario revealed that many of the desired characteristics such as flights having unique callsigns and airport arrival rate demand exceeding the airport arrival rate capacity can also be achieved in the seedscenario by enhancing the automated scenario generation process.A method for altering the entry time of flights to get the desired landing rate was described as an example of such enhancement.and confirm the findings.Hyo-Sang provided the HITL-scenario, a copy of the MACS software that he had used for the HITL and the adaptation data required for running that version of MACS.Hyo-Sang also described the manual scenario generation process for MACS simulations that had been used prior to the automated scenario generation process described in this paper.Authors are grateful to Dr. Min Xue, Dr. Antony Evans, Shannon Zelinski and Dr. Banavar Sridhar for reviewing the paper and providing feedback.Contributions of the other ATM Testbed team members-John Robinson, James Murphy, Alan Lee, Chok Fung (Jack) Lai, Phu (Phil) Huynh and Huu Huynh to the idea, design and development of the scenario generation capability is gratefully acknowledged.Figure 1 .1Figure 1.Example IDM experimental setup.
+Figure 2 .2Figure 2. Simulation Architect view for composing MACS scenario generation.
+descent for selecting the initial position of the flight.A value of -5 minutes for example would result in the selection of the position five minutes (or slightly more because track-data might not be available exactly at 5 minutes) prior to the time the flight reaches the top-of-descent point.At the current stage of development, there is a single option associated with each of the other inputs needed for generating MACS scenarios.The only option available for the Entry State Method is From Track.Inclusion of the From Track block the scenario generator to use actual track data and the Mach transition altitude, determined using Base of Aircraft Data (BADA) 11 aircraft performance model and the specified atmospheric model, to determine the state of the flight such as altitude, heading, calibrated airspeed and Mach number at the entry location.The only option for Aircraft Performance Model is BADA Model block, and for Airspace Adaptation Database is National Flight Data Center (NFDC) Database block.Two options for the Atmosphere Model are Rapid Refresh block and Standard Atmosphere block.The steps for MACS scenario generation starting from loading and filtering the traffic data to output of scenario data in a file are summarized in Fig. 4. The first step consists of loading RD, EV and IFF files from ATM-data-warehouse and filtering traffic data according to the filters specified on the Simulation Architect, and creating the flight data structure.The second step consists of assigning a BADA aircraft model in the flight data structure based on aircraft type and BADA Synonym List, and sorting the flight-plans of each flight by time.BADA Synonym List enables mapping of aircraft types that do not exist in the BADA database to the ones that exist in the database.The next step consists of finding the entry track data of the flights based on the simulation start time and the Entry Track Method specified on the Simulation Architect.Entry track data consist of time, latitude and longitude, altitude, groundspeed, course heading, Rate of Climb or Descent (ROCD) and sector ID of the entry point.The last flight-plan prior to entry track time is determined in the
+Figure 3 .3Figure 3. Inputs and associated options for MACS scenario generation.
+Figure 4 .4Figure 4. Summary of MACS scenario generation steps.
+Figure 5 .5Figure 5. Summary of scenario and MACS output data generation steps.
+tis the predicted landing time, E t is the entry time (takeoff time for aircraft on the ground), R l is the route length and .Avg V is the average cruise groundspeed, which is determined by averaging the actual cruise speed derived from track-data within the top-of-climb and top-of-descent interval.Predicted landing rate comparison with the actual landing rate is useful for sanity check.
+Figure 6.Flights with the same call-sign.
+Figure 7 .7Figure 7. Flights with the same aircraft-type.
+Figure 8 .8Figure 8. Entry point IAS.
+Figure 9 .9Figure 9. Seed-scenario KEWR landing rate.
+Figure 11 .11Figure 11.MACS simulated using HITL-scenario versus HITL-scenario KEWR landing.rate.
+Table 1 .1Summary of seed-scenario results.#ParameterOnce RepeatedUnique1.Call-sign28192902.Aircraft-type1124353.Destination airport0114.Landing runway1345.MACS flight-plan148471956.ATC flight-plan174412157.Beacon-code256212778.Departure airports50681189.Entry point altitude736113410. Entry point IAS775813511. Entry point sector-ID47237012. Aircraft weight52429
+Table 2 .2Summary of HITL-scenario results.#ParameterOnce RepeatedUnique1.Call-sign19101912.Aircraft-type1020303.Destination airport0114.Landing runway0115.MACS flight-plan64411056.ATC flight-plan80401207.Beacon-code18151868.Departure airports4150919.Entry point altitude35468110. Entry point IAS23143711. Entry point sector-ID03312. Aircraft weight31619
+Table 3 .3Comparison of seed-scenario with HITL-scenario.Seed-scenarioHITL-scenario
+		
+		
+
+			
+AcknowledgementsThe authors thank Dr. Antony Evans and Dr. Hyo-Sang Yoo.This paper would not have been possible without their help.Tony described the IDM concept and ran MACS with the HITL-scenario to identify some issues
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+Introductionhe automated scenario generation capability in the Air Traffic Management (ATM) Testbed (ATMTB) has been used to generate Multi-Aircraft Control System (MACS) scenarios for Human-in-the-Loop (HITL) evaluations.MACS is a distributed simulation system with multiple pseudo-pilot and air traffic controller stations that is frequently used for National Aeronautics and Space Administration (NASA)'s evaluations of air traffic management concepts. 1utomated scenario generation has been used for creating MACS scenarios for Dynamic Routes for Arrivals in Weather (DRAW) 2 and Integrated Demand Management 3 HITL experiments.It has also been used for creating Airspace Target Generator (ATG) scenarios, used in realistic airport surface traffic simulation, for the Airspace Technology Demonstration (ATD-2) 4 subproject.Automated scenario generation is currently being used to generate MACS scenarios for Instrument Flight Rules (IFR) traffic, Visual Flight Rules (VFR) traffic and the expected Urban Air Mobility (UAM) traffic for evaluations under the ATM-eXploration (ATM-X) project 5 to enable simulation of future UAM vehicles to operate in the National Airspace System (NAS).This paper describes a two-step automated process for creating MACS traffic scenarios according to the desired scenario characteristics.The first step of the two-step procedure described in this paper employs the ATMTB automated scenario generation process first described in Ref. 6.The second step, introduced in this paper, enhances the scenario, created by the first step, by eliminating flights with unreasonable values associated with parameters such as cruise speed and cruise altitude, and selecting/adjusting flights based on desired scenario characteristics specified by the user such as route length, entry time, landing rate and ratio of number of flights inside to outside the terminal area (for example with respect to the Time-Based Flow Management freeze horizon).Prior to the automated scenario generation capability in ATMTB, traffic scenarios for MACS were generated manually by first creating an initial scenario (seed-scenario) by selecting flight-plans from recorded air traffic data
+II. BackgroundThe research focus for IDM 7 Trajectory Based Operations (TBO) concepts is improving the efficiency and predictability of air traffic operations.Many of the air traffic management tools and technologies in the enroute, terminal-area and surface domains developed both by the Federal Aviation Administration (FAA) and NASA that are used for operations in the National Airspace System have been difficult to use in the congested Northeast region airspace due to a mix of high traffic-volume, weather conditions, the proximity of major airports in the New York Metroplex and in neighboring centers, airspace geometry, and operational procedures for separating the flows in and out of major airports.TBO concepts seek to collaboratively organize aircraft trajectories into well-managed flows that match traffic demand to the available capacity by initially leveraging FAA and NASA pre-departure, enroute, arrivaldeparture and surface technologies. 8he charter of the IDM project is to explore ways of integrating near-term to mid-term NextGen traffic management systems to improve efficiency in situations when the traffic demand exceeds capacity of resources such as airports and airspace.In the IDM concept, FAA's Traffic Flow Management System (TFMS) and the Time-Based Flow Management (TBFM) system are used. 7In the current system, TFMS is used strategically for determining departure delays at the airports of origin in response to constraints at destination airports and flow constrained areas (FCA).TBFM is a terminal-area traffic management system that tactically assigns a scheduled time of arrival (STA) to arrivals at the metering locations such as meter-fixes, meter-arcs and runway threshold based on capacity constraints at those locations.TFMS uses a Ration-By-Schedule algorithm that is based on the STA filed by the aircraft operator while TBFM computes an estimated time of arrival (ETA) using track and flight-plan data for its STA assignment.IDM seeks to establish coordination between strategic TFMS and tactical TBFM decisions for reducing delays by using TFMS to precondition traffic into the airspace domain of the TBFM system.Unfortunately, incorrect capacity forecast, delayed departure from the airport, wind and weather introduce uncertainty to the arrival time forecast, which disrupts the schedule and sequence intended by preconditioning.TBFM then has to impose additional delays to adjust the schedule for complying with the capacity constraints at the metering locations.Given that the uncertainty is higher and the cost of delay is lower when the aircraft are on the ground compared to when they are airborne and close to the TBFM freeze-horizon boundary, a proper balance between TFMS and TBFM delays is needed for reducing fuel consumption (by delaying as little as possible while airborne), maintaining the airline schedule and fully utilizing the available airport capacity.Several HITL and Automation-In-The-Loop experiments have been completed to investigate the operational feasibility of the IDM concept under realistic conditions. 9,10 ecently, fast-time Monte-Carlo simulations are also being developed for IDM concept evaluations. 11In these experiments, MACS simulates air traffic data based on the input traffic and weather/wind scenario files; it also provides a high-fidelity air traffic control simulation environment for controller and pilot interactions.The progress made on the IDM project will be continued for developing a concept of operations and accompanying system architecture that evaluates the integration of FAA's systems and NASA's Airspace Technology Demonstration (ATD) ground-based and airborne systems for a future service-oriented airspace system. 8This concept of operations needs to include the operation of new entrants such as supersonic aircraft, space launch vehicles, high-altitude long endurance platforms, Unmanned Aerial Systems and Urban Air Mobility vehicles in addition to the traditional airspace users like airlines and general aviation aircraft.The concept of operations developed is expected to be evaluated in a HITL simulation in the future using Northeast region scenarios developed with service provider and user inputs.
+III. Traffic Scenario SelectionThis section describes the analysis of runway configurations at JFK, EWR, LGA and TEB with the objective of identifying days with high-volume of arrival and departure traffic for creating MACS traffic scenarios for TBO studies.Because traffic flow patterns on the airport surface and in the terminal airspace depend on the runway configuration (runways used for arrivals and runways used for departures), hourly arrival and departure data for every day in 2017 were obtained for JFK, EWR, LGA and TEB airports from the FAA's Aviation System Performance Metrics (ASPM) database using the information in the Throughput Analysis Standard Report.These data were processed to determine the total number of hours each configuration was in use and the total number of operations (sum of arrivals and departures) conducted in each configuration during 2017.The top five configurations based on the percentage of hours in use are summarized in Table 1.The second column in the table shows the arrival runways such as 31L and 31R and departure runways such as 31L separated by a vertical bar (see the first row of the second column in Table 1).The third column shows the total number of hours the particular configuration was in use during the year.The fourth, fifth and the sixth columns present the total number of arrivals, departures and their sum, for the corresponding configurations, respectively.Finally, the seventh and the eighth columns list the percentages based on the total number of hours the airport was in operation and the total number of operations during the year.These percentages were computed by removing data corresponding to when the airport was closed or the configuration information was absent.Next, the configurations at JFK, EWR, LGA and TEB were considered together at every hour of every day in 2017 to determine the most frequently used combinations of configurations.These combinations are summarized in Table 2. Comparing these tables, one observes that the top five configurations at JFK, EWR, LGA and TEB in Table 1 are used 74%, 84%, 86% and 96% of the time (based on hours), respectively, while the top five combinations of the configurations in Table 2 are only used 17% of the time.Of the 687 unique combinations of the configurations observed in the 2017 data, ten were used  2% of the time, 12 were used 1% of the time and the remaining 665 were used less than 1% of the time.The top ten and the top 22 combinations were used 28% and 40% of the time, respectively.It also turned out that the top five configurations listed in Table 2 consist of combinations of the top five most frequently used configurations at the four airports listed in Table 1.The configuration in the second row in Table 2 is composed of the most frequently used configuration at JFK, EWR, LGA and TEB in Table 1.Finally, the sum of the operations at JFK, EWR, LGA and TEB were computed for every hour of every day in 2017 and the configuration data were sorted in non-increasing order of the sum of operations to identify dates and times with large number of operations.These results are summarized in Table 3.While archived traffic data do exist  3).This interval was also chosen because it had balanced operations (ratio of arrivals to departures close to one) with 865 arrivals and 784 departures.The MACS scenario was generated using the 5/23/2017 air traffic data archived in the ATM-data-warehouse to ensure that the traffic simulation would result in most flights arriving at JFK, EWR, LGA and TEB within this six-hour period.The MACS scenario generation is described next.
+IV.Step
+1: Automated Scenario Generation ProcessThe automated scenario generation process in ATMTB is initiated by dragging and dropping predefined blocks (user defined and testbed native), specifying the block properties and linking the blocks graphically using the Simulation Architect, where the Simulation Architect application is launched from the web frontend as described in Ref. 6.While the details are available in Ref. 6, the description below is included as background for the second step of the two-step scenario generation process.MACS scenarios are generated by processing recorded air traffic data archived in the ATM-data-warehouse, which is a platform for collecting, archiving, processing, querying and retrieving ATM data.Processed data derived from FAA's System-Wide Information Management (SWIM) data are available in the ATM-data-warehouse in three different types of files-Reduced Record (RD), Event Data (EV) and Integrated Flight Format (IFF).RD files contain a single record for each flight, where the record contains information such as the reference time, unique key, aircraft ID, aircraft type, beacon code, airline, origin (airport or Fix-Radial-Distance (FRD)), takeoff/landing runway, destination airport, top-of-climb/top-of-descent time, runway threshold arrival/departure time, flight-plan (including route) data, and sector/center transition list.EV files contain multiple records for events related to each flight such as reference time, unique key, aircraft ID, aircraft type, event time and event type.MACS scenario generation currently processes takeoff/landing and crossing events, which includes sector, center and TRACON crossings.IFF files contain multiple records for each flight, where the records contain all flight-plans including amended flight-plans and trackdata.Figure 2 shows the various inputs that have to be specified for MACS scenario generation and the choices associated with them.Data contained in the RD and EV records are especially useful for filtering the traffic data for building the scenarios.The IFF data are useful for augmenting the traffic data derived from RD and EV records.Three types of filters are currently available.RD String Filters are used for selecting records from RD files by matching specified strings to those in the records.The user selects the desired filter from the list of filters and provides a list of strings appropriate for the selected filter.For example, airport code such as KEWR is a string that is compatible with the Arrival/Departure Airport filter.Similarly, 22L is an appropriate string in the list of strings with the Landing Runway filter option.The RD Airport Proximity filter is used for selecting flights to/from airports either inside or outside the specified region by processing RD records.The filter is set up by selecting from a list of options related to the properties like Reference Location and Reference Distance and inputting the values needed by the properties.or a named fix, along the flight-plan for connecting the entry point to the flight-plan, and builds the MACS route for the flight starting at the entry point and ending at the landing runway.Entry state data are determined in the sixth-step using trackdata, which is specified by selecting the From Track block, the only available Entry State Method, on the Simulation Architect.Entry state data consist of true heading, calibrated airspeed, Mach, flight state (overflight, arrival or departure), and in-Mach or in-CAS mode at the entry point.MACS requires a target waypoint with speed and altitude constraints to be specified.The target waypoint is specified based on the flight state at the entry point.For flights in takeoff and climb phase at the entry point, the first waypoint after top-of-climb is chosen to be the target waypoint.If the flight is in cruise phase at the entry point, the next waypoint is chosen to be the target waypoint; if the next waypoint is beyond top-of-descent, the next waypoint with speed and altitude constraints on the approach route is chosen as the target waypoint; else, the airport is chosen as the target waypoint.If the flight is in descent phase at the entry point, the next waypoint with speed and altitude constraints on the approach route is chosen as the target waypoint.If the approach route is missing, the airport is chosen as the target waypoint.Data for several comment fields in the MACS scenario file are generated in the seventh-step.These data are useful for debugging and analysis.Values for all the data fields specified in the header of the version of MACS being used are assigned in the eighth-step based on the computations done in the earlier steps.The scenario data are output in a file in the last-step shown in Fig. 3.
+V.Step 2: Scenario RefinementThe automated scenario generation process described in the previous section was employed to create a MACS scenario file for arrivals to JFK, EWR, LGA and TEB using 5/23/2017 traffic data archived in ATM data warehouse.All flights, including the ones with flight plans and the ones without flight plans, landing during the 18 through 23 UTC interval were considered for inclusion in the scenario.For flights without flight plans, the track data were processed to create flight-plans by specifying their route as a sequence of latitude-longitude pairs from the starting location to the destination airport.Cruise altitude and cruise speed were assigned based on the maximum altitude and maximum groundspeed seen in the track-data of these flights.The simulation start-time for creating the MACS scenario was chosen to be 12 UTC, which is six-hours prior to 18 UTC, to ensure that all flights in the scenario are able to land within the 18 UTC to 23 UTC time interval.The automated scenario generation process with these traffic data and scenario parameters resulted in the MACS scenario file with 808 flights, 57 fewer arrivals compared to 865 reported in ASPM.Starting with the scenario file with 808 flights, the scenario refinement steps in Fig. 4 were employed to improve the data quality and to adjust the scenario for meeting experiment requirements.Flights with route-length of less than 20 nautical-miles eliminated in the first step reduced the number of flights by 17.In the second step, none of the flights were removed by filtering based on cruise speed because the minimum speed of 127 knots and the maximum speed of 571 knots are reasonable.Next, in step 3, filtering based on cruise altitude of more than 600 feet eliminated 22 additional flights whose cruise altitude was zero, most likely because of missing altitude information.VFR aircraft without a transponder with altitude reporting capability are not required to provide altitude reports automatically to air traffic control.The possibility of assigning a reasonable cruise altitude based on the performance characteristics of the type of aircraft and the length of route could be investigated in the future.In step 4, the entry time filter was implemented to remove flights with an entry time of 30 minutes past the simulation start-time as suggested in Ref. 6.This step did not filter any flights because the earliest entry time in this dataset was one-hour and 28-minutes.The internal to external flights ratio filter (step 5) is designed to eliminate a number of shorter and longer flights from the flights in the scenario file to achieve the desired ratio.Flights are categorized as internal flights if the length of route is less than a prescribed threshold and external otherwise.A threshold value of 400 nautical-miles was used in this study.Of the 769 remaining flights at the end of stepfour in Fig. 4, 238 were categorized as internal flights and 531 as external flights with the resulting internal to external flights ratio of 0.45.Let, the desired ratio be r , 1x be the number of external flights in the dataset, 2x be the number of internal flights in the dataset, 1sx be the number of selected external flights and 2sx be the number of selected internal flights.The procedure for selecting 1sx and 2sxsuch that 2 1 s s x r x  (1)is given by: and ifss xx x x x r rx      (3)These two solutions can be written together as follows using the Iverson's notation:            2 1 1 2 1 1 2 2 1 2 1 1 2 2 1 1 s s x x rx x x rx x r x rx x rx rx x x                (4)where the logical expressions within the square-brackets mean a value of one or zero depending on whether they are true or false.Table 4 shows the number of internal and external flights for different values of r .Observe that a large value of r like 300 results in only internal flights to be selected; with zero external flights selected, the desired internal to external ratio is  .Finally, the internal and external fights are selected by first sorting the lists of internal flights and external flights in non-increasing order of route length, and then picking the required number starting from the top of the two lists.Alternatively, the required numbers can be selected randomly from the two unordered lists of internal and external flights.Step 5 for selecting internal and external flights based on their desired ratio, though implemented, was not applied; all 769 flights in the scenario were accepted.Figure 5 shows the hourly arrival traffic count considering arrivals to i is temporally separated from the leading aircraft 1 i  by more than t  , set the scheduled arrival time of the following aircraft to its originally proposed time of arrival; if not separated, add t  to the scheduled arrival time of the leading aircraft and assign it to the following aircraft as the scheduled time of arrival.Thus, this scheduler only delays aircraft.The resulting delay is given as( ) ( ( ) ( 1)) ( )( 1)p s p s i t t i t i t i t i t             (7)where the logical expression inside the square-brackets means a value of one when true and zero otherwise.Scheduling results were generated by imposing an hourly arrival capacity constraint of 100 aircraft.Figure 6 shows the original unconstrained hourly arrival traffic counts as a function of time (shown earlier in Fig. 5) and the traffic counts resulting from scheduling traffic using Eq.(6) to meet the specified capacity constraint.The average delay was found to be 49 minutes, maximum delay was one-hour and 44 minutes, and total delay was 634 hours.To achieve the desired arrival schedule of traffic simulated by MACS, the entry time of the flights have to be adjusted.Because the flight time is given as:  Figures 7 and8 show the histograms of the original entry times and the new scheduled entry times.Observe the reduction in the number of flights in the bins and the spreading of the flights and entry times to beyond 12 hours past 12 UTC in Fig. 8.f p E t t t  (8)The scheduling procedure described in this section is useful both for IDM HITL and fast-time MACS based simulations.This procedure can be used for allocating ground delay in response to airport capacity constraint forecast to achieve strategic traffic flow management objectives.The same procedure can then be used to allocate airborne delay to flights in the terminal area in response to actual airport capacity constraint to achieve tactical traffic flow management objectives.A slightly modified version of the scheduler in Eq. ( 6), ( ) ( 1)ss t i t i t    (10)where the delay, t  , could be changing as a function of time, can be used for increasing the arrival traffic demand beyond the capacity of the airport as was needed for the manually modified HITL scenario in Ref. 6; the HITL scenario in Ref. 6 achieved demand exceeding capacity by squeezing six-hours of traffic into five hours.
+VI. ConclusionsThe two-step procedure for automated scenario generation for Multi-Aircraft Control System based traffic simulation was described.The first step utilized the scenario generation process currently being used by the Air Traffic Management Testbed in development at NASA Ames Research Center.The second step, which implemented refinements to the scenario output from the first step, for meeting the objectives of the Human-in-the-Loop experiments and fast-time simulations, was also described.Flights were filtered in the second step based on route length, cruise speed, cruise altitude, entry time and the ratio of internal to external flights.The procedure for selecting internal and external flights was described.Finally, first-come first-served schedulers were described for curtailing arrival traffic demand to meet the airport arrival capacity constraints, and to increase arrival traffic demand over the airport capacity to meet Human-in-the-Loop experiment objectives.To determine the most frequently used runway configurations and the ones used during the busiest periods in terms of the number of operations (sum of arrivals and departures), runway configurations used during every hour of every day in 2017 and the associated numbers of arrivals and departure were obtained from the FAA's Aviation System Performance Metrics database.Results of analysis were presented in tables to summarize top five most frequently used configurations at John F. Kennedy, Newark Liberty, LaGuardia and Teterboro airports both individually and together.This analysis led to the identification of 5/23/2017 as a busy traffic day on which to base the scenario generation.Flights with and without flight plans in the 5/23/2017 traffic data were processed using the two-step procedure and the scheduling procedure to generate the results.These results show that the automated procedures discussed in the paper can be used to generate traffic scenarios that meet the requirements of Human-in-the-Loop experiments and fast-time simulations for evaluation of air traffic concepts.The automated process can replace the tedious manual scenario generation process; it is less error prone and makes it possible to generate large number of scenarios needed for Monte-Carlo evaluations, which is very difficult to achieve with the manual process.Fig. 11Fig. 1 Hourly sum of traffic counts at JFK, EWR, LGA and TEB on 5/23/2017 based on ASPM data.
+Fig. 22Fig. 2 Inputs and associated options for MACS scenario generation.
+Fig. 3 .3Fig. 3. Summary of MACS scenario generation steps.
+Fig. 4 .4Fig. 4 Scenario refinement steps.
+Fig. 55Fig. 5 Actual and predicted hourly arrival traffic counts at JFK, EWR, LGA and TEB taken together.
+Fig. 7 Fig. 6 .Fig. 8768Fig. 7 Histogram of the original entry times.
+Table 1 Top five most frequently used configurations at JFK, EWR, LGA and TEB1AirportConfigurationHoursArrivalsDepartures Operations % Hours% OperationsJFK31L, 31R | 31L3,817102,88995,443198,3324344JFK13L | 13R1,02026,72928,29755,0261112JFK13L, 22L | 13R74124,97822,64347,621810JFK22L, 22R | 22R69015,81715,90331,72077JFK4L, 4R | 4L49310,35410,09520,44954EWR 22L | 22R3,58294,37497,409191,7834044EWR 4R | 4L2,91472,91676,997149,9133334EWR 11, 22L | 22R49618,85616,27035,12658EWR 22R | 22R3342,6731,7864,45931EWR 4L | 4L2872,5011,8914,39231LGA22 | 132,04146,92746,22293,1492425LGA31 | 41,51936,90636,39473,3001820LGA4 | 131,38532,57633,40665,9821618LGA22 | 311,21028,71528,70657,4211416LGA31 | 311,17521,09821,29442,3921411TEB19 | 244,05039,00237,75276,7544748TEB6 | 13,00125,57525,66551,2403532TEB19, 24 | 246447,4037,94315,34679TEB1, 6 | 15835,0665,26410,33066TEB24 | 241251,6541,7383,39212
+Table 3 Top five hours with most operations at JFK, EWR, LGA and TEB taken together3ConfigurationArrivals Departures Operations Local Time UTC DateJFK-13L | 13R; EWR-22L | 22R; LGA-22 | 13; TEB-19 | 2413018331318229/13/2017JFK-13L, 22L | 13R; EWR-4R, 11 | 4L; LGA-22 | 13; TEB-6 | 115215430618226/7/2017JFK-31L, 31R | 31L; EWR-22L | 22R; LGA-22 | 13; TEB-19 | 2415814430218225/23/2017JFK-13L, 22L | 13R; EWR-11,22L | 22R; LGA-22 | 13; TEB-191461553011722 11/21/2017| 24JFK-13L, 22L | 13R; EWR-11,22L | 22R; LGA-22 | 13; TEB-1914715129818228/10/2017| 24
+Table 2 Top five most frequently used combinations of JFK, EWR, LGA and TEB configurations2ConfigurationHours Arrivals Departures Operations % Hours % OperationsJFK-31L, 31R | 31L; EWR-4R | 4L; LGA-31 | 4; TEB-6 | 141038,99838,82277,82055JFK-31L, 31R | 31L; EWR-22L| 22R; LGA-22 | 13; TEB-19 |29025,23726,83152,0683324JFK-31L, 31R | 31L; EWR-22L| 22R; LGA-22 | 31; TEB-19 |27927,21826,88854,1063424JFK-31L, 31R | 31L; EWR-4R | 4L; LGA-31 | 4; TEB-1, 6 | 126425,90126,77952,68033JFK-31L, 31R | 31L; EWR-4R | 4L; LGA-4 | 13; TEB-6 | 126424,72725,93350,66033
+Table 4 Number of internal and external flights based on r values r4InternalExternal005310.251325310.52384760.7523831712382383002380t is the original entry time, the new scheduled entry time, sE t , can be determined as:
+		
+		
+
+			
+AcknowledgementsAuthors are grateful to Dr. Min Xue, Dr. Antony Evans, Confesor Santiago, William Chan and Katharine Lee for reviewing the paper and providing feedback.Discussions with Dr. Robert Windhorst are gratefully acknowledged.
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+	
+	EEC Note
+	Eurocontrol Experimental Centre, "User Manual for the Base of Aircraft Data (BADA) Revision 3.6," EEC Note No. 10/04, Eurocontrol Experimental Centre Publications Office, B.P. 15, 91222 -Bretigny-sur-orge, France, July, 2004.
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+II. Sensitivity Study MethodThis section describes the sensitivity analysis method used for determining the affect that departure and arrival capacity reduction at one major airport has on the departure and arrival delays at the other 33 major airports in the continental United States.The 34 airports considered in this study are tracked in the Operational Evolution Plan (OEP) of the Federal Aviation Administration (FAA) and are referred to as OEP airports.The sensitivity determination method consists of conducting an ACES simulation with the 34 airport departure and arrival capacities set at their most common settings for establishing the baseline delays.Then, performing a series of ACES runs with airport departure and arrival capacities reduced at each airport one at a time, while maintaining baseline capacity value at the other airports, to determine the change in arrival and departure delays at each of the 34 airports.The Airspace Concept Evaluation System (ACES) was used to do the simulations.ACES is a comprehensive computational model of the national airspace system consisting of air traffic control and traffic flow management models of air route traffic control centers, terminal radar approach controls (TRACON), airports and the air traffic control system command center (ATCSCC). 2It simulates flight trajectories through the enroute-phase of flights, where the enroute-phase for piston-props is 6,000 feet, for turboprops is 8,000 feet and for jet aircraft is 10,000 feet.A queuing model simulates the surface movement and flight through the terminal airspace.Thus, with continuous aircraft dynamics and discrete air traffic control and traffic flow management events, ACES is a hybrid-system.The traffic flow management and air traffic control models in ACES use airport and sector capacity thresholds for delaying flights while they are on the ground and during their enroute phase to ensure that these capacity thresholds are not exceeded.Some of the ACES outputs are arrival and departure counts at airports, traffic counts in sectors and air traffic system performance metrics including arrival, departure, enroute and total delays.Validation studies in Refs. 3 and 4 have shown that ACES generates realistic delays and airport operational metrics similar to those observed in the real-world.Due to these capabilities, ACES was chosen as the system for conducting the airport departure and arrival rate sensitivity study discussed in this paper.
+III. Simulation Inputs and OutputsInput for ACES simulations consists of scenario files containing capacity data (airport arrival and departure capacities, and sector capacities), traffic data (scheduled departure times and flight-plans), and adaptation data (sector/center geometric data).These inputs are described below.Delay metrics, the outputs of ACES, are defined in this section.
+A. Airport CapacitiesTo determine airport departure and arrival capacities, four-months of data spanning the period from March 1, 2006 through June 30, 2006 reported in the FAA's Aviation System Performance Metrics (ASPM) database were collected.This database can be accessed via the web site: http://www.apo.data.faa.gov/.Airport capacity data for a particular airport can be obtained by selecting the Analysis tab and choosing Airport, Weather and Hourly radio buttons on the graphical user interface.Table 1 shows the airport capacity data for Hartsfield-Jackson Atlanta International airport during each hour of March 17, 2006.The first column shows the local hour and the second column lists the landing and takeoff conditions at that hour.Instrument approach condition is indicated by IA and visual approach condition by VA.The airport departure rate (ADR), which is the number of takeoffs per hour, is tabulated in the third column.The airport arrival rate (AAR), which is the number of landings per hour, is listed in the fourth column of the table.Finally, the total capacity of the airport, which is the sum of the ADR and AAR, is given in the last column of the table.In addition to the items in Table 1, the airport capacity data contain the actual number of arrivals and departures during the hour, cloud-ceiling, visibility, temperature, windspeed, wind-angle and arrival and departure runway configurations.
+Baseline CapacitiesThe data of the type listed in Table 1 were analyzed via scripts written in the Matlab language 5 to determine the most frequently used total capacities, along with the associated arrival and departure capacities, for each of the 74 ASPM airports including the 34 OEP airports.Honolulu International airport, which is one of the 35 OEP airports, was excluded from analysis because this study is devoted to airports within the continental United States.After obtaining the most frequently assigned total capacity -mode capacity from the entire dataset, instances with total capacities equal to the selected mode capacity were placed in a subset.Departure and arrival capacities were then selected from this subset based on the minimum of the cost function given in Eq. (1):2 ) ( AAR ADR C C J ! = (1) ADR Cis the airport departure rate and AAR C is the airport arrival rate.Observe that a minimum value of the function is obtained when the ADR is equal to the AAR.Table 2 lists the selected ADR and AAR values corresponding to the mode value of total capacities.The first and the seventh columns list the International Civil Aviation Organization codes for the airports.The second and the eighth columns indicate whether the airport is included in the OEP or not.Mode values of the total capacities are given in columns five and eleven.The frequency of occurrence of the mode value of the total capacity for each of the 74 airports is given as a percentage of the total of 2928 (24 hours !122 days) possible instances in columns six and twelve.The ADR and AAR values listed in this table were used in the ACES simulation for generating the baseline delay values.
+Reduced CapacitiesMatlab scripts were also used to identify instances where total capacities, Total C , were close to 50% of the baseline capacities, Mode C , listed in Table 2.The desire was to identify instances in real data when ADR and AAR were severely reduced.The ADR and AAR values corresponding to 50% capacities were obtained based on the minimum of Eq. (2):2 2 ) 5 . 0 ( ) ( Total Mode AAR ADR C C C C J ! + ! = (2)These ADR and AAR values for the 34 OEP airports are listed in Table 3.This second set consists of the reduced airport departure and arrival capacities that were used in ACES simulations for comparisons against the baseline capacities listed in Table 2.Note that the reduced total capacities are not exactly 50% of the mode capacity; they are as close to 50% as possible based on the actual four-months of airport capacity data that were analyzed.For example, the reduced total capacity of Cincinnati/Northern Kentucky International Airport (KCVG) in Table 3 is 69% of the baseline total capacity of 156 aircraft/hour in Table 2.
+B. Flight-Plans and Adaptation DataFlight-plans for the simulations were derived from the Aircraft Situation Display to Industry (ASDI) data, which is provided via the FAA's Enhanced Traffic Management System (ETMS) 6 , spanning the period from zero Coordinated Universal Time (UTC) on 17 March 2006 to zero UTC on 19 March 2006.These days were selected because 1) they were within the March 1, 2006 to June 30, 2006 time period and 2) they had experienced high traffic-volume, low weather impact and low delays.There were 48,258 departures on the 17 th (a Friday) and 35,394 departures on the 18 th (a Saturday) according to the Centers: Summary of Domestic Operations Report in the FAA's   Air Traffic Operations Network (OPSNET) database. 7Delay data obtained from the OPSNET database for these days are provided in Table 4.The second row of the table lists the total number of aircraft delayed by fifteenminutes or more.The third and the fourth rows show the number of aircraft delayed due to weather and due to traffic-volume.Total delay is given in the fifth row.Average delay given in the sixth row is obtained as the ratio of the total delay to the total number of aircraft delayed by fifteen-minutes or more.It can be verified that these two days are low delay days by comparing the total time delay values in Table 4 with those in
+C. Flight Schedule and ConnectivityThe flight connectivity data, data conditioning steps and delay metrics are described in this section.
+Flight Connectivity DataFlight connectivity data relating the same physical aircraft to two or more flights segments were obtained from the Bureau of Transportation Statistics (BTS) for the two days.Airline flightnumbers were used as tail-numbers for flights not found in the BTS data.The airline flight-numbers, aircraft tailnumbers and the associated flight-plans for all the flights were then included in the Flight Data Set (FDS) file.The subsequent step consists of assigning a departure time to the flights in the FDS file.Scheduled departure times derived from the BTS data are assigned to the flights in the FDS file found in the BTS data.For flights that are not in the BTS data, proposed departure times from flight-plan messages in the ASDI data are assigned as scheduled departure times.In instances when the route of flight is available but the departure time is not, average taxi times reported in the FAA's Aviation System Performance Metrics (ASPM) database are subtracted from the departure message times reported in the ASDI data to estimate the gate departure times.Scheduled departure times are then set to these gate departure times.After assigning a scheduled departure time for every flight, an ACES simulation is run without airport and sector capacity constraints to compute the unconstrained arrival time of each flight at its destination airport.These computed arrival times are then used as scheduled arrival times at the destination airports of the flights.
+Data ConditioningData conditioning steps are needed to compensate for missing and incomplete data.Although the data conditioning steps taken introduce some errors in the simulation, they help keep most flights in the simulation.Errors are due to discrepancies between the airline flight schedule and the simulated flight schedule Although departure schedules are provided as ACES input, arrival schedules for the flights are created during the configuration step of ACES.These computed arrival times need to be earlier than the scheduled departure times of the next segment of the flights.Data in the initial FDS file are therefore processed further to ensure that flight connectivity is preserved and that the arrival and departure schedules linked to the same physical aircraft account for the turn-around-time.Turn-around-time is defined as the time required for unloading the aircraft after arrival at the gate and preparing it for departure.Turn-around-time was assumed to be 40minutes irrespective of the size of the aircraft.The procedure for checking flight connectivity and turnaround-times is summarized in Fig. 1.The process is begun by running an ACES simulation with the initial FDS file and storing the results in the output database.The output database is examined to retrieve flights with a common tail-number.These flights are sorted in time and then a check is performed to determine if the destination airport of the previous flight segment is the same as the origin of the next segment.If the check fails, a new tailnumber is assigned to the subsequent flight segments.For example, consider the four flight segments in Table 5.Since the first segment of the flight ends at Los Angles International (KLAX) and the next segment begins at KLAX, these two segments are proper.The third segment starts at Chicago O'Hare International (KORD) which indicates that the flight connectivity between the second and the third leg is broken.A new tail-number, N12345-1, is assigned in the FDS file to associate this flight with a different aircraft.Tail-numbers of the subsequent segments are also altered.This means that the tail-number of the fourth segment in Table 5 is also altered to N12345-1 because it shares its airport of origin with the airport of destination of segment three.Next, the scheduled arrival and departure times of the flight segments are examined to determine if there is adequate turn-around-time between the segments.If it is determined that the condition described by Eq. ( 3) is not met, the scheduled departure time is altered to meet the condition.The amount of change in the departure time also appears in the scheduled time of arrival of this flight segment at the next airport.Since the unimpeded flight time between a pair of origin-destination airports is a constant, a change in departure schedule alters the arrival schedule by the same amount.Once the schedule of a flight segment is altered, schedules of subsequent flight segments are also altered to ensure that the turn-around-time requirement is met.The process summarized in Fig. 1 was applied to the initial FDS file that contained data for 98,674 flights operating out of 2,669 U. S. and foreign airports that were operated during the 48-hour period from March 17 th to the 18 th .Flight schedules and tail-numbers were altered for 37,638 flights to create the modified FDS file.
+D. Selection of Time Periods for Capacity ReductionSince the system-wide impact is a function of the time of day when ADR or AAR is reduced, peak-demand times were identified for each airport.A three-hour period around the peak demand time was identified as the time for ADR and AAR reduction at each airport.These times are provided in Table 6.The second and seventh columns list the two dates -3/17/2006 and 3/18/2006 associated with start-times and end-times for reduction of the ADR and AAR values.
+E. Delay MetricsThe delay metrics described below are ACES outputs that have been used for the study described in this paper.Scheduled times are employed in the simulation to provide the datum for computation of delays.Delays associated with the departure and arrival, which are defined below, are computed as those in Ref. 3. Scheduled takeoff time, stt t , is defined as:utot sgdt stt t t t + = ,(4)The gate arrival delay, gad t , is obtained as:sgat agat gad t t t ! = . (9)Substituting Eqs. ( 7) and ( 8) in Eq. ( 9) and using the definition in Eq. ( 6), it is seen that) ( ) ( utit atit uft aft dd gad t t t t t t ! + ! + = . (10)Equation (10) shows that the departure delay is accounted as part of the arrival delay.Arrival delay can be reduced by absorbing a part of the departure delay in flight.These metrics were computed with the baseline and reduced airport capacities to study the system-wide impact of capacity reduction at the 34 OEP airports.Results of this study are discussed in the next section.
+IV. ResultsResults obtained via ACES simulations with baseline capacities are described in Subsection A and those obtained using reduced capacities are discussed in Subsection B.
+A. Baseline Capacity ResultsA simulation was conducted with the conditioned FDS file, baseline sector capacities and baseline airport departure and arrival capacities listed in Table 2.Aircraft-counts in each sector resulting from the baseline ACES simulation were retrieved from the output database and added together to compute the total number of aircraft in the continental United States above 10,000 feet altitude at one-minute intervals.This time history of aircraft count was then compared with the time history of the actual number of flights, above 10,000 feet altitude.Actual flights for those days, recorded in the ASDI data, were processed using NASA's Future ATM Concepts Evaluation Tool (FACET). 9The two time histories are shown in Fig. 2. Observe that the ACES simulation starts with all aircraft on the ground, whereas in the actual air traffic system there are always flights that are airborne.Figure 2 shows that the simulated traffic catches up with the actual traffic around four UTC.The general trend of the simulated traffic is similar to the actual traffic for the twenty-four hours between eight UTC on 17 March 2006 and eight UTC on 18 March 2006 (location marked 32 UTC in Fig. 2).Differences between the time histories are both due to issues with the actual flight data and with the simulation.Several issues related to the quality of ASDI data are described in Ref. 10.These issues make it difficult to exactly determine how many flights are in the airspace at a given instant of time.Flight-plan amendments, cancellations and pop-up flights are not included in the simulation.Flights with track information but missing flight-plans in ASDI data are not included in the simulation.Additionally, the trajectory flown by the real aircraft can be different than the one synthesized in the simulation.During the simulation, aircraft were delayed on the ground and in the air to ensure that the airport and sector capacities were not exceeded.Figure 3 shows the baseline ADR value of 96, scheduled takeoff demand and the achieved takeoff rate at the Hartsfield-Jackson Atlanta airport as a function of time.The time along the abscissa is with respect to 17 March 2006, 0:00 UTC.The dashed line shows the baseline ADR value.The scheduled demand is shown with a solid-line marked with crosses (x) and the achieved departure rate, measured as the number of aircraft that departed in one-hour time period, is shown with another solid-line marked with circles (o).Observe that the scheduled demand was greater than the ADR value, whereas the achieved ('actual') departure rate is close to the ADR value.Comparing the scheduled demand and the achieved rate graphs in Fig. 3 between the locations marked as 28 UTC and 32 UTC, it is seen that the excess demand is modulated by shifting the flights to later times.Actual departure rates beyond 44:00 UTC should be ignored because departed flights that did not reach their destination airports prior to termination of the simulation were not counted.The arrival rate was also controlled in ACES to guarantee that the baseline AAR capacities are not exceeded.Figure 4 shows the baseline AAR value of 96, scheduled arrival demand and the achieved arrival rate at the Hartsfield-Jackson Atlanta airport.Observe that the arrival rate constraint was also met by delaying flights, which is reflected in the duration of the achieved arrival rate being close to the AAR value.It should be noted that most of the arrival delays are realized prior to departure at the departure airport and minimally in the airborne phase.In this sense, delays are mostly realized (imposed) at airports of origin both for ADR constraints at airports of origin and AAR constraints at the airports of destination.This is also the way most of the delays occur in the real air traffic system.For example, controlled departure times are issued at airports of origin during a Ground Delay Program at a destination airport.The values of dd t and gad t for each of the 34 OEP airports were obtained from the ACES baseline simulation.Table 7 lists these values along with the number of aircraft that departed from and arrived at each airport and the number of aircraft that landed at each airport during the twenty-four hour period spanning from eight UTC on 17 March 2006 to eight UTC on 18 March 2006 (location marked as 32 UTC in Fig. 2).Columns one and six list the airports.Departure-counts are listed in columns two and seven, and the total departure delays in minutes obtained by summing the departure delays of aircraft delayed by 15-minutes or more are given in columns three and eight.This 15minutes delay metric is commonly used by the FAA for assessing the performance of the air traffic system.Arrival-counts are provided in columns four and nine, and the total arrival delays in minutes obtained as the sum of arrival delays of aircraft delayed by 15-minutes or more are listed in columns five and ten.It should be noted that the delays in Table 7 cannot be compared with the OPSNET delays given in Table 4 because of their definitions.Delays in ACES are compared against schedule, whereas delays in OPSNET are compared with respect to the time when pilot requests permission to depart.In ACES, once a flight incurs departure delay, it can continue to incur departure delays as it arrives and departs from other airports.In the real system, it is possible that departure delay is only accounted once.Delays would not accrue in subsequent flight segments, if the air traffic controller permits the flight to depart soon after departure request is made by the pilot.Departure delay per flight is obtained as the ratio of the total departure delays to the departure-counts and the arrival delay per flight is obtained as the ratio of the total arrival delays to the arrival-counts.These ratios, obtained using the data in Table 7, are shown in Fig. 5.This figure shows that Hartsfield-Jackson Atlanta International (KATL) flights experience the most departure and arrival delays.One of the reasons is apparent from Fig. 3, which shows that the ratio of peak departure demand to departure capacity is 1.7.For comparison, Chicago O'Hare (KORD), which has similar ADR and AAR values as Atlanta, has a peak departure demand to capacity ratio of 1.1.Flights departing from George Bush Intercontinental/Houston Airport (KIAH) and flights arriving at Fort Lauderdale/Hollywood International (KFLL) also experience significant delays.The large difference between   2).
+B. Reduced Capacity ResultsOne-hundred-and-two ACES simulations were conducted with reduced ADR and AAR capacities listed in Table 3 for the time-durations given in Table 6.The baseline ADR and AAR values for the non-OEP airports listed in Table 2 were kept for all the simulations, only the values for OEP airports were altered for the sensitivity study.The first set of 34 ACES simulations were conducted by changing the ADR value for each OEP airport one at a time, while keeping the baseline ADR values for the other airports.The baseline AAR values for all OEP airports were kept for this set of simulations.Figure 6 shows an example of the achieved departure rate in response to ADR reduction at the Hartsfield-Jackson Atlanta airport from the baseline rate of 96 aircraft per hour to 48 aircraft per hour during 13:00 UTC through 16:00 UTC.The dashed-line in the graph shows the ADR value and the solid-line marked with circles shows the achieved rate.The scheduled departure demand is shown by the solid-line marked with crosses.The impact of the ADR reduction at Atlanta airport on the departure and arrival delays at the other airports is shown in Fig. 7.This figure shows the percentage increase or decrease in the delay values compared to the baseline delay values at those airports, which are given in columns three, five, eight and ten of Table 7.A key observation is that as departure delays at Atlanta increase, arrival delays at the other airports also increase.This is an expected result based on Eq. ( 10); it is interesting that the departure delays at some of the airports increase.This effect is explained by the fact that departures from these airports are connected to arrivals from Atlanta.The same physical aircraft arriving from Atlanta is flown out of these airports to other out-bound destinations.An arrival delay associated with these flights shows up as departure delay for the connected out-bound flights.Finally, one observes that the arrival delays also increase at Atlanta although the AAR values were not changed.This again is due to delayed departures from airports that depart aircraft for Atlanta.The impact of ADR reduction at each of the 34 OEP airports on the departure delays at other OEP airports is summarized in Table 8.The first column of this table lists the airport whose ADR was reduced and the header row indicates the impacted airport.A value of 89 in the first element of the first row states that total departure delay of flights delayed by 15-minutes or more at Atlanta increased by 89% compared to the baseline departure delay value of 151,897 minutes (see Table 7) due to reduced ADR at Atlanta.Similarly, the second element of the second row shows that the departure delay increased by 57% at Boston Logan airport due to reduced ADR at the Boston Logan airport.Note that the percentage values in the table have been rounded.Viewing Table 8 as a matrix, it is seen that the diagonal elements have a higher value compared to the off-diagonal terms.This is an expected result because ADR reduction at the airport directly affects departures from that airport.Closer examination reveals that for some of the airports, departure delays do not increase significantly with reduced ADR values.It was determined that for these airports, the departure demand is either lower or only slightly greater than the reduced ADR values.The only two airports -KMEM and KPHX for which the reduced ADR values were found to be same as the baseline values in the four months of operational data (see Tables 2 and3), additional departure delays were not expected.The effect of ADR reduction on the arrival delays is shown in Table 9. Viewing the data in Table 9 as a matrix, it is seen that the values of diagonal elements are low, which indicates that reduced ADR at an airport does not significantly increase arrival delays at that airport.Some airports -Atlanta (KATL), Houston (KIAH), John F. Kennedy (KJFK), San Francisco (KSFO) and Salt Lake City (KSLC) did not follow this trend.Reduced ADR at these airports had the effect of increasing arrival delays at these airports.Examining the rows of Table 9, it is seen that the off-diagonal terms are large for some airports.For example, the value of 41 in the first row under the KDFW heading means that total delays of flights arriving at Dallas/Fort Worth (KDFW) that were delayed by fifteenminutes or more increased by 41% compared to the baseline value in Table 7 due to reduced ADR at Atlanta.Increase in arrival delay should be expected because the delay caused by ADR reduction at the airports of origin can be expected to be propagated to the airports of destination.The significance of increase in delay should be judged by comparing the baseline delay value for the airport against the baseline delay values of other airports, which are given in Table 7.For example, an increase of 50% in delays at Atlanta is considerably more significant compared to the same increase at Salt Lake City.The next set of 34 ACES simulations were conducted with reduced AAR values at each of the 34 OEP airports.Baseline ADR values were kept for all the airports.Figure 8 shows the impact of AAR reduction at Atlanta on the other OEP airports.The bar-graphs show that the total arrival delay due to flights arriving late by fifteen-minutes or more increases by more than 90% compared to the baseline arrival delay in Table 7.The figure shows that departure delays at several airports increase due to reduced AAR at Atlanta.This is to be expected because the arrival constraint at Atlanta is met by delaying the out-bound flights to Atlanta at their airports of origin.Arrival delay at Atlanta also contributes to departure delay at Atlanta due to in-bound out-bound flight connectivity.This departure delay then propagates as arrival delay at other airports.In some instances, the departure and arrival delays are reduced slightly at other airports.This is essentially due to shifting of the departure and arrival times of the affected flights to times of lower demand at these airports.The results shown in both Figs.7 and 8 demonstrate that the impact of capacity reduction at one airport on the delays at another airport is complicated because of network (flight-connectivity) effects.Mathematical modeling of these effects is difficult, and therefore, a simulation capability like ACES is required for such an analysis.The impact of reduction of AAR at each airport on the departure delays at 34 OEP airports is summarized in Table 10.Data trends in this table are similar to those seen in Fig. 8.It should be noted that departure delays at La Guardia (KLGA), Minneapolis-Saint Paul (KMSP), Chicago O'Hare (KORD), San Francisco (KSFO) and Salt Lake City (KSLC) increase significantly due to their own reduced AAR rates.The sensitivity of arrival delays at the 34 OEP airports to reduced AAR at other airports is summarized in Table 11.This table shows that the reducing AAR at the airports, increases arrival delays significantly.Delays increase by more than 100% at Cleveland-Hopkins (KCLE), Charlotte/Douglas (KCLT), Newark Liberty (KEWR), Washington Dulles (KIAD), John F Kennedy (KJFK), La Guardia (KLGA), Minneapolis-Saint Paul (KMSP), Chicago O'Hare (KORD), Philadelphia (KPHL), Phoenix Sky Harbor (KPHX) and Salt Lake City (KSLC).The off-diagonal terms show that arrival delays also increase considerably at some airports due to AAR reduction at other airports.Instances are also seen where arrival delays decrease by a small amount.0 0 0 2 1 KPDX 0 0 0 -1 -1 0 1 -1 -2 2 1 -2 0 1 -3 4 KPHL 0 0 1 1 0 0 0 0 0 0 0 3 0 0 0 1 KPHX 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KPIT 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KSAN 0 0 -1 -2 1 2 0 -1 1 1 0 -1 -1 0 1 2 -1 KSEA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KSFO 0 0 0 0 0 -1 0 6 2 -1 0 0 0 0 8 11 KSLC 0 0 -2 -2 -1 -1 0 4 2 1 0 -3 -1 0 1 3 0 KSTL 0 -1 -1 0 0 -1 0 0 0 0 0 -1 0 0 0 0 KTPA 0 0 1 0 0 0 0 -10 KDEN 3 -1 1 -1 0 0 1 5 -2 -1 2 4 4 -3 1 3 KDFW 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 KDTW 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 KEWR 0 1 0 0 1 1 1 0 0 0 0 0 -1 0 0 0 KFLL -1 1 0 0 0 0 0 0 -1 0 0 0 0 0 0 1 KIAD 0 0 0 0 0 0 0 -3 0 0 0 0 -2 1 0 -1 KIAH 0 0 2 0 1 2 0 -1 0 1 2 2 -2 3 2 3 KJFK 0 1 0 0 0 1 0 -3 2 0 0 0 -1 -1 0 1 KLAS 0 0 1 0 0 0 0 2 0 1 0 15 1 4 1 0 KLAX 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KLGA 95 2 1 0 2 2 -1 -2 2 0 3 0 -1 1 0 4 KMCO 0 2 -2 0 0 1 0 -410 0 -1 0 1 0 -1 1 0 0 0 0 0 0 -1 0 KCLE 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 KCLT 0 0 1 1 2 1 1 1 1 1 1 0 0 0 1 1 KCVG 0 0 1 1 0 1 1 0 1 1 1 0 0 0 0 1 KDCA 0 0 0 0 0 0 0 0 0 1 0 0 0 0 -1 0 KDEN 0 0 -1 -1 2 1 5 4 2 2 2 -1 1 1 2 3 KDFW 0 1 0 2 0 0 0 3 1 1 1 0 0 0 0 2 KDTW 0 0 0 1 0 1 1 1 0 0 0 0 0 0 1 1 KEWR 0 2 2 3 5 2 3 0 2 4 3 2 2 2 1 2 KFLL 0 0 0 0 1 0 0 1 0 2 1 1 0 3 -2 1 KIAD 0 0 -1 2 1 0 0 0 0 1 0 0 0 1 0 2 KIAH 0 1 280 KSAN 0 0 0 2 2 0 -2 1 -1 1 -1 -1 1 1 2 1 KSEA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 KSFO 0 1 1 1 0 3 0 9 2 1 0 0 1 5 8 10 KSLC 0 0 0 4 0 1 -1 4 -1 0 0 -2 1 2 4 2 KSTL 0 0 0 0 0 0 -1 1 0 1 1 -1 0 0 1 0 KTPA 0 0 2 0 1 0 1 1 0 1 1 1 0 1 00 0 0 -1 0 0 1 0 0 0 0 0 0 0 1 KCLE 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 KCLT 1 1 0 1 0 2 1 1 1 0 3 0 0 0 2 1 KCVG 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 KDCA 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 KDEN 5 -1 1 0 -1 2 3 0 0 1 1 3 4 3 1 -1 KDFW 0 0 1 1 0 1 2 1 1 1 1 3 2 2 0 0 KDTW 0 0 2 1 0 2 0 0 0 0 1 1 0 -1 0 0 KEWR 0 3 3 3 2 2 3 4 0 1 4 1 0 1 4 1 KFLL 0 1 1 1 0 0 1 0 0 0 2 1 0 1 1 2 KIAD 0 0 0 0 0 4 4 4 2 0 1 3 -1 0 1 0 KIAH 1 0 1 7 1 4 4 -1 2 2 4 5 -1 4 3 2 KJFK 0 3 0 1 2 3 2 4 0 1 1 4 1 2 4 4 KLAS 0 0 5 3 0 8 2 5 1 7 0 21 2 8 4 1 KLAX 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KLGA 2 2 4 2 4 3 7 1 3 0 8 1 -1 1 -1 3 5 KMCO 0 0 1 1 0 1 1 0 1 0 0 0 0 0 -1 -1 KMDW 0 0 2 0 0 3 0 3 0 0 0 0 0 0 2 0 KMEM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KMIA 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0 2 KMSP 1 140 0 0 KSAN 1 -1 -2 0 0 0 0 1 1 2 0 4 0 2 1 -1 KSEA 0 0 0 1 0 1 0 3 0 0 0 0 0 1 0 0 KSFO 0 0 0 0 0 2 3 4 1 3 0 8 17 11 1 0 KSLC 0 -1 1 -3 0 1 -1 5 -1 4 0 7 5 77 2 -1 KSTL -1 -1 0 0 -1 0 2 0 1 0 2 0 0 1 0 -1 KTPA 1 0 3 1 0 0 0 2 0 0 1 1 0 1 1 2The final set of 34 ACES simulations were conducted with both ADR and AAR reduced together at each airport, one at a time, while keeping baseline ADR and AAR values at the other airports to complete the sensitivity study.Tables 12 and13 summarize these results.Table 12 presents the impact on departure delays at the 34 OEP airports and Table 13 shows the impact on arrival delays.Both these tables show that the departure and arrival delays increase substantially at the airports were capacity is reduced.Other observations made in the previous tables remain the same for these tables too.The data presented in Tables 12 and13 show that the percentage change in departure and arrival delays at the affected airports due to both reduced ADR and AAR capacities is close to the maximum of delay change due to reduced ADR capacities or AAR capacities given in Tables 8 through 11.For example, departure delay increase of 93% at Dallas Fort Worth (KDFW) due to both ADR and AAR reduction at Hartsfield-Jackson Atlanta (KATL) (see Table 12) is closer to 85% increase in departure delays due AAR reduction at KATL (see Table 10) compared to 39% increase in departure delays due ADR reduction at KATL (see Table 8).These initial results show that it might not be possible to simply add the impact due to ADR capacity reduction to that due to AAR reduction to derive the combined impact of both ADR and AAR capacity reduction.The utility of the sensitivity data in Tables 8 through 13 for developing delay forecasting models remains to be seen.The results also provide insight into flight demand between pairs of airports.For example, the impact of capacity reduction at Atlanta on delays at Dallas Fort Worth (KDFW) is much more compared to those at San Francisco International (KSFO).This insight can also be gained by analyzing origin-destination pairs in the ACES FDS file.System-wide impact due to each airport is easily determined by first using the percent change in delays given in the rows of Tables 8 through 13 with the baseline delay values reported in Table 7 for determination of delay increase or decrease at each affected airport, and then adding these delay values.Figure 9 shows the increase in system-wide departure delays due to ADR and AAR reduction, obtained using the values in Table 12. Figure 10 depicts the impact on system-wide arrival delays obtained using values in Table 13.Both Figs. 9 and 10, show that capacity constraints at Atlanta, compared to constraints at other airports, have a significantly higher impact on the total system departure and arrival delays.One of the reasons is that there are significantly more flights with connected segments out of Atlanta compared to any other airport.Atlanta had 1,347 connected flights compared to 1,017 at Chicago, the airport with the next higher number of connected flights.In the real system the delays at            Atlanta might be considerably less because of the following reasons.Fifty-percent capacity for three hours with a peak demand capacity ratio of 3.3 (see Fig. 6), which means three times the demand, is extreme.When delays are this severe, flights are cancelled in the real system.Flights were not cancelled during the ACES simulations.0 3 -2 0 2 0 0 1 0 0 0 KBWI 0 1 0 -2 1 -1 0 1 -2 2 1 -2 -2 0 -1 0 KCLE 0 1 1 29 1 2 1 -1 1 2 1 0 2 0 -1 0 KCLT 0 0 -1 -1 28 2 -1 -2 4 2 1 -1 -1 0 0 1 KCVG 0 0 0 0 0 6 0 0 0 1 0 0 0 0 -1 0 KDCA 0 1 -1 1 0 0 6 0 1 1 0 0 0 0 -1 0 KDEN 0 0 1 0 -1 1 2 20 -2 0 1 1 2 0 1 4 KDFW 0 0 0 1 0 0 1 3 38 0 0 0 0 0 0 0 KDTW 0 0 0 1 0 2 1 -1 -1 10 0 -1 1 0 0 0 KEWR 0 11 4 4 7 4 1 -2 0 19 14 1 8 0 0 -1 KFLL 0 -1 -2 -2 2 -1 0 -1 3 3 1 20 -1 0 -1 2 KIAD 0 1 -2 4 2 3 0 0 3 3 6 0 14 0 0 1 KIAH 0 0 -1 1 1 1 3 -2 6 2 -1 -1 1 0 0 1 KJFK 0 12 -180 0 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 KSAN 0 0 1 0 -1 -1 0 -2 0 0 0 0 2 0 2 1 KSEA 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 KSFO 0 0 -1 -1 -1 -1 -1 6 -2 -1 0 0 -2 0 -2 31 KSLC 0 -1 -2 0 -1 -3 1 11 5 -1 1 0 -1 0 0 7 KSTL 0 -1 -2 -2 0 -2 2 2 -2 1 0 -2 1 0 -2 -4 KTPA 0 0 -2 -1 2 0 -20 -4 0 -1 0 0 -1 1 1 KMDW 0 0 1 3 0 0 0 1 -1 -1 0 0 0 0 1 0 KMEM 1 0 2 -2 3 -1 1 0 0 -2 0 0 0 -3 3 0 KMIA 0 -1 1 0 0 -1 0 0 -1 0 0 -1 0 0 -1 0 KMSP 0 -1 0 1 0 0 -1 5 -2 6 0 0 0 1 0 1 KORD 0 0 2 1 -1 2 0 -1 2 9 -1 -1 0 -1 -4 -2 KPDX 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KPHL 0 1 -1 0 1 -1 0 0 -3 0 1 0 0 1 -2 0 KPHX -1 -2 5 5 0 -2 -1 14 0 0 2 0 -1 -2 -3 13 4 KPIT 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 -1 0 KSAN 0 0 0 0 1 0 0 -1 0 0 0 0 0 0 0 2 KSEA 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 1 KSFO -1 0 2 -1 0 1 -3 2 1 -2 -3 -1 -1 0 4 17 KSLC 0 0 3 6 -1 0 1 -1 1 -3 -3 -2 -1 -1 -1 2 2 KSTL 0 -1 0 2 0 2 -3 -1 -6 1 -2 -2 0 0 -2 0 KTPA 0 -1 1 -1 -1 1 -2 2 -1 1 0 0 -1 0 0 -10 0 -1 0 -1 0 1 0 4 0 -1 0 -2 -1 0 KBWI 0 -1 -1 -6 -1 -1 0 0 -1 0 0 0 -1 1 1 0 -1 KCLE 0 0 1 0 0 1 -1 0 0 1 -1 -1 -1 0 1 0 0 KCLT -1 -1 0 2 -1 1 -1 0 1 0 9 1 0 0 -2 0 KCVG 0 0 0 0 0 0 0 0 0 0 0 1 0 0 -1 0 KDCA 0 0 0 0 0 0 -1 2 0 0 0 1 -1 0 -1 -1 -1 KDEN 4 -1 -1 4 0 0 2 2 2 0 5 3 -1 3 1 -1 KDFW 0 0 0 0 0 2 0 -2 0 0 0 1 -1 1 0 0 KDTW 0 0 0 -6 -1 1 1 1 -1 0 1 1 -1 -1 1 -1 KEWR 0 -1 0 -1 -1 -1 2 -1 0 0 1 3 -1 -2 1 1 0 KFLL 2 2 0 0 -1 1 -1 -1 2 0 5 1 0 1 1 1 KIAD 0 -1 0 1 1 -1 0 1 -1 0 3 3 -2 2 0 2 KIAH 1 -1 -2 -2 -1 2 -1 -1 -1 1 1 0 -3 2 -1 -2 KJFK -1 0 -1 0 -2 -2 1 -2 -1 1 -2 0 0 0 -2 1 KLAS 0 1 2 -2 1 0 0 1 1 1 3 10 -2 6 0 3 KLAX 0 0 0 0 0 0 0 0 0 1 0 2 0 1 0 0 KLGA 153 -2 9 1 -2 -2 5 -2 0 3 5 2 -2 1 0 0 KMCO 0 1 -1 1 0 0 -2 0 1 0 3 1 0 0 1 0 KMDW 0 1 48 0 0 0 0 2 0 0 -1 1 0 0 1 0 KMEM 2 -2 -1 65 0 -4 1 -1 0 2 4 2 -3 1 0 2 KMIA 0 -1 2 -1 16 0 0 0 1 0 0 1 -1 0 -1 1 KMSP 0 0 4 0 0 405 -2 1 0 0 4 1 -1 -1 -3 3 -1 KORD 0 -1 -1 2 -1 5 275 -2 0 0 3 12 -1 -1 -2 2 -1 KPDX 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 KPHL 0 1 3 -72 KMCO 0 1 0 -1 -1 1 -1 1 -1 1 0 1 -1 0 0 2 KMDW 0 0 1 3 0 0 0 1 0 5 0 0 0 0 0 0 KMEM 0 0 1 2 0 2 1 -1 6 4 -2 0 2 0 -1 2 KMIA 0 -1 -1 0 0 -1 -1 -1 0 0 -1 -2 -1 0 0 -1 KMSP 0 1 0 5 0100 0 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 KSAN 0 0 -1 -2 2 2 1 -3 1 1 0 0 -1 0 0 -2 KSEA 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 KSFO 0 -1 -2 -3 -1 0 -1 13 0 -1 0 -1 -2 0 6 34 KSLC -1 -1 -3 -1 -4 -1 0 10 7 0 -1 -1 -2 0 -1 8 KSTL 0 -1 -3 -3 -1 -2 1 2 -2 1 0 -2 -1 0 -2 -2 KTPA 0 1 -1 -12 0 -1 0 -1 1 0 KBWI 1 -2 1 -2 -1 3 -1 2 -2 0 1 -1 2 -1 1 5 KCLE 1 0 6 0 0 0 0 1 -1 0 2 0 0 -1 0 -1 KCLT 2 0 2 0 4 6 5 -1 1 0 2 1 0 -2 -3 2 1 KCVG 0 1 1 0 0 0 1 -3 0 0 0 1 0 0 1 0 KDCA 1 1 0 0 -1 1 2 -3 1 0 1 -1 0 -1 1 3 KDEN 2 -1 1 0 0 3 5 2 -1 3 -5 17 -3 1 1 4 KDFW 0 0 0 1 0 0 4 -4 0 0 1 1 -1 0 -1 3 0 KDTW 1 -1 1 -3 -1 2 1 0 -1 0 1 2 1 -2 1 -1 KEWR 2 4 1 1 5 4 17 -5 1 0 23 -1 0 -3 0 3 KFLL 0 5 2 -1 -3 1 -1 -2 1 0 -1 1 2 -1 -1 1 15 KIAD 3 0 1 2 -2 0 1 0 0 0 5 0 0 0 13 4 KIAH 2 -2 4 2 -1 7 0 -1 -1 1 -2 3 -4 2 6 3 KJFK 2 2 -2 -1 1 4 2 -3 1 -1 9 -1Figure 2 .2Figure 2. ACES simulated aircraft counts and actual aircraft counts comparison.
+Figure 3 .3Figure 3. Departure rate achieved at Hartsfield-Jackson Atlanta airport with baseline airport and sector capacities.
+Figure 4 .4Figure 4. Arrival rate achieved at Hartsfield-Jackson Atlanta airport with baseline airport and sector capacities.
+Figure 5 .5Figure 5. Baseline departure and arrival delays at the 34 OEP airports.
+Figure 6 .6Figure 6.Departure rate achieved at Hartsfield-Jackson Atlanta airport with reduced ADR.
+Figure 9 .9Figure 9. System-wide impact of ADR and AAR reduction on departure delays.
+Table
+Table 2 .2Baseline capacities for the 74 ASPM airports.. Hartsfield-Jackson Atlanta International airportcapacity on March 17, 2006.Local Hour Weather ADR AAR Total0IA96961IA96962IA96963IA96964IA96965IA96966IA96787IA96828VA96949VA969410VA969411VA969412VA969413VA969414VA969415VA969416VA969417VA969418VA969419VA969420VA969421VA969422VA969423VA9694
+Table 8 of8Ref. 8. Along with the flight-plan data, adaptation data and capacity data are required for ACES simulation.Sector and center geometry definitions in the January 1, 2005 adaptation data obtained from ETMS have been used to generate the results in this paper.Baseline sector capacity values are also derived from January 1, 2005 ETMS data tables.
+Table 4 .4OPSNET delay data.Date3/17/2006 3/18/2006 3/19/2006# Aircraft Delayed7831441Weather1661199Volume396129Total Delay (min.)22,05414,21070,119Average Delay (min.)28.1729.8548.66
+Table 3 .3Reduced capacities for the 34 OEP airports.Airport ADR AAR Total Airport ADR AAR TotalKATL484896 KLGA2550KBOS262652 KMCO3672KBWI282856 KMDW2448KCLE282856 KMEM56116KCLT303060 KMIA3262KCVG5157108 KMSP2652KDCA262652 KORD50100KDEN6262124 KPDX3264KDFW5659115 KPHL2654KDTW6048108 KPHX48108KEWR303060 KPIT4080KFLL181836 KSAN2856KIAD303262 KSEA2856KIAH484896 KSFO2752KJFK202040 KSLC4080KLAS303464 KSTL3264KLAX5357110 KTPA1939
+Table 5 .5Flight segments operated by the same physical aircraft.Segment Tail-Origin Destinationnumber1N12345 KSFO KLAX2N12345 KLAX KDEN3N12345 KORD KIAD4N12345 KIADKORD
+Table 6 .6Time periods for reduced ADR and AAR values at the 34 OEP airports.Airport Start-Start-End-End-Airport Start-Start-End-End-datetimedatetimedatetimedatetime(UTC)(UTC)(UTC)(UTC)KATL3/1713:00 03/17 16:00 KLGA03/17 19:00 03/1722:00KBOS3/1723:00 03/182:00 KMCO 03/17 20:00 03/1723:00KBWI3/1720:00 03/17 23:00 KMDW 03/17 23:00 03/182:00KCLE3/1723:00 03/182:00 KMEM 03/17 13:00 03/1716:00KCLT3/1723:00 03/182:00 KMIA03/17 23:00 03/182:00KCVG 3/1723:00 03/182:00 KMSP03/17 23:00 03/182:00KDCA 3/1723:00 03/182:00 KORD03/180:00 03/183:00KDEN 3/1716:00 03/17 19:00 KPDX03/17 14:00 03/1717:00KDFW 3/1723:00 03/182:00 KPHL03/17 23:00 03/182:00KDTW 3/1722:00 03/181:00 KPHX03/17 16:00 03/1719:00KEWR 3/1723:00 03/182:00 KPIT03/17 19:00 03/1722:00KFLL3/1721:00 03/180:00 KSAN03/17 15:00 03/1718:00KIAD3/1720:00 03/17 23:00 KSEA03/181:00 03/184:00KIAH3/1718:00 03/17 21:00 KSFO03/17 18:00 03/1721:00KJFK3/1721:00 03/180:00 KSLC03/17 16:00 03/1719:00KLAS3/1723:00 03/182:00 KSTL03/17 18:00 03/1721:00KLAX 3/183:00 03/186:00 KTPA03/17 21:00 03/180:00where sgdttatt=tagdt+tatot,(5)where agdttdd=tatt!tstt.(6)Scheduled gate arrival time, sgat t , is defined as:tsgat=tstt+tuft+tutit,(7)where stt t is the scheduled takeoff time (wheels-off time), uft t is the unimpeded flight time and utit t is the unimpededtaxi-in time. Actual gate arrival time, agat t , is similarly defined is terms of the actual takeoff time, att t , actual flighttime, aft t , and the actual taxi-in time, atit t , as:tagat=tatt+taft+tatit.t is the scheduled gate departure time and utot t is the unimpeded (assuming it is the only aircraft) taxi-out time.Recollect that the scheduled gate departure time is available in the FDS file and that the unimpeded taxi times for the airports are obtained from the ASPM database.The actual takeoff time, att t , is similarly defined as: t is the actual gate departure time and atot t is the actual taxi-out time.Actual times are not real ones but simulated times.Departure delay is then obtained as:
+Table 7 .7Baseline delay results for 34 OEP airports.Airport Dep.Dep. DelayArr.Arr. DelayAirport Dep.Dep. DelayArr.Arr. Delaycount! 15 min.count! 15 min.count! 15 min.count! 15min.KATL1,761151,897 1,787166,517 KLGA64714,71718,934KBOS63312,13956813,336 KMCO56017,17820,659KBWI41812,5584269,586 KMDW4544,1945,509KCLE4184,1704083,707 KMEM6137,9963,278KCLT73510,1047356,940 KMIA5897,13011,928KCVG7216,6536063,368 KMSP7493,9425,657KDCA42810,8764336,157 KORD1,55811,015 1,55215,074KDEN8753,1748775,233 KPDX3721,6192,506KDFW1,0354,508 1,0016,009 KPHL80221,18210,715KDTW7514,8607275,714 KPHX95328,81217,893KEWR76021,31671314,047 KPIT3573,5613,524KFLL44811,49948720,481 KSAN3372,4743,617KIAD5968,0256107,532 KSEA4992,1963,184KIAH93045,94284312,642 KSFO5928,74619,072KJFK57412,6205087,847 KSLC6992,9343,610KLAS93517,49682616,825 KSTL4202,7984,258KLAX1,0136,1768607,700 KTPA4065,7346,431departure arrival
+Table 8 .8Impact of ADR reduction at one airport on departure delays at other OEP airports.100.0090.0080.00Change in delay (%)30.00 40.00 50.00 60.00 70.0020.0010.000.00KATLKBOSKBWIKCLEKCLTKCVGKDCAKDENKDFWKDTWKEWRKFLLKIADKIAHKJFKKLASKLAXKLGAKMCOKMDWKMEMKMIAKMSPKORDKPDXKPHLKPHXKPITKSANKSEAKSFOKSLCKSTLKTPAAirportdeparture arrival Figure 7. Impact of ADR reduction at Hartsfield-Jackson Atlanta airport on delays at other OEP airports.
+Table 9 .9Impact of ADR reduction at one airport on arrival delays at other OEP airports.AirportKATLKBOSKBWIKCLEKCLTKCVGKDCAKDENKDFWKDTWKEWRKFLLKIADKIAHKJFKKLASKLAXKATL37101734263536274127106135514KBOS02311060022106-11KBWI
+Table 10 .10Impact of AAR reduction at one airport on departure delays at other OEP airports.100.0090.0080.0070.00Change in Delay (%)20.00 30.00 40.00 50.00 60.0010.000.00-10.00-20.00KATLKBOSKBWIKCLEKCLTKCVGKDCAKDENKDFWKDTWKEWRKFLLKIADKIAHKJFKKLASKLAXKLGAKMCOKMDWKMEMKMIAKMSPKORDKPDXKPHLKPHXKPITKSANKSEAKSFOKSLCKSTLKTPAAirportdeparture arrival Figure 8. Impact of AAR reduction at Hartsfield-Jackson Atlanta airport on delays at other OEP airports.
+Table 11 .11Impact of AAR reduction at one airport on arrival delays at other OEP airports.AirportKATLKBOSKBWIKCLEKCLTKCVGKDCAKDENKDFWKDTWKEWRKFLLKIADKIAHKJFKKLASKLAXKATL96-701238528-114277-122-65KBOS0471-1000200-1-103-10KBWI016220-10-310-20-1-10KCLE0011020200-110-10111KCLT001517701-1010-1-1-111KCVG00000530-1-20000010KDCA01000-1480-20-1-1000-1KDEN00121235811-100-1-24KDFW010000-118001000-10KDTW001-1000-1-2510-100-11KEWR022700-10-26115-102-12KFLL0-1103-330-1004310-22KIAD00142220030-10213KIAH0002-1-230-21-1-153020KJFK05183920-3-10-2012411KLAS001300-1-3100000985KLAX00000000000000028KLGA015-21112-334-1030-40-1-242KMCO0011100
+Table 12 .12Impact of ADR and AAR reduction at one airport on departure delays at other OEP airports.AirportKATLKBOSKBWIKCLEKCLTKCVGKDCAKDENKDFWKDTWKEWRKFLLKIADKIAHKJFKKLASKLAXKATL1009155617413527938792432513012KBOS05820104-302011010KBWI01111-100-211-2-1000KCLE011341210120020-11KCLT00006121-2421-1-1001KCVG000002800010010-10KDCA01-1100110110000-10KDEN001-1-13187112000-14KDFW0001001411100000-10KDTW0001021-1-1360-11000KEWR01264652-20195628000KFLL0-1-1-12-100330330001KIAD02-2534003460540-10KIAH01060323114-10130-11KJFK01418271105-121010002KLAS0000001244100007727KLAX00000003000000112KLGA023-11896295846-35601
+Table 13 .13Impact of ADR and AAR reduction at one airport on arrival delays at other OEP airports.2-2-17
+Airport Increase in total system delay (minutes) arrival delay 256,989 minutesFigure 10.System-wide impact of ADR and AAR reduction on arrival delays.800006000040000200000KATLKORDKLGAKPHLKMSPKEWRKLASKJFKKIAHKPHXKSFOKBOSKSLCKIADKCLTKDFWKDENKFLLKTPAKMDWKMIAKMEMKCLEKPDXKCVGKSEAKDCAKDTWKLAXKMCOKSANKPITKBWIKSTL800006000040000200000KATLKORDKLGAKMSPKPHXKLASKPHLKEWRKSFOKJFKKIAHKCLTKFLLKSLCKIADKBOSKDFWKTPAKDENKCLEKSEAKMEMKMDWKDCAKCVGKLAXKDTWKMIAKMCOKPDXKSANKBWIKSTLKPIT
+Airport Increase in total system delay (minutes) departure delay 215,807 minutes
+		
+		
+
+			
+AcknowledgmentsThe authors wish to thank Dr. Robert Windhorst of NASA Ames Research Center for his support of this study.We also thank the Raytheon Team for enhancing the flight-connectivity functionality in the Airspace Concept Evaluation System (ACES), without which this study would not have been possible.Finally, we thank Tom Romer of NASA Ames Research Center for suggesting additional ways of examining the data and critiquing our results.His comments have helped improve the paper.
+			
+
+			
+The results presented in this paper were generated with a single day of air traffic data.The trends seen in the results are expected to hold for days with similar characteristics as those of the day used for computing the results.If the demand patterns change, numerical values will change but the method described in the paper can be used to generate the new sensitivity matrices.The impact of capacity reduction was studied by altering capacities one airport at a time.On the typical day multiple airports are impacted.The impact of combinations of ADR and AAR capacity reductions at multiple airports has not been studied.
+V. SummaryThis paper described a method for sensitivity study in which the airport departure rate (ADR) and airport arrival rate (AAR) were reduced at each of the 34 major airports in the United States, one at a time, and the impact on the departure and arrival delays at these airports was assessed.To compute these delay values, the Airspace Concept Evaluation System (ACES) was used.One-hundred-and-three ACES simulations were conducted to complete the study.In the first set of 34 runs, only the ADR values were altered.The AAR values were kept at their baseline level.In the second set of 34 runs, the AAR values were changed.The ADR values were kept at their baseline level.Both the ADR and AAR values were reduced for the final set of 34 simulations.The results obtained show that ADR reduction at an airport directly increases the departure delay at that airport.This departure delay then appears as arrival delay at the other airports.It was observed that the departure delays at other airports increase indirectly due to flight-connectivity effects.Reduction of AAR was seen to increase the arrival delay at the affected airport.Passing back of this arrival delay causes the departure delay to increase at the airports sending flights to this affected airport.Flight-connectivity was responsible for causing departure delays at the affected airport.Data tables in the paper provide numerical values that quantify the degree of impact of capacity reduction at one major airport on another.			
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+		AIAA- 2003-5626
+	
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+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, TX
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+			American Institute of Aeronautics and Astronautics
+			August 11-14, 2003
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+	Chatterji, G. B., Sridhar, S., Kim, D., "Analysis of ETMS Data Quality for Traffic Flow Management Decisions," AIAA- 2003-5626, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, TX, August 11-14, 2003.
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+I. Introductionhis paper is motivated by the need for predicting wheels-off time at airports where advanced automation systems, such as the Surface Decision Support System (SDSS), that depend on surveillance information derived from Airport Surface Detection Equipment Model-X (ASDE-X) type systems will not be available.At airports with SDSS type systems, aircraft surface movement will be scheduled, which will reduce runway entry time and wheelsoff time uncertainty.Additionally, it will be possible to generate better estimates by using surveillance information.For example, the taxiway entry time of an aircraft waiting at the spot can be predicted more accurately by predicting the trajectories of aircraft in the movement areas starting from the current locations from surveillance data.At less equipped airports, wheels-off time will have to be estimated under the current day conditions with uncertainties associated with aircraft movement in the ramp-area and in the movement-area.Wheels-off time predictions are needed for coordinating departure release times with downstream facilities to meet flow management restrictions imposed by them.These restrictions are imposed for ensuring adequate separation between aircraft, creating orderly flows in terminal areas, and protecting sectors and airports from being overwhelmed by demand.Two of the commonly used restrictions that could benefit from wheels-off time estimation are Call For Release (CFR) and Expect Departure Clearance Time (EDCT).Accuracy requirement for CFR is actual wheels-off time within twominutes early to one-minute late window with respect to the estimated wheels-off time.The requirement for EDCT is plus-minus five minutes.In the first part of the paper, the initial set of 77 major U. S. airports in the Aviation System Performance Metrics (ASPM) database is reduced to the set of 29 airports after eliminating ASDE-X, small-hub and non-hub airports.This reduced set contains 27 medium-hub airports and two large-hub airports.Nine of these airports are being considered for surface surveillance systems.Currently, single radar-based Low Cost Ground Surveillance (LCGS) is
+II. Selection of Non-ASDE-X AirportsIn this section, the sources of taxi-out delay, taxi-out time, number of commercial operations, traffic flow management initiative caused delay counts, passenger enplanement counts, type of hub airport and type of surface surveillance equipment at the airport used for determining the non-ASDE-X airports in this study are described.Most of the data were derived from the Federal Aviation Administration's (FAA) Aviation System Performance Metrics (ASPM) and Operations Network (OPSNET) databases containing historical traffic counts and delay statistics.Data were also derived from the Terminal Area Forecast (TAF) Summary for fiscal years 2011-2040 report.The 77 airports included in the ASPM and OPSNET databases are listed in Table A-1 in the Appendix.These airports are referred to by their airport code in the rest of the document.The first report obtained from the ASPM database is the "Analysis By Airport Report (compared to flight plan)" for calendar year 2011.This report includes average taxi-out time and average taxi-out delay for each of the 77 major U. S. airports.Taxi-out time is the difference between the actual wheels-off time and the actual gate-out time.Taxi-out delay is difference between the taxi-out time and the unimpeded taxi-out time.Taxi-out times and delays are in minutes.Only data from itinerant flights to or from the ASPM 77 airports or operated by one of the ASPM 29 carriers are included.Itinerant flights are those that land at an airport, arriving from outside the airport area, or depart an airport and leave the airport area.Data on flights by ASPM carriers to international and domestic non-ASPM airports are also kept in the ASPM database.General aviation and military flights are excluded.Table A-2 in the Appendix lists the average taxi-out delay and the taxi-out delay ratio derived from this report.The average taxiout delay ratio is defined as the ratio of average taxi-out delay to average taxi-out time.The second report, "OPSNET: Airport Operations: Standard Report" for calendar year 2011 is obtained from the OPSNET database.This same report can also be obtained from the FAA's Air Traffic Activity System (ATADS) database.This report contains air-carrier, air-taxi, general aviation and military itinerant operations, and civil and military local operations.Local operations are performed by aircraft that remain in the local traffic pattern, execute simulated instrument approaches or low passes at the airport, and fly to or from the airport and a designated practice area within a 20-mile radius of the tower.Airport operations include all arrivals and departures at the airport; overflights are not included.OPSNET defines air-carrier as aircraft with a seating capacity of more than 60 seats or a maximum payload capacity of more than 18,000 pounds carrying passengers or cargo for hire or compensation and air-taxi as aircraft with a seating capacity of 60 or less or a maximum payload capacity of 18,000 pounds carrying passengers or cargo for hire or compensation.Air-carrier includes U. S. and foreign flagged carriers.Table A-2 in the Appendix lists the sum of itinerant air-carrier and air-taxi operations (commercial operations as defined by TAF report) for each of the 77 major U. S. airports based on the 2011 report.The third report, "OPSNET: Delays: Delay Types Report" was obtained from OPSNET database to get TMIfrom delay counts for calendar year 2011.These counts are also listed in Table A-2 in the Appendix.OPSNET defines TMI-from delays as traffic management initiative delays from a national or local traffic management initiative as experienced by aircraft departing from the selected facilities.These initiatives include departure spacing (DSP), enroute spacing (ESP), arrival spacing (ASP), miles-in-trail (MIT), minutes-in-trail (MINIT), Expect Departure Clearance Time (EDCT), Ground Stop (GS), and Severe Weather Avoidance Plan (SWAP).It should be understood that these delays are charged to facilities asking for the delays and not to the ones providing the delays.For example, 11,132 aircraft were delayed at Atlanta (see ATL TMI-from delay counts in Table A-2 in the Appendix) to comply with traffic management initiatives of other facilities.From this study's perspective, TMIfrom delay counts (number of aircraft experiencing TMI-from delays) indicates instances when improved wheels-off time estimate at the departure airport could help meet metering and spacing constraints imposed by others.The fourth report derived from OPSNET database, "OPSNET: Delays: EDCT/GS/TMI By Cause Report" provided the TMI-to weather delay counts and TMI-to volume delay counts.TMI-to delays is defined as delays resulting from a national or local traffic management initiative reported in OPSNET and charged to the facility that is the originating cause of the restriction.These initiatives also include DSP, ESP, ASP, MIT, MINIT, EDCT, GS, and SWAP.These delays may be experienced by aircraft at another facility, but are charged to the causal facility.For example, 5,561 aircraft were delayed at other airports due to weather caused restrictions imposed by Atlanta as listed in Table A-2 in the Appendix.TMI-to volume delays are caused by restrictions imposed for moderating the traffic demand.In addition to weather and volume delay counts and minutes, the report contains delays due to equipment, runway and other causes.Passenger enplanements data listed in Table A-2 in the Appendix were obtained from the main FAA website in the "Passenger Boarding and All-Cargo Data" Section (see Ref. 4).The main source of enplanement statistics is the U. S. Department of Transportation (DOT).Scheduled and nonscheduled U. S. certificated aircarriers, commuter air-carriers, and small certificated aircarriers submit data to DOT on Form 41 Schedule T-100.Foreign flag air carriers submit data to DOT on Form 41 Schedule T-100(f).In addition, an annual survey of airtaxi/commercial operators, who report their nonscheduled activity on FAA Form 1800-31, is conducted by the FAA.As one would expect, passenger enplanement is highly correlated to the number of commercial operations.The value of the correlation coefficient (also known as Pearson's correlation coefficient and Pearson's productmoment correlation coefficient) was found to be 96.7% and the p-value was found to be zero to six decimal places, which indicates highly significant correlation.Figure 1 shows the plot of passenger enplanements as a function of commercial operations based on the data in Table A-2 in the Appendix.The dotted line indicates a linear fit between enplanements and commercial operations.The coefficient of determination (R 2 value) was found to be 0.935.A closer look at enplanement data and commercial operations data in Table A-2 in the Appendix indicates some data inconsistencies.For example, 3,634 air-carrier and air-taxi operations were conducted at Oxnard, CA (OXR), but the number of enplanements is just 3. Similarly, numbers look very low for GYY, TEB, and VNY.The following response was received from the FAA when asked for an explanation for the discrepancy.The enplanement numbers posted by the FAA include all revenue passengers that boarded a flight conducted by a large certificated, commuter, or foreign air-carrier.They also include the enplanements for on demand air-taxi operators.However, these small operators are not required to report their passenger enplanements to the FAA.So while FAA has some data, it can vary from year to year based on whether the operators voluntarily report their passenger activity.This impacts the non-primary airports like TEB and VNY that serve corporate and business flights which may have a significant number of Part 135 on demand operations.FAA facility pay level data listed in Table A-2 in the Appendix were obtained from the "OPSNET: Facility Information: Detail Report" derived from the OPSNET database.Reference 5 provides a complete description of the formula for pay setting.The facility pay level varies between "null" and 12. Null level is indicated by a zero in the table.Since facility pay level is based on a formula that considers both the number of operations and complexity factors such as, tower with or without radar, performance characteristics of aircraft using the airport, runway and taxiway layout, proximity to other airports, military operations and terrain, it can be used as a metric for categorizing airports.Correlation between the number of operations and the levels given in Table A-2 was found to be 68.6%.The correlation improved to 78% with 0 levels excluded.In both these instances, the correlations were determined to be significant with p-value of zero up to six decimal places.It is thus seen that number of operations dominates the formula for pay setting.ASPM and OPSNET databases provide operational statistics on Core airports, Operational Evolution Partnership (OEP 35) airports, 45 airports tracked in OPSNET (OPSNET 45) and the 77 airports tracked in ASPM.Core airports are the 30 busiest commercial U. S. airports that serve as hubs for airline operations at major metropolitan areas.OEP 35 airports are commercial U.S. airports with significant activity.These airports serve major metropolitan areas and also serve as hubs for airline operations.More than 70 percent of passengers move through these airports.The Venn-diagram in Fig. 2 shows the airport codes of the Core, OEP 35, OPSNET 45 and ASPM 77 airports.The rectangular box shows all the 77 ASPM airports.The 30 Core airports are enclosed in the innermost circle with thick solid boundary.All the Core airports and five additional airports enclosed in the circle with dotted line boundary form the OEP 35 airport set.OPSNET 45 airports are enclosed in the largest circle with a thin solid boundary.Observe that OPSNET 45 set contains all the Core and OEP 35 airports except Honolulu International airport (HNL).The 31 airports outside the circles only belong to ASPM 77.The airports in Fig. 2 were identified as large-hub airports, medium-hub airports, small-hub airports and non-hub airports based on their designation in Ref. 6.A large-hub airport is defined as an airport with 1% or more of total U. S. passenger enplanements.A medium-hub airport is defined as an airport with 0.25% to 0.99% of total U. S. passenger enplanements.An airport with 0.05% to 0.249% of total U. S. passenger enplanements is categorized as a small-hub airport.Finally, an airport with less than 0.05% of total U. S. passenger enplanements is termed a non-hub airport.All Core airports other than Memphis International airport (MEM) are large-hub airports.These 29 airport codes inside the smallest circle are shown in red without a superscript.The 34 medium-hub and 6 non-hub airports are indicated in black and magenta, and by "m", and "*" superscripts, respectively.The airport codes of 8 small-hub airports are in blue and underlined.Figure 3 shows the airport codes of ASDE-X airports, Low Cost Ground Surveillance (LCGS) airports and Airport Surface Surveillance Capability (ASSC) airports.List of names of airports with ASDE-X, LCGS and ASSC were obtained from Refs.7-9.LCGS is based on single surface movement radar concept.FAA is currently evaluating LCGS at Spokane (GEG), Manchester (MHT), San Jose (SJC), Reno (RNO) and Long Beach (LGB).Airport codes of LCGS airports are indicated in blue with a "*" superscript.As opposed to ASDE-X that uses radar, multilateration and Automatic Dependent Surveillance-Broadcast (ADS-B), ASSC derives data from just multilateration and ADS-B.FAA expects ASSC to begin tracking transponder-equipped aircraft and ADS-B equipped ground vehicles by 2017 at Portland (PDX), Anchorage (ANC), Kansas City (MCI), New Orleans (MSY), Pittsburgh (PIT), San Francisco (SFO), Cincinnati (CVG), Cleveland (CLE) and Andrews Air Force Base.ASSC airport codes in green are underlined.Airports without a surface Figure 3. Airports with ASDE-X, LCGS and ASSC surface surveillance systems, and airports without surface surveillance systems.surveillance system are indicated with airport codes in black with an "n" superscript.The remaining 35 airports in red are ASDE-X airports.After removing all the ASDE-X airports, small-hub and non-hub airports from Fig. 3, the remaining 29 airports shown in Fig. 4 are considered as suitable candidates for further analysis.San Francisco and Tampa are the only two large-hub airports remaining in this set.The other 27 are medium-hub airports.Nine airports in this set will either have LCGS or ASSC systems for surface surveillance.Since surveillance data can be used for comparing estimates against reality, these airports could be considered for near term development and testing of wheels-off time estimation methods.To identify airports that could benefit from wheelsoff time estimation, the important metrics are, 1) taxi-out delay, 2) TMI-from delay counts and 3) number of commercial operations.Without a suitable means for accounting for taxiway delays due to interactions between arriving and departing aircraft, one could resort to estimating wheels-off time based on the single value of taxi-time for the airport from ASPM database.This however, would lead to larger wheels-off time prediction errors at airports with larger taxi-out delays.Thus, airports with larger taxi-out delays can be expected to benefit more by being able to reduce larger wheels-off time prediction errors by employing a wheels-off time estimation method.Airports with TMI-from delays have to ensure that the affected aircraft depart at times coordinated with downstream facilities so that the restrictions imposed by them are met.Airports with higher TMI-from delay counts have to depart more aircraft on time; therefore, wheels-off time estimate can be expected to have a greater impact at these airports.Finally, improved predictability of taxi-time and wheelsoff time has the potential of improving planning and scheduling for greater surface movement efficiency at busier airports, the ones with large number of commercial operations.In addition to estimating taxi-out time, gate departure time needs to be known or estimated for wheels-off time prediction since wheels-off time is obtained by adding the taxi-out time to the gate departure time.Unfortunately, gate departure time is difficult to estimate.If airlines are unable to provide gate departure times prior to actual departure, the only choices are scheduled departure time from the Official Airline Guide (OAG) or proposed departure time from filed flight-plans.These times, however, are not accurate.It is also difficult to estimate gate departure delay by observing airport state data.The study in Ref. 3 found the correlation between gate departure delay and metrics derived from airport state data such as, number of aircraft on the surface, airport departure rate, wind and visibility to be quite low.Like Ref. 3, this study also assumes that gate departure time is known.To group the 29 airports shown in Fig. 4 based on the values of taxi-out delay, TMI-from delay counts and number of commercial operations, and the other metrics listed in Table A-2 in the Appendix, the multiple-metric K-Means classifier described in Ref. 10 is used.The K-Means method partitions data into specified number of groups such that the means associated with the groups are as widely separated as possible.Data elements are then labeled based on their closeness to the group means for reducing the variance.Group means are then re-computed based on the elements assigned to the groups.The process of assignment of elements to the groups and computation of group means is continued till convergence is achieved, that is, group means do not change with successive iterations.The  1.The minimum, mean, standard deviation and maximum values for each group are listed in the last four columns.Airport codes in each group are arranged in the non-increasing order.For example, ONT has the maximum taxi-out delay in Group 1 and OGG has the minimum taxi-out delay in Group 1.The three groups can be considered to be low, medium and high taxi-out delay groups.Groups obtained with TMI-from delay counts and with number of commercial operations are summarized in Tables 2 and3.Observe from Table 2 that Raleigh-Durham (RDU) has to comply with more departure restrictions compared to the other airports.San Francisco (SFO), which has the most taxi-out delays and number of commercial operations, is a member of Group 2 in Table 2 with TMI-from delay counts of 1,848 in the year 2011 (see Table A-2 in the Appendix).Grouping based on taxi-out delay ratio and FAA level are summarized in Tables 4 and5.Comparing Table 1 to Table 4, it is seen that ten airports (ONT, MSY, BUR, SJC, OAK, DAL RDU, SAT, TUS and ABQ) in Table 1 move one level higher in Table 4.These airports have higher average taxi-out delays compared to their nominal taxi-out times.Comparing Table 3   two levels higher, and only two airports-SJU and SFO moved one level down.This reconfirms the finding that the number of operations is significantly correlated to the FAA pay level.Thus, FAA pay levels can be used in lieu of number of commercial operations.Finally, grouping based on TMI-to weather delay counts and TMI-to volume delay counts are given in Tables 6 and7, respectively.Only airports that delayed at least 10 aircraft in the year 2011 were considered for grouping in Tables 6 and7.This reduced the set of 29 airports to just 8 based on TMI-to weather delay counts.San Francisco, the sole member of Group 3 in Table 6, is known to be severely affected by visibility.In 2011, it was responsible for causing over 18,000 aircraft bound for SFO to be delayed elsewhere.Only three airports-CLE, SFO and DAL in Table 7 caused aircraft bound for those airports to be delayed elsewhere due to traffic volume.It is reasonable to expect that departures would be affected when arrivals are impacted by weather and traffic volume.Wheels-off time estimates at these airports would have to consider weather and traffic volume conditions.The grouping results discussed above in Tables 1 through 7 considered a single metric for classification.Groups can also be formed by first creating a composite ID for each airport based on single metric classifications and then placing all the airports with the same ID in a group.For example, CVG is a member of Group 1 based on taxi-out delay, Group 2 based on TMI-from delays and Group 2 based on number of commercial operations, therefore its composite ID is (1,2,2).Similarly, the composite ID of IND is (1,2,2).Thus, CVG and IND belong to the same group based on their composite ID.This method is described in Ref. 10. Table 8 lists the airport grouping with composite ID constructed based on Tables 1, 2 and3.Following this procedure, a member of group with Group ID (3,3,3) would be expected to benefit the most from wheels-off time estimation.Mean values of taxi-out delay, TMI-from delay counts and number of commercial operations for each group of airports are listed in the columns with headings-Mean 1, Mean 2 and Mean 3, respectively.Airport codes of airports that will receive ASSC for surface surveillance are shown in green boldface type and underlined.Airport code of San Jose, where LCGS is being tested, is shown in blue boldface type with "*" superscript.Table 8 shows that other than groups with IDs (2, 1, 1), (2,2,1) and (2,3,2), there is at least one airport in the group that will have a surveillance system.These airports should be initially targeted for developing and testing wheels-off time estimation methods.Track data (position as a function time) from surveillance can be used to determine gate, gate departure time, spot crossing time, ramp-area path and taxiway path needed for developing wheels-off time estimation methods.The estimated wheels-off time can be compared with the actual wheels-off time also using actual track data.The nine groups in Table 8 can be reduced further by merging smaller adjacent groups.Table 9 presents such a grouping by first giving preference to number of commercial operations and then to TMIfrom delay counts using the Group IDs.The first group consisting of Group IDS (1, 1, 1), (2, 1, 1) and (2, 2, 1) has MSY and SJC, two airports that will have surface surveillance systems.The second group of airports consisting of Group IDs (1, 1, 2), (2,1,2), (1,2,2), (2,2,2) and (2,3,2) has six airports-ANC, PDX, CVG, CLE, MCI and PIT that will receive ASSC.One of these airports can be chosen to represent the second set.The third group consisting of Group ID (3,2,3) has SFO as its sole member, an airport that will have ASSC.Thus, SJC with LCGS, CLE with ASSC and SFO with ASSC are good choices for representing the three groups in Table 9.
+III. Wheels-off Time Estimation MethodIn this section, the procedure for estimating wheel-off time at the 29 non-ASDE-X airports in Table 9 is described.It is assumed that airport geometry is available.Taxiways and runways are usually represented by polygons, where the locations of the vertices of polygons are specified by Cartesian coordinates with respect to a frame of reference.Locations of gates and spots are also specified with respect to the same frame of reference.Gate and spot locations and the polygons can be processed to create the node-link graph of the airport.An example of a node-link graph is shown in Fig. 5. Reference 11 describes the procedure for creating the node-link graph using taxiway and runway polygons.Any physical path for going from one location to another on the airport surface is represented by a sequence of nodes and links in this node-link graph.A location on the node-link graph is thus equivalent to a location on the physical airport surface.Since polygons, which are area elements, are represented by links, which are line elements, traversing along the links can be thought of as traversing along the taxiway centerline.Given this node-link graph representation, the first step of the proposed wheels-off time prediction consists of representing the taxi clearance issued by the controller as a path in the node-link graph.Taxiway clearance is specified as an ordered list of taxiway segments that the aircraft is required to follow after pushback from the gate to runway.Mapping from taxiway segments to polygons and from polygons to links is used to determine the path in the node-link graph.In this early phase of development, aircraft position data acquired by ASDE-X during surface movement at Dallas-Fort Worth airport are derived from recorded SDSS logs to identify path from gate to runway in the node-link graph.Polygon containing the aircraft position is identified and then polygon to link mapping is used to identify the corresponding link.The sequence of links then determines the path.The next step of the wheels-off time prediction consists of integrating the aircraft equations of motion along the path in the node-link graph.Starting with the gate location and the gate pushback time, this process generates a time history of positions along the path.This is the classical procedure of open-loop trajectory prediction.If this was the only aircraft moving on the surface, open-loop prediction would be reasonable.In reality however, aircraft moving on the surface interact with each other as the arrivals taxi-in towards their gates and departures taxi-out towards the runways.Aircraft have to stop at intersections to let other aircraft pass.Similarly, they have to often stop and wait for the active runway to be clear prior to crossing it.Separation rules also have to be followed.Departures also have to queue and wait prior to entering the runway so that there is adequate separation with respect to the prior aircraft that took off from the same runway.These rules have been programmed in the Surface Operation Simulator and Scheduler (SOSS) that is being developed at NASA Ames Research Center.SOSS uses kinematic models of different types of aircraft and the node-link graph to simulate surface traffic.While SOSS has been designed to work with schedulers for optimizing surface operations, SOSS has been used without a scheduler in this study.Routes from gate to runway, gate departure times and aircraft types are input to SOSS to simulate surface traffic for generating the results discussed in the section below.
+IV. ResultsThe SOSS-model-based method, described in Section III, and a data-driven method are evaluated.The first method consists of using SOSS.This means that ramp-area, movement-area and queue-area speeds specified for each aircraft type are used in the prediction.The second method is a data-driven method in which average taxi-times derived from several days of actual data are added to the gate-out time or the spot crossing time for predictions.Nineteen hours, 5:00 am to 12:00 midnight, of each day of Dallas-Fort Worth surface traffic data derived from 8/8/2011 to 8/13/2011 SDSS logs were processed to create inputs for the two methods and for validating the predictions.These days had good weather.There were a total of 5,208 arrivals and 5,256 departures.Table 10  the number of arrivals and departures, and the flow configuration on each day.DFW is operated in the southflow configuration 70% of the time. 3On the 11th, DFW switched from south-flow configuration to north-flow configuration at 2:00 p.m. local time and then back to southflow configuration at 5:00 p.m. DFW switched from north-flow configuration to south-flow configuration at 8:30 a.m. on the 13 th .The SOSS-based method and the data-driven method were used to generate estimates of queue-area entry time, runway entry time, wheels-off time, queue-area entry sequence and runway entry sequence.These times and sequences were compared against actual values derived from SDSS logs.Queue-area entry, runway entry and wheels-off time are defined using Fig. 6 as an example.Figure 6 shows the locations of queue-area entry nodes, hold nodes and departure nodes related to runway 17R.Observe that the queue-area entry nodes are placed such that the queue is set up in the correct order.For example, an aircraft on taxiway J could enter the queue-area earlier if the entry-node were placed closer to the intersection of taxiways J and Y than an aircraft that enters upstream on taxiway J (near the queue-entry node on taxiway J depicted in the figure) and still be behind the upstream aircraft.To maintain the correct order of entry into the queue, the queue-entry node is placed at the last entry node along the taxiway in the queue-area.In SOSS simulation, aircraft movement is simulated from gate to spot, from spot to runway hold node, from hold node to departure node and from departure node to wheels-off.For the example in Fig. 6, queues are formed along taxiways EF, EG and EH and taxiways J, K and L as aircraft wait to reach the runway hold nodes.SOSS computes spot crossing time, runway hold node arrival time, wait time at the hold node, departure node arrival time, wait time at the departure node and wheels-off time.Time from departure node to wheels-off is specified for different types of aircraft.Queue-entry time is determined as the time when SOSS simulated aircraft position is at or just prior to the queue-area entry node.Runway entry time is determined by adding the wait time at the hold node to the hold node arrival time.Actual hold node arrival time, runway entry time and wheels-off time are obtained by processing the track data obtained from SDSS logs with airport geometry data.Results obtained using the two methods are discussed below.
+SOSS-Model-BasedFigure 7 shows the histogram of the difference between the actual queue-area entrance time of the departures and the estimated queue-area entry times derived from SOSS simulation of 19 hours of 8/11/2011 Dallas-Fort Worth surface traffic consisting of 897 arrivals and 915 departures.August 11 was challenging because of airport configuration change from south-flow to north-flow and then back to south-flow.During the south-flow to northflow change, departures for 35L left their gates and queued in the queue-area and waited for a long time for departures and arrivals in the previous south-flow configuration to clear the runways.Queue-area entry time results shown in Fig. 7 were computed with respect to spot crossing time.This means that the SOSS simulation used spot position and time as initial conditions.The histogram in Fig. 7 shows the maximum error to be 21 minutes.It was determined that about 80% of the actual departures arrived within two-minutes early to one-minute window with respect to SOSS-based prediction of queue-area entry time.The two-minutes early to one-minute late window is   Figure 10.Cumulative absolute value of wheelsoff time estimation error.used as the wheels-off time requirement in Ref. 12 for Precision Departure Capability for Call For Release.Later on results are presented with respect to gate departure time.Gate-based results were found to be worse than the spotbased results.Figure 8 shows the cumulative absolute estimation error.For example, absolute value of queue-area entry time error is less than two-minutes for 87% and less than five-minutes for 97% of the departures.The main source of queue-area entry time error is the aircraft speeds assumed in SOSS.Actual maximum speeds in SDSS logs were found to be much higher in several instances compared to the nominal speeds assumed for the aircraft type in SOSS.The maximum speed difference was found to be 16 knots compared to SOSS speed of 15 knots.Average difference was found to be 8 knots for August 11 data.These findings suggest that SOSS nominal speeds can be better tuned to improve the estimates.Figure 9 shows the histogram of the difference between the actual wheels-off time of the flights and the estimated wheels-off time derived from SOSS simulation with reference to spot crossing.Maximum wheels-off time error was found to be 24 minutes.47% of the departures were within the two-minutes early to one-minute late window and 59% were within the plus-minus two-minute window.86% were within the plus-minus five-minute window.The cumulative absolute wheels-off time estimation error is given in Fig. 10.Comparing Fig. 9 to Fig. 7 and Fig. 10 to Fig. 8, it may be seen that queue-area entry time estimation is much better than wheels-off time estimation.Part of the reason is that Dallas-Fort Worth has multiple queues in the queuing-area from which flights exit to enter the runway.Currently a simple logic of first-in first-out based on the queue-area entry time is being used to determine runway entry order.In the real operations, flights that need to comply with traffic flow management initiatives are given priority.Better prediction of runway entry time, which directly affects wheels-off time prediction, might require knowledge of flight priority.Queue-area entry time, runway entry time and wheels-off time estimation errors were computed with spot and gate as references for the six days listed in Table 10.The two-minute early to one-minute late compliance results are summarized in Table 11.The next set of results is for queue-area entry and runway entry sequences.To determine queue-area entry sequence for SOSS simulated aircraft and actual aircraft (based on SDSS track data), time of arrival at the queue entry nodes are sorted in increasing order for departures going to each runway.Sequence error is then computed as the difference between the actual aircraft and SOSS simulated aircraft positions in the sorted lists.This same procedure is repeated to determine runway entry sequence error.It should be noted that the runway entry sequence is same as the wheels-off sequence because only one aircraft is permitted to be on an active runway at a time.Table 12 shows the percentages of departures without sequence errors on the six days.Table 13 shows percentages with at most one sequence error.This means that the actual aircraft was either in the correct sequence or was just ahead or just behind the SOSS predicted sequence.These results show that sequence errors are reduced when spot crossing time is used as a reference for SOSS-based predictions.Maximum spot-based queue-area and runway entry sequence errors were found to be 13 and 12, respectively, on August 8 data.The highest gate-based queue-area and runway entry sequence errors were found to be 12 and 12, respectively, also on August 8 data.Results for the six days show that there is a significant loss of estimation accuracy from queue-area entry to runway entry.The loss is less from runway entry to wheels-off.Gate-based results are worse than spot-based results because of imprecise gate-out time information.Gate-out time is estimated based on the proximity of the SDSS reported position to the gate and SDSS reported speed, which indicates movement.
+SDSS-Based Average Taxi-TimeThis method assumes that historical taxi-time data from spot and gate to queue-area entry locations and wheelsoff are available.This method like the SOSS-based method, discussed in the previous section, does not assume that surface surveillance information is available in real-time.Historical information can be derived from the Out-Off-On-In (OOOI) data provided by airlines and Automatic Dependent Surveillance (ADS) position reports provided by ADS equipped aircraft.To compute the spot and gate to queue-area and wheels-off taxi-times, six days of surface data were processed to identify the unique 226 spot-runway and 522 gate-runway combinations.Next, the number of departures associated with spot-runway and gate-runway were counted.Analysis showed that 25% of the spotrunway combinations were used by a single aircraft, 36% were used by two or fewer aircraft and 61% were used by 10 or fewer aircraft.The maximum number of times the spot-runway combination was used was 301 times.Of the 522 gate-runway combinations, 31% were used by only one departure, 45% were used by two or fewer departures and 57% were used by 10 or fewer departures.The maximum number of times was 38.The six days of taxi-time data were averaged and assigned to each spot-runway and gate-runway pair.Queue-entry and wheels-off times were predicted by adding the spot crossing time or the gate departure time to the average taxi-times.The actual queueentry time and wheels-off time of the departures were compared to these predictions.Table 14 shows the percentages of departures that could be predicted within the two-minutes early to oneminute late compliance window.This table also shows that spotbased estimates are a bit better than gate-based estimates.Comparing the gate-based results in Table 11 to those in Table 14, it is seen that both queue-area entry time and wheelsoff time errors are less with this method.One of the reasons for better results is that the average taxitime is same as the actual taxi-time in instances of single departures associated with a spot-runway or gate-runway pair.The second reason is that the taxi-times based on actual track data include the influence of the actual path (not the idealized node-link path) and speed.The maximum queue-entry time and wheels-off time errors were 29 minutes and 26 minutes on August 8, respectively, when spot crossing time was used as the reference for estimation.Wheels-off time could be predicted within plus-minus five-minutes for at least 90% of the departures.This minimum value of 90% was obtained for August 11 data.Maximum queue-entry time error of 27 minutes and wheels-off time error of 24 minutes were obtained with gate-based predictions of August 8 departures.At a minimum, wheels-off time of 89% percent of departures in each of the six days could be predicted within plus-minus five-minutes.The percentage, 89%, was lowest for August 11 departures.August 13 gate-based wheels-off result of 55.6% in Table 14 are close to 59.2% obtained with the neural network in Ref. 3. Wheels-off time compliance of 53.5% of neural network predictions with respect to six days, August 7 through 12, of training data is comparable to the average compliance of 51.5% in Table 14.Queue-area entry and runway entry sequence error results are given in Tables 15 and16.Maximum queue-area entry and runway entry sequence errors were obtained with August 8 data.For spot-based estimates, these were 14 and 12 departures.For gate-based estimates, these errors were 11 and 10.The sequence errors for each of the six days were found to be very close to those obtained with the method described in the previous section.Comparing the results in Tables 15 and16 to Tables 12 and13, it is seen that the gate-based estimates are a bit better with the SDSS-based Average Taxi-time model compared to with the SOSS-based simulation method.These results suggest that the Average Taxi-Time model could be used for predictions at all airports without resorting to a more detailed simulation based approach.This method is also simple to implement, it does not require airport geometry and the Gate-out and wheels-off data reported by airlines can be used to determine the average gate to wheels-off taxi-time needed by this model.The method is also computationally efficient because an addition operation is required for estimating wheels-off time and sorting is required to estimate the runway entry sequence.
+V. Conclusions and Future WorkIn the first part of this paper, 29 airports were identified for development and testing of wheels-off estimation methods after removing airports with Airport Surface Detection Equipment Model-X (ASDE-X), small-hub airports and non-hub airports from the set of 77 major U. S. airports tracked in the Federal Aviation Administration's Aviation System Performance Metrics database.These 29 airports were classified into three groups using a K-Means procedure based on taxi-out delay, traffic management delay counts and number of commercial operations.Within these three groups, San Jose International, Cleveland-Hopkins International and San Francisco International are recommended for further development and validation of wheels-off time estimation methods.San Jose has the least number of commercial operations and San Francisco has the most.In the second part of the paper, a simulation based method and a data-driven method, which uses historical taxi-time information, for estimating queue-area entry time, runway entry time and wheels-off time were described.Queue-area entry time, runway entry time and wheelsoff time estimates were generated with reference to spot crossing time and gate departure time for six days of August 2011 Dallas-Fort Worth surface traffic data.These estimates were compared with the actual values determined by processing actual ASDE-X based aircraft position data.The main findings are as follows.Spot-based estimates are better compared to gate-based estimates.The data-driven method produces better gate-based estimates compared to the Surface Operation Simulator and Scheduler (SOSS) based method assuming model speeds.If surface surveillance data are unavailable, the data-driven method could be used for estimating queue-area entry time, runway entry time and wheels-off time.This method could also be used for estimating queue-area and runway entry sequence.These conclusions are expected to be equally applicable to airports such as San Jose, Cleveland and San Francisco.The next step consists of creating a geometry and node-link model for the San Jose airport with the data received from the City of San Jose.Several days of San Jose airport surface surveillance data acquired via the Low Cost Ground Surveillance (LCGS) system will be processed to create the parameters and inputs needed by the datadriven model and by the SOSS-based simulation.The estimates generated by these methods will then be compared with LCGS derived values.Figure 1 .1Figure 1.Correlation between commercial operations and passenger enplanements.
+Figure 2 .2Figure 2. ASPM 77 airports categorized as largehub, medium-hub, small-hub and nonhub airports.
+Figure 4 .4Figure 4. Airports remaining after removing ASDE-X airports, and small-hub and non-hub airports.
+Figure 5 .5Figure 5. Node-link graph of Dalla-Fort Worth airport.
+Figure 8 .8Figure 8. Cumulative absolute value of the queuearea entry time estimation error.
+Figure 7 .7Figure 7. Queue-area entry time estimation error.
+Figure 9 .9Figure 9. Wheels-off time estimation error.Figure10.Cumulative absolute value of wheels-
+Table 3 .3Grouping based on number of commercial operations.Group IDAirportsMin.MeanStd.Max.57,64890,43018,572 116,4142PDX, ANC, CLE, TPA, CVG, RDU, BNA, IND, MCI, SJU, OAK, PIT127,723 153,339 22,103 190,1083SFO386,941 386,9410386,9411DAL, SAT, AUS, SJC, MSY, SMF, OGG, ABQ, BUF, PBI, OMA, JAX, ONT, RSW, BUR, TUS
+Table 1 .1Grouping based on average taxi-out delays in minutes.Group IDAirportsMin.Mean Std.Max.1ONT, MSY, BUR, IND, AUS, SJU, CVG, SJC, OAK, DAL, ANC, OGG1.51.90.22.12RDU, PIT, CLE, SAT, TUS, ABQ, BNA, RSW, TPA, BUF, PBI, PDX, JAX, MCI, OMA, SMF2.22.50.33.23SFO4.44.40.04.4
+Table 4 .4Grouping based on average taxi-out delay ratio in percentage.three groups obtained based on taxi-out delay are summarized in TableGroup IDAirportsMin.MeanStd. Max.1AUS, OGG, IND, SJU, ANC, CVG13.915.61.517.32ONT, CLE, PIT, SMF, BNA, TPA, PDX, RSW, MSY, OAK, BUF, MCI, DAL, OMA, BUR, PBI, JAX, SJC17.919.91.322.03SFO, ABQ, SAT, RDU, TUS23.324.21.526.8
+Table 2 .2to Table5, it is seen that 13 airports (ABQ, AUS, JAX, MSY, PBI, BUF, DAL, RSW, TUS, CVG, TPA, CLE and PIT) moved one level up in Table5, one airport-SAT jumped Grouping based on TMI-from delay counts.Group IDAirportsMin.MeanStd. Max.1MSY, PDX, AUS, SAT, OMA, DAL, SMF, BUR, SJC, OAK, TUS, ABQ, ONT, SJU, ANC, OGG104592849052CVG, PIT, CLE, IND, SFO, BUF, TPA, MCI, BNA, PBI, JAX, RSW1,1721,772412 2,5313RDU3,5573,55703,557Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org|DOI: 10.2514/6.2013-4274Copyright© 2013 by the American Institute of Aeronautics and Astronautics, Inc.The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes.
+Table 5 .5Grouping based on FAA level.Group IDAirportsMin.MeanStd.Max.1BUR, OGG, SJC, SJU, OMA, ONT, SMF66.60.572ABQ, AUS, BNA, IND, JAX, MCI, MSY, PBI, RDU, SFO, ANC, BUF, DAL, OAK, PDX, RSW, TUS88.60.593CVG, TPA, CLE, PIT, SAT1010.40.611
+Table 6 .6Grouping based on TMI-to weather delay counts.Group IDAirportsMin.MeanStd.Max.1CLE, IND, TPA, RDU, CVG, SAT1849491462DAL30030003003SFO18,679 18,6790 18,679Table 7. Grouping based on TMI-tovolume delay counts.Group IDAirports Counts1CLE112SFO243DAL173
+Table 9 .9Grouping based on preferences to number of commercial operations and TMI-from delay counts.Group IDAirportsMean 1Mean 2Mean 31, 1, 1AUS, OGG, BUR, DAL, MSY, ONT, SJC*1.946897,4062, 1, 1OMA, SMF, ABQ, SAT, TUS2.450390,6712, 2, 1BUF, JAX, PBI, RSW2.31,42577,9201, 1, 2ANC, SJU, OAK1.8221 151,6432, 1, 2PDX2.3887 190,1081, 2, 2CVG, IND22,209 149,2392, 2, 2BNA, CLE, MCI, PIT, TPA2.51,859 150,4312, 3, 2RDU3.23,557 144,3993, 2, 3SFO4.41,848 386,941
+Table 8 .8Grouping based on taxi-out delay, TMI-from delay counts and number of commercial operations.Group IDAirportsMean 1Mean 2Mean 31, 1, 1AUS, OGG, BUR, DAL, MSY, ONT, SJC*1.946897,4061, 1, 2ANC, SJU, OAK1.8221 151,6431, 2, 2CVG, IND2.02,209 149,2392, 1, 1OMA, SMF, ABQ, SAT, TUS2.450390,6712, 1, 2PDX2.3887 190,1082, 2, 1BUF, JAX, PBI, RSW2.31,42577,9202, 2, 2BNA, CLE, MCI, PIT, TPA2.51,859 150,4312, 3, 2RDU3.23,557 144,3993, 2, 3SFO4.41,848 386,941
+Table 10 .10lists Selected days.Date# Arrivals # DeparturesDayFlow8/8/2011868881MondaySouth8/9/2011870860TuesdaySouth8/10/2011900902Wednesday South8/11/2011897915ThursdaySouth, North, South8/12/2011888892FridaySouth8/13/2011785806SaturdayNorth, SouthFigure 6.Examples of queue-area entry nodes, hold nodes and departure nodes.
+Table 11 .11Compliance within two-minutes early to one-minute late.DateSpot-based Queue (%) Runway (%) Wheels-off (%) Queue (%) Runway (%) Wheels-off (%) Gate-based885.864.058.260.047.146.3983.753.750.363.344.540.91084.758.553.361.047.044.91180.152.647.059.542.438.61287.358.051.863.049.046.11384.660.454.262.451.447.5Table 12. No sequence error.DateSpot-based Queue (%) Runway (%) Queue (%) Runway (%) Gate-based870.462.049.547.1965.951.752.744.41072.959.148.845.01169.154.851.445.21272.262.653.647.91372.564.552.048.3
+Table 13 .13At most one sequence error.DateSpot-based Queue (%) Runway (%) Queue (%) Runway (%) Gate-based894.690.785.983.2993.383.585.577.21094.690.784.181.21193.086.385.980.01292.989.084.981.41395.291.485.081.8
+Table 14 .14Compliance within two-minutes early to one-minute late.DateSpot-based Queue (%) Wheels-off (%) Queue (%) Wheels-off (%) Gate-based886.956.577.750.6986.250.077.748.11086.652.578.451.61181.754.674.552.11286.853.377.250.91386.157.477.855.6
+Table 16 .16At most one sequence error.DateSpot-based Queue (%) Runway (%) Queue (%) Runway (%) Gate-based896.091.490.387.1993.484.588.381.51095.690.691.985.51193.983.290.781.11294.189.590.586.91395.489.790.286.0
+Table 15 .15No sequence error.models if gate departure time based wheels-off time and runway entry sequence predictions are desired.node-linkDateSpot-based Queue (%) Runway (%) Queue (%) Runway (%) Gate-based871.764.963.152.4970.054.260.051.01071.758.461.550.81168.352.263.047.31274.660.459.452.71373.458.661.851.9
+Table A -A1. 77 ASPM airports.#Airport CodeAirport NameLocation1ABQAlbuquerque International SunportAlbuquerque, New Mexico2ANCTed Stevens Anchorage InternationalAnchorage, Alaska3ATLHartsfield -Jackson Atlanta InternationalAtlanta, Georgia4AUSAustin-Bergstrom InternationalAustin, Texas5BDLBradley InternationalWindsor Locks, Connecticut6BHMBirmingham-Shuttlesworth InternationalBirmingham, Alabama7BNANashville InternationalNashville, Tennessee8BOSGeneral Edward Lawrence Logan InternationalBoston, Massachusetts9BUFBuffalo Niagara InternationalBuffalo, New York10BURBob HopeBurbank, California11BWIBaltimore/Washington International Thurgood Marshall Baltimore, Maryland12CLECleveland-Hopkins InternationalCleveland, Ohio13CLTCharlotte/Douglas InternationalCharlotte, North Carolina14CVGCincinnati/Northern Kentucky InternationalCovington, Kentucky15DALDallas Love FieldDallas, Texas16DAYJames M Cox Dayton InternationalDayton, Ohio17DCARonald Reagan Washington NationalWashington, District of Columbia18DENDenver InternationalDenver, Colorado19DFWDallas/Fort Worth InternationalDallas-Fort Worth, Texas20DTWDetroit Metropolitan Wayne CountyDetroit, Michigan21EWRNewark Liberty InternationalNewark, New Jersey22FLLFort Lauderdale/Hollywood InternationalFort Lauderdale, Florida23GYYGary/Chicago InternationalGary, Indiana24HNLHonolulu InternationalHonolulu, Hawaii25HOUWilliam P HobbyHouston, Texas26HPNWestchester CountyWhite Plains, New York27IADWashington Dulles InternationalWashington, District of Columbia28IAHGeorge Bush Intercontinental/HoustonHouston, Texas29INDIndianapolis InternationalIndianapolis, Indiana30ISPLong Island Mac ArthurNew York, New York31JAXJacksonville InternationalJacksonville, Florida32JFKJohn F Kennedy InternationalNew York, New York33LASMc Carran InternationalLas Vegas, Nevada34LAXLos Angeles InternationalLos Angeles, California35LGALa GuardiaNew York, New York36LGBLong Beach (Daugherty Field)Long Beach, California36MCIKansas City InternationalKansas City, Missouri38MCOOrlando InternationalOrlando, Florida39MDWChicago Midway InternationalChicago, Illinois40MEMMemphis InternationalMemphis, Tennessee41MHTManchesterManchester, New Hampshire42MIAMiami InternationalMiami, Florida43MKEGeneral Mitchell InternationalMilwaukee, Wisconsin44MSPMinneapolis-St Paul International/Wold-ChamberlainMinneapolis, Minnesota
+Table A -A1. 77 ASPM airports (Contd.).Table A-2. Airport metrics.# # Airport Airport Code Code Taxi-out Avg.Taxi-out Avg.from TMI-Airport Name Air-carrier ItinerantWeather TMI-toVolume TMI-toLocation EnplanementsLevel45MSY DelayLouis Armstrong New Orleans International Delay Delay and Air-taxi DelayDelayNew Orleans, Louisiana46OAK (min.)Metropolitan Oakland International Ratio (%) Counts Ops.CountsCountsOakland, California147ABQOGG2.51Kahului 23.929399,7990Kahului, Hawaii 0 2,768,4359248ANCOMA1.66Eppley Airfield 14.0132186,6989Omaha, Nebraska 2 2,354,9878349ATLONT7.52Ontario International 37.0 11,132916,8245,561Ontario, California 1,271 44,414,12112450AUSORD1.97Chicago O'Hare International 17.3 858 113,1110Chicago, Illinois 0 4,436,6619551BDLOXR2.67Oxnard 20.81,22986,8380Oxnard, California 0 2,772,3157652BHMPBI2.34Palm Beach International 19.8 36358,6940West Palm Beach, Florida 0 1,429,2828753BNAPDX2.43Portland International 21.1 1,576142,2472Portland, Oregon 0 4,673,0479854BOSPHL5.2Philadelphia International 28.7 5,734355,6078,964Philadelphia, Pennsylvania 80 14,180,730 10955BUFPHX2.38Phoenix Sky Harbor International 19.7 1,764 80,6450Phoenix, Arizona 0 2,582,59781056BURPIT2.07Pittsburgh International 18.9 41267,7260Pittsburgh, Pennsylvania 0 2,144,91571157BWIPSP3.35Palm Springs International 26.7 4,715258,540561Palm Springs, California 97 11,067,31991258CLEPVD2.74Theodore Francis Green State 21.9 2,203 179,382146Providence, Rhode Island 11 4,401,033101359CLTRDU5.52Raleigh-Durham International 30.9 4,308 513,8021,468Raleigh/Durham, North Carolina 1,558 19,022,535 121460CVGRFD1.93Chicago/Rockford International 13.9 2,531 157,36720Chicago/Rockford, Illinois 1 3,422,466 111561DALRSW1.78Southwest Florida International 19.2 458 116,414300Fort Myers, Florida 173 3,852,886862 16 DAYSAN2.57San Diego International 19.8 82148,2170San Diego, California 0 1,247,33381763DCASAT4.3San Antonio International 26.7 7,565278,757895San Antonio, Texas 95 9,053,004101864DENSDF3.71Louisville International -Standiford Field 26.5 3,302 630,9691,702Louisville, Kentucky 7 25,667,4991265 19 DFWSEA3.26Seattle-Tacoma International 22.7 5,296 640,5411,592Seattle, Washington 5 27,518,3581266 20 DTWSFO3.25San Francisco International 18.5 4,149436,534997San Francisco, California 303 15,716,8651167 21 EWRSJC8.49Norman Y. Mineta San Jose International 40.4 6,476 402,988 26,201San Jose, California 1,419 16,814,092102268FLLSJU4.16Luis Munoz Marin International 26.2 4,397 227,06125San Juan, Puerto Rico 20 11,332,466969 23 GYYSLC0.09Salt Lake City International 0.8 101,5740Salt Lake City, Utah 0 1,42002470HNLSMF1.96Sacramento International 15.5 29206,4460Sacramento, California 0 8,689,6991171 25 HOUSNA1.8John Wayne-Orange County 19.9 593139,280112Santa Ana, California 238 4,753,55482672HPNSTL2.39Lambert-St Louis International 18.8 1,946 64,60159St Louis, Missouri 171 972,38572773IADSWF4.04Stewart International 25.2 5,766314,384899Newburgh, New York 350 11,044,383112874IAHTEB4.84Teterboro 29.34,031516,708797Teterboro, New Jersey 166 19,306,660122975INDTPA2.02Tampa International 16.6 1,886141,11149Tampa, Florida 0 3,670,39693076ISPTUS1.51Tucson International 17.5 13619,8280Tucson, Arizona 0 781,39673177JAXVNY2.22Van Nuys 18.01,19177,3150Van Nuys, California 0 2,700,514932JFK8.532.79,296405,9768,89697523,664,8321033LAS3.7926.62,700484,19441613119,872,6171134LAX3.9626.64,503583,16780832130,528,7371135LGA11.6747.3 10,716364,14019,8453,19511,989,2271036LGB2.2316.729736,839001,512,212836MCI2.2119.61,603136,398005,011,000938 MCO3.6727.33,831300,075952917,250,4151139 MDW2.9927.11,855209,789661429,134,576840 MEM2.1414.11,883291,700550114,344,2131041 MHT2.0318.21,14551,376001,342,308542MIA3.4321.22,623375,20924712218,342,15812
+			Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4274Copyright© 2013 by the American Institute of Aeronautics and Astronautics, Inc.The U.S. Government has a royalty-free license to exercise all rights under the copyright claimed herein for Governmental purposes.
+		
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+AcknowledgementsThe authors thank Dr. Tatsuya Kotegawa, Dr. Waqar Malik, William Chan and Dr. Banavar Sridhar for their careful review and suggestions for improving the paper.
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+AppendixThe 77 major U. S. airports in the ASPM database are listed in Table .A-1. Table A-			
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+	
+	
+		2015 Integrated Communication, Navigation and Surveillance Conference (ICNS)
+		
+			IEEE
+			March 2012
+		
+	
+	NextGen Implementation Plan
+	9 Federal Aviation Administration, "NextGen Implementation Plan," March 2012, URL: http://www.faa.gov/nextgen/implementation/media/NextGen_Implementation_Plan_2012.pdf [cited: 2/25/2013].
+
+
+
+	
+		Characterization of Days Based on Analysis of National Airspace System Performance Metrics
+		
+			GanoChatterji
+		
+		
+			BassamMusaffar
+		
+		10.2514/6.2007-6449
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Hilton Head, South Carolina
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 20-23, 2007
+		
+	
+	AIAA-2007-6449
+	10 Chatterji, G. B., and Musaffar, B., "Characterization of Days Based on Analysis of National Airspace System Performance Metrics," AIAA-2007-6449, Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit, Hilton Head, South Carolina, Aug. 20-23, 2007.
+
+
+
+	
+		Automating the Process of Terminal Area Node-Link Model Generation
+		
+			Hak-TaeLee
+		
+		
+			ThomasRomer
+		
+		10.2514/6.2008-7101
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Honolulu, Hawaii
+		
+			American Institute of Aeronautics and Astronautics
+			August 18-21, 2008
+			12
+		
+	
+	AIAA 2008-7101
+	Lee, H., and Romer, T. F., "Automating the Process of Terminal Area Node-Link Model Generation," AIAA 2008-7101, Proc. AIAA Modeling and Simulation Technologies Conference and Exhibit, Honolulu, Hawaii, August 18-21, 2008. 12
+
+
+
+	
+		Characterization of Tactical Departure Scheduling in the National Airspace System
+		
+			RichardCapps
+		
+		
+			ShawnEngelland
+		
+		10.2514/6.2011-6835
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			September 20-22, 2011
+		
+	
+	AIAA 2011-6835
+	Capps A., and Engelland, S. A., "Characterization of Tactical Departure Scheduling in the National Airspace System," AIAA 2011-6835, Proc. AIAA Aviation Technology, Integration and Operations Conference (ATIO), Virginia Beach, VA, September 20-22, 2011.
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+				
+			
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+	
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+I. Introductionhis paper is motivated by the need for improving departure scheduling advisories.A specific application is for Precision Departure Release Capability (PDRC) which has the goal of using surface trajectory based off-time predictions for Call for Release (CFR).Accurate wheels-off time estimation is important to PDRC both for 1) providing an initial estimate to the Traffic Management Advisor (TMA) for inserting a flight into a constrained overhead stream and for scheduling an inbound flight to a TMA-metered airport, and 2) determining when to release the aircraft from the gate so that its actual wheels-off time corresponds to the wheels-off time coordinated with the Air Route Traffic Control Center.Improved wheels-off time prediction would also benefit traffic flow management (TFM).TFM techniques first estimate traffic demand considering both airborne aircraft that are detected by the air traffic control radars and aircraft on the ground that are scheduled to depart within the forecasting interval, and then
+II. Dallas-Fort Worth Airport Geometry and OperationsDallas-Fort Worth International Airport (DFW) is the third busiest airport in the United States.According to the March 2011 FAA Administrator's Factbook 7 , 652,000 operations were conducted at DFW in 2010 compared to 950,000 operations at Hartsfield-Jackson Atlanta International, the busiest US airport, and 883,000 operations at Chicago O'Hare International, the second busiest US airport.DFW is physically one of the largest airports in the United States and the world with an area spanning five nautical-miles east to west and three nautical-miles north to south.The airport has seven physical runways shown in Fig. A-1 in the Appendix.These runways are operated in the south-flow and north-flow configurations.The runways in south-flow configuration are designated as, 13L, 13R, 17L, 17C, 17R, 18L and 18R.The runways in the north-flow configuration are designated as, 31L, 31R, 35L, 35C, 35R, 36L and 36R.These designations also indicate the runway heading with respect to north; they are physically painted on the two ends of each runway.To get insight into DFW operations, hourly runway configurations (arrival and departure runways) for each day in 2011 were obtained by generating the "Daily Weather by Hour Report," from the ASPM database.These data were then processed to determine runways that are used for both arrivals and departures, runways that are used only for arrivals and runways that are used solely for departures.Table 1 lists the runways and the time duration of usage during 2011.This table shows that 10 of the 14 runways (considering the same physical runway to be two different runways based on the approach direction) are used both for arrivals and departures.Of these, 18R is used most often.Summing the runway utilization in both directions of the physical runways, it is seen that 17C/35C, 17R/35L, 18L/36R, 18R/36L, 17L/35R and 13R/31L usage is within 20% of each other.Runway 13L/31R usage is 27% of the usage of the most utilized runway, 18R/36L.Data in Table 1 show that runways 17C, 17R, 18L and 18R are utilized more often compared to the other runways; thus, south-flow is the dominant flow direction in DFW operations.Further analysis of ASPM runway configuration data revealed that 76 arrivaldeparture runway configurations out of 254 theoretically possible configurations were used in 2011.Number of theoretically possible combinations is obtained using,∑ ∑ ≤ ≤ ≤ ≤         +         = 7 1 7 1 7 7 k k k k n (1)where        k 7means the number of ways in which "k" runways can be chosen out of seven runways.The two summations are for the seven south-flow and seven north-flow runways.Table 2 lists the top ten configurations along with the percentage of time they were used with respect to 8,760 hours in the year.Observe that the ten configurations listed in Table 2 were used 95% of the time.Summing the percentage use of the 76 south-flow and north-flow configurations, it is seen that the south-flow configuration is used 70% of the time and the north-flow configuration is used 30% of the time.Reference 6 also cited the same numbers.Passenger and cargo traffic in and out of the 233 gates cross 59 spots to exit and enter a network of 96 taxiways to travel to and from the seven physical runways.The taxiways intersect at 215 locations.This includes intersection   of taxiways with runways.The taxiways consist of 360 segments partitioned by the intersections.These numbers were obtained by analyzing DFW geometry data derived from SODAA.The DFW airport design is also characterized by multiple runways, high-speed exit taxiways, non-intersecting runways, three towers, and perimeter taxiways near 35L and 35C that permit aircraft to taxi to and from the gates without crossing active runways.These features reduce congestion on the airport surface; delays are mostly weather related and independent of surface operations. 6This suggests that it might be possible to estimate taxi time with reasonable accuracy for different weather conditions at DFW.To estimate wheels-off time, estimates of gate departure time and taxi time are needed.Of these two estimates, gate departure time estimate is much more difficult to compute.In the absence of airline provided gate departure time, the choices are limited to the scheduled departure time from the Official Airline Guide (OAG) and the proposed departure time included in the filed flightplan.These times provide approximate estimates of gate-out time.Reference 1 proposes the use of pre-departure event times to improve estimate of gate-out time.The central idea employed in Ref. 1 is that gate-out time becomes more certain as completion of each step taken by airline and air traffic for preparing the flight for departure are reported via the Aircraft Communication Addressing and Reporting System (ACARS).Taxi time is a function of distance between the gate and the runway, taxi-speed and congestion of the surface.Since a taxi route is specified, distance along the route can be determined.Taxi-speed differences between flights are significant; taxi-speed is a function of pilot preference, stops needed at intersections and congestion along the route to the runway threshold.To determine gate to runway distances and taxi-out times to the runways at DFW, one week-spanning 7 August 2011 through 13 August 2011 of surface traffic data were obtained by processing SMS logs.The chosen seven days had good weather, and consisted of 6,284 departures.After discarding flights with more than 60 minutes of gate departure delay and 30 minutes of gate to runway entry time, 5,822 departures were considered for further analysis.It was determined that 393 unique gate-runway combinations were used by these aircraft.Figure 1 shows the gate to runway distance distribution and Fig. 2 shows the average taxi speed distribution of 5,822 departures.Average taxi speed is obtained as the ratio of gate to runway distance to taxi-out time.Average, standard deviation and maximum gate to runway distance were found to be 1.6, 0.6 and 4.0 nautical-miles, respectively.The average, standard deviation and maximum of the average taxi-out speed were found to be 13, 3.6 and 34.5 knots, respectively.Average and standard deviation of the taxi-out time were determined to be 7.8 and 3.6 minutes, respectively.
+III. Correlation Between Metrics and Gate to Wheels-off Time and Gate Departure DelayCorrelation coefficients between gate to wheels-off time (SMS Log Data Item 4 in the Appendix) and variables selected from the two sets discussed in the SMS Log Data and ASPM Data sections in the Appendix were computed to make an assessment of their suitability as predictors of gate to wheels-off time in a neural network and a linear model framework.The procedure was repeated for correlations with respect to gate departure delay (SMS Log Data Item 15 in the Appendix) to ascertain the ability of these variables to predict gate departure delay.Prior to computing the correlation coefficients, data were conditioned as follows.SMS Log data for flights with absolute value of the gate departure delay greater than the specified threshold of 60 minutes were discarded.The data were found to contain large negative gate departure delays.Another check was performed for gate to runway taxi time with a threshold value of 30 minutes.Only flights with gate to runway entry time of less than 30 minutes were considered.Total number of samples prior to pruning is 6,284.After pruning 5,822 remain.462 samples removed represent a 7.4% loss with respect to 6,284 samples.Table 3 shows the correlation (with 100% being perfect correlation) and p-value of the selected variables with gate to wheels-off time.Here, correlation means the cross-correlation coefficient derived from the covariance matrix of the two variables being compared and the p-value is the probability of obtaining a correlation as large as the observed value by random chance, when the true correlation between the variables is zero.p-value of less than 0.05 is considered to be significant.The gate to runway distance has the highest correlation followed by average taxi-out delay and number of departures on the surface.Wind angle, visibility and temperature were found to be negatively correlated.Weak negative correlation with visibility and temperature is reasonable in that as visibility and temperature decrease, one would expect taxi-time to increase a bit.Wind angle correlation is difficult to interpret without examining wind velocity components relative to the surface trajectory.Few variables that are independent of each other (contain different type of information) with high correlation can be used to develop a linear model or a neural network model for predicting gate to wheels-off time.Such models could be adequate for CFR at DFW because in current operations, CFR is initiated after pushback from the gate.This means that the gate departure time is known.At some other airports, for example at San Jose International airport, airlines are asked to inform air traffic control (ATC) some time (for example, 15 minutes) prior to ready for departure when the flight is impacted by CFR.Gate departure time uncertainty can be expected to be small in this scenario therefore wheels-off time could be predicted with these models.For predicting gate departure time, the only available information about when an aircraft might leave the gate is the scheduled gate departure time.Improvement beyond the scheduled gate departure time is possible if metrics derived from observed or estimated airport state are found to be good predictors of gate departure delay.This is examined next.Table 4 shows the correlation of the selected metrics with gate departure delay.The table shows that time of day at the scheduled gate departure time has the highest correlation followed by average gate departure delay in the previous 15-minutes.Average taxi-in delay in previous 15-minutes is similarly correlated with gate departure delay.While unexpected, temperature was found to be mathematically correlated to gate departure delay.It turns out that temperature increase from morning to afternoon and then decrease in the evening is somewhat correlated to the schedule of the departure pushes.The degree of correlation can be expected to change with different weather conditions.Comparing Tables 3 and4, it is seen that the degree of correlation of the selected metrics with gate departure delay is much lower compared to with gate to wheels-off time.This suggests that constructing a model for reliable prediction of gate departure delay based on these metrics might be difficult.Furthermore, wheels-off time prediction at the scheduled gate departure time requires that the metrics computed based on airport state data at scheduled departure time be correlated to gate to wheels-off time at actual gate departure time.These correlations are likely to be worse compared to the correlations in Table 3 which are based on airport state data at or close to the actual gate departure time and not at an earlier time.These issues have not been examined further in this paper.The rest of the paper assumes that gate departure time is known and a prediction of gate to wheels-off time is needed for wheels-off time estimate.
+IV. Neural Network Model and ResultsA three-layer neural network with seven nodes in the input layer, 20 nodes in the hidden layer and one node in the output layer was designed to predict gate to wheels-off time.Such a neural network is shown in Fig. 3.The seven selected inputs are, 1) gate to runway distance, 2) average taxi-out delay in previous 15-minutes, 3) number of departures on surface at actual gate departure time, 4) average taxi-out delay of departures on same runway in previous 15-minutes, 5) average taxi-out delay of departures to same fix in previous 15-minutes, 6) wind angle and 7) ATC set airport arrival rate.Gate departure count in previous 15 minutes (#6 in Table 3) and number of departures to same fix in previous 15 minutes (#7 in Table 3) were not considered as inputs for the neural network because they were found to be significantly correlated to the other inputs.For example, correlation between metric #6 and #3 is 53.5% and between #7 and #5 is 43.5%.The single output is the gate to wheels-off time.Maximum absolute value of each input was determined for the entire seven day  dataset.Inputs were then normalized with these values.The gate to wheels-off time data used for training the neural network were also normalized by the maximum absolute value obtained from seven days of gate to wheels-off time data.Figure 3 shows that the inputs are multiplied by weights and summed together with a bias at each hidden layer node and input to the sigmoid function, which is real-valued and differentiable.This means that the neural network had 140 weights and 20 biases for seven inputs and 20 nodes in the hidden layer.The output of the sigmoid functions in the hidden layer are multiplied with another set of weights and summed together with a bias and input to sigmoid functions in the output layer.Since this neural network has 20 nodes in the hidden layer and one node in the output layer, there are 20 weights and one bias between the hidden and output layers.The 160 neural network weights and 21 biases were initialized with values between -1 and 1 using a uniform random number generator.These weights were then adjusted using the standard gradientbased back-propagation algorithm in the neural network training step.Four-hundred iterations resulted in reduced error between the gate to wheels-off time predicted by the neural network and the gate to wheels-off time used for training the network as shown in Fig.     runway distance correlation with gate to wheels-off time of 66.3%.Average and standard deviation of the error with respect to the gate to wheels-off time training data turned out to be 23 seconds and 2.6 minutes, respectively.The average and standard deviation of gate to wheels-off time in the six day training set is 8.9 and 3.8 minutes, respectively.A histogram of the training error is shown in Fig. 5.Reference 8 notes that while the compliance window for CFR varies by facility and that a nationwide standard does not exist, information from traffic managers and inter-facility agreements ask for flights to depart within a three-minute window, two-minutes early to one-minute late, with respect to the coordinated departure time.The reason for allowing departures to be two-minutes early compared to one-minute late is because it is easier to slow down the flight compared to accelerating it for merging into the constrained flow.Considering the neural network predicted gate to wheels-off time to be the coordinated departure time and the difference of the actual gate to wheels-off time with respect to this predicted time to be delay, 53.5% of the flights were found to be in compliance with the CFR window based on the error distribution in Fig. 5.This increased to 61.2% when the window was expanded to allow two-minute early to two-minute late departures.For the 13 August gate to wheels-off time test data, the correlation with the neural network generated gate to wheels-off time estimate is 74%.Average and standard deviation of the error with respect to the test data are 35 seconds and 2.3 minutes.These values can be compared to the average and standard deviation of gate to wheels-off time of 8.7 minutes and 3.4 minutes.Gate to wheels-off time test data are shown in Fig. 6 and the departure distribution of the flights whose gate to wheels-off time are in Fig. 6 are shown in Fig. 7 in 15-minute bins.The error distribution is given in Fig. 8. Actual gate to wheels-off times for 59.2% of the flights in the test set were found to be within the two-minute early and one-minute late CFR window with respect to the neural network predicted gate to wheels-off times.This result compares favorably with the observation in Ref. 8, which is based on onemonth of data, that 69.2% of aircraft subject to CFRs in which TMA automation was utilized were compliant with the CFR window.Compliance of the test set improved to 66.5% when the window was expanded to allow twominute early to two-minute late departures.These results confirm that the neural network performance on the test data is as good as it is on the training data.Results discussed in this section show that the selected metrics can be used as inputs to a neural network for generating gate to wheels-off time predictions for CFR after gate pushback.The performance of the neural network can be improved further by removing outliers from the training and test sets.This would require flights with unusual delay to be identified and removed from the training and test sets based on detailed analysis of surface trajectory of each flight.To compare the results obtained with the neural network with the earlier study reported in Ref. 5, a linear model was set up as follows:∑ ≤ ≤ = 7 1 k k k x c y (2)with 1x through 7x representing the seven non-normalized neural network inputs, 1 c through 7 c representing the corresponding coefficients and y representing the non-normalized gate to wheels-off time.These seven coefficients were computed using the leastsquares method with the left and the right hand sides of Eq. ( 2) derived from six days of data that had been used earlier to train the neural network.The numerical values of the coefficients are given in Table 5. Inputs derived from one day of data used for testing the neural network were then multiplied with these coefficients and summed to generate gate to wheelsoff time predictions for comparison with the actual gate to wheels-off time.Results obtained with the linear model matched the distributions shown in Fig. 5 and 8 based on the Mahalanobis distance metric, which is the ratio of the Euclidean Norm of the error (data in Figs. 5 and8) and the standard deviation of the gate to wheels-off time distributions used for training and testing.Mahalanobis distance metric values were determined to be 49.55 and 49.02 with neural network and linear model outputs with respect to training data, respectively.With respect to test data, the values were found to be 19.42 and 19.29 with neural network and linear model outputs, respectively.Actual gate to wheels-off times of 62.1% of the flights in the test set were found to be within the two-minute early and one-minute late CFR window with the linear model.CFR compliance is a bit better than 59.2% obtained with the neural network.These results do not show a benefit of using the neural network over a simple linear model for DFW traffic.It remains to be seen if the neural network would perform better on surface data from other airports.Results obtained with both the neural network and linear model validate the suitability of the chosen metrics for predicting gate to wheels-off time.For follow on work, these metrics will be computed using data from airports where SMS will not be available and used with the neural network and linear model to assess the accuracy of gate to wheels-off time predictions.
+V. ConclusionsCorrelation of airport state metrics derived from the Aviation System Performance Metrics database and Surface Management System logs with gate to wheels-off time and gate departure delay were examined to identify metrics with significant correlation as inputs for a neural network.Gate to runway distance was found to have the highest correlation of 66.3% with gate to wheels-off time.Scheduled departure time of day was found to have the highest correlation of 13.8% with gate departure delay.Given low correlation with gate departure delay, this study did not attempt to develop a model for predicting gate departure time.Instead, gate departure time was assumed to be known.The neural network was trained with six days of data to predict gate to wheels-off time.After training, the correlation with gate to wheels-off time predicted by the neural network and that used for training increased by 6% to 72.4% compared to 66.3% correlation with gate to runway distance.Average and standard deviation of the error with respect to the gate to wheels-off time training data were found to be 23 seconds and 2.6 minutes.One day of data were used for testing the neural network.A 74% correlation was found between these test set data and the neural network generated gate to wheels-off time estimate.Average and standard deviation of the error with respect to the test data were determined to be 35 seconds and 2.3 minutes.These results show that the neural network performance on the test data is comparable to its performance on the training data.Actual wheels-off times for 59% of the departures in the test set were found to be within the two-minute early to one-minute late Call for Release window with respect to the trained neural network predicted wheels-off times.This result is comparable to 69% compliance within the Call for Release window reported in an earlier study.Results based on analysis of Dallas-Fort Worth data show that it is feasible to use the selected metrics as inputs to a neural network for generating gate to wheels-off time predictions for Call for Release after gate pushback.Results obtained with a linear model, with coefficients obtained using the least-squares method, were found to be as good as those obtained with the neural network based on the Mahalanobis distance metric.While a clear benefit of using a neural network over the simple linear model was not found for Dallas-Fort Worth traffic, it remains to be seen if it would perform better on surface data from other airports.Both the approaches suggest that the selected metrics can be used for predicting gate to wheels off time.These metrics will be computed using data from airports where the Surface Management System will be unavailable and used with the neural network and the linear model to determine the accuracy with which gate to wheels-off time can be predicted.
+Parameters for Modeling Gate to Wheels-off Time and Gate Departure DelayThe variables chosen for predicting gate to wheels-off time and gate departure delay are discussed in this section.These input variables were obtained by processing seven days, 7 August 2011 through 13 August 2011, of SMS logs and ASPM data.
+SMS Log DataSurface trajectory data, consisting of a sequence of latitudes and longitudes as a function of time, of every flight departing DFW were analyzed to determine the following:1. Actual gate-out time in hours, minutes and seconds.2. Time of day in 15-minute interval.For example, 02:45 means 2 hours and 45 minutes past 00:00 local time.Actual gate-out time is within this 15-minute bin. 3. Actual wheels-off time in hours, minutes and seconds.4. Actual wheels-off time minus actual gate-out time.This is the sum of time spent in the ramp area, time taken to taxi to the runway and time spent on runway till the wheels are off the ground.5. Flight ID. 6. Departure gate ID. 7. Departure runway ID. 8. Name of the departure fix.9. Name of the destination airport.10.Actual gate to runway distance in nautical-miles based on surface trajectory.11.Actual taxi-out time of the flight in seconds.This is the gate to runway entrance time; sum of time spent in ramp area and taxi time to the runway entry.12. Aircraft type.For example, Boeing 747-400.13.Airline name.14.Scheduled gate departure time in hours, minutes and seconds.15.Gate departure delay.This is the difference between the actual gate departure time and the scheduled departure time.16.Number of departures on the surface at the actual gate departure time.These flights are out of the gate and moving towards departure runways.17.Number of arrivals on the surface at the actual gate departure time.These flights have landed and are moving towards arrival gates.18. Number of takeoffs from the same runway as this flight in the previous 15-minute interval with respect to the time of day.19.Average taxi-out delay of departures in seconds using the same runway as this flight in the previous 15minute interval with respect to the time of day.Taxi-out delay of each departure is computed as the difference of the actual taxi-out time and the unimpeded taxi-out time, where the unimpeded taxi-out time is computed as the ratio of the actual taxi-out distance to the average speed of 13 knots.20.Number of takeoffs that used the same departure fix as this flight in the previous 15-minute interval with respect to the time of day.21.Average taxi-out delay of departures in seconds using the same fix in the previous 15-minute interval with respect to the time of day.Taxi-out delay is computed in the same manner as in Item 19.22. Number of departures to the same destination airport in the previous 15-minute interval with respect to time of day.23.Average taxi-out delay of departures in seconds to the same destination airport in the previous 15-minute interval with respect to time of day.Taxi-out delay is computed in the same way as in Item 19.24.Number of departures from all gates in the previous15-minute interval with respect to the time of day.25.Number of takeoffs from all runways in the previous15-minute interval with respect to the time of day.26.Number of arrivals at all gates in the previous15-minute interval with respect to the time of day.27.Number of landings on all runways in the previous15-minute interval with respect to the time of day.28.Time of day in 15-minute interval such that the scheduled gate-out time is within this time interval.31.Average taxi-out delay of departures from the same runway as this flight in the previous 15-minute interval with respect to time of day.Taxi-out delay is computed in the same way as in Item 19.32.Average taxi-out delay of departures through the same departure fix in the previous 15-minute interval with respect to time of day.Taxi-out delay is computed in the same way as in Item 19.33.Average taxi-out delay of departures to the same destination airport in the previous 15-minute interval with respect to time of day.Taxi-out delay is computed in the same way as in Item 19.Note that the variables 29 through 33 are with reference to time of day related to the scheduled gate departure time.The other variables are with respect to the actual gate departure time.While some variables, such as 16 and 17, are with respect to the actual gate departure time, most variables are with respect to a broader interval of the time of day.Many variables, such as 1 and 3-15 are flight specific.Other variables like 16-23 and 29-33 are aggregate metrics based on other flights.Additionally, variables like 18-27 and 31-33 are based on aggregate metrics in the 15-minute time interval just prior to either the actual gate departure time or the scheduled gate departure time.It is assumed that these variables can be computed with flight plan data and Out-Off-On-In (OOOI) data provided by airlines available in the current air traffic system.Aggregate metrics 24-27 consider traffic to and from all gates and runways.Thus, they represent general state of airport operations.Time of day variables 2 and 28 are used for relating variables derived from the SMS logs and ASPM data.Variables based on ASPM data are discussed next.
+ASPM DataFederal Aviation Administration's (FAA) Aviation System Performance Metrics (ASPM) database, which is accessible on the web for authorized users, provides detailed data on flights to and from the 77 major U. S. airports (ASPM 77 airports) and flights operated by 29 major carriers (ASPM 29 carriers).Flights operated by ASPM carriers to international and domestic non-ASPM airports are also included.ASPM database also contains information on airport weather, runway configuration, and arrival and departure rates.Data in ASPM provide insight into air traffic and air carrier activity.FAA uses these data for monitoring airport efficiency, aspects of system performance, and retrospective trend analysis studies.Two different reports were extracted from the ASPM database for Dallas-Fort Worth airport operations spanning the period of 7 August 2011 through 13 August 2011.The first report, "Daily Weather by Quarter Hour Report," provided weather (visual meteorological condition or instrument meteorological condition), ceiling in feet, visibility in statute miles, temperature in degrees Fahrenheit, wind angle in degrees, wind speed in knots, arrival/departure runway configuration, airport departure rate and airport arrival rate in fifteen minute intervals as a function of local time.The second report, "Analysis By Airport By Quarter Hour Report (compared to flight plan)," includes numbers of scheduled departures/arrivals and departures/arrivals used for metric computation, percentages of on-time gate departures, airport departures and gate arrivals, average gate departure delay, average taxi-out time, average taxi-out delay, average airport departure delay, average taxi-in delay, and average gate arrival delay.Times and delays are in minutes.Numbers of scheduled arrivals and departures are based on carrier published schedules.Numbers of arrivals and departures for metric computation are based on itinerant flights to/from the ASPM 77 airports or operated by one of the ASPM 29 carriers.General aviation and military flights are excluded.Percent on-time gate departures is computed as the ratio of the number of flights that departed within 15-minutes past the flight plan gateout time to the number of departures for metric computation.Percent on-time airport departures is given as the ratio of the number of flights that departed within 15-minutes past the flight plan wheels-off time to the number of departures for metric computation.Percent on-time gate arrivals is determined as the ratio of the number of flights that arrive at the gate less than 15-minutes late compared to the flight plan gate-out time plus the scheduled block time to the total number of arrivals for metric computation.Taxi-out/taxi-in delay is the difference between taxiout/taxi-in time and unimpeded taxi-out/taxi-in time.Airport departure delay is computed as the difference between the actual wheels-off time and the sum of flight plan gate-out time and unimpeded taxi-out time.Average gate arrival delay is determined by adding minutes of gate arrival delay of one-minute or more, and dividing it by number of arrivals for metric computation.Gate arrival delay is defined as the difference between the actual gate-in time and the flight plan gate-in time.ASPM data from the two reports were processed to determine the following:1. Time of day in 15-minute intervals in hours and minutes format with respect to 00:00 local time.Figure 1 .1Figure 1.Gate to runway distance distribution.Figure 2. Average taxi speed distribution.
+Figure 2 .2Figure 1.Gate to runway distance distribution.Figure 2. Average taxi speed distribution.
+Figure 3 .3Figure 3. Neural network.
+Figure 4 .4Figure 4. Neural network convergence.
+Figure 7 .7Figure 7. Test set departure distribution.
+Figure 6 .6Figure 6.Test set data.Figure 5. Training error distribution.
+Figure 5 .5Figure 6.Test set data.Figure 5. Training error distribution.
+Figure 8 .8Figure 8. Test error distribution.
+Figure A- 1 .1Figure A-1.Dallas-Fort Worth airport layout.
+2 .2Meteorological condition-Visual Meteorological condition (VMC) or Instrument Meteorological Condition (IMC).3. Visibility in statute miles.4. Temperature in degrees Fahrenheit. 5. Wind angle in degrees.6. Wind speed in knots.7. Runway configuration indicating runways used for arrivals and runways used for departures.
+Table 1 .12011 runway usage summary.RunwaysRunwaysRunwaysUsed for bothUsageUsedUsageUsed onlyUsageArrivals and(hours)only for(hours)for(hours)DeparturesArrivalsDepartures18R6,96017L5,84435L2,55617R6,14213R5,62413L33517C6,11918L5,16736R2,56935C2,54731L2,53736L2,52635R2,31831R2,208Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5699
+Table 2 .22011top-ten runway configurations.ArrivalDepartureUsage (%)Flow13R,17C,17L,18R17R,18L45.4South31R,35C,35R,36L 31L,35L,36R22.7North13R,17C,17L,18R17R,18R9.7South17C,17L,18R17R,18L4.7South13R,17C,17L,18R 13L,17R,18L3.1South13R,17C,18R17R,18L2.9South35C,35R,36L31L,35L,36R2.9North31R,35C,36L31L,35L,36R1.5North13R,17C,17L17R,18R1.2South13R,17C,17L17R,18L0.8South
+Table 3 .3Gate to wheels-off correlations.#MetricCorrelation (%)p-value1 Gate to runway distance66.30.002 Average taxi-out delay in previous 15-minutes36.70.003 Number of departures on surface at actual gate departure time36.20.004 Average taxi-out delay of departures on same runway in previous 15-minutes23.50.005 Average taxi-out delay of departures to same fix in previous 15-minutes22.90.006 Gate departure count previous 15-minutes13.20.007 Number of departures to same fix in previous 15-minutes12.60.008 Wind angle-12.00.009 ATC set airport arrival rate9.70.00Number of departures on same runway in previous 15-minutes9.40.00Average taxi-in delay in previous 15-minutes8.30.00Wind speed on surface7.50.00Average taxi-out delay of departures to same destination in previous 15-minutes6.90.00ATC set airport departure rate6.00.00Number of departures to same destination in previous 15-minutes5.50.00Number of arrivals on surface at actual gate departure time5.30.00Visibility-4.40.00Average gate departure delay in previous 15-minutes3.80.00Takeoff count previous 15-minutes3.60.00Time of day at gate departure3.00.02Landing count previous 15-minutes2.50.05Temperature-2.20.09Gate arrival count previous 15-minutes2.00.13Average gate arrival delay in previous 15-minutes1.60.23Meteorological condition (VMC or IMC)1.30.33
+Table 4 .44. Note that the error is dimensionless because the training and neural network outputs are normalized.Six days, 7 August 2011 through 12 August 2011, of data were used for training the neural network and one day, 13 August 2011, of data were used for evaluating the gate to wheels-off time estimation ability of the neural network.Correlation between the gate to wheels-off time used for training and that generated by the neural network after training on the same set of neural network input data was found to be 72.4%.This is an improvement over gate to Gate departure delay correlations.#MetricCorrelation (%)p-value1Time of day at scheduled gate departure13.80.002Average gate departure delay in previous 15-minutes11.00.003Temperature10.10.004Average taxi-in delay in previous 15-minutes9.60.005Landing count previous 15-minutes8.90.006Gate arrival count previous 15-minutes7.70.007Number of arrivals on surface at scheduled gate departure time6.50.008Wind angle-5.30.009Average gate arrival delay in previous 15-minutes5.10.0010 Average taxi-out delay of departures to same destination in previous 15-minutes4.90.0011 Takeoff count previous 15-minutes4.80.0012 ATC set airport departure rate4.20.0013 Average taxi-out delay in previous 15-minutes3.50.0114 Number of departures on surface at scheduled gate departure time3.10.0315 Gate departure count previous 15-minutes3.00.0416 Wind speed on surface-1.20.4017 Visibility1.10.4318 Average taxi-out delay of departures on same runway in previous 15-minutes0.60.6619 ATC set airport arrival rate0.60.6920 Gate to runway distance0.50.7321 Average taxi-out delay of departures to same fix in previous 15-minutes-0.40.8022 Meteorological condition (VMC or IMC)-0.10.93
+Table 5 .5Linear model coefficients.Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5699CoefficientValue1 c241.18282 c10.00573 c10.15214 c0.10495 c0.03696 c-0.24917 c2.4563
+Time is given in the same format as Item 2 of this list.Observe that Item 2 is with reference to actual gate-out time while Item 28 is with respect to scheduled gate-out time.29.Number of departures on the surface at the scheduled gate departure time.Similar to Item 16 except at scheduled, not actual, gate departure time.30.Number of arrivals on the surface at the scheduled gate departure time.Similar to Item 17. by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5699Downloaded
+			Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5699
+		
+		
+			
+			
+			
+
+				
+
+
+	
+		Improved Prediction of Gate Departure Times Using Pre-Departure Events
+		
+			LaraCook
+		
+		
+			StephenAtkins
+		
+		
+			YoonJung
+		
+		10.2514/6.2008-8919
+	
+	
+		The 26th Congress of ICAS and 8th AIAA ATIO
+		Anchorage, Alaska
+		
+			American Institute of Aeronautics and Astronautics
+			September 14-19, 2008
+		
+	
+	AIAA 2008-8919
+	Cook, L. S., Atkins, S., and Jung, Y., "Improved Prediction of Gate Departure Times Using Pre-Departure Events," AIAA 2008-8919, Proc. 26 th Congress of International Council of the Aeronautical Sciences (ICAS), Anchorage, Alaska, September 14-19, 2008.
+
+
+
+	
+		Aircraft Taxi Times at U.S. Domestic Airports
+		
+			DerekRobinson
+		
+		
+			DanielMurphy
+		
+		10.2514/6.2010-9147
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
+		
+			American Institute of Aeronautics and Astronautics
+			September 13-15, 2010
+		
+	
+	Aircraft Taxi Times at U. S. Domestic Airports
+	Robinson, D. P., and Murphy, D. J., "Aircraft Taxi Times at U. S. Domestic Airports," AIAA 2010-9147, Proc. 10th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Fort Worth, TX, September 13-15, 2010.
+
+
+
+	
+		An Analytical Queuing Model of Airport Departure Processes for Taxi Out Time Prediction
+		
+			IoannisSimaiakis
+		
+		
+			NikolasPyrgiotis
+		
+		10.2514/6.2010-9148
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
+		
+			American Institute of Aeronautics and Astronautics
+			September 13-15, 2010
+		
+	
+	Simaiakis, I., and Pyrgiotis, N., "An Analytical Queuing Model of Airport Departure Process for Taxi Out Time Prediction," AIAA 2010-9148, Proc. 10th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Fort Worth, TX, September 13-15, 2010.
+
+
+
+	
+		Quantitative Analysis of Uncertainty in Airport Surface Operations
+		
+			DavidRappaport
+		
+		
+			PeterYu
+		
+		
+			KatyGriffin
+		
+		
+			ChrisDaviau
+		
+		10.2514/6.2009-6987
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			September 21-23, 2009
+		
+	
+	Rappaport, D. B., Yu, P., Griffin, K., and Daviau, C., "Quantitative Analysis of Uncertainty in Airport Surface Operations," AIAA 2009-6987, Proc. 9th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Hilton Head, SC, September 21-23, 2009.
+
+
+
+	
+		Relationship Between Airport Efficiency and Surface Traffic
+		
+			MatthewKistler
+		
+		
+			GautamGupta
+		
+		10.2514/6.2009-7078
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			September 21-23, 2009
+		
+	
+	Kistler, M. S., and Gupta, G., "Relationship between Airport Efficiency and Surface Traffic," AIAA 2009-7078, Proc. 9th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Hilton Head, SC, September 21-23, 2009.
+
+
+
+	
+		Air Traffic Management System Development and Integration (ATMSDI) Acquisition CTO-05--Surface Management System CTOD-2--Airport Site Surveys
+		
+			AtmsdiRaytheon
+		
+		
+			Team
+		
+		NAS2-00015
+	
+	
+		Contract Number
+		Moffett Field, CA
+		
+			June 5, 2001
+			
+		
+		
+			NASA Ames Research Center
+		
+	
+	Raytheon ATMSDI Team, "Air Traffic Management System Development and Integration (ATMSDI) Acquisition CTO-05- -Surface Management System CTOD-2--Airport Site Surveys," Contract Number NAS2-00015, NASA Ames Research Center, Moffett Field, CA 94035-1000, June 5, 2001.
+
+
+
+	
+		Foreword from Acting Administrator of the Health Resources and Services Administration (HRSA) and Associate Administrator of the Federal Office of Rural Health Policy (FORHP), U.S. Department of Health and Human Services
+		
+			JimMacrae
+		
+		
+			TomMorris
+		
+		10.1353/hpu.2016.0195
+		
+	
+	
+		Journal of Health Care for the Poor and Underserved
+		Journal of Health Care for the Poor and Underserved
+		1548-6869
+		
+			27
+			4A
+			
+			March 2011
+			Project MUSE
+		
+	
+	Administrator's Fact Book. cited: 4/24/2012
+	Assistant Administrator for Financial Services, "Administrator's Fact Book," Federal Aviation Administration, U. S. Department of Transportation, March 2011, URL: http://www.faa.gov/about/office_org/headquarters_offices/aba/admin_factbook/media/201103.pdf [cited: 4/24/2012].
+
+
+
+	
+		Characterization of Tactical Departure Scheduling in the National Airspace System
+		
+			RichardCapps
+		
+		
+			ShawnEngelland
+		
+		10.2514/6.2011-6835
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
+		
+			American Institute of Aeronautics and Astronautics
+			September 20-22, 2011
+		
+	
+	AIAA 2011-6835
+	Capps, A., and Engelland, S. A., "Characterization of Tactical Departure Scheduling in the National Airspace System," AIAA 2011-6835, Proc. 11th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Virginia Beach, VA, September 20-22, 2011.
+
+
+
+	
+		Observations of Departure Processes at Logan Airport to Support the Development of Departure Planning Tools
+		
+			HusniRIdris
+		
+		
+			IoannisAnagnostakis
+		
+		
+			BertrandDelcaire
+		
+		
+			RJohnHansman
+		
+		
+			John-PaulClarke
+		
+		
+			EricFeron
+		
+		
+			AmedeoROdoni
+		
+		10.2514/atcq.7.4.229
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			7
+			4
+			
+			
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	15-minute airport departure rate set by air traffic control.
+
+
+
+	
+		Estimating One-Parameter Airport Arrival Capacity Distributions for Air Traffic Flow Management
+		
+			TashaRInniss
+		
+		
+			MichaelOBall
+		
+		10.2514/atcq.12.3.223
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			12
+			3
+			
+			
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	15-minute airport arrival rate set by air traffic control.
+
+
+
+	
+		Supplementary file 1.
+		10.7554/elife.04535.023
+		
+			null
+			eLife Sciences Publications, Ltd
+			11
+		
+	
+	Average gate departure delay in the previous 15-minute interval with respect to the time of day
+	Average gate departure delay in the previous 15-minute interval with respect to the time of day. 11. Average taxi-out delay in the previous 15-minute interval with respect to the time of day. 12. Average taxi-in delay in the previous 15-minute interval with respect to the time of day. 13. Average gate arrival delay in the previous 15-minute interval with respect to the time of day. The first 9 items are measured and directly available at the tower. The remaining four items can be calculated based on flight plan and OOOI data.
+
+
+				
+			
+		
+	
diff --git a/file130.txt b/file130.txt
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+		
+I. Introductionn the current national airspace system, design of sectors have evolved over a long period of time based on incremental addition of new technologies and procedures for air traffic control.Each sector has a fixed capacity.When these capacities are exceeded by traffic demand, traffic flows are restriced to bring the demand below capacity.The concept in Ref. 1 suggests that instead of restricting traffic, which causes delays, airspace capacity can be increased by partitioning the airspace differently.Motivated by this concept, several methods for airspace partitioning that are described in Refs. 2 through 6 have been developed.These methods use some measure of controller workload to guide the design.In the future, with increased level of automation, airspace design might not be guided by controller workload considerations.Depending on how different the future design is from the current design, the controller's ability to actively separate aircraft might be limited.It might be useful to carry some of the design features of the current system into the future one, if some role for human controller is envisioned in the future air traffic control system.The motivation for computing metrics for the existing sectors is to capture some of the design features of the current sectors.Since the design of current sectors is based on the routes of flight and controller workload considerations, metrics related to controller workload can be expected to capure the design features.][9][10][11] These studies are limited to sectors in few centers.A comprehensive study of sectors in all the twenty centers is unavailable.In this paper, thirty-three traffic and geometric metrics from Refs. 7 to 11 are computed for 364 higher altitude sectors in each of the twenty centers, and in eight geographical regions.Higher altitude sectors were chosen because the benefits of airspace partitioning are expected to be realized in these sectors first.Data presented in this paper describes the design of the current sectors and will be found to be useful for comparing the designs of future airspace partitions.The paper is organized as follows.Section II describes the method for computing the metrics for each sector.The maximum number of aircraft in sectors obtained using simulated track-data are compared with those obtained using actual track-data in this section.Evaluation of sectors based on the traffic metrics is described in Section III and on geometric metrics in Section IV.Finally, the paper is summarized in Section V.
+II. Computational MethodThis section describes two methods for computing the fifteen sector traffic metrics as a function of time.The first method processes the actual track-data, and the second method processes simulated traffic data.The main benefit of using actual track-data is that they best represent traffic resulting from the demand and capacity constraints of the particular day.These data reflect flow control and separation assurance actions, and airline operational control actions such as: cancellations and creation of new flights.Along with their benefits, they suffer from some data-quality issues discussed in Ref. 12 that can lead to erroneous values of the metrics.Simulated traffic data do not suffer from many of the data quality issues, but they do not include all of the realworld effects.Trajectories of aircraft are simulated using mathematical models; they can be expected to differ from those actually flown.Simulated traffic data have to be compared against actual traffic data for validation.These limitations not withstanding, a simulation offers far greater flexibility in designing scenarios, including future traffic growth scenarios, for evaluating the desired metrics.Simulation also provides a mechanism for eliminating control actions inherent in the real system.For example, aircraft are permitted to violate separation minimums, which only happen as an operational error in the real system.Given a choice between using actual position data, "track-data," and simulated data, it is often desirable to use the actual track-data because they represent reality.Due to this reason, ten of the fifteen measures have been computed using the actual track-data derived from a recorded Airline Situation Display to Industry (ASDI) file.The remaining five metrics, number of jet aircraft, number of non-jet aircraft, conflict-count, average airspeed and variance in airspeed, were computed using position data simulated using the Airspace Concept Evaluation System (ACES) with flight-plans extracted from the same ASDI file.The reason for using simulated data is that the information needed for computing these metrics is provided in the flight plan and not in the actual track-data.
+A. Track-DataThe ASDI subsystem of the Enhanced Traffic Management System (ETMS) 13 disseminates real-time air traffic data, associated with different message types, to aviation industry.Of the different ASDI messages and supported data types described in Ref. 13, only the track/flight data-block messages, TZ messages, were used for computing the traffic metrics.TZ messages contain time-stamp, ARTCC identifier, aircraft identification (ACID), groundspeed, altitude, latitude and longitude.Over 9 million TZ messages were extracted from the recorded ASDI file, containing 48-hours of traffic data spanning the period from zero Coordinated Universal Time (UTC) on 17 March 2006 to zero UTC on 19 March 2006.Traffic data for these days were selected because of high traffic-volume, low weather impact and low delays.It should be noted that the traffic patterns vary from day to day and from season to season depending on demand, capacity, wind patterns and weather.Numerical values of the metrics in the sectors can be expected to vary based on the traffic pattern.The results presented in this paper are for a nominal day, which is defined as a high-volume, low-weather, and low-delay day.The time-sorted TZ messages were stored in data structures based on ACID.Data structures were then examined for each ACID to determine all the flights associated with that ACID.Individual flights were identified based on temporal gaps in the associated time-stamps.A temporal spacing of over thirty minutes was assumed to be due to a different flight.After identifying each flight, duplicate messages within one-minute periods were removed.This reduced the data by about 15%.Each flight's latitude, longitude and altitude derived from TZ messages were used to determine the sector the flight was in at each instant of time, where the sectors were as defined in May 2007 ETMS adaptation data.An efficient procedure for locating aircraft in a sector, described in Ref. 14, was used to identify the sectors.The latitudes and longitudes were then transformed into Cartesian coordinates with respect to a horizontal frame of reference using Oblique Stereographic Projection. 2 A first-order filter with altitude time-history input was used to generate time-history of climb/descent rate of each flight.Time, Flight ID, climb/descent rate, position and the sector corresponding to the position were input into a MySQL 15 database.This database was then queried to extract data related to flights in each sector and to sort them in time.These time-sorted data were then used to determine the numbers of aircraft in climb, in cruise, and in descent, and their sum (total number of aircraft) in the sectors.The data and the results are for 364 higher altitude sectors shown in Fig. 1.Each of these sectors has a floor of 17,100 feet altitude or higher.Fifty of the sectors have a floor between 17,100 feet and 24,000 feet; the rest have a floor at or above 24,000 feet altitude.The ceilings of these sectors are between 24,000 feet and 99,900 feet altitude.Several sectors in Salt Lake, Minneapolis, New York, DC, Boston and Los Angeles centers (see Fig. 1) were not included in the analysis because their floors are below 17,100 feet altitude.
+B. ACES SimulationACES is a comprehensive computational model of the national airspace system consisting of air traffic control and traffic flow management models of ARTCCs, terminal-radar-approach-controls (TRACON), airports and the air-traffic-control-system-command-center (ATCSCC). 16It simulates flight trajectories through the enroute-phase of flight, where enroute-phase for jet aircraft is above 10,000 feet.A queuing model simulates surface movement and flight through terminal airspace.Traffic flow management and air traffic control models in ACES use airport and sector capacity thresholds for delaying flights, while they are on the ground and during their enroutephase.Some of the ACES outputs are arrival and departure counts at airports, trafficcounts in sectors and air traffic system performance metrics including arrival, departure, enroute and total delays.Earlier validation studies in Refs.17 and 18 have shown that ACES generates realistic delays and airport operational metrics similar to those observed in the real-world.Due to these capabilities, ACES was chosen for simulating traffic.
+Simulation InputsACES simulation inputs include files containing capacity data (airport arrival and departure capacities, and sector capacities), traffic data (scheduled departure times and flight-plans), and adaptation data (sector/center geometric data).The actual airport arrival and departure rates specified at the 74 major U. S. airports on 17th and 18th March 2006 were specified as airport capacities in ACES.Sector capacity data were derived from the ETMS data tables.Sector capacity is defined as the maximum number of aircraft allowed in a sector at any one time during a fifteen-minute time interval.Capacity values, known as Monitor Alert Parameter (MAP), are used in the ETMS to trigger traffic flow management initiatives for demand reduction.Capacity thresholds are set to ensure that air traffic controllers are able to separate aircraft traversing the sector airspace.MAP values and the number of sectors with those values out of 364 higher altitude   1.Flight-plans for the simulation were derived from the same ASDI file used for computing the traffic-count metrics discussed in the previous subsection.Flight connectivity data relating the same physical aircraft to two or more flights segments were obtained from the Bureau of Transportation Statistics (BTS) for the two days.Airline flight-numbers, aircraft tail-numbers and the associated flight-plans for all flights were included in the ACES input file.Scheduled departure times derived from the BTS data were assigned as departure times.Proposed departure times from flight-plan messages in the ASDI data, or actual departure times in ASDI data minus average taxi times associated with airports of departure were assigned as departure times when scheduled times were not available in the BTS data.After assigning scheduled departure times for flights, an ACES simulation was run without airport and sector capacity constraints to compute unconstrained arrival times of flights at their destination airports.These were then set to scheduled arrival times at destination airports.A series of steps described in Ref. 19 were then taken to ensure that flight connectivity was preserved and that the arrival and departure schedules were compatible with turnaround-time requirements, time required for unloading aircraft after arrival at the gate and preparing it for departure.Final traffic schedule was generated after making the required flight schedules and tail-number changes.Sector and center geometry definitions needed for the simulation were obtained from the May 2007 ETMS adaptation data.
+Simulation OutputsACES writes out identification information (ID) and position coordinates of flights that are in each sector to an output database at the specified rate during simulation.After completion of the simulation, flight IDs associated with a sector and their position time-histories were extracted from the database for computation of the five traffic metrics, number of jet aircraft, number of non-jet aircraft, conflictcount, average airspeed and variance in airspeed.This process was repeated for every sector.
+C. Comparison of Tack-Data and ACES Simulation ResultsGiven that there are differences between the actual traffic data from the field and ACES simulated data, it is necessary that a comparison between the values of metrics obtained by processing actual track-data and ACES simulation be done for establishing the validity of the results.This was accomplished by comparing 1) the total number of aircraft in higher altitude airspace and 2) numbers of higher altitude sectors grouped according to the peak total-counts (trafficcounts), peak climb-counts, peak cruise-counts and peak descent-counts in each hour simulated by ACES with those obtained by processing track messages.Numbers of aircraft in sectors shown in Fig. 1 were retrieved from the simulation output and added together to compute the total number of aircraft in ten-second intervals.This time-history is shown with that of the actual number of flights in Fig. 2. Observe that the ACES simulation starts with all aircraft on the ground, whereas in the actual air traffic system there are always flights that are airborne.Figure 2 shows that the simulated traffic catches up with the actual traffic.The general trends of simulated and actual traffic are similar for the twenty-four hours between eight UTC on 17 March 2006 and eight UTC on 18 March 2006 (location marked 32 UTC in Fig. 2).Some of the differences between the time-histories are attributable to the issues with simulated and actual flight data discussed earlier in this section.Figure 3. Sectors with peak traffic-count in the first five levels, listed in Table 2, using actual track-data.For comparison based on peak traffic-counts, aircraft were counted in each higher altitude sector.The maximum of 60 values, one for each minute, provided the maximum number of aircraft, peak trafficcount, using actual track data for that hour.Figures 3 and4 show the number of sectors with peak traffic-count values in the first five levels listed in Table 2 during each one-hour period.The graphs in the figures viewed in conjunction with Fig. 2 show that as traffic increases, there are fewer sectors with low peak traffic-counts as would be expected.The maximum numbers of sectors for the four graphs, Level 2 through 5, in Fig. 3 were found to be 217 at 28:00 UTC, 190 at 22:00 UTC, 85 at 23:00 UTC and 12 at 18:00 UTC.In Fig. 4 these are 222 at 29:00 UTC, 176 at 24:00 UTC, 55 at 23:00 UTC and 12 at 17:00 UTC.The information contained in the individual graphs in Figs. 3 and4 can be combined and presented in terms of a vertically stacked bar chart for each one-hour period of the day.Such bar charts provide cumulative information as explained by the following example.Consider the bar charts obtained by processing the actual trackdata shown in Fig. 5.Of the eight levels listed in Table 2 and included in Fig. 5, only levels one through five are visible in the bar charts.Numbers of sectors with a peak traffic-count of four aircraft or less are shown in the bottom bar charts.Numbers of sectors with the next higher level are placed on top of this layer, and so on.Cumulative counts obtained by summing the levels below each chosen level determine the numbers of sectors with peak traffic-counts below the thresholds implicitly defined by levels in Table 2.For example, the top of Level 2 histogram at 23:00 UTC indicates that at any time of the day, at least 99 sectors have a peak traffic-count of nine aircraft or less.This is defined as the lower cumulative count.The maximum value of the top of the Level 2 histogram is 364 sectors at 10:00 UTC.This maximum value is defined as upper cumulative count.Table 3 summarizes these results obtained using actual track-data and ACES simulation.The time-histories shown in Figs. 3 and4 compare reasonably well; they show that similar peak traffic-counts are obtained in approximately the same number of higher altitude sectors using actual track-data and ACES simulated track-data.Values listed in the second and third  2, using ACES data.Figure 5. Time-history of bar charts of number of sectors grouped in eight peak traffic-count levels using track-data.Table 3. Numbers of sectors with peak traffic-counts below thresholds using ACES simulation compared to those obtained using track-data.3 also suggest that the peak traffic-count statistics computed using actual track-data and ACES are comparable.Analysis of peak climb-counts, peak cruise-counts and peak descent-counts using actual track-data and ACES simulated data also compared well.
+III. Traffic MetricsA considerable amount of research has been devoted to the synthesis of traffic dependent metrics and their application to modeling sector complexity and air traffic controller's workload. 8,9Traffic metrics that were found to be especially pertinent for modeling workload perceived by controller in Ref. 8 are listed in Table 4.These metrics were computed for today's higher altitude sectors shown in Fig. 1 and the results are presented in the following subsections.
+A. Traffic-count MetricsPeak traffic-count or the maximum number of aircraft has been found to be the most important contributor to controller workload.Research has found that controller activity and attentiveness are highly correlated to peak traffic-count. 10Maximum number of aircraft in higher altitude sectors computed using actual track-data were discussed in Section II.The time histories of the numbers of sectors with peak traffic-count levels were shown in Fig. 3 and the corresponding bar charts were shown in Fig. 5.Maximum numbers of aircraft in the climb phase (peak climb-count), in the cruise phase (peak cruise-count), and in the descent phase (peak descent-count) were computed in the same manner as the peak traffic-count in a sector.Aircraft with a climb rate of 200 feet/minute or more were considered to be climbing and those with a descent rate greater than or equal to 200 feet/minute were considered to be descending.Aircraft with climb or descent rates less than 200 feet/minute were considered to be cruising.Maximum number of jet aircraft and non-jet aircraft (turboprops and piston-props) in each hour in every sector were computed using ACES simulated track-data at ten second intervals.Since actual track-data do not contain aircraft type information, flight plans do, ACES simulated track-data that are based on flight-plans were used.Although it is possible to relate actual track-data to the flight-plans using the aircraft IDs that are common to both the message types, it does require an efficient data structure, significant memory and computation.ACES simulated data were also used for computing conflict-counts.Since jet aircraft cruise at approximately 8 miles/minute, trajectory data generated by ACES were written to the output database at tensecond intervals.Aircraft within each sector were selected from the database for checking conflicts, which means that conflicts between aircraft in neighboring sectors were not checked.This limitation arose because the database did not contain information on which sector is next to which one.Not knowing the sector neighborhood relationship meant that the only way to check for all such conflicts was to consider all the flights at each time-  3).Cumulative counts out of 364 counts listed in the last two columns are based on the time history of bar charts of numbers of sectors in peak climb-count levels like the one shown in Fig. 5. Cumulative counts were discussed earlier to explain the contents of Table 3.The value of 364 in the sixth and seventh columns indicates that at no time were there more than 14 aircraft in climb in a higher altitude sector.Tables 6 through 10 should be interpreted in the same way as Table 5. Observe that the thresholds in Tables 9 and 10 are not the ones in Table 2. Since there are far fewer non-jet aircraft and conflicts compared to the number of aircraft in a sector, the thresholds for non-jet aircraft counts and conflict-counts have a smaller range compared to those in Table 2.The time to conflict is computed as the ratio of the range to the range-rate (time derivative of range).The average time-to-go, defined as C12 in Ref. 8, considers each aircraft in the sector one at a time for determining the aircraft with which it is predicted to conflict.Next, the time-to-go is used for identifying those aircraft (conflict set) with which conflict is predicted in the near term (for example, in less than ten minutes).Minimum time-to-go is then determined for each conflict set, summed and divided by the number of sets to compute the average.Minimum values of the 1) two horizontal separation metrics, 2) two vertical separation metrics, and 3) one timeto-go metric were computed for each hour using actual track-data.Time histories and historgrams of sectors were created in the same manner as discussed for the traffic-count metrics.The trends in these data are summarized in Tables 11 through 15.The category no-conflict in Tables 11 and12 means that aircraft in the sector were outside each others vertical bounds, therefore horizontal separation was not computed for them.Horizontal separation is measured in nauticalmiles and the vertical separation is measured in feet.Time-to-go is measured in seconds.The categories no-conflict in Tables 13 and14 means that aircraft were outside each others horizontal bounds, and 'at most one aircraft' in
+C. Flow MetricsThe three flow metrics discussed in this section are, average transit-time, average airspeed and the variance in airspeed of aircraft in the sector.Sector transit-time is defined as the time taken by the aircraft to cross the sector.The difference between the entry and exit times, obtained by processing the actual track-data provided the transit-time.Average transit-time for each sector was obtained by considering the transittime of all the aircraft that went though the sector during the 24-hour period.Figure 6 shows the histogram of the average transit-time in minutes of 364 higher altitude sectors.The minimum, mean, standard-deviation, and maximum values of the distribution shown in Fig. 6 were found to be 2.8 minutes, 8 minutes, 2.8 minutes and 21 minutes.Average airspeed was computed every ten-second in each sector using the ACES simulated data.Maximum average-airspeed within each hour was computed with 360 such values.Time histories and bar charts of sectors were then created with the maximum average-airspeed values.Results are summarized in Fig. 7 and Table 16.Observe that airspeed of higher altitude traffic generally lies in the 400 knots to 500 knots range.Like the average airspeed, the variance of airspeed (knots 2 ) was computed at ten-second intervals and then the maximum value was selected for each hour for each sector.Variance of airspeed is defined in Eq. 26 in Ref. 8. Time history of the bar charts of sectors with the maximum airspeed-variance in six-levels is shown in Fig. 8. Table 17 lists the five-levels, minimum, maximum, average, and maximum number of sectors at each of the five-levels, and the cumulative lower and upper bounds obtained using the bar charts in Fig. 8.The computations done with airspeed could have also been done with groundspeed, which is available in the actual track-data, but it was not used because groundspeed depends on winds, which vary from day to day.Airspeed on the other hand is a function of aircraft performance characteristics and is independent of winds.
+IV. Geometric MetricsGeometric metrics are described in this section.Sector geometry features such as airways, navigational aids and airports define the kind and the frequency of tasks performed by the controller.Therefore, they contribute to controller workload directly and operational errors indirectly. 10,11Sector geometry metrics, including those proposed in Refs. 10 and 11, considered in this study are listed in Table 18.Data resulting from computations on higher altitude sectors are discussed in the subsections below.Geometric metrics listed in Table 18 are grouped into five categories.The first category "Geographical Location" consists of metrics one through three.The second category "Sector Dimensions" consists of metrics four through seven.The third category "Shape Attributes" consists of metrics eight through ten.The fourth category "Route Attributes" comprises of metrics 11 through 15.The last category "NeighborhoodAttributes" contains metrics 16 through 18.
+A. Geographical LocationThe number of higher altitude sectors in each center was determined from the first three characters of Sector IDs of sectors with a base of 17,100 feet and above.For example, "ZAB37" indicates that the sector belongs to the Albuquerque center (ZAB).Counting all these sectors shown in Fig. 1 with the same first three letters provided the number of sectors in each center shown in Table 19.The 20 centers listed in Table 19 are organized in eight geographical regions listed in Table 20.The number of sectors in each region is obtained by summing the values corresponding to the centers that form the region.These results are given in the fourth column of Table 20.The numbers of lowaltitude, high-altitude and super-high-altitude sectors as defined in May 2007 ETMS adaptation data were found to be five, 248 and 111, respectively.The five low-altitude sectors had a base at or above 17,100 feet altitude.
+B. Sector DimensionsTo determine the dimensions of each sector, the volume of each subsector was computed.A subsector is defined as a polygonal prism with a boundary defined by a polygon and a constant height.The complex geometric shape of a sector is achieved by placing subsectors on top, and to the side of other subsectors.The volume of a sector is the sum of the volumes of its subsectors.Figure 9 shows the histogram of the volume of sectors, in cubic-nautical-miles.Height of each sector was determined by subtracting the lower bound of the lowest subsector from the upper bound of the highest subsector.The distribution of the heights of the sectors is shown in Fig. 10.The histogram in Fig. 10 shows a bi-modal distribution with sector below 16,000 feet and above 56,000 feet.Dividing sector volumes with sector heights resulted in the reference-areas of the sectors.The distribution of the sector reference-areas is shown in Fig. 11.Reference-lengths of the sectors were obtained by taking the square-root of the reference-areas.The histogram of the reference-lengths is given in Fig. 12.Minimum, mean, maximum and standard deviation values of sector volume, height, reference-area and reference-length are summarized in Table 21.
+C. Shape AttributesAspect-ratio, the ratio of the length to width, of the sectors was determined by computing the moments of inertia of the sectors.Prior to the computation of moments of inertia, centroids of the sectors had to be computed.Since a sector is composed of subsectors, the centroid of each subsector was determined first, and then, these centroids were weighted with the volumes of the subsectors to obtain the centroid of the sector.The moment of inertia tensor of  each subsector was computed with respect to the frame of reference located at the centroids, and then the parallel axis theorem was employed to determine the moment of inertia tensor with respect to the frame of reference located at the centroid of the sector.These moment of inertia tensors were then summed up to determine the moment of inertia tensor of the sector.Mathematical expressions for computing the centroid and the moment of inertia tensor for a two-dimensional polygonal object are given in Ref. 20.These equations were extended to three-dimensional polygonal prisms for generating the results discussed here.The eigenvalues of the moment of inertia tensor are the principal moments of inertia about the principal axes, which are the eigenvectors corresponding to the eigenvalues.If 11 I , 22 I and 33 I are the principal moments of inertia, the dimensions of a rectangular prism with the same principal moments of inertia as the sector (polygonal prism) are:( )3 1 ; 12 ! ! " = i I S V l ii i (1)where, ( )33 22 11 2 1 I I I S + + =and V is the volume of the sector.The aspect-ratio is given by the ratio of dimensions in Eq. (1), j i l l / such that j i ! .Figure 13 shows the distribution of the aspect-ratio of the 364 sectors.Sectors with an aspect-ratio close to one are of square shape, while the sector with an aspect-ratio closer to seven is a highly elongated rectangle.The minimum, mean, maximum and standard deviation aspect-ratio are 1.0, 2.0, 6.6, and 0.86, respectively.Reference 7 notes that the traffic pattern is usually highly parallel and less complicated in elongated sectors.If the principal moments of inertia are distinct, the principal axes are uniquely specified.If two or all three of the principal moments are the same, there is no choice of a preferred axis.Once the three principal axes were obtained, the two principal axes with larger projections on the horizontal plane were selected.Of these, the one with the larger eigenvalue was chosen as the preferred axis of the sector. Figure 14 shows the preferred axis of the sectors.This figure shows that most of the sectors are aligned along East-West direction.Along the East and West Coasts, sectors are oriented in the North-South direction.It is interesting that the sectors are aligned along the major traffic flow directions.The number of subsectors in a sector is an indicator of the shape complexity of the sector.Table 22 lists the number of sectors containing the same number of subsectors.This table shows that most of the sectors have a single subsector.One sector has 13 subsectors.The mean and the standard deviation of the number of subsectors were found to be 1.7 and 1.4.
+D. Route AttributesThree types of navigation aids were counted to determine the number of navigation aids in sectors.These three types of radio navigation aids commonly employed by aircraft for navigation along routes are Very-High-Frequency Omnidirectional Range (VOR), VOR collocated with Distance Measuring Equipment (VOR-DME), and VOR collocated with Tactical Air Navigation System (VORTAC).The known latitude and longitude of each navigation aid were used for locating it in the sector. Figure 15 shows the distribution of sectors as a function of the number of navigation aids enclosed within their boundaries.There are a total of 1055 navigation aids.Some of these are shared by multiple sectors.Six sectors had no navigation aids and one sector had a maximum of 14 navigation aids.The mean and standard deviation of the number of navaids in a sector is 3.9 and 2.3.Next, the number of intersections in sectors was computed.Intersections are defined as locations where airways, Victor Airways and Jet Routes, intersect.Intersections are specified by latitude, longitude and altitude.This makes it possible to locate them in sectors.Figure 16 shows the histogram of sectors based on the number of intersections enclosed within the sectors.Sixty-four sectors had no intersections, 328 sectors had ten or fewer intersections, 36 sectors had more than 10 intersections, and one sector had 22 intersections.Mean and standard deviation were determined to be 4.3 and 4.1 intersections.Airways in sectors were determined using the association between the intersections and airways.Since an airway can be associated with several intersections within a sector, each associated airway is counted only once.An airway that went across the sector without passing through an intersection in the sector could not be counted.It may be possible to improve the airway count by using the association between the navaids and airways in addition to that between intersections and airways.Victor Airways were counted below 18,000 feet altitude and Jet Routes at or above 18,000 feet altitude.Both were counted in sectors whose base was below 18,000 feet and top above 18,000 feet altitude.A histogram of sectors as a function of the number of airways is given in Fig. 17.Airways were not found in sixty-four sectors because those sectors did not have any intersections.334 sectors had ten or fewer airways while 30 sectors had more than ten airways.The maximum number of airways was found to be 17 in only one sector.Mean and standard deviation values were determined to be 5 and 3.7 airways.Seventy-four major airports in the United States that are in the Federal Aviation Administration's (FAA) Aviation System Performance Metrics (ASPM) 21 were located within the horizontal confines of the higher altitude sectors and counted.Table 23 lists the numbers of sectors and the corresponding numbers of 74 ASPM airports.Eightythree sectors had one or more major U. S. airports.Only 16 sectors had two or more airports.Controllers have to ensure that aircraft do not enter a Special Use Airspace (SUA).This monitoring function adds to controller workload.Prohibited areas, military operations areas, alert areas, warning areas, and national security areas are considered to be SUA.Boundary and height information of the 1017 SUAs obtained from the FAA were used for locating them in sectors.Of the 364 sectors, only two sectors, ZHU 59 and ZDV 47 were found to completely contain one and two SUAs, respectively.The rest partially contained the SUAs.Statistics of sectors partially containing SUAs are discussed in the next subsection.
+E. Neighborhood AttributesSectors above, below and to the sides of sectors were counted to determine the number of surrounding sectors.Minimum, mean, maximum and standard deviation of the number of surrounding sectors were determined to be 4, 13.3, 30 and 3.9.Sector 22 in Atlanta Center was found to have 30 sectors surrounding it.There were 202 sectors that had 13 or fewer sectors surrounding them, while 162 sectors had more than 13 sectors surrounding them.The distribution of the sectors as a function of number of surrounding sectors is given in Fig. 18.SUA polygons were related to a data structure of grid cells using the method described in Ref. 14.The mapping of the grid cells to the sectors was then used to identify the set of sectors that the SUAs could be in.The altitude range of the sectors was compared with the altitude range of the SUAs to determine if there was any overlap.Sectors with overlap were deemed to partially or fully contain these SUAs.If a SUA was associated with a single sector, that sector was considered to completely contain that SUA.In instances where the SUA was associated with more than one sector, the SUA was considered to be partially contained in the associated sectors.With the elimination of two sectors that fully contained SUAs, analysis was done on the remaining 362 sectors.SUAs were not found in 268 sectors.Eighty-one sectors were found to have five or fewer SUAs.Only 15 sectors were found to have more than five SUAs.Sector 26 in Houston Center contained 19 SUAs.Table 24 summarizes these results.Distance with respect to the center of the sector to the closest one of the 74 ASPM airports outside the sector was determined by first establishing which of them were within the confines of the sector boundary.These airports were excluded and distances to the airports outside the sector boundary were computed.The minimum of these distances gave the distance to the closest airport outside the sector. Figure 19 gives the histogram of the sectors with respect to distances to the closest airports.The closest airport outside a sector was 20.6 nautical-miles.The average and maximum distances were 116.5 and 422.2 nautical-miles.The standard deviation was 57.8 nautical-miles.A major airport was within 200 nautical-miles from the 335 sectors (both inside and outside the sector).Only 29 sectors were farther than 200 nautical-miles from a major airport.
+V. SummaryThis paper was motivated by the problem of determining the design features of the current sectors so that future designs can be compared with the current design.Since the current design is based on tools and techniques used by controllers, a large departure from this design guided by automation needs will have implications in controllers being able to manage traffic in the new sectors.Since this study is focused on current sectors, fifteen traffic metrics related and eighteen geometric metrics related to controller workload were used to characterize the design of current sectors.Numerical values of these metrics were computed for sectors with a base of 17,100 feet and above in the current U. S. airspace.The fifteen traffic metrics were classified into three categories: seven traffic-count metrics, five separation metrics and three flow metrics.The eighteen geometric metrics were classified into five categories: three geographical location metrics, four sector dimension metrics, three shape attribute metrics, five route attribute metrics and three neighborhood attribute metrics.Ten out of the fifteen traffic metrics were computed using actual aircraft position data obtained from the field and the remaining five were computed using simulated aircraft position data.Use of simulated data provided an easy means of computing conflict-count, aircraft type and airspeed metrics.Conflictcounts are difficult to determine from actual data because controllers make sure that aircraft are separated.Only in rare occasions operational errors occur when separation minimums are violated.Aircraft type and airspeed information is available in the flight-plan therefore it is straightforward to carry this information in a simulation.It is possible to relate the flight-plan data to aircraft position update messages using aircraft identification tag that is common to both, but the processing is more involved.Maximum numbers of aircraft in sectors during the 24-hour period were computed using both actual and simulated data.Time histories were presented to show that the results obtained using simulated data compare very well with those obtained using actual data.Data corresponding to the distribution of sectors as a function of the traffic and geometric metric values were provided in tables and in bar charts.These results show that most sectors in the current airspace have fewer than 20 aircraft at any given time.Most sectors have less than five aircraft in climb phase, fifteen in cruise phase and five in descent phase.Most of the traffic at higher altitude sectors is jet traffic.It was shown that about 98% of the sectors have fewer that three pairs of aircraft in conflict in simulation.Horizontal and vertical separation metrics indicated that aircraft fly at the same altitude in most sectors.Airspeed was found to lie in a narrow range of 400 to 500 knots.Sector transit time was found to be normally distributed with a mean of eight minutes and standard deviation of three minutes.The maximum transit time was found to be 21 minutes.A wide variation was found in sector volume, area, height and length.Most sectors were found to be elongated with an aspect ratio of two and aligned with the main traffic flows.The number of subsectors, which is a measure of sector shape complexity, was found to be less than three in most sectors.Of the 364 sectors considered in this study, 328 sectors had ten or fewer airway intersections.Maximum number of airways in a sector was determined to be 17.On an average, a sector was surrounded by 13 other sectors.The maximum number of sectors surrounding a sector was found to be 30.None of the 1017 Special Use Airspaces (SUAs) were found in 268 sectors.Eighty-one sectors had five or fewer SUAs.The maximum number of SUAs in a sector was determined to be 19.A major U. S. airport was found within 200 nautical-miles from each of the 335 sectors.Only 29 sectors were farther than 200 nautical-miles from one of the 74 major U. S. airports.Figure 1 .1Figure 1.View of 364 higher altitude sectors with a floor at or above 17,100 feet altitude.
+Figure 2 .2Figure 2. Actual and ACES simulated aircraftcounts.
+Figure 4 .4Figure 4. Sectors with peak traffic-count in the first five levels, listed in Table2, using ACES data.
+Figure 9 .9Figure 9. Sector volume histogram.Figure10.Sector height histogram.
+Figure 11 .11Figure 11.Sector reference-area histogram.Figure12.Sector reference-length histogram.
+Figure 12 .12Figure 11.Sector reference-area histogram.Figure12.Sector reference-length histogram.
+Figure 13 .13Figure 13.Sector aspect-ratio histogram.Figure14.Preferred axis of the sectors.
+Figure 14 .14Figure 13.Sector aspect-ratio histogram.Figure14.Preferred axis of the sectors.
+Figure 16 .16Figure 16.Sector intersections histogram.
+Figure 17 .17Figure 17.Sector airways histogram.
+Figure 18 .18Figure 18.Surrounding sectors histogram.
+Figure 19 .19Figure 19.Distance to closest airport outside the sector histogram.
+Table 1 .1Sector capacities.MAP No. ofsectors911011151210131514231562164517281882193820222131231
+Table 1 .1The average MAP value is approximately 17 and the most frequent MAP value is 18 for data in Table
+Table 2 .2Peak traffic-count levels.Level Peak Traffic-Count Range10 -425 -9310 -14415 -19520 -24625 -29730 -348> 34
+Table 4 .4Traffic metrics.NumberMetricDataType1Maximum number of aircraftActualCount2Maximum number of aircraft in climbActualCount3Maximum number of aircraft in cruise ActualCount4Maximum number of aircraft inActualCountdescent5Maximum number of jet aircraftSimulated Count6Maximum number of non-jet aircraftSimulated Count7Peak conflict-countSimulated Count8Average horizontal separation betweenActualSeparationaircraft in sector9Minimum horizontal separationActualSeparationbetween aircraft in sector10Average vertical separation betweenActualSeparationaircraft in sector11Minimum vertical separation betweenActualSeparationaircraft in sector12Average time-to-go to conflictActualSeparation13Sector average transit timeActualFlow14Average airspeedSimulated Flow15Variance in airspeed of aircraftSimulated Flow
+Table 5 .5Numbers of sectors with peak climb-counts using actual track-data.Level Min. Mean Max. Threshold CumulativeCumulativeLowerUpper1282323363528236321408010359364301515364364
+Table 7 .7Numbers of sectors with peak descent-counts using actual track-data.Level Min. Mean Max. Threshold CumulativeCumulativeLowerUpper1323345364532336420194110363364300115364364
+Table 8 .8Numbers of sectors with peak jet-counts using ACES data.Level Min. Mean Max. Threshold CumulativeCumulativeLowerUpper171123415734122213421910144363319417515305364402252203523645031225362364600230364364
+Table 9 .9Numbers of sectors with peak non-jet-counts using ACES data.Level PeakMin. Mean Max. Threshold CumulativeCumulativeNon-LowerUpperjetCount102022783611202361213661172306364320164333493644304154358364540165362364650026364364
+Table 6 .6Numbers of sectors with peak cruise-counts using actual track-data.These distances are added and the average minimum separation is computed.The second horizontal separation metric, C9 in Ref. 8, is the minimum horizontal separation obtained within the altitude bands.It is the minimum of the horizontal distances, rather than the average, computed in the previous metric.The two vertical separation metrics are computed in a similar manner as the horizontal separation metrics.The first vertical separation metric, C8 in Ref. 8, places a horizontal neighborhood of ten nautical-miles around each aircraft in the sector.Vertical separation distance to the closest aircraft in the horizontal neighborhood is computed.The distances are then summed up to determine the average vertical separation.The second metric, C10 in Ref. 8, is the minimum of these vertical separation distances.Level Min. Mean Max. Threshold CumulativeCumulativeLowerUpper1191363405193402241402201017536430701491532436440153620355364502625361364600330363364700035363364800142364364
+Table 10 .10Numbers of sectors with peak conflict-counts using ACES data.Level PeakMin. Mean Max. Threshold CumulativeCumulativeConflict-LowerUpperCount10361423471363472117154232222436432045903314364430174143473645404125359364650146362364760027363364870018364364
+Table 1111Level Min.Min. Mean Max. Threshold CumulativeCumulativeSeparationLowerUpperRange10-47612216057616025-9156095109321439-165601411911662623644No-041202! 0364364conflictTable 12. Numbers of sectors with minimum minimum-horizontal-separation (nautical-miles) using actual track-data.Level Min.Min. Mean Max. Threshold CumulativeCumulativeSeparationLowerUpperRange10-49326834459334425-91422341011036139-165233901661623644No-041202! 0364conflict. Numbers of sectors with minimum average-minimum-horizontal-separation (nautical-miles) using actual track-data.
+Table 13 .13Numbers of sectors with minimum average-minimum-vertical-separation (feet) using actual track-data.Level Min.Min. Mean Max. Threshold CumulativeCumulativeSeparationLowerUpperRange10-999104281353100010435321000-970011224397011223643No-060242! 0364364conflict
+Table 14 .14Numbers of sectors with minimum minimum-vertical-separation (feet) using actual track-data.Level Min.Min. Mean Max. Threshold CumulativeCumulativeSeparationLowerUpperRange10-999105287359100010535921000-97005163497011223643No-060242! 0364364conflict
+Table 15 .15Numbers of sectors with minimum average-minimum-time-to-go (seconds) using actual track-data.Level Min.Min. Mean Max. Threshold CumulativeCumulativeTime-to-LowerUppergo Range10-11913126211120132112120-2391594144240343363240-35982658360423574360-47911124480483585480-5991724600593616At most399305! 0oneaircraftFigure 7. Time-history of histograms of number of sectors grouped in six peak average-airspeed levels in Table16using ACES data.Table16.Numbers of sectors with maximum average-airspeed (knots) using ACES data.Figure8.Time-history of histograms of number of sectors grouped in five peak airspeed-variance levels in Table17using ACES data.
+Table 17 .17Numbers of sectors with maximum airspeed-variance (knots 2 ) using ACES data.Level Max.Min. Mean Max. Threshold CumulativeCumulativeAirspeed-LowerUppervarianceRange10-29331373273033327230-592513318760197358360-8957413890326364490-119018371203613645120-140013141364364
+Table 18 .18Sector geometry metrics.NumberMetricCategory1Number of sectors in centersGeographical2Number of sectors in eight airGeographicaltraffic control regions3Sector-type: low, high or super-Geographicalhigh4Sector volumeDimensions5Sector heightDimensions6Sector areaDimensions7Sector lengthDimensions8Aspect ratio -length/widthShape9Principal directionShape10Number of subsectorsShape11Number of navaidsRoute12Number of intersectionsRoute13Number of airwaysRoute14Number of airportsRoute15Special Use Airspace completelyRouteinside16Number of surrounding sectorsNeighborhood17Special Use Airspace containedNeighborhoodpartially18Distance to closest major airportNeighborhood
+Table 19 .19Numbers of sectors in centers.Center ID ZAB ZAU ZBW ZDC ZDV ZFW ZHU ZIDZJX ZKC# sectors23251319281817252027Center ID ZLA ZLC ZMA ZME ZMP ZNY ZOA ZOB ZSEZTL# sectors168621181011271022
+Table 20 .20Centers in eight regions.RegionsCode Centers# sectorsCentral RegionACEZKC27Eastern RegionAEA ZNY, ZDC29Great Lakes RegionAGL ZMP, ZAU, ZID, ZOB95New England RegionANE ZBW13Northwest Mountain Region ANM ZSE, ZLC, ZDV46Southern RegionASOZME, ZTL, ZJX, ZMA69Southwest RegionASW ZAB, ZFW, ZHU58Western Pacific RegionAWP ZOA, ZLA27
+Table 21 .21Summary of sector dimensions.MetricMinimum MeanMaximum Std. Dev.Volume (cubic-1,909 77,775572, 21086,034nautical-mile)Height (feet)4,000 47,54182,85030,164Area (square-1,6789,39745,7786,369nautical-mile)Length (nautical-419321428mile)
+Table 22 .22Numbers of subsectors in sectors.# Subsectors# Sectors1224297322465563718391121131
+Table 23 .23Numbers of 74 ASPM airports in sectors.# Airports# Sectors02811672103551
+Table 24 .24Number of SUAs contained partially in sectors.# SUAs# Sectors# SUAs# Sectors0268711318222291312101481115812363191
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+
+			
+AcknowledgementsThe authors thank Dr. Hak-Tae Lee of University of California Santa Cruz for generalizing the moment of inertia equations from two-dimensional polygons to three-dimensional polygonal prisms and providing the Java program that was used for computing the principal directions and aspect ratio of sectors.We also thank Christopher Gleim from the University of Louisville who as a part of his internship task wrote the computer program for the Special Use Airspace analysis discussed in this paper.
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+
+			
+
+				
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+	
+		Initial Concepts for Dynamic Airspace Configuration
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+			BanavarSridhar
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+		10.2514/6.2007-7763
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+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
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+	Kopardekar, P., Bilimoria, K., and Sridhar, B., "Initial Concepts for Dynamic Airspace Configuration," Proceedings of 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Belfast, Northern Ireland, September 18-20, 2007.
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+			GanoChatterji
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+			AlexandreBayen
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+		AIAA Guidance, Navigation and Control Conference and Exhibit
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+			August 20-23, 2007
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+	Martinez, S., Chatterji, G. B., Sun, D., and Bayen, A., "A Weighted-Graph Approach for Dynamic Airspace Configuration," Proceedings of AIAA Guidance, Navigation, and Control Conference, Hilton Head, SC, August 20-23, 2007.
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+	Pawlak, W. S., Goel, V., Rothenberg, D. B., and Brinton, C. R., "Comparison of Algorithms for the Dynamic Resectorization of Airspace," Proceedings of AIAA Guidance, Navigation, and Control Conference, Boston, MA, August 10-12, 1998.
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+	Basu A., Mitchell, J. S. B., and Sabhnani, G., "Geometric Algorithms for Optimal Design and Air Traffic Controller Workload Balancing," 16 th Fall Workshop on Computational and Combinatorial Geometry, Northampton, MA, November 10- 11, 2006.
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+	Yousefi, A., Donohue, G. L., and Qureshi, K. M., "Investigation of En route Metrics for Model Validation and Airspace Design Using the Total Airport and Airspace Modeler (TAAM)," Proceedings of the 5 th Eurocontrol/FAA ATM R&D Conference, Budapest, Hungary, June 23-27, 2003.
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+	Chatterji, G. B., and Sridhar, B., "Measures for Air Traffic Controller Workload Prediction," Proceedings of AIAA Aircraft, Technology, Integration, and Operations Forum, Los Angeles, CA, October 16-18, 2001.
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+		Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis
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+	Kopardekar, P., et al, "Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis," Proceedings of 7th USA/Europe ATM R&D Seminar, Barcelona, Spain, July 2-5, 2007.
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+	Report
+	Goldman, S., Manning, C., and Pfleiderer, E., "Static Sector Characteristics and Operational Errors," Report No.
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+		Federal Aviation Administration: 800 Independence Ave. S.W., Washington, DC 20591: Internet:
+		
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+		
+		10.4135/9781483384757.n93
+		AM-06/4
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+		Federal Regulatory Directory: The Essential Guide to the History, Organization, and Impact of U.S. Federal Regulation
+		Washington, DC
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+	DOT/FAA/AM-06/4, Office of Aerospace Medicine, Federal Aviation Administration, 800 Independence Ave., S. W., Washington, DC 20591, March, 2006.
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+		Analysis of ETMS Data Quality for Traffic Flow Management Decisions
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+			GanoChatterji
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+			BanavarSridhar
+		
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+			DouglasKim
+		
+		10.2514/6.2003-5626
+		ASDI-FD-001
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, TX
+		
+			American Institute of Aeronautics and Astronautics
+			August 11-14, 2003. August 4, 2000
+		
+		
+			Volpe National Transportation Systems Center
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+	Analysis of ETMS Data Quality for Traffic Flow Management Decisions. Version 4.0, Report No. Automation Applications Division, DTS-56, 55 Broadway Street, Cambridge, MA 02142
+	Chatterji, G. B., Sridhar, S., Kim, D., "Analysis of ETMS Data Quality for Traffic Flow Management Decisions," AIAA- 2003-5626, Proceedings of AIAA Guidance, Navigation, and Control Conference, Austin, TX, August 11-14, 2003. 13 Volpe National Transportation Systems Center, "Aircraft Situation Display To Industry: Functional Description and Interface Control Document," Version 4.0, Report No. ASDI-FD-001, Volpe National Transportation Systems Center, Automation Applications Division, DTS-56, 55 Broadway Street, Cambridge, MA 02142, August 4, 2000.
+
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+	
+		Description and Analysis of a High Fidelity Airspace Model for the Airspace Concept Evaluation System
+		
+			ScottSahlman
+		
+		10.2514/6.2007-6877
+		
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			August 20-23, 2007. 14 July 2008
+		
+	
+	AIAA-2007-6877
+	Sahlman, S., "Description and Analysis of a High Fidelity Airspace Model for the Airspace Concept Evaluation System," AIAA-2007-6877, Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, Hilton Head, SC, August 20-23, 2007. 15 URL: http://www.mysql.com/[cited 14 July 2008].
+
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+	
+		Build 4 of the Airspace Concept Evaluation System
+		
+			LMeyn
+		
+	
+	
+		Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit
+		AIAA Modeling and Simulation Technologies Conference and ExhibitKeystone, Colorado
+		
+			August 21-24, 2006
+		
+	
+	AIAA-2006-6110
+	16 Meyn, L., et al, "Build 4 of the Airspace Concept Evaluation System," AIAA-2006-6110, Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, Keystone, Colorado, August 21-24, 2006.
+
+
+
+	
+		Validating the Airspace Concept Evaluation System for Different Weather Days
+		
+			ShannonZelinski
+		
+		
+			LarryMeyn
+		
+		10.2514/6.2006-6115
+		AIAA 2006-6115
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		San Francisco, CA; Keystone, CO
+		
+			American Institute of Aeronautics and Astronautics
+			August 15-18, 2005 18 Zelinski,. August 21-24, 2006
+		
+	
+	Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit
+	Zelinski, S. J., "Validating The Airspace Concept Evaluation System Using Real World Data," AIAA 2005-6491, Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, San Francisco, CA, August 15-18, 2005 18 Zelinski, S. J., and Meyn, L., "Validating The Airspace Concept Evaluation System For Different Weather Days," AIAA 2006-6115, Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, Keystone, CO, August 21-24, 2006.
+
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+		Impact of Airport Capacity Constraints on National Airspace System Delays
+		
+			GanoChatterji
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+			YunZheng
+		
+		10.2514/6.2007-7712
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+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			September 18-20, 2007
+		
+	
+	AIAA-2007-7712
+	Chatterji, G. B., and Zheng, Y., "Impact of Airport Capacity Constraints on National Airspace System Delays," AIAA- 2007-7712, Proceedings of 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Belfast, Northern Ireland, September 18-20, 2007.
+
+
+
+	
+		Automating the Process of Terminal Area Node-Link Model Generation
+		
+			Hak-TaeLee
+		
+		
+			ThomasRomer
+		
+		10.2514/6.2008-7101
+		
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Honolulu, Hawaii
+		
+			American Institute of Aeronautics and Astronautics
+			August 18-21, 2008. 14 July 2008
+		
+	
+	Lee, H., and Romer T. F., "Automating the Process of Terminal Area Node-Link Model Generation," Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, Honolulu, Hawaii, August 18-21, 2008. 21 URL: http://aspm.faa.gov/aspm/entryASPM.asp [cited 14 July 2008]
+
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+				
+			
+		
+	
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+II. ApproachThe DWC candidates are shown in Table 1.DWC1 and DWC2 are the two primary candidates, both achieving a desirable unmitigated collision risk of 5%.Unmitigated collision risk is the likelihood that two aircraft would violate the DAA Well Clear definition if neither aircraft used a DAA system.DWC1 achieves the minimum maneuver initial range (MIR), which is the range between aircraft when the UAS must start maneuvering away in order to maintain DWC, whereas DWC2 is simple because it does not have a time component.DWC3 and DWC4 are backup candidates carried forward from previous analysis in case DWC1 and DWC2 do not perform well in certain categories of metrics.DWC3 achieves a "risky" unmitigated collision risk of 7%.It was once proposed for terminal area UAS operations.DWC4 achieves an unmitigated collision risk between 3.5-4% and was considered a "safer" candidate that achieves an unmitigated collision risk of less than 5%.In addition to the four DWC candidates selected in [5], the Phase 1 DWC definition was also evaluated for comparison.The DWC candidates are defined by thresholds of three parameters: horizontal miss distance (HMD), vertical separation threshold (h), and   .HMD*, h*, and   * are the specific values of these parameters used to define each DWC candidate.The HMD parameter is the predicted minimum horizontal distance during an encounter, assuming constant velocities and straight line flight.The h parameter is the current altitude difference between the two aircraft.For all candidates, h* is 450 ft, which is equal to the component used in the Phase 1 MOPS.The definition of   is𝜏 𝑚𝑜𝑑 = { - 𝑟 2 -𝐷 𝑚𝑜𝑑 2 𝑟𝑟̇, >   , 0,  ≤   (1) where  and ̇ are horizontal range and range rate, respectively, between the UAS and the intruder.  is the distance modification and defines the radius of a cylinder around the UAS.In this analysis,   is set equal to the horizontal miss distance threshold, HMD *, in the DWC definitions.A loss of DWC (LoDWC) occurs when all three parameters, computed from aircraft states, fall below their respective thresholds.One million uncorrelated encounters between one low-C-SWaP UAS and one non-cooperative intruder, and one million uncorrelated encounters between one Phase 1 UAS and one non-cooperative intruder were simulated.Uncorrelated encounters are situations where intervention from ATC is unlikely, such that aircraft blunder into close proximity.The UAS trajectory is sampled from NASA's Airspace Concept Evaluation System (ACES) UAS database [6], and the intruder trajectory is sampled from Lincoln Laboratory's Uncorrelated Encounter Model [7].Next, the encounters are simulated using the Detect and AvoID Alerting Logic for Unmanned Systems (DAIDALUS) as the DAA alerting and guidance algorithm [8].DAIDALUS is a reference algorithm used to validate the Phase 1 MOPS, and was used in this study as a representative DAA algorithm.DAIDALUS generates maneuver guidance by projecting candidate vertical and horizontal DAA maneuvers to determine which would result in conflicts, and which can be used to aid the pilot in resolving the situation manually.The evaluation for each DWC consisted of two scenarios: one nominal (or unmitigated) and one mitigated.DAIDALUS issues three types of alerts in increasing levels of severity: preventive, corrective, and warning.The lowest level, preventive, is primarily used to alert the pilot to not maneuver vertically when the aircraft are separated vertically by 450-700 feet.The second level, corrective, indicates that a LoDWC is predicted and an avoidance maneuver is necessary, but there is still time for coordination with ATC.The highest level, warning, indicates that a LoDWC is imminent, an immediate avoidance maneuver is needed, and coordination with ATC before maneuvering is not a requirement.Upon alerting, DAIDALUS generates corresponding preventive, corrective, and warning guidance indicating a range of conflict-free headings and altitudes for a pilot to select from in order to maintain DWC separation.In the event that a LoDWC cannot be avoided, DAIDALUS also generates regain DWC guidance, a range of heading or altitude that can be executed to increase separation at CPA and regain DWC effectively.DAIDALUS alerts are issued based on a buffered DWC volume.Specifically, the HMD* used by the DAA alerting and guidance algorithm is scaled by a factor of 1.52 to be consistent with the parameters referenced in the Phase 1 MOPS [3].This buffer is meant to guard against maneuvering intruders and surveillance uncertainties (there are none in this work).In the simulation, the ownship only maneuvers when a corrective or warning alert is received.For maneuver guidance computation, the low C-SWaP UAS turn rate was assumed to be 7 deg/sec, which is suitable for UAS speeds from 40 to 100 kts and results in approximately the same bank angle and load factor as the Phase 1 UAS turn rate, which was assumed to be 3 deg/sec.The simulations of low C-SWaP UAS were performed using perfect (truth) surveillance, i.e., with no track uncertainty or range limitations, in order to evaluate the mitigated performance of low C-SWaP without confounding factors (such as limited surveillance range or sensor noise).However, to assess the potential impacts of limited detection ranges on safety, simulations were also run with 2 NM, 3 NM, and 4 NM surveillance ranges applied; the results of these simulations are presented in Section III.The simulations of Phase 1 UAS were run with truth surveillance (i.e., no track uncertainty) constrained by a Phase 1 radar field, defined as ±8 NM range, ±15° elevation, and ±110° azimuth.The reason for using the Phase 1 radar surveillance volume is to faciliate comparison to a previous study of the Phase 1 UAS [9].Upon completion of the simulations, metrics are computed to compare the performance of the DWC definitions.
+A. MetricsThe safety and operational suitability metrics provide an indication of whether the system will be able to operate safely without interfering with the operations of other aircraft and without causing DAIDALUS to alert unnecessarily.These metrics and their formulation are shown in Table 2 and Table 3.A Near Mid-Air Collision (NMAC) occurs when the separation between two aircraft is less than 500 ft horizontally and 100 ft vertically [10].If the ratio is less than one, then the mitigated system reduces the risk of NMAC.For example, a risk ratio of 0.1 indicates a 90% reduction in risk.Small values are desirable.LoDWC Ratio (|, ℎ ) (|, ℎ ) Similar to the NMAC risk ratio, if the LoDWC risk ratio is less than one, then the mitigated system reduces the risk of LoDWC.Small values are desirable.Given the same risk ratio, systems with lower alert ratios are desirable, since fewer alerts indicate fewer unnecessary maneuvers.
+Alerting time and range relative to the LoDWC pointThis metric can help inform alerting timeline requirements, and thus, sensor range requirements.
+B. EncountersThis study assessed two sets of encounters: one set with encounters between one low C-SWaP UAS and one noncooperative intruder, and the other set with encounters between one Phase 1 UAS and one non-cooperative intruder.All encounters contains 1 Hz aircraft states, and a majority of encounters last 180 seconds.These encounters were generated by pairing one projected UAS trajectory generated by NASA's UAS mission flights, and one intruder trajectory sampled from MIT Lincoln Laboratory's Uncorrelated Encounter Model [7].NASA's UAS mission flights consist of 19 different types of missions, including aerial imaging and mapping, law enforcement, and air quality monitoring.The demand and mission profiles were generated based on subject matter experts' opinions and socioeconomic analysis [11].The trajectories cover the entire continental US.These aircraft models are defined in a way similar to those in the Eurocontrol Base of Aircraft Data (BADA) [12].In the low C-SWaP encounter set, two types of UAS were considered: the RA-7 AAI Shadow B and the MQ-19 AAI Aerosonde.The Phase 1 encounter set includes trajectories for seven types of UAS: Cessna 208 Caravan, Cessna 510 Citation Mustang, AAI Aerosonde, MQ-9A Reaper, RQ-4A Global Hawk, Shadow B, and Socata Trinidad.The Uncorrelated Encounter Model is derived from radar data of observed aircraft operations under visual flight rules (VFR) in the National Airspace System.The model is sampled to produce random aircraft trajectories that are statistically representative of non-cooperative trajectories.Each encounter is specified by the initial positions and orientations of the two aircraft in the simulation and the nominal dynamic maneuvers that may occur leading up to the time of closest approach (TCA).Filters were applied to the ownship and intruder speeds and altitudes to ensure that the dynamics of the sampled trajectories are within the bounds for low C-SWaP UAS or Phase 1 UAS, and the intruders they are expected to encounter.The intruder characteristics from the Uncorrelated Encounter Model are the same in both sets of encounters.The low C-SWaP UAS speeds are constrained to be between 40 and 100 kts, and the Phase 1 UAS speeds are constrained to be between 40 and 250 kts, 50 kts more than permitted by the Phase 1 MOPS so as to explore potential safety issues for faster UASs.The intruder speeds range from 0 to 170 kts, the 95 percentile speed for non-cooperative intruders in the Uncorrelated Encounter Model [7].Intruders with zero speed represent aircraft like helicopters that are hovering.The encounters occur at altitudes between 500 ft above ground level (AGL) and 10,999 ft mean sea level (MSL), in airspace classes E and G.Although Class E only goes up to 10,000 ft when it is adjacent to class B or class C airspace, altitudes up to 10,999 feet were included to represent a few UAS missions that are flown slightly above 10,000 ft.The resulting altitude and speed distributions are shown in Ownship Type
+C. Pilot Response ModelUpon alerting, DAIDALUS provides guidance indicating a range of conflict-free headings and altitudes.The SC-228 standard pilot model created by Lincoln Laboratory [13] is then used to select and execute an appropriate maneuver.Encounters with an alert within the first 5 seconds of the encounter are excluded from the analysis to ensure that the pilot response model is given adequate time to resolve the conflict.Only horizontal maneuvers were executed because vertical maneuvers against non-cooperative intruders are much less robust in most situations due to the uncertainties in non-cooperative sensors' vertical measurements.The pilot response model was executed in deterministic mode, meaning that the ownship always maneuvers horizontally in the direction of the minimum suggested maneuver.In the event that the minimum suggestion is inconclusive, the ownship will turn left, as a preference for left turns was observed in human-in-the-loop experiments [13].For this analysis, the pilot model chooses the minimum heading change suggested by the guidance bands.Although variability was observed in humanin-the loop experiments, the minimum heading change was used in the analysis to isolate the effect of the DAA Well Clear definition from other parameters.After the first alert is received, the pilot response model has a 5 sec initial delay representing the time it takes the pilot to perceive the alert and devise a plan.For corrective alerts, there is an 11 sec ATC coordination time representing the time it takes the pilot to communicate the intended maneuver with ATC and receive approval.The ATC coordination time is then followed by a 3 sec execution delay representing the time it takes the pilot to enter the maneuver command into the control station and transmit this command to the UAS.The ownship may perform multiple maneuvers per encounter to resolve a conflict.The time between pilot response model decisions is determined by the alert state, as shown in Table 4.For example, if the pilot model chooses a maneuver during a warning alert state, then the situation will be reevaluated after 9 seconds (the decision update period), and a different subsequent maneuver can be issued at that time, if needed.All delays and times (e.g., the 11 sec ATC coordination time, 3 sec execution delay, etc.) are the mean values of distributions observed from human-in-the loop studies used to build the pilot response model [13].
+III. ResultsThis section presents the metrics that were evaluated.Although HMD* and   * are not completely independent (because the definition of   * is dependent on HMD*), this study may be able to provide some insight into the effect of HMD* and   * on the metrics.Because DWC1 and DWC3 have the same   * but different HMD*s, HMD* likely causes any difference in metrics between DWC1 and DWC3.Likewise, because DWC1 and DWC2 have similar HMD* but different   *,   * likely causes any difference in metrics between DWC1 and DWC2.Results for low C-SWaP UAS will be presented first (Section A), and results for Phase 1 UAS will be presented in the following subsection (Section B).All results are for mitigated encounters, unless otherwise specified.
+A. Low C-SWaP UAS ResultsA.1 Safety Metrics Fig. 3 shows the NMAC risk ratios (left) and LoDWC ratios (right) for the four DWC candidates.The causes of NMACs after DAA maneuvers include the following:1. Intruder and ownship maneuvers 2. Surveillance volume limitation and sensor uncertainties (none in this simulation) 3. Guidance ineffectiveness or instability of guidance 4. Pilot response unable to keep up with the situation For any specific encounter leading to an NMAC, all causes could have contributed to it.For example, analysis of a few select encounters leading to NMACs indicates an intruder maneuver near the UAS, causing the conflict guidance bands to saturate, leaving no conflict-free heading available.In this situation, the WCR guidance comes up, is executed, but changes turn directions multiple times during the UAS's maneuver.Combined with the pilot response delay, this instability of guidance can cause a chase situation, resulting in the DAA system's failure to avoid an NMAC.The LoDWCs result from similar causes.Fig. 5 puts NMACs into two categories.Unresolved NMAC risk is comprised of encounters that lead to nominal NMACs (i.e., without a DAA system) and which still have NMACs with the DAA system.Induced NMAC is comprised of encounters that do not have nominal NMACs but develop into NMACs with the DAA maneuver in response to DAA guidance.The unresolved and induced LoDWCs are defined in a similar way.An important observation of the NMAC risk ratios shown in Fig. 3 is that they are all fairly small and there is no statistically significant difference among them, even when compared to the Phase 1 DWC, This suggests that, given sufficient surveillance volume (infinite for this simulation) and small surveillance uncertainties (none for this simulation), all candidate DWCs are likely to be acceptable in terms of their resulting DAA performance to avoid NMACs.Interestingly, the Phase 1 DWC does not perform better with its large volume.In reality, finite surveillance volume and sensor uncertainties will increase the NMAC risk ratios.In terms of LoDWC ratio, DAA is unable to avoid LoDWC in about 10% of the encounters.DWC2 has the lowest value of .09but is only marginally lower than DWC1's .10.DWC3, DWC4, and the Phase 1 DWC all have comparable values (0.12).Unresolved risk ratios comprise the majority of the LoDWC counts.Intruder and ownship maneuvers are likely to be the main cause of these unresolved LoDWCs.Adding a buffer to the heading selected by the pilot response model was tested in this study, but showed no improvement.
+Fig. 3 Safety RatiosOne of the trends observed among the encounters with NMACs was that the intruder or ownship had a nominal (scheduled) maneuver late during the encounter.Nominal maneuvers are maneuvers that are part of the original unmitigated encounter.To analyze the impact of this trend, NMAC risk ratios and LoDWC ratios were computed for the subset of encounters where neither the ownship nor intruder has a nominal maneuver within 30 seconds of nominal TCA (Fig. 4), and for the subset of encounters with late maneuvers-i.e., where either the ownship or intruder has a nominal maneuver within 30 seconds of nominal TCA (Fig. 5).Compared to the safety ratios for all encounters (Fig. 3), the safety ratios without maneuvering (Fig. 4) are much lower.Phase 1 now has the highest LoDWC ratio, whereas previously, the LoDWC ratios for all encounters were comparable among DWC3, DWC4, and Phase 1.Since the Phase 1 DWC is the largest and has the longest timeline, it is likely that maneuvers that occurred 30 seconds or more before the TCA contribute a sizable number to the LoDWC risk.
+Fig. 4 Safety Ratios without Ownship or Intruder ManeuveringIn contrast, the NMAC risk ratios and LoDWC ratios with late maneuvers (Fig. 5) are much higher compared to ratios where encounters with maneuvers are excluded.This suggests that one reason the risk ratios for all encounters are comparable is because the risk comes primarily from encounters with late maneuvers (and hence, late alerts), which cannot be mitigated by any DWC.DWC3 is the least robust to late maneuvers with the highest LoDWC ratio, the second-highest NMAC risk ratio, and the most unresolved NMACs.
+Fig. 5 Safety Ratios with Ownship or Intruder Late ManeuveringThe system operating characteristic (Fig. 6) allows simultaneous evaluation of safety and operational suitability.The alert ratio measures the alert frequency relative to the unmitigated NMAC frequency, so it is independent of the encounter definition.Ideally, low values of both metrics are preferred and therefore the closer a system is to the origin the better.HMD* appears to have the largest effect on alert ratio; DWC1 and DWC3 have the same   *, but DWC1 has a larger HMD* and alerts more frequently.DWC3 has the lowest alert ratio because it has the smallest HMD*.
+Fig. 6 System Operating Characteristic for Low C-SWaP Encounters
+A.2. Operational Suitability MetricsAlerting time and range are computed based on the first alert of any level that occurs in an encounter.Alerting time is the projected time to unmitigated LoDWC when the alert occurs.Only encounters that have an unmitigated LoDWC are included in this metric.Fig. 7 shows the cumulative distribution function for alerting time and range.The cumulative distribution function is the probability that alerting time or range will be less than or equal to the values on the x-axis.For example, the alerting range plot shows that 60% of encounters run with DWC4 alert at range of 3 NM or less, and all encounters run with DWC4 alert within 6 NM.Mitigated encounters that, with DAA maneuvers, still result in a LoDWC (dashed lines) have on average later alert times and shorter ranges than all encounters with an alert (solid lines).This suggests that many LoDWCs may be caused by late nominal (non-DAA) maneuvers.Alerting time and range are driven more by   than by HMD (as indicated by the larger difference between DWC1 and DWC2 than between DWC1 and DWC3).DWC2, which has no   , has the earliest alerting time relative to LoDWC and the smallest alerting range.This implies the surveillance range required to provide the alerting timeline for DWC2 is likely smaller than those for other DWCs.
+A.3. Effect of Surveillance Range on Safety MetricsTo assess the potential impact of limited surveillance ranges on safety, NMAC risk ratios and LoDWC ratios were compared for simulations run with a 2 NM, 3 NM, and 4 NM surveillance range limit (shown in Fig. 8).The NMAC risk ratios for DWC 1, 2, and 3 are largely insensitive to reduced surveillance ranges.On the other hand, the NMAC risk ratios for DWC4 and Phase 1 experience large increases when the surveillance range is reduced to 2 NM.For the DWC4 and Phase 1 volumes, the intruder is sometimes not observed until loss of Well Clear has already occurred (particularly during higher speed encounters), and DAIDALUS's regain DWC guidance is likely not as effective in avoiding NMACs as its maintain DWC guidance.The LoDWC risk ratios for DWC1 and DWC2 increase noticeably while the value for DWC3 stays constant at 2 NM.The LoDWC risk ratios for DWC4 and the Phase DWC increase the most because 2 NM is inside of their DWC volume for some encounters.
+B. Phase I UAS ResultsAnalysis of the Phase 1 UAS encounters was performed on a set of one million encounters between one Phase 1 UAS and one non-cooperative intruder.The same DWC volumes used to evaluate the low C-SWaP UAS encounters were used to evaluate the Phase 1 UAS encounters in order to understand the effect of high speed UAS and baseline any additional differences when comparing to Phase 1 results.For the Phase 1 UAS results, truth surveillance data were constrained by the Phase 1 radar field of view, defined as ±8 NM range, ±15° elevation, and ±110° azimuth.
+B.1. Safety MetricsFig. 9 shows the NMAC risk ratios (left) and LoDWC ratios (right).Results similar to those for low C-SWaP UAS are desirable because this would corroborate the notion that the same DWC can be applied to both low C-SWaP UAS and Phase 1 UAS.As with the low C-SWaP UAS results, the NMAC risk ratios and LoDWC ratios are comparable among the DWC candidates.However, the risk ratios are approximately five times larger than the risk ratios for low C-SWaP UAS, and the LoDWC ratios are approximately two times larger than the LoDWC ratios for low C-SWaP UAS.This difference is primarily due to the limited 110° bearing range, which results in undetected intruders and therefore unresolved NMACs and LoDWCs.When the Phase 1 risk ratios and LoDWC ratios are computed without bearing and elevation limitations (as shown in Fig. 10), the results are much closer to the ratios obtained using the low C-SWaP encounter set (Fig. 3).Safety Ratios with Full Field of View Fig. 11 compares the above safety metrics to the alert ratio, providing insight into the potential tradeoff between safety and operational suitability.Like the low C-SWaP encounter set (Fig. 6), HMD has the largest effect on alert ratio; larger volumes result in significantly higher alert ratios, while maintaining similar safety.
+B.3 Effect of Ownship Speed on SafetyThe Phase 1 UAS encounters encompass both low C-SWaP and high-performance aircraft against a VFR intruder.To assess the sensitivity of the safety metrics to ownship speed, the results were binned by maximum ownship speed shown in Table 5.The relative frequency of encounters in each of these bins is shown in Fig. 13.Frequency of Encounters per Bin Fig. 14 shows the risk ratios binned by maximum ownship speed.There are no mitigated NMACs when the ownship aircraft has a maximum speed greater than 200 knots (Speed Bin 4), but this could be caused by the few number of encounters in Speed Bin 4. Likewise, DWC3 appears to induce NMACs for Speed Bin 3, but this is not statistically significant.In general, the risk ratios in Speed Bin 1 are highest, mainly because UAS in Speed Bin 1 are more likely to have overtaking aircraft from the rear, outside the radar's field of view.UAS in speed bins 2, 3, and 4 usually fly faster than the intruder and are therefore less likely to have undetected intruders approach from the rear.
+Fig. 14 NMAC Risk Ratios Binned by SpeedFig. 15 shows the LoDWC ratios binned by maximum ownship speed.For slower aircraft (Speed Bin 1), LoDWC ratios are all comparable.For faster aircraft (Speed Bins 2, 3, 4),   * seems to have a larger effect (low   * leads to lower LoDWC ratios).DWC2, the only DWC with a zero   *, consistently leads to the lowest LoDWC risk ratio.Although not modeled in this work, sensor uncertainties are likely to increase the NMAC and LoDWC risk ratio.For example, a MITRE study [9] yielded a NMAC risk ratio of 0.22 for a class of UAS similar to those in the Speed Bin 1 when taking into account sensor uncertainties (compared to 0.15 without uncertainty in this simulation), and a LoDWC risk ratio of 0.42 for a class of UAS similar to those in the Speed Bin 1 when taking into account sensor uncertainties (compared to 0.28 without uncertainty in this simulation).
+Fig. 15LoDWC Ratios Binned by Speed
+IV. Conclusion and Future WorkThis analysis evaluated four potential Detect-and-Avoid (DAA) Well Clear (DWC) definitions for UAS encountering non-cooperative aircraft using safety and operational suitability metrics.Two sets of encounters were used to evaluate the metrics in the two operational contexts of interest: one with encounters between low C-SWaP UAS and non-cooperative intruders, and another with encounters between Phase 1 UAS and non-cooperative intruders.The low C-SWaP UAS analysis shows that NMAC risk and LoDWC ratios are not sensitive to DWC parameters under perfect surveillance.Furthermore, safety and operational suitability are not dependent on   *; this indicates that   * may not be necessary in a DWC definition.  * affects mainly alerting performance (timing and range), whereas safety and operational suitability are of primary concern for a DAA Well Clear definition for low C-SWaP UAS.Because   requires a larger tracking range without providing any additional safety or operational suitability benefit, the results give preference to DWC2, which has no temporal parameter.The Phase 1 UAS analysis results generally follow the same trends as the low C-SWaP UAS analysis results.In terms of safety, with no radar field of view applied, the risk ratio and LoDWC ratios for Phase 1 UAS are comparable to those for low C-SWaP UAS.This seems to support the hypothesis that the final non-cooperative DWC definition for low C-SWaP UAS is also applicable to Phase 1 UAS.With the Phase 1 radar field of view applied, NMAC and LoDWC risk ratios increase noticeably due to a large number of undetected intruders approaching from the rear of the UAS.The Phase 1 UAS analysis results were also binned by maximum ownship speed.As ownship speed increases, the LoDWC risk ratios seem to be driven more by   * than by HMD*.A smaller   * seems to have little effect on safety at low speeds and also reduces the safety ratios at high speeds.This again corroborates the result that   is not needed in a definition for low C-SWaP UAS and Phase 1 UAS against non-cooperative intruders.Based on the findings presented in this paper as well as a companion paper [15], SC-228 has selected DWC2 (2200 ft, 450 ft, 0   *) for low C-SWaP UAS and Phase 1 UAS encountering non-cooperative aircraft.The following tasks can be considered as follow-on efforts to this analysis:•Fig
+Fig. 77Fig. 7 Alerting Time and Range.Solid lines are all encounters.Dashed lines are encounters with LoDWC.
+Fig. 88Fig. 8 Safety Ratios for Limited Surveillance Ranges
+Fig. 99Fig. 9 Safety Ratios
+Fig. 11 System11Fig. 11 System Operating Characteristic for Phase 1 Encounters B.2 Operational Suitability Metrics Fig. 12 illustrates the time of alert, prior to unmitigated LoDWC (left) and range at time of first alert (right).The time of alerts has a noticeable negative portion (the non-zero cumulative frequency at 0 alert time) because many intruders enter the DWC volume undetected by the limited surveillance volume.As expected, the larger Phase 1 volume alerts sooner and at larger separations.Similar trends are seen compared to the low C-SWaP UAS analysis (Fig. 7); again, alerting time and range are driven more by   than HMD.DWC2, which has no   , has the earliest alerting time relative to LoDWC and the smallest alerting range.
+Fig. 12 Alerting12Fig. 12 Alerting Time and Range.Solid lines are all encounters.Dashed lines are encounters with LoDWC.
+Fig. 1313Fig. 13Frequency of Encounters per Bin
+Additional safety analyses using DWC2, and potentially test lower HMD values with 0   to see if the DAA Well Clear definition can be further reduced • Access the effects of intruder speed or relative speed on safety performance • Development and validation of low C-SWaP sensor requirements, and associated guidance and alerting • Human factors evaluation of low C-SWaP DWC to validate the alerting timeline, and understand ATC response to and pilot acceptability of the low C-SWaP DWC.
+
+
+
+Table 11DWC CandidatesDWC1DWC2DWC3DWC4Phase 1HMD*2000 ft2200 ft1500 ft2500 ft4000 fth*450 ft450 ft450 ft450 ft450 ft𝜏 𝑚𝑜𝑑 *15 s0 s15 s25 s35 s
+Table 22Safety Metrics
+Table 3 Operational Suitability Metrics3MetricNotesAlert Ratio𝑃(𝐴𝑙𝑒𝑟𝑡|𝑒𝑛𝑐𝑜𝑢𝑛𝑡𝑒𝑟, 𝑤𝑖𝑡ℎ 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛)𝑃(𝑁𝑀𝐴𝐶|𝑒𝑛𝑐𝑜𝑢𝑛𝑡𝑒𝑟, 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑚𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛)
+Table 4 Pilot Response Model Decision Update Times4Alert ConditionDecision Update Period (s)No Alert24Preventive Alert15Corrective Alert9Warning Alert9Regain DAA Well Clear Guidance3
+Table 5 -Ownship Speed Bins5BinMaximum Ownship Speed Range140 -100 knots2100 -150 knots3150 -200 knots4200+ knots
+		
+		
+
+			
+This material is based upon work supported by the National Aeronautics and Space Administration under Air Force Contract No. FA8702-15-D-0001.Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Aeronautics and Space Administration.areas.These standards are documented in the Phase 1 Minimum Operational Performance Standards (MOPS) for DAA systems [3] and air-to-air radar [4] published by the RTCA Special Committee 228 (SC-228) in 2017, as well as the corresponding Technical Standard Orders (TSO), TSO-C211 and TSO-C212 published by the Federal Aviation Administration (FAA) in October 2017.The UAS in the Phase 1 MOPS are assumed to be equipped with Automatic Dependent Surveillance-Broadcast (ADS-B)
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+I. Introduction3][4][5][6] The largest environmental impacts for enroute air traffic comes from emissions of carbon-dioxide and nitrogen-oxides, and persistent contrail formations.It has been shown that commercial aircraft can reduce climate impact due to these factors by modifying their trajectories, although this often comes at the cost of increased fuel consumption. 7Such an increase in fuel consumption represents an increase in the operational cost incurred by an airline.The three largest environmental impacts for enroute air traffic include direct emissions of greenhouse gases such as carbon dioxide (CO 2 ), emissions of nitrogen oxides (NO X ), and persistent contrails.CO 2 and NO X emissions are a function of fuel burn therefore reducing fuel consumption results in emissions reductions.Various procedures have been proposed in the past to reduce the persistent contrail formation, including promising approaches based on changing aircraft flight altitudes.Mannstein 8 proposed a strategy to reduce the climate impact of contrails significantly by only small changes in individual flight altitude.Williams 9, 10 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restriction policies.However, these restrictions generally imply more fuel burn, thus more emissions.Sridhar, 11 Chen, 12 and Wei 13 proposed contrail reduction strategies by altering an aircraft's cruising altitude in a fuel-efficient way, but these strategies did not address the environmental impact from aircraft emissions.Recently, the Absolute Global Temperature Potential (AGTP), a climate assessment metric that adapts a linear system for modeling the global temperature response to aviation emissions and contrails, was introduced in Ref. 14  and 15 to study the combined effect of CO 2 emissions and contrail formation on the reduction strategies.Chen et al. 7 evaluate both the reduction in environmental cost and the increase in operational costs for the climate reduction strategy by applying the same flight altitude change for all aircraft in each of the twenty U.S. Air Traffic Control Centers.A detailed climate reduction method for individual aircraft was not addressed in that study.This paper follows the research reported in Ref. 7 and develops an climate impact reduction algorithm for individual aircraft.The goal of this work is to identify flights for which the environmental cost of climate impact reduction outweighs the increase in operational cost on an individual aircraft basis.To determine this, the changes in cost imposed upon both the airlines and society are considered using the cost of fuel and the social cost of carbon [16][17][18] by developing a trajectory modification algorithm that modifies individual aircraft trajectories.A trajectory modification algorithm has been developed to minimize the aircraft environmental cost by reducing AGTP 14,15,19 due to both contrails and CO 2 emissions.The increase in fuel consumption that leads to higher fuel costs imposed on airlines is also computed by the algorithm.This research aims to identify flights that yield the most environmental benefit for the least operational cost from climate impact reduction strategies.The remainder of the paper is organized as follows.Section II provides a description of the climate impact model, the cost model, and the climate impact reduction method.Next, Section III shows the results and analyses of climate impact reductions for flights from eight different airlines.Finally, Section IV presents a summary and conclusions.
+II. Models and Methods
+II.A. Climate Impact ModelThe climate response to aviation emission and contrails can be modeled as outputs from a series of linear dynamic systems.The carbon cycle models describe the changes to the CO 2 concentration due to the transport and absorption of CO 2 by the land mass and various ocean layers.The Radiative Forcing (RF) for CO 2 emissions is made of a steady-state component and three exponentially decaying components. 20ontrails occur at different regions of the earth and add non-uniform sources of energy to the atmosphere.The latest estimates indicate that contrails caused by aircraft may be causing more climate warming today than all the residual CO 2 emitted by aircraft. 21The net RF for contrails includes the effect of trapping outgoing longwave radiation from the Earth and that of reflecting incoming shortwave radiation from the sun.Energy Forcing (EF) is the net energy flux induced to the atmosphere by a unit length of contrail over its lifetime.Estimates of EF given the RF forcing due to contrails are described in Ref. 22.The EF is expressed as joules/km of contrails.NO X increases the amount of ozone in the atmosphere while decreasing the amount of methane in the atmosphere.The amount of ozone produced depends on the lifetime of NO X that varies from days to weeks in the upper troposphere.The RF associated with NO X is made up of short-lived positive RF due to ozone and a negative RF due to methane and methane-induced ozone and the combined effect results in a net RF due to NO X . 23Research in Ref. 6 shows NO X has relatively small effect for the climate reduction strategies compared to CO 2 and contrails, therefore its effect is ignored in this paper.The lifetime associated with different emissions and contrails varies from a few hours to several hundred years.The impact of certain gases depends on the amount and location of the emission, and the decisionmaking horizon, H in years, when the impact is estimated.These variations make it necessary to develop a common yardstick to measure the impact of various gases.Several climate metrics have been developed to assess the impact of the aviation emissions. 24Using linear climate response models, the Absolute Global Temperature Potential (AGTP) measures the mean surface temperature change because of different aircraft emissions and persistent contrail formations. 19AGTP provides a way to express the combined environmental cost of CO 2 and NO X emissions, and contrails as a function of the fuel cost.For simplicity, the RF due to contrails is assumed to be independent of the location of the contrails.The near surface temperature change ∆T for each flight can be approximated as∆T = ∆T CO2 + ∆T Con ,(1)where ∆T CO2 is the contribution to AGTP from CO 2 emissions in Kelvin (K) and ∆T Con is the contribution to AGTP from contrails in K. ∆T CO2 is a linear function of the additional CO 2 emissions and ∆T Con is a linear function of the contrail formation time.The coefficients of the linear functions, also known as pulse AGTP, depend on the linear models for RF, the specific forcing because of CO 2 , energy forcing because of contrails, energy balance model and the duration of the climate effect horizon. 14Using the coefficients described in Ref. 6, at the time horizon of H, Eq.( 1) can be rewritten as∆T H = AGTP H CO2 E CO2 + AGTP H Con L Con ,(2)where ∆T H is the temperature changes due to both CO 2 and contrails for the time horizon of H in K, AGTP H CO2 is the coefficient of AGTP due to CO 2 for the time horizon of H in K/kg, AGTP H Con is the coefficient of AGTP due to contrails for the time horizon of H in K/km, E CO2 is the amount of CO 2 emissions in kg, and L Con is the contrail length in km.A list of pulse AGTP coefficients used in this paper is shown in Table 1.The details of the fuel burn, emissions, and contrail models are described in Ref. 12.The details of the climate model can be found in Ref. 6.
+II.B. Cost ModelThe total social cost of fuel consumption is comprised of the private cost of paying for fuel, borne by airlines and in turn their passengers, and the external cost of environmental damage, borne by societies, present and future.The social cost of carbon (SCC) is the cost, in monetary terms, to society of emitting an additional metric ton of carbon dioxide.It is often used to determine how much investment should be undertaken in order to mitigate the effects of carbon dioxide emissions.It also represents the theoretical value of a carbon tax for a perfect market.This is particularly suitable because asking or requiring airlines to increase fuel costs to reduce contrail formation would be a form of tax on contrail-induced environmental damage.The United States Government combines results from the three most prominent climate models to determine a suitable measure for the social cost of carbon and recently adopted a value of $36 United States Dollars (USD) in 2007 dollars, which is equivalent to $41 USD in 2013 dollars. 25This is the value used for the purpose of this research.Fuel costs historically represent as much as 33% of aircraft operating costs with an increasing trend.The fuel cost for individual flights are likely to increase if otherwise-quasi-optimal trajectories are modified in a way that is detrimental to fuel efficiency so as to avoid contrail favorable regions.For the purpose of this work, the price of jet fuel of $4 USD per US gallon was used in this paper.The social cost of carbon can be used to quantify the environmental cost of CO 2 emission.Using the social cost of carbon dioxide as an estimate of environmental cost of CO 2 , the additional contribution to environmental cost from CO 2 emissions, ∆Cost CO2 , can be formulated as∆Cost CO2 = SCC • ∆E CO2 1000 ,(3)where SCC is the social cost of carbon in dollar per metric ton, and ∆E CO2 is the change in CO 2 emissions in kg.In order to quantify the environmental cost of contrails, the environmental cost of temperature changes, specifically one Kelvin of AGTP, was defined using the SCC and the AGTP coefficient of CO 2 for time horizon H years,ECK H = SCC 1000 • AGTP H CO2 ,(4)where ECK H is the equivalent environmental cost of temperature change in dollars per Kelvin for the time horizon of H years. Assume that the surface temperature is reduced after the climate impact reduction (∆T H < 0), the total environmental cost reduction ∆Cost H Env can be formulated as∆Cost H Env = ECK • (-∆T H ). (5)Note that ∆Cost H Env is postive after the climate impact reduction.The environmental net benefit, N B H Env , is defined asN B H Env = ∆Cost H Env -∆Cost Opr ,(6)where ∆Cost Opr is the additional operational cost of applying the climate impact reduction.Only the cost of additional fuel burn is considered as additional operational cost in this paper.If the environmental cost reduction ∆Cost H Env is greater than the additional operational cost ∆Cost Opr , the environmental net benefit N B H Env is positive.
+II.C. Climate Impact ReductionA preliminary trajectory modification algorithm has been developed.The goal of the algorithm is to reduce the total AGTP effect of a flight by modifying its trajectory.Previous study in Ref. 7 shows that the climate effect can be reduced efficiently by applying the same flight altitude change for all aircraft in each of the twenty U.S. Air Traffic Control Centers.This algorithm follows that concept and focuses on modifying the flight profile for individual aircraft; it allows aircraft to deviate no more than one flight level (2,000 feet) above or below the original flight path.It is assumed airlines choose to fly at, or close to, each aircraft optimal operating conditions and, at least approximately, along the most fuel-efficient trajectory, given the weather and traffic conditions at the time of flight.Aircraft can potentially reduce climate impact by avoiding contrail favorable regions either by climbing to a higher cruise altitude of descending to a lower cruise altitude.For most typical commercial aviation cruise altitudes, flying higher will generally yield higher fuel efficiency.However, flight ceiling and mechanical safety constraints often limit aircraft maximum cruise altitudes.The algorithm evaluates the environmental cost for the period of a flight cruise segment.The total environmental cost is calculated as the combined AGTP effect of CO 2 emissions due to fuel consumption and persistent contrail production caused by flying through contrail regions.The algorithm allows the flight to make one altitude change, meaning climbing 2,000 feet or descending 2,000 feet then returning to the original cruise altitude.The algorithm computes the combined environmental cost and operational cost of all flight segments at the cruise altitude and 2,000 feet above and below it and finds the path that will maximize the environmental net benefit.If this alternative results an environmental net benefit, then the flight path is altered to incorporate this change.Figure 1 shows an example flight modification for one of the flights tested.The grey blocks represent the contrail regions.The contrail regions were computed based on the weather data at the aircraft's take-off time and are assumed to be static during the flight.The blue line is the original flight path and the green line is the new path after modification.As indicated in the figure, the new path tried to avoid the contrail regions by flying 2,000 feet lower than the original flight path.The new path will result in reduction in ∆T Con by avoiding the contrail regions but increase ∆T CO2 due to additional fuel burn at a given time horizon H.The net changes in ∆T H is negative, meaning the net climate impact is reduced after the flight path modification.The environmental cost reduction ∆Cost H Env is increased because of the reduction in ∆T H , and the operational cost ∆Cost Opr is also increased because of the additional fuel burn.The net environmental benefit is the difference of the two costs.If the environmental cost saving is greater than the additional operational cost, it will result in environmental net benefit In reality, it is not possible to know the exact contrail regions to avoid before flying.The forecast data is required to predict the contrail regions so that the algorithm can determine the path to reduce the climate impact.Using actual weather data in the algorithm is like having perfect forecast data, which is not realistic.Figure 2 shows the same example of flight modification with the predicted and actual contrails regions.The grey blocks represent the contrail regions and the black grid blocks represent the predicted contrail regions.The predicted contrail regions were computed based on the one-hour weather forecast data at the aircraft's take-off time for the entire flight.The algorithm modified the flight trajectory based on predicted contrail regions, and use the actual contrail regions to determine the actual environmental cost.The blue line is the original flight path and the green line is the new path after modification.As indicated by the green line, because of the inaccuracy in the forecast data, the flight would fly through some contrail regions then lower the altitude before it reaches the black grid blocks.The flight would also fly back to the original cruise altitude after the predicted contrails regions is clear of contrails but the actual contrails still exist.It would still result in reduction in environmental cost but the benefit would be reduced because of the inaccuracy in the forecast data.
+III. Results
+III.A. Using actual weather dataThe trajectory modification algorithm analyzed 12,787 flights using actual flight track data from the Enhanced Traffic Management System of April 23, 2010.These are all flights carried by one of eight major US airlines that operated the most flights on the day: American Airlines (AAL), America West Airlines (AWE), ExpressJet Airlines (BTA), Delta Airlines (DAL), American Eagle Airlines (EGF), SkyWest Airlines (SKW), Southwest Airlines (SWA), and United Airlines (UAL).The contrail model uses atmospheric temperature and humidity data retrieved from the Rapid Updated Cycle (RUC) data, provided by the National Oceanic and Atmospheric Administration (NOAA).The actual and one-hour forecast data based on the take-off time were used to find the contrail regions along each flight path.The day was selected because there were large portions of US airspace covered by the contrail regions.A time horizon of 25 years was used in the climate model; the study in Ref. 7 shows that the environmental benefit after applying the climate impact reduction strategy for time horizon of 25 years is more significant than for time horizon of 50 and 100 years.Figure 3 shows the additional operational cost (the fuel cost increase in this paper), against the environmental cost reduction using actual weather data for the flights with positive net benefit for each airline, in descending order of total net benefit, after the reduction strategy.Each blue dot represents a flight with net benefit (when the environmental cost reduction is greater than operational cost) greater than zero.From a policy perspective, the most desirable flight modifications reduce the net environmental cost by the most while increasing the fuel cost by the least, as these will result in the greatest net benefit.Graphically, these points can be found in the bottom-right corner of the figure.In the figure, Airlines #1, #2, and #3 have a similar pattern.They show more blue dots at the bottom-right corner, while Airlines #4, #6, #7, and #8 show a similar pattern with less blue dots than the others and most of the dots are on the lower of the left-half side.This is mainly because Airlines #1, #2, and #3 have more long-haul flights that will benefit more from climate impact reduction by avoiding long contrails.Airlines #4, #6, #7, and #8 have more short-haul flights therefore the environmental cost reduction of each flight is smaller.Airlines #4, #6 have more blue dots than Airlines #7, and #8 simply because they have more flights during the day.Airline #5 has many short-haul flights and also some long-haul flights therefore the plot is a mix of the two patterns.These observations are consistent with the findings in Ref. 26.The climate impact reduction algorithm was able to achieve a net benefit for 3,067 of the 12,787 flights (24%).The total net benefit is $843,416, or equivalent to a reduction of around 20,000 tons of carbon emissions.The net benefit per flight is $275.Among the 3,067 flights, there are 77 flights resulting in net benefit greater than $1,000.The total net benefit among the 77 flights is $95,482, or $1240 per flight.These flights could be the most cost-efficient candidates for applying the climate reduction maneuver.The results for each of the eight airlines are summarized in Table 2.In the table, it shows Airline #3 has the highest percentage of flights resulting in net benefit, at 43.1% even though the total net benefit is not the highest.This is because Airline #3 has more long-haul and less short-haul flights than the others.Airlines #1 and #2 are next at 29.0%, then Airline #4 at 24.3%.The other four airlines, which have mostly short-haul flights, have percentages less than 20%.This suggests long-haul flights would be better candidates for climate impact reduction than the short haul flights.In reality, it is not possible to know the exact contrail regions to avoid before flying.In this subsection, the one-hour forecast data based on the flight take-off time were used to predict the contrail regions.The climate reduction algorithm used the predicted contrail regions to modify the flight trajectories and used the actual weather data to compute the environmental cost reductions.Because of the inaccuracy in the forecast data, the performance of the climate reduction algorithm was reduced.Figure 4 shows the same example in Fig. 3 using forecast data for the algorithm.Because of the inaccuracy in the forecast data, it is possible that the flights would fly through some contrail regions or would climb or descend without contrail regions present, therefore resulting in lower environmental net benefit or even negative net benefit.It can be seen in the figure that the blue dots were shifted toward the left side compared to the blue dots in Fig. 3.The red dots represent the flights with negative net benefit, where the increases in the operational costs are larger than the environmental cost reductions.The flights with negative net benefit are the group of flights with small environmental cost reduction using actual data (bottom-left corner in Fig. 3).Only applying the algorithm to flights with large net benefit (blue dots on the right side) would avoid negative net benefits.Using the one-hour forecast data, the climate reduction algorithm was still able to reduce the net benefit for 2,043 of the 12,787 flights (16%) on the selected day; the algorithm identified 2,959 flights for climate reduction and 916 of them ended up with negative net benefit because of the inaccuracy of the forecast data.Also, among the 3,067 flights that could have received net benefit if actual weather data were used, 515 of them were not identified for a maneuver using forecast data.The total environmental net benefit was reduced from $843,416 to $499,256 when using forecast data compared to knowing the actual weather condition, which is a 41% reduction.The net benefit per flight for this one day was $169.The results for each airline are summarized in Table 2. Using weather forecast data, Airlines #1, #2, and #3 still result in the most total net benefits among the eight airlines, mainly because the three airlines have mostly long-haul flights.Airline #3 remains having the most net benefit per flight.As indicated in the table, inaccurate forecast data have significant impact on the performance of the climate reduction algorithm for all airlines.
+IV. ConclusionsA algorithm has been developed that modifies the trajectories of individual flights to evaluate the effect of environmental cost and operational cost of flights in the United States National Airspace System.The algorithm identifies flights of which the environmental cost of climate impact reduction outweighs the increase in operational cost on an individual aircraft basis and modifies their trajectories to achieve the maximum environmental net benefit, which is the difference between the reduction in environmental cost and the additional operational cost.The result shows on a selected day, 24% of the flights can achieve environmental net benefit using actual weather data and 16% of the flights can achieve environmental net benefit using weather forecast data, resulting in net benefit of around $840,000 and $500,000, respectively.It also suggests that the long-haul flights would be better candidates in cost-efficient climate impact reduction than the short haul flights.Future work of this study includes using a more detail contrail model, 27 designing more operational viable routing, and update the actual and forecast weather data along the flights.Figure 1 .1Figure 1.Flight profile (blue line: baseline, green line: after reduction) and contrail regions (grey areas) on April 23, 2010.
+Figure 2 .2Figure 2. Flight profile (blue line: baseline, green line: after reduction), actual contrail regions (grey areas), and predicted contrail regions (black grids) on April 23, 2010.
+Table 1 .1Pulse AGTP values for CO2 and contrails for three different time horizonsTime HorizonH = 10 years H = 25 years H = 100 yearsAGTP H CO2 , K/kg6.0×10 -166.7×10 -165.1×10 -16AGTP H Con , K/km1.5×10 -133.0×10 -145.1×10 -15
+Table 2 .2Number of flights and net benefit (NB) before and after climate reduction algorithm using actual weather dataAirline total flights with NB%total NB NB per flight NB > $1000 total NB#1229066529.0% $202,901$30529$34234#2180152229.0% $147,772$2837$8101#3103544643.1% $141,684$31822$30236#4170641524.3% $105,976$2558$9362#5121223719.6% $69,685$2945$6042#6215934015.7% $63,912$1883$3778#7114122019.3% $58,305$2652$2318#8144322215.4% $53,181$2401$1401Total12787306724.0% $843,416$27577$95482
+Table 3 .3Number of flights and net benefit (NB) before and after climate reduction algorithm using forecast weather data
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-3016
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-3016
+			Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-3016
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-3016
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-3016
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-3016
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+		ex-nomination 24, 27, 31–2; in humanism: Catholic 69; technological French translation 6, 33 70; traditional 73 hyperconformity 2–3 design 81–3, 118–19; Bauhaus 71 dropping out 101–3 Internet 99; cybercops 101; cyberculture and business 9 effraction 90; break and entry 86; see implosion 4, 50, 94–8, 111, 122; and also symbolic exchange consciousness 83; and nationalism Einsteinism 18, 23 103 electricity: light 48–9; and language 49; and implosion 96–7 Japan 1 Eskimos 107–8, 110–11, 116 Jesus 104, 116 Expo ’67 5, 59, 92, 100; Christian j’explique rien 5 Pavilion 104; Québec Pavilion 5, 92 Expo ’92 4 Latin character 44; Gallic 7, 56, 57, 58; extensions of man 68, 85, 90; mediatic Gallicized name 53; opposed to 58 53; outering 12 liberalism 46, 103–4; cool media 105 families 101; human 102; mafia 101; M et M 58 McLuhan’s 56; commune-ist 116 Ma – Ma – Ma – Ma 58–9 figure and ground 21, 26, 35 Mac 53, 54, 58; Macbeth 54; MacBett French McLuhan 1, 2, 20, 76–8, 98; 57; Macheath 54; Big Mac 58 new 77 Le mac 62 Mack 55 galaxies 39, 41–2, 44, 99, 109, 116; McLuhan: Counterblast 118; Du and detribalization 107; Gutenberg cliché à l’archétype 119–20; 4, 14, 18, 26, 42–3, 47, 51, 85, Explorations in Communication 121; galactic shifts 38; galaxie 16; From Cliché to Archetype 119; MacLuhan 56; and tribalism 106 La galaxie Gutenberg 4, 44; The gap in historical experience 8, 91–2, Gutenberg Galaxy 4, 8, 18, 26, 49– 99, 106 50, 99, 107, 109; The Mechanical Gen-X 43, 105 Bride 18, 24–5, 27–9, 31–2, 34, 107; Global Village 4, 94, 100, 107, 111, Letters 15, 21, 55; The Medium is 121; global consciousness 102–3; the Massage 9, 26, 68; Message et and idiocy 12; and nomadology massage 44; Mutations 1990 44; 110–11; and teamness 9 Pour comprendre les médias 44, 87; grammatology 7, 39–41; écriture 37, 39, Through the Vanishing Point 120; 41; and logocentrism 40 Understanding Media 8, 13, 18–19, 23–4, 29, 68, 78, 85, 95; War and happenings 83, 119–20 Peace in the Global Village 16, 26 hemispheres 25 McLuhanacy 3, 84; McLuhanatic 108 McLuhan renaissance 1, 10, 12, 99
+		10.4324/9780203005217-18
+		#3 1035 435 128 65 $90,672 $208 #4 1706 388 87 48 $68,721 $177 #5 1212 215 60 29 $40,522 $188 #6 2159 306 125 69 $27,576 $90 #7 1141 231 67 53 $35,155 $152 #8 1443 213 69 31 $29,358 $138 Total 12787 2959 916 515 $499
+	
+	
+		McLuhan and Baudrillard
+		
+			Routledge
+			
+			901
+			
+		
+	
+	total flights identified flights with neg
+	total flights identified flights with neg. NB missed flights total NB NB per flight #1 2290 674 235 139 $121,351 $180 #2 1801 497 145 81 $85,901 $173 #3 1035 435 128 65 $90,672 $208 #4 1706 388 87 48 $68,721 $177 #5 1212 215 60 29 $40,522 $188 #6 2159 306 125 69 $27,576 $90 #7 1141 231 67 53 $35,155 $152 #8 1443 213 69 31 $29,358 $138 Total 12787 2959 916 515 $499,256 $169
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+	
+		
+			IWaitz
+		
+		
+			JTownsend
+		
+		
+			JCutcher-Gershenfeld
+		
+		
+			EGreitzer
+		
+		
+			JKerrebrock
+		
+		Report to the United States Congress: Aviation and the Environment, A National Vision, Framework for Goals and Recommended Actions
+		London, UK
+		
+			December 2004
+		
+	
+	Tech. rep
+	Partnership for AiR Transportation Noise and Emissions Reduction
+	Waitz, I., Townsend, J., Cutcher-Gershenfeld, J., Greitzer, E., and Kerrebrock, J., "Report to the United States Congress: Aviation and the Environment, A National Vision, Framework for Goals and Recommended Actions," Tech. rep., Partnership for AiR Transportation Noise and Emissions Reduction, London, UK, December 2004.
+
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+	
+		Potential to reduce the climate impact of aviation by flight level changes
+		
+			UlrichSchumann
+		
+		
+			KasparGraf
+		
+		
+			HermannMannstein
+		
+		10.2514/6.2011-3376
+	
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+		3rd AIAA Atmospheric Space Environments Conference
+		Honolulu, HI
+		
+			American Institute of Aeronautics and Astronautics
+			June 2011
+		
+	
+	Schumann, U., Graf, K., and Mannstein, H., "Potential to reduce the climate impact of aviation by flight level changes," 3rd AIAA Atmospheric Space Environments Conference, AIAA, Honolulu, HI, June 2011.
+
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+	
+		Dynamics of Implementation of Mitigating Measures to Reduce Commercial Aviation's Environmental Impacts
+		
+			RahulKar
+		
+		
+			PhilippeBonnefoy
+		
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+			RJohnHansman
+		
+		
+			SgourisSgouridis
+		
+		10.2514/6.2009-6935
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			September 2009
+		
+	
+	Kar, R., Bonnefoy, P., Hansman, R. J., and Sgouridis, S., "Dynamics of Implementation of Mitigating Measures to Re- duce Commercial Aviation's Environmental Impacts," 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), AIAA, Hilton Head, SC, September 2009.
+
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+	
+		Use of Hyperspace Trade Analyses to Evaluate Environmental and Performance Tradeoffs for Cruise and Approach Operations
+		
+			MGregoryO'neill
+		
+		
+			Jean-MarieDumont
+		
+		
+			RJohnHansman
+		
+		10.2514/6.2012-5507
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		Indianapolis, IN
+		
+			American Institute of Aeronautics and Astronautics
+			September 2012
+		
+	
+	O'Neill, M. G., Dumont, J.-M., and Hansman, R. J., "Use of Hyperspace Trade Analyses to Evaluate Environmental and Performance Tradeoffs for Cruise and Approach Operations," 12th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), AIAA, Indianapolis, IN, September 2012.
+
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+
+	
+		Call for Papers
+		
+			BSridhar
+		
+		
+			NYChen
+		
+		
+			HKNg
+		
+		10.1027/2192-0923/a000067
+	
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+		Aviation Psychology and Applied Human Factors
+		Aviation Psychology and Applied Human Factors
+		2192-0923
+		2192-0931
+		
+			4
+			2
+			
+			June 2013
+			Hogrefe Publishing Group
+			Chicago, IL
+		
+	
+	Tenth USA
+	Sridhar, B., Chen, N. Y., and Ng, H. K., "Energy Efficient Contrail Mitigation Strategies for Reducing the Environmental Impact of Aviation," Tenth USA/Europe Air Traffic Management Research and Development Seminar , Chicago, IL, June 2013.
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+		Aircraft Trajectory Design Based on Reducing the Combined Effects of Carbon-Di-Oxide, Oxides of Nitrogen and Contrails
+		
+			BanavarSridhar
+		
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+			NeilYChen
+		
+		
+			HokNg
+		
+		10.2514/6.2014-0807
+	
+	
+		AIAA Modeling and Simulation Technologies Conference
+		National Harbor, MD
+		
+			American Institute of Aeronautics and Astronautics
+			Jan 2014
+		
+	
+	Sridhar, B., Chen, N. Y., and Ng, H. K., "Aircraft Trajectory Design Based on Reducing the Combined Effects of Carbon- Dioxide, Oxides of Nitrogen and Contrails," AIAA Modeling and Simulation Technologies (MST) Conference, National Harbor, MD, Jan 2014.
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+		Evaluating Tradeoff between Environmental Impact and Operational Costs for Enroute Air Traffic
+		
+			NeilYChen
+		
+		
+			BanavarSridhar
+		
+		
+			HokNg
+		
+		
+			JinhuaLi
+		
+		10.2514/6.2014-1464
+	
+	
+		AIAA Guidance, Navigation, and Control Conference
+		National Harbor, MD
+		
+			American Institute of Aeronautics and Astronautics
+			January 2014
+		
+	
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+		
+I. IntroductionT O ENSURE smooth air traffic flow and safety in the presence of disruptions caused by uncertainties, innovative modeling and design methods are needed in traffic flow management.One of the main functions of traffic flow management is to predict and resolve demand-capacity imbalances at the sector level.Thus, an accurate sector prediction model that can account for traffic flow uncertainty and weather impact is an essential component of traffic flow management.Efforts have been made in the past to perform sector-demand predictions.Traditionally, models used in air traffic control and flow management are based on simulating the trajectories of individual aircraft.Deterministic forecasting of sector demand is routinely done within the enhanced traffic management system (ETMS), which relies on the computation of each aircraft's entry and exit times at each sector along the path of flight.Gilbo and Smith [1] proposed, acknowledging the uncertainty in the predictions, a regression model for improving aggregate traffic demand prediction in ETMS.A more recent traffic flow management simulation tool, the Future Automation Concepts Evaluation Tool (FACET) [2], was used to propagate the trajectories of the proposed flights forward in time and use them to count the number of aircraft in each sector for demand forecasting and establish confidence bounds on the forecasts [3].These trajectory-based models predict the behavior of the National Airspace System adequately for short durations of up to 20 min and their accuracy is impacted by weather and trajectory prediction uncertainties [4][5][6].In addition, these prediction models are openloop, which means the traffic flow management (TFM) actions are not accounted in the models; therefore, the prediction does not reflect the actual sector demand after the TFM management actions.The objective of this paper is to develop an empirical sectordemand prediction model that accounts for TFM actions, including air traffic control and airline actions, and that accounts for both shortterm (less than 30 min) and midterm (30 min to 2 h) predictions.The model consists of two parts: the open-loop prediction and the TFM action model.The open-loop predictions, similar to the traditional methods, are used to determine the possibility of demand-capacity imbalances at a future time, and help decide whether to activate the TFM action.The TFM action model simulates the demand reduction caused by the TFM actions.The closed-loop prediction represents the net result of the open-loop prediction and the TFM actions.The periodic autoregressive model and its variants [7,8] were used to build the model.The model considers both historical traffic flows to capture the midterm trend and flows in the near past to capture the transient response.In addition, for severe weather cases, the weatherimpacted TFM action was modeled using weather forecast information.The proposed model provides both open-and closed-loop sector-demand predictions.Open-loop prediction is adequate for short durations.When looking at predictions for long durations, open-loop models produce large errors due to their inability to capture traffic initiatives and airline actions during the planning period.A combination of closed-loop and open-loop models provide decision-makers the full range of traffic behavior.The remainder of the paper is organized as follows.Section II provides the sector-demand data and a description of the open-and closed-loop sector-demand prediction models.Next, in Sec.III, a weather factor is introduced and the TFM action model that considers weather is described.The results and performance of the models are demonstrated in Sec.IV.Finally, a summary and conclusions are presented in Sec.V.
+II. Data and Model
+A. Sector-Demand DataThe air traffic demand data were recorded from the Aircraft Situation Display to Industry (ASDI) data generated by the Federal Aviation Administration's ETMS.The ASDI data provide the locations of all aircraft at 1 min intervals.The sector demand, defined as the number of aircraft in each sector at a given time, can be computed using the ASDI data.Since traffic flow management decisions are made by comparing the peak number of aircraft in a sector during a 15 min interval with the sector's monitor alert parameter (MAP) value, the 15 min peak sector demand was used to build the models.A day is defined as a 24 h interval starting at 0400 hrs local time, since most of the aircraft departing on the previous day would have landed before 0400 hrs.The  The average trend of sector demand on different days can be observed in Fig. 1, which shows the variation of 15 min peak sector demand in September 2007.In this figure, each horizontal strip represents one day of 15 min peak sector demand, and each vertical strip represents the peak sector demand at the same time of day during the entire month.As shown, the horizontal strips on 1 September, 8 September, 15 September, 22 September, and 29 September, which are Saturdays, have lower demands than the others.The blue vertical regions on the left and right show the offpeak traffic in the early morning and the late night.A vertical light blue region at around 1200 hrs divides the sector demand into a morning rush left of the region and an afternoon peak right of it.The sector-demand prediction model presented in the next section captures these variations in the demand.
+B. Demand Prediction ModelSector demand, defined as the number of aircraft in a sector, is the result of planned inflow and outflow and TFM actions.Figure 2a shows the block diagram of the current sector-demand system, where d k is the sector demand at the kth time step and d kp is the sector demand at the (k p)th time step.In the system, the traffic flow manager monitors the sector-demand prediction based on enhanced traffic management system (ETMS), denoted as dETMS kp ; if the prediction is high, TFM is activated to reduce the demand in the sector.The top half of the diagram, shown in the dashed box, is considered as an open loop; the bottom half, with the TFM action, is considered as a feedforward loop with negative gain.In the sectordemand prediction model, shown in Fig. 2b,fd open kp f open k;p d 1 . . . d k e open k d kp d open kp f TFM k;p d open kp e TFM k (1)To implement the prediction model in Eq. ( 1), f open k;p and f TFM k;p need to be identified using historical data.In reality, it is not possible to identify the open-loop sector demand when TFM is in action because of the absence of data to verify the validity of the models during high demand.However, the open-loop model can be identified using data during low demand, since no TFM action is involved.With the assumption that the behavior of open-loop models are similar during low-and high-demand periods, the open-loop prediction model validated for low demand is also used during high demand.
+C. Periodic Autoregressive Sector-Demand ModelAutoregressive models have been used for short-term hourly air traffic delay prediction [9,10].This research extends the delay prediction approach to open-loop sector-demand prediction.The TFM action model is incorporated in the prediction model and can be identified once the open-loop model is identified.A 24 h period, starting at 0400 hrs local time, is divided into 96 15 min intervals.Given the observed 15 min peak sector demands for n days, the sector-demand data matrix is defined as   can then be solved explicitly [11].For high-demand cases, TFM action is active.The action is modeled as a negative linear feedforward gain based on the open-loop prediction and the threshold, formulated asD d 1;d kp k;p d k k;p k;p k;p d k k;p d threshold e k (4)where k;p and k;p are the least-squares solution of Eq. ( 3) using low-demand data, k;p is the feedforward gain, and e k is the error of the model.Note that k;p is equal to zero for low-demand cases.With k;p and k;p known, the least-squares solution of k;p for highdemand cases, denoted as k;p , can be solved explicitly using highdemand data.On a day m other than the n days in the data set, the p-step prediction of the sector demand at the kth time step, dkp;m , based on the observed sector demand, d k;m , can then be expressed as  4) and ( 5) is referred to as the periodic autoregressive (PAR) sector-demand prediction model.dopenAs an example, peak sector-demand data in August 2007 were used to construct the data matrix in Eq. ( 2).Equations ( 3) and ( 4) were used to identify the model parameters k;p , k;p , and k;p , where k 1; . . .; 96 and p 1 for one step, or 15-min-ahead prediction.The peak sector demands on 3 September 2007 were predicted using Eq. ( 5).The prediction results for sector ZID93 are shown in Fig. 3.The black dots represent the sector demand in a 1 min interval, the blue line represents the 15 min peak sector demand, the green line represents the 15-min-ahead sector-demand prediction, and the red line is the MAP value.The root-mean-squared (rms) error between the actual peak sector demand and the 15 min demand prediction for the day is 1.96.The rms error during the hours that most aircraft fly, 0700 to 2300 hrs EDT, is 2.23.The rms error when the demand is great than 50% of the MAP value is 2.63.The model can be extended by using the cumulative sum of the past sector demands as an observation instead of using a single observation d k;m in Eq. ( 5), since the sum includes more information than a single observation and has less noise compared with the single peak sector demand.Following the definition of the sector-demand matrix D in Eq. ( 2), where d k is the kth column of D, the cumulative p-step-ahead sector-demand model at time step k can be described in terms of the cumulative sum of q past sector demands asd
+III. Weather FactorWeather has a big influence on air traffic sector demand and the uncertainty in weather may cause error in the predictions [5,12].If a severe storm blocks a sector or regions near it, the sector capacity may drop dramatically, causing the TFM in action to reduce the sector demand [13,14].A weather factor that models the TFM action on severe weather days in the sector-demand prediction is derived in this section.To model the weather impact on TFM action, an accurate weather forecast product with a high update rate is required.In addition, to capture the impact on all low, high, and superhigh sectors, the storm echo tops information is useful.The weather data used in this paper was provided by the Corridor Integrated Weather System (CIWS) [15], which provides both accurate precipitation and echo tops data and is updated every 5 min.In addition, CIWS provides precipitation and echo tops forecasts at 5 min intervals up to 2 h in the future.The weather factor used to model the TFM action was chosen to be the sector weather index, defined as the percentage of area covered by the storm with precipitation vertically integrated liquid (VIL) level 3 and above.Only storms with the echo tops above the lower boundary of the sector are considered.The sector weather index at time k is formulated asw k A w k A (8)where A is the area of the sector and A w k is the area of the sector covered by storms with the echo tops at or above the lower bound of the sector.The sector weather index is a number between 0 and 1 and is often expressed in terms of a percent in the figures in this paper.Note that if time k is a future time, the weather forecast is used to determine A w k .It is possible to use other definitions of a sector weather index [13,14].Figure 4a shows a snap shot of the CIWS data for the high-altitude sectors at Indianapolis center (ZID) on a severe weather day.The red spots indicate the storms with VIL level 3 and above, and the echo tops at 35,000 ft.As shown in this figure, most of the sector ZID93 is covered by the storm.The sector weather index for ZID93 on 16 August 2007 is shown in the red curve in Fig. 4b.Also shown is the actual sector demand on the same day in the blue curve.Note that the sector weather index is greater than 30% from 1800 to 2000 hrs Eastern Daylight Time (EDT), and the sector demand clearly drops during the same period.Traffic reduction due to weather impact can be modeled in many different ways [16].In this research, the open-loop prediction was first estimated, and then the prediction was adjusted by the TFM action based on the sector weather index.Assume that the TFM action is active when the sector weather factor exceeds w low , and TFM blocks out the entire sector when the weather factor reaches w high .The sector-demand reduction rate is modeled as the power law distribution, 1 w k w low =w high w low , where is the power of the distribution.To reflect the thresholds, the sector weather index in Eq. ( 8) is redefined asw k 8 < : w low if A w k =A w low A w k =A if w low < A w k =A < w high w high if w high A w k =A (9)To model the TFM action on the sector-demand prediction model, the weather forecast is used to compute the predicted sector weather index.Assume at time k, the predicted sector weather index at time k p is w kp , the PAR sector-demand prediction model in Eq. ( 7) can be rewritten as    (10) Using the echo tops information provides a more representative weather index, especially for the high sectors.If there are storms with low echo tops located at some high sectors, the weather might have minimal impact on the sector demand.The sector demand and weather index for sector ZID93 on two different days is shown in Fig. 5.Both days have severe storms, but one has high echo tops, while the other has low echo tops.The sector demands on severe weather days were compared with the average sector demand on the rest of the days in the same month.In Fig. 5a, the sector demand on 16 August 2007 is lower than the average between 1800 and 2000 hrs EDT because of the high weather index during the period, as indicated in Fig. 5c.The blue line in Fig. 5c shows the weather index considering the area covered by storms without the echo tops information, and the red line is the weather index considering the echo tops at 35,000 ft and above.In this case, the two lines are close.This suggests that there are severe storms in the area and most of the echo tops are higher than the lower bound of sector ZID93.On the other hand, on 23 October 2007, there is no demand reduction compared to the average of the other days in October 2007 during 1800 and 2000 hrs EDT, shown in Fig. 5b, even though there are storms in the sector during the period, as shown in Fig. 5d.The red line in Fig. 5d is substantially lower than the blue line, which means even though there are storms in the sector, most the echo tops are lower than the low boundary of the sector and have minor impact on the sector demand.In the next section, the sector weather index refers to the index with the echo tops information.
+IV. ResultsThe sector demands of 25 high and superhigh sectors in ZID were investigated in this research.The sector demands for the month of August 2007 were used to build the PAR sector-demand prediction model, described in Eqs. ( 2) and (4).The time step used in the models is 15 min.Once the parameters were identified, Eq. ( 7) was used to perform the sector-demand prediction for the month of September 2007.Starting from the 15 min prediction model, up to 2 h prediction model were built and evaluated.The results of four superhigh sectors ZID91, ZID92, ZID93, and ZID94, and four high sectors ZID81, ZID82, ZID83, and ZID84 in the southwest region of ZID were presented.The prediction results for the eight sectors are summarized in Table 1.Only the errors from 0700 to 2300 hrs were computed.The results include open-loop predictions on low-demand days, when TFM is inactive, and closed-loop predictions when TFM is activated.Note that the errors of the PAR model are not sensitive to the look ahead time.In general, the errors are larger with longer look ahead time, but only slightly.The errors of the 120 min prediction is 2.97% larger than the 15 min prediction on average.For all the high and superhigh sector in ZID, the results are similar.The errors are between 1.77 and 2.44 for the 15 min prediction, and between 1.82 and 2.56 for the 120 min prediction.Even though the differences between the errors are small, the same trends hold for the majority of sectors tested.When the predicted sector-demands are lower than the demand threshold d threshold , defined as sector MAP value subtracted by 4, the TFM actions are inactive so the model is open-loop.When the predicted demand is higher than d threshold , TFM actions are activated so the closed-loop predictions are computed.Among the sectors tested, the TFM actions in the model are more active in ZID81 and ZID93, as more occurrences of TFM actions were triggered.The prediction errors of open-loop predictions on low-demand days, and closed-loop prediction at ZID81 and ZID93 are summarized in Table 2.The sector-demand prediction for bad weather days uses the weather factor described in the previous section to model the TFM action, formulated in Eqs. ( 9) and ( 10), with w low 0, w high 1, and 1.The days with peak weather factors greater than 30% are considered bad weather days.For the days and sectors tested, there are four cases of severe weather periods: ZID83 on 16 August 2007 between 1600-2200 hrs EDT, ZID93 on 16 August 2007 between 1600-2200 hrs EDT, ZID82 on 21 August 2007 between 0600-1400 hrs EDT, and ZID92 on 21 August 2007 between 0800-1400 EDT, shown in Fig. 6.Since all these cases happened in August 2007, the model is built using data for July 2007.Two types of weatherimpacted TFM action models are built: one uses the actual weather information and the other uses the forecast weather information.Using the actual weather information represents the cases with perfect weather forecast.It is used to evaluate the impact of weather forecast accuracy on the model.The average closed-loop prediction errors of the four severe weather periods in August 2007 are shown in Fig. 7.It is noted that in all four cases, both the model using actual weather information (red dashed line) and the model using forecast weather (green dasheddotted line) produce smaller errors than the open-loop model (blue solid line).The model using forecast weather performs as well as the model using actual weather when the prediction time is small (less than 30 min).However, with longer prediction time (more than 60 min), the performance starts to decay and the errors are closer to the open-loop model.As an example, in Fig. 7b, the closed-loop sector-demand prediction model using actual weather information improves the 15 min prediction over the open-loop model by 36%, the 60 min prediction by 43%, and the 120 min prediction by 41%.For the model using forecast weather, the improvement is 37% for the 15 min prediction, 44% for the 60 min prediction, and down to 23% for the 120 min prediction.This suggests that with longer prediction time, the forecast inaccuracy might effect the performance of the TFM action model, resulting in larger error in the prediction model.open k;p is the open-loop prediction model and dopen kp is the open-loop prediction, which is used to determine whether to activate the TFM action.When dopen kp is high, TFM is active.f TFM k;p is the model of the TFM action based on the open-loop prediction.d open k is the actual open-loop sector demand, which is the sum of dopen kp and the open-loop prediction error,e open k .e TFM k is the error of the TFM action model.The model in Fig. 2b can be formulated as
+Fig. 1 Fig. 212Fig. 1 Fifteen-minute peak sector demand at sector ZID93 in September 2007.
+solution of k;p and k;p that minimizes e open k T e open k
+Fig. 33Fig.3Sector demand and peak sector demand at sector ZID93 on 3 September 2007.
+Fig. 44Weather data, sector demand, and weather index on a severe weather day.
+Fig. 55Fig. 5 Sector demand and weather indices with and without echo tops information on 16 August and 23 October 2007.
+15 min peak sector demand, denoted as d k , where k 1; . . .; 96.Presented as Paper 2009-6195 at the AIAA Guidance, Navigation, andControl Conference, Chicago, IL, 10-13 August 2009; received 27 August2009; revision received 2 August 2010; accepted for publication 5 August2010. This material is declared a work of the U.S. Government and is notsubject to copyright protection in the United States. Copies of this paper maybe made for personal or internal use, on condition that the copier pay the$10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 RosewoodDrive, Danvers, MA 01923; include the code 0731-5090/10 and $10.00 incorrespondence with the CCC.Senior Scientist for Air Transportation Systems, Aviation SystemsDivision, Mail Stop 210-10. Fellow AIAA.* Research Aerospace Engineer, Systems Modeling and Optimization Branch, Mail Stop 210-10.Member AIAA.†
+in 2007 Hour of the dayFor lowdemand time periods, TFM is inactive; therefore, open-loop demand is the same as actual demand.A sector-demand threshold d threshold , usually a small number lower than the sector MAP value, is used to define whether the demand is high or low.The demand is classified as high when d threshold > 0 and low when d threshold 0. Consider the sector demands that satisfy d kp;j d threshold , the least-squaresDate ← MAP4567892 6 4. . .1 d 2;1 . . . d 96;1 . . . . . . . . .3 7 5(2)d 1;n d 2;n . . . d 96;nwhere d i;j represents the 15 min peak sector demand at time step i on day j.For September 2007, D has a dimension of 30 by 96, and Fig.1shows the image of the matrix D. Letting d k be the kth column of D, the p-step-ahead open-loop sector-demand prediction model at the kth time step can be described in the form of a first-order periodic autoregressive model:d open kp k;p d k k;p e open k (3)where k;p and k;p are the coefficients that map the sector demand at time k to the open-loop sector demand at time k p.
+10 11 12 13 14 15 16 17 18 19 20 21 22 239/19/3209/59/79/9 9/11159/139/159/17109/199/219/23 9/2559/279/2901230
+and k;p are the coefficients that map the cumulative sector demand at the kth time step to the sector demand at the (k p) th time step, and k;p is the TFM action gain.Once the least-squares solution of coefficients k;p , k;p , and k;p are identified, the p-step prediction of the sector demand at the kth time step for a day m, dkp;m , based on the observed cumulative sector demand,kp k;pX kd i k;pik q1k;pk;pX kd i k;p d thresholde k(6)ik q1where k;p X kd i;mik q1can be expressed asdopen kp;m k;pX kd i;m k;pik q1dkp;mdopen kp;mk;pdopen kp;md threshold(7)
+Table 11Sector-demand prediction errors of the PAR model in September 2007 (the unit is the number of aircraft)SectorAverage prediction rms error from 0700 to 2300 hrs EDTName MAP 15 min 30 min 45 min 60 min 90 min 120 minZID81172.202.302.312.312.292.31ZID82161.771.821.841.771.801.82ZID83161.811.831.841.831.841.85ZID84162.092.132.102.122.102.07ZID91192.342.422.432.392.432.46ZID92171.921.981.951.961.981.99ZID93192.442.552.542.522.592.56ZID94172.192.262.272.232.242.23
+Table 22Open-and closed-loop sector-demand prediction errors of the PAR model in September 2007 (the unit is the number of aircraft)SectorAverage prediction rms error from 0700 to 2300 hrs EDTNameMAPType15 min30 min45 min60 min90 min120 minZID8117Open2.202.302.332.322.302.25ZID8117Closed2.202.302.312.312.292.31ZID9319Open2.392.502.502.502.532.56ZID9319Closed2.442.552.542.522.592.56
+		
+		
+			
+V. ConclusionsA class of periodic autoregressive (PAR) models with management-action-embedded for sector-demand prediction is used for predicting air traffic demand in a sector between 15 min and 2 h in the future.The open-loop model was first identified using lowdemand data, assuming no traffic flow management (TFM) action, then the TFM action model was identified using high-demand data.The closed-loop model is the net result of the open-loop and the TFM action models.The proposed PAR model captures both the midterm trend based on the historical data and the short-term transient response based on the near-past observation.For the sectors tested, the model provides the demand predictions with an average rootmean-squared (rms) error between 1.77 and 2.44 in the 15 min prediction and between 1.82 and 2.56 in the 120 min prediction.The performance of the prediction only decays slightly as the prediction interval is increased from 15 min to 2 h, with an error increase of 2.97%.For the sector-demand prediction in the presence of severe weather, the paper introduced the concept of a weather factor to model the TFM actions.For severe weather days, the model uses the storm precipitation and echo tops to form the TFM action model using the weather factor and then adjusts the open-loop prediction.The model improves the closed-loop sector-demand prediction by as much as 37% for the 15 min prediction, 44% for the 60 min prediction, and 23% for the 120 min prediction on the days and sectors tested.In addition to traditional trajectory-based sector-demand prediction methods that predict only the open-loop behavior of the National Airspace System adequately for short durations of up to 20 min and are vulnerable to weather uncertainties, the managementembedded PAR models provide a reliable short-to midterm (both open-and closed-loop) sector-demand prediction that accounts for non-weather-and weather-impacted TFM actions.A combination of closed-loop and open-loop models provide decision-makers with the full range of traffic behavior.			
+			
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+
+
+	
+		Model Estimates of Traffic Reduction in Storm Impacted En Route Airspace
+		
+			BrianMartin
+		
+		10.2514/6.2007-7889
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 2007
+		
+	
+	Martin, B., "Model Estimates of Traffic Reduction in Storm Impacted En Route Airspace," 7th AIAA Aviation Technology, Integration, and Operations Conference, AIAA Paper 2007-7889, Belfast, Northern Ireland, Sept. 2007.
+
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+				
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+I. IntroductionC ontrails are clouds that are visible trails of water vapor made by the exhaust of aircraft engines.They appear and persist if the aircraft is flying in certain atmospheric conditions.The environmental impact of aircraft-induced persistent contrails has drawn attention in recent years. 1 Persistent contrails reduce incoming solar radiation and outgoing thermal radiation in a way that accumulates heat. 2 The global mean contrail cover observed in 1992 is estimated to double by 2015 and to quadruple by 2050 due to the increase in air traffic. 3Studies suggest that the environmental impact from persistent contrails may be three to four times, 4 or even ten times, 5 larger than that from aviation emissions.Therefore, methods to reduce aircraft-induced persistent contrails need to be explored to minimize the impact on the global environment.Efforts have been made in the past years to identify and reduce persistent contrail production.Gierens 6 and Noppel 7 reviewed various strategies for contrail avoidance including changing engine architecture, enhancing airframe and engine integration, using alternate fuels, and modifying traffic flow management procedures.Among the traffic flow management solutions, Mannstein 8 proposed a strategy to reduce the climate impact of contrails significantly by small changes to each aircraft's flight altitude.Campbell 9 presented a mixed integer programming methodology to optimally reroute aircraft trajectories to avoid the formation of persistent contrails.Both methodologies require a flexible flight plan and onboard contrail detection system.Fichter 10 showed that the global annual mean contrail coverage could be reduced by reducing the cruise altitude.Williams 11,12 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restrictions.These restrictions generally require more fuel burn and add congestion to the already crowded airspace at lower altitudes.The objective of this paper is to derive a class of indices that can identify and predict, up to six hours in advance, regions of airspace with high potential for contrail formation.Traffic and weather forecasts were used to generate the predicted contrail frequency index.The indices are used to identify air traffic control centers and altitudes with high contrail formation activities over the next one to six hours.The method uses actual air traffic data and provides a one-hour temporal resolution of predicted contrail frequency.The results show that the predicted indices are highly correlated with the actual contrail frequencies and have a high success rate in identifying the centers and flight levels with high contrail frequencies over the next one to three hours.The remainder of the paper is organized as follows.Section II provides the descriptions of atmospheric and aircraft data and the contrail model used in this paper.Section III describes contrail frequency index, predicted contrail frequency index, and their use for contrail reduction strategies.The results are demonstrated in Section IV.Finally, a summary and conclusions are presented in Section V.
+II. Atmospheric and Aircraft Data and Contrail ModelThe atmospheric data, contrail model, and aircraft data used to generate the contrail formation frequency are described in this section.
+A. Atmospheric DataContrails can be observed from surface observation data 13 and detected by satellite data. 14Duda 15 has related the observations to numerical weather analysis output and demonstrated that persistent contrail formation can be computed using atmospheric temperature and humidity data retrieved from the Rapid Updated Cycle (RUC) data, provided by the National Oceanic and Atmospheric Administration (NOAA).Contrails can persist when ambient air is supersaturated with respect to ice, which means the environmental relative humidity with respect to ice (RHi) is greater than one hundred percent. 16The RHi can be computed from relative humidity with respect to water (RHw) and temperature, which are available in the RUC data.The one-hour, two-hour, three-hour, and six-hour forecasts and the forty-kilometer (40km) resolution RUC data were used in this paper.The data have a temporal resolution of one hour, a horizontal resolution of 40 kilometers, and isobaric pressure levels from 100 to 1000 hectopascals (hPa) in 25 hPa increments.The vertical range is from 150 hPa to 400 hPa, which are equivalent to 23, 600 feet to 44, 400 feet in the standard atmosphere.As an example, snap shots of temperature and RHw contours at 8AM eastern daylight time (EDT) on August 1, 2007 at a pressure altitude of 250 hPa, or about 34, 100 feet, are shown in Fig. 1.
+B. Contrail ModelContrails are clouds produced by aircraft operating at high altitudes.Persistent contrail formation areas are defined as areas with RHi greater than or equal to 100%.RHi can be computed from RHw and temperature using the saturation vapor pressure coefficients of Alduchov, 17 formulated as RHi = RHw × 6.0612e 18.102T /(249.52+T ) 6.1162e 22.577T /(237.78+T ) ,where T is the temperature in Celsius.The temperature and relative humidity shown in Fig. 1 can be translated to RHi.The 40-km RUC data consist of a grid of (113 × 151) data points at each isobaric pressure level.The altitude level index, l, is defined as l = 1 . . .11 corresponding to isobaric pressure levels at 400, 375, . . ., 150 hPa.The level index, isobaric pressure level, and approximate corresponding flight levels are listed in Table 1.The potential persistent contrail formation matrix (contrail matrix) at hour h at level l is defined asR l h =     r 1,1 r 1,2 . . .    ,(2)where r i,j is 1 if RHi ≥ 100% at grid (i, j), and 0 if RHi < 100%.To indicate the location of the twenty U.S. air traffic control centers in the grid, the center grid matrix is defined asC k =     c 1,    ,(3)where k is the center index corresponding to the twenty continental U.S. air space control centers (see Table 2), and c i,j is one if the grid point is within the center and zero if not.The potential persistent contrail formation coverage ratio (contrail coverage ratio) of one center can be defined by the total area of the contrail regions in the center divided by the area of the center.Assuming all of the grid points have the same area, the contrail coverage ratio of center k at time t at altitude level l can be defined as113 i=1 151 j=1 r i,j c i,j 113 i=1 151 j=1 c i,j ,(4)where r i,j is an element of R l t , and c i,j is an element of C k .As an example, the contrail area at flight level 341 at 8AM EDT on August 1, 2007 is shown in Fig. 2a.The corresponding center contrail coverage ratio, computed from Eq. ( 4), is shown in Fig. 2b.The center contrail coverage ratio indicates the portion of center airspace that would form contrails when aircraft fly through it.When the ratio is zero, there will be no contrail formed in the center.
+C. Aircraft DataContrails form when aircraft fly through a potential contrail formation area.Aircraft locations are needed to determine the contrail formation frequency.The aircraft data used in this paper were extracted from the Federal Aviation Administration's Aircraft Situation Display to Industry (ASDI) data.The ASDI has a sampling rate of one minute.The same grid used in the RUC data was used to generate the aircraft position matrix.The aircraft position matrix is defined asA l t =     a 1,    ,(5)where a i,j is the number of aircraft within grid (i, j) flying closest to altitude level l at time t.The aircraft position matrix indicates the air traffic density in the grid scale at different altitudes.
+III. MethodologyThe concept of contrail frequency index, predicted contrail frequency index, and its use for contrail reduction strategies are described in this section.
+A. Contrail Frequency IndexAs discussed in the previous section, the size and coverage ratio of the persistent contrail formation areas are not sufficient indications of severity of contrail activities.The center contrail frequency index consists of both potential contrail formation area and air traffic information.It is defined as the number of aircraft flying through an area that would form persistent contrails at time t at level l.It is formulated as113 i=1 151 j=1 r i,j c i,j a i,j ,(6)where r i,j , c i,j , and a i,j are defined in Eq. ( 2), (3), and (5).As an example, the center contrail frequency indices at flight level 341 were computed at 8AM EDT on August 1, 2007 and are shown in Fig. 3.Even though in Fig. 2b the contrail coverage ratio of Houston Center is higher than Atlanta Center, in Fig. 3 the contrail frequency of Houston Center is zero.Figure 4a
+B. Predicted Contrail Frequency IndexThe contrail frequency index derived in the previous section indicates the actual contrail activities.For strategic planning, prediction of the contrail frequency for the next few hours is needed.The predicted contrail frequency index is defined as a convolution of traffic data and atmospheric conditions.They are similar to the concept of Weather Impacted Traffic Index (WITI) introduced by Callaham et al. 18 and Sridhar, 19 and the three dimensional index derived by Chen. 20The index consists of the RUC forecast data and the predicted aircraft locations.The center predicted contrail frequency index is defined as the predicted number of aircraft flying through the forecasted potential contrail area at time t at level l in center k.It is formulated as113 i=1 151 j=1 r i,j c i,j a i,j ,(7)where r i,j is defined in Eq. ( 2) with RUC forecast data, c i,j is defined in (3), and a i,j is defined in (5). a i,j is based on the historic air traffic data during the planning period.As in the case of WITI, the index is affected more by the changing atmospheric conditions than by small daily variations to the nominal traffic plan. 19In Eq. ( 7) the coefficient a i,j can be thought of as an air traffic weighting coefficient.
+C. Contrail Reduction StrategyThe feasibility of using predicted contrail frequency index for contrail reduction is investigated.The center predicted contrail frequency index can be used to identify the flight level that would have formed the most contrails and find an alternate altitude with less contrail activities.The contrail frequency index after the contrail reduction strategy has been applied is formulated as113 i=1 151 j=1 r i,j c i,j âi,j ,(8)where r i,j and c i,j are defined in Eqs. ( 2) and ( 3), and âi,j is defined in Eq, ( 5) with the aircraft location after the contrail reduction strategy is applied.The contrail reduction strategies need to consider extra fuel burn to minimize overall environmental impact and not to add congestions in the center.The strategy in Ref. 21 uses the predicted contrail frequency index to identify the area that would have formed the most contrail activities, and change the cruise altitudes of a group of aircraft to reduce contrails.The changes need to have minimal extra fuel utilization and maintain the air traffic density below airspace capacity.In general, changing the cruise altitude of a group of aircraft will not increase the air traffic density within the center and sectors.
+IV. ResultsThe temperature and relative humidity from RUC data and aircraft position from ASDI data in 2007 were processed and analyzed.Figure 5 shows four average hourly indices at each of the twenty continental U.S. centers at different altitudes in 2007.They are the contrail coverage ratio derived in Section II.B, the aircraft position matrix derived in Section II.C, the contrail frequency index derived in Section III.A, and the contrail frequency density derived later in this section.As shown in the figure, most of the contrails were formed between flight level 301 and 387.These flight levels account for 78% of contrail frequency over all centers and altitudes.Seattle Center (ZSE), Oakland Center (ZOA), Los Angeles Center (ZLA), and New York Center (ZNY) have lower contrail activities.The reasons are there are less flight activities at ZSE, ZOA, and ZLA, and the size of ZNY is small bringing the index low.To observe the density of the center contrail frequency index, the center contrail frequency density is defined by the center contrail frequency index divided by the number of grid points in the center, formulated as113 i=1 151 j=1 r i,j c i,j a i,j 113 i=1 151 j=1 c i,j ,(9)where r i,j , c i,j , and a i,j are defined in Eq. ( 2), (3), and (5). Figure 5d shows the average hourly center contrail frequency density at each of the eleven flight levels and twenty centers.It is shown that there are high contrail activities between flight level 320 and 363, having some centers with density higher than 0.5.The highest contrail density is 0.69 at flight level 363 at Indianapolis Center (ZID).Figure 6 shows the contrail frequency density at flight level 363 on a U.S. map.As shown in the figures, Indianapolis Center (ZID) has the highest contrail density, and its surrounding five centers, Kansas City Center (ZKC), Chicago Center (ZAU), Memphis Center (ZME), Cleveland Center (ZOB), and Atlanta Center (ZTL), also have high contrail density ranged from 0.45 to 0.57.The seasonal variation can be observed by the monthly average center contrail frequency at ZID in 2007, as shown in Fig. 7.In general, there are less contrail activities in summer.The contrail frequency index provides a way to quantify the contrail activities.Next, for the predicted contrail frequency index, one-hour, two-hour, three-hour, and six-hour predicted indices in August 2007 were generated using Eq. ( 7) and analyzed.a i,j in Eq. ( 7) was based on the air traffic data on the same day of week of July 15-21.For example, to generate the predicted contrail frequency indices on August 1, 8, 15, 22, and 29, the air traffic data on July 18 was used since they are all Wednesdays.As an example, actual and one-hour predicted contrail frequency indices at flight level 363 at Indianapolis center are shown in Fig. 8a.As shown in the figure, the one-hour predicted contrail index is highly correlated with actual index, with a correlation coefficient of 0.94.It is mentioned in Ref. 19 that the index is affected more by the changing atmospheric conditions than by small daily variations to the nominal traffic plan.To show the effect on different choices of the historical air traffic data used, the predicted contrail frequency index was regenerated using a i,j computed by the average air traffic in July 2007.The indices at flight level 363 at Indianapolis Center using the average traffic in July 2007 are shown in Fig. 8b with a correlation coefficient of 0.94.The result is very similar to using the air traffic data on July 15-21.Table 3 shows the average correlation coefficients between actual and predicted indices in twenty centers at different flight levels using historical data of July 15-21 and the average of July 2007.Note that there is no significant difference between the two types of historical data.The accuracy of the prediction decays with longer prediction time.The correlations at flight level 414 and 444 are small mainly because of the lower frequency of contrail formation and the resulting higher sensitivity to the noise.The mean correlation coefficients between actual index and one-hour, two-hour, three-hour, and six-hour predicted index are 0.85, 0.72, 0.64, and 0.52 using July 15-21 data, and 0.84, 0.72, 0.63, and 0.51 respectively.To use the prediction contrail frequency index for contrail reduction strategies, center prediction indices were generated and analyzed.Figure 9  In general, when the actual contrail frequency is high, the predicted contrail frequency is high for prediction up to three hours.The six-hour predicted index is under-predicted, most likely due to the prediction inaccuracy.For implementing a contrail reduction strategy, the centers with high contrail frequency indices need to be identified.As an example, the contrail reduction strategy may be enabled when the centers have indices higher than 100.This would affect seven centers including Los Angeles Center (ZLA), Salt Lake City Center (ZLC), Albuquerque Center (ZAB), Dallas/Fort Worth Center (ZFW), Houston Center (ZHU), Jacksonville Center (ZJX), and Miami Center (ZMA).All of the one-hour, two-hour, and three-hour prediction indices are able to correctly identify the centers that need a reduction strategy with the exception that the threehour predicted index fails to identify ZHU.There is one case that the one-hour prediction falsely identifying Denver Center (ZDV) with low contrail activity as having an index greater than 100.The success rate of the identification is defined as the rate of the predicted contrail index correctly identifying the center with high or low contrail activities.In this example, the one-hour, two-hour, three-hour, and six-hour predicted indices have success rates of 95%, 100% , 95% and 65% for identifying the correct centers respectively.The performance of predicted indices for identifying centers with high contrail frequency index is shown in Table 4.As expected, the success rate decays with longer prediction time due to the prediction inaccuracy.Also noticeable is that the success rate decays with higher threshold.There is a 83.47% success rate to identify centers with contrail frequency index greater than 100 using one-hour predicted index, 69.24% using two-hour index, 58.31% using three-hour index, and down to 38.92% using six-hour index.It is harder to successfully identify centers with index greater than 400.There is a 76.19% success rate using one-hour index, and down to 21.99% using six-hour index.
+V. ConclusionsThis paper described a methodology to predict contrail frequency index for contrail reduction.A class of predicted indices that reflects the severity of airspace contrail formation frequency was derived.The indices consist of weather forecast and actual and historical air traffic data.The results show that the predicted indices are affected more by changing atmospheric conditions than by small daily variations of traffic.For the data tested, the one-hour predicted contrail index is highly correlated with the actual index, resulting in an average correlation coefficient of 0.85 and is lower with longer prediction time.The average correlation coefficient between the actual index and the two-hour, three-hour, and six-hour predicted index are 0.72, 0.64, and 0.52, respectively.In terms of developing strategies for contrail reduction, there is a 83.47% success rate to identify centers with contrail frequency index greater than a threshold, 69.24% using two-hour index, 58.31% using three-hour index, and 38.92% using six-hour index.The method of using predicted contrail frequency index for contrail reduction shows promise but requires detailed future evaluation in a fast-time traffic flow management simulation environment.Relative humidity with respect to water
+Figure 1 .1Figure 1.Contours of temperature and RHw at 34,100 feet at 8AM EDT on August 1, 2007.
+Figure 2 .2Figure 2. Potential persistent contrail formation area and coverage ratio at flight level 341 at 8AM EDT on August 1, 2007.
+Figure 3 .3Figure 3. Center contrail frequencies at flight level 341 at 8AM EDT on August 1, 2007.
+Figure 4 .4Figure 4. Aircraft location and persistent contrail formation areas at 8AM EDT on August 1, 2007.
+Figure 5 .Figure 6 .Figure 7 .567Figure 5. Average hourly center contrail coverage ratio, air traffic density, contrail frequency and contrail frequency density in 2007.
+Use of average air traffic data in July 2007
+Figure 8 .8Figure 8. Actual and predicted center contrail frequency index at flight level 363 at Indianapolis Center in August 2007.
+shows the actual and predicted center contrail frequency indices at flight level 363 at 8PM EDT on August 1, 2007.The blue bars are the actual, and the light blue, green, orange, and red color bar are the one-hour, two-hour, three-hour, and six-hour predicted contrail frequency indices computed by Eq. (7) using traffic data on July 18, 2007.As shown in the figure, the actual contrail frequency index and the one-hour, two-hour, and three-hour predicted contrail frequency indices are correlated.
+Table 1 .1Altitude level index, isobaric pressure level, and pressure altitude.level index1234567891011pressure level (hPa ) 400 375 350 325 300 275 250 225 200 175 150flight level (100 feet) 236 251 267 283 301 320 341 363 387 414 444
+Table 2 .2Center index of twenty continental U.S. air traffic control centers.IndexNameIndexName1Seattle Center (ZSE)11Chicago Center (ZAU)2Oakland Center (ZOA)12Indianapolis Center (ZID)3Los Angeles Center (ZLA)13Memphis Center (ZME)4Salt Lake City Center (ZLC)14Cleveland Center (ZOB)5Denver Center (ZDV)15Washington D. C. Center (ZDC)6Albuquerque Center (ZAB)16Atlanta Center (ZTL)7Minneapolis Center (ZMP)17Jacksonville Center (ZJX)8Kansas City Center (ZKC)18Miami Center (ZMA)9Dallas/Fort Worth Center (ZFW)19Boston Center (ZBW)10Houston Center (ZHU)20New York Center (ZNY)
+Table 3 .3Average correlation coefficient between actual and predicted contrail frequency index over twenty U.S. centers in August 2007.Two types of historical data are used, air traffic on July 15-21, 2007 (ref. 1) and the average air traffic in July 2007 (ref.2).prediction timeflight levelone-hourtwo-hourthree-hoursix-hourref. 1 ref. 2 ref. 1 ref. 2 ref. 1 ref. 2 ref. 1 ref. 24440.640.630.580.570.530.530.460.464140.730.690.660.630.600.570.540.503870.890.900.810.820.730.740.570.583630.920.920.790.790.700.690.550.543410.910.910.770.770.680.680.530.523200.910.900.770.750.680.670.530.533010.880.880.730.740.650.650.530.532830.860.860.710.710.620.610.500.482670.860.840.700.700.620.610.490.482510.860.850.700.700.630.620.500.502360.870.860.710.710.630.610.500.48350Actual300one-hourContrail frequency index100 150 200 250two-hour three-hour six-hour500ZSEZOAZLAZLCZDVZABZMPZKCZFWZHUZAUZIDZMEZOBZDCZTLZJXZMAZBWZNYFigure 9. Actual and predicted center contrail frequency index at flight level 363 at 8PM EDT on August 1, 2007.
+Table 4 .4Success percentage of identifying the center with index greater than the threshold index for twenty U.S. centers in August 2007.thresholdprediction time one-hour two-hour three-hour six-hour10083.47%69.24%58.31%38.92%20081.43%64.37%51.27%30.69%30079.14%60.75%46.36%26.18%40076.19%57.28%41.69%21.99%
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+
+	
+		Reducing the climate change impacts of aviation by restricting cruise altitudes
+		
+			VictoriaWilliams
+		
+		
+			RobertBNoland
+		
+		
+			RalfToumi
+		
+		10.1016/s1361-9209(02)00013-5
+	
+	
+		Transportation Research Part D: Transport and Environment
+		Transportation Research Part D: Transport and Environment
+		1361-9209
+		
+			7
+			6
+			
+			November 2002
+			Elsevier BV
+		
+	
+	Williams, V., Noland, R. B., and Toumi, R., "Reducing the climate change impacts of aviation by restricting cruise altitudes," Transportation Research. Part D, Transport and environment, Vol. 7, No. 5, November 2002, pp. 451-464.
+
+
+
+	
+		Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation
+		
+			VictoriaWilliams
+		
+		
+			RobertBNoland
+		
+		10.1016/j.trd.2005.04.003
+	
+	
+		Transportation Research Part D: Transport and Environment
+		Transportation Research Part D: Transport and Environment
+		1361-9209
+		
+			10
+			4
+			
+			July 2005
+			Elsevier BV
+		
+	
+	Williams, V. and Noland, R. B., "Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation," Transportation Research. Part D, Transport and environment, Vol. 10, No. 4, July 2005, pp. 269-280.
+
+
+
+	
+		Contrail Frequency over the United States from Surface Observations
+		
+			PatrickMinnis
+		
+		
+			JKirkAyers
+		
+		
+			MicheleLNordeen
+		
+		
+			StevenPWeaver
+		
+		10.1175/1520-0442(2003)016<3447:cfotus>2.0.co;2
+	
+	
+		Journal of Climate
+		J. Climate
+		0894-8755
+		1520-0442
+		
+			16
+			21
+			
+			November 2003
+			American Meteorological Society
+		
+	
+	Minnis, P., Ayers, J. K., Nordeen, M. L., and Weaver, S. P., "Contrail Frequency over the United States from Surface Observations," Journal of Climate, Vol. 16, No. 21, November 2003, pp. 34473462.
+
+
+
+	
+		Contrail coverage derived from 2001 AVHRR data over the continental United States of America and surrounding areas
+		
+			RabindraPalikonda
+		
+		
+			PatrickMinnis
+		
+		
+			DavidPDuda
+		
+		
+			HermannMannstein
+		
+		10.1127/0941-2948/2005/0051
+	
+	
+		Meteorologische Zeitschrift
+		metz
+		0941-2948
+		
+			14
+			4
+			
+			August 2005
+			Schweizerbart
+		
+	
+	Palikonda, R., Minnis, P., Duda, D. P., and Mannstein, H., "Contrail coverage derived from 2001 AVHRR data over the continental United States of America and surrounding areas," Meteorologische Zeitschrift, Vol. 14, No. 4, August 2005, pp. 525-536.
+
+
+
+	
+		Relating observations of contrail persistence to numerical weather analysis output
+		
+			RPalikonda
+		
+		
+			PMinnis
+		
+		
+			DPDuda
+		
+		
+			HMannstein
+		
+	
+	
+		Atmospheric Chemistry and Physics
+		
+			9
+			4
+			
+			February 2009
+		
+	
+	Palikonda, R., Minnis, P., Duda, D. P., and Mannstein, H., "Relating observations of contrail persistence to numerical weather analysis output," Atmospheric Chemistry and Physics, Vol. 9, No. 4, February 2009, pp. 1357-1364.
+
+
+
+	
+		Estimated contrail frequency and coverage over the contiguous United States from numerical weather prediction analyses and flight track data
+		
+			DavidPDuda
+		
+		
+			PatrickMinnis
+		
+		
+			RabindraPalikonda
+		
+		10.1127/0941-2948/2005/0050
+	
+	
+		Meteorologische Zeitschrift
+		metz
+		0941-2948
+		
+			14
+			4
+			
+			June-July 2003
+			Schweizerbart
+			Friedrichshafen at Lake Constance, Germany
+		
+	
+	Duda, D. P., Minnis, P., Costulis, P. K., and Palikonda, R., "CONUS Contrail Frequency Estimated from RUC and Flight Track Data," European Conference on Aviation, Atmosphere, and Climate, Friedrichshafen at Lake Constance, Germany, June- July 2003.
+
+
+
+	
+		Improved Magnus Form Approximation of Saturation Vapor Pressure
+		
+			OlegAAlduchov
+		
+		
+			RobertEEskridge
+		
+		10.1175/1520-0450(1996)035<0601:imfaos>2.0.co;2
+	
+	
+		Journal of Applied Meteorology
+		J. Appl. Meteor.
+		0894-8763
+		1520-0450
+		
+			35
+			4
+			
+			April 1996
+			American Meteorological Society
+		
+	
+	Alduchov, O. A. and Eskridge, R. E., "Improved Magnus Form Approximation of Saturation Vapor Pressure," Journal of Applied Meteorology, Vol. 35, No. 4, April 1996, pp. 601-609.
+
+
+
+	
+		Assessing NAS Performance: Normalizing for the Effects of Weather
+		
+			MBCallaham
+		
+		
+			JSDearmon
+		
+		
+			ACooper
+		
+		
+			JHGoodfriend
+		
+		
+			DMoch-Mooney
+		
+		
+			GSolomos
+		
+	
+	
+		4th USA/Europe Air Traffic Management R&D Symposium
+		Santa Fe, NM
+		
+			December 2001
+		
+	
+	Callaham, M. B., DeArmon, J. S., Cooper, A., Goodfriend, J. H., Moch-Mooney, D., and Solomos, G., "Assessing NAS Performance: Normalizing for the Effects of Weather," 4th USA/Europe Air Traffic Management R&D Symposium, Santa Fe, NM, December 2001.
+
+
+
+	
+		Relationship Between Weather, Traffic and Delay Based on Empirical Methods
+		
+			BanavarSridhar
+		
+		
+			SeanSwei
+		
+		10.2514/6.2006-7760
+	
+	
+		6th AIAA Aviation Technology, Integration and Operations Conference (ATIO)
+		Wichita, KS
+		
+			American Institute of Aeronautics and Astronautics
+			September 2006
+		
+	
+	Sridhar, B. and Swei, S., "Relationship between Weather, Traffic and Delay Based on Empirical Methods," 6th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Wichita, KS, September 2006.
+
+
+
+	
+		Estimation of Air Traffic Delay Using Three Dimensional Weather Information
+		
+			NeilChen
+		
+		
+			BanavarSridhar
+		
+		10.2514/6.2008-8916
+	
+	
+		The 26th Congress of ICAS and 8th AIAA ATIO
+		Anchrorage, AK
+		
+			American Institute of Aeronautics and Astronautics
+			September 2008
+		
+	
+	Chen, N. Y. and Sridhar, B., "Estimation of Air Traffic Delay Using Three Dimensional Weather Information," 8th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Anchrorage, AK, September 2008.
+
+
+
+	
+		Fuel efficient strategies for reducing contrail formations in United States airspace
+		
+			BanavarSridhar
+		
+		
+			NeilYChen
+		
+		10.1109/dasc.2010.5655533
+	
+	
+		29th Digital Avionics Systems Conference
+		Salt Lake City, UT
+		
+			IEEE
+			October 2010
+		
+	
+	to appear
+	Sridhar, B., Chen, N. Y., and Ng, H. K., "Fuel Efficient Strategies for Reducing Contrail Formations in United State National Air Space," 29th Digital Avionics Systems Conference, Salt Lake City, UT, October 2010, to appear.
+
+
+				
+			
+		
+	
diff --git a/file135.txt b/file135.txt
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+	
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+		
+I. IntroductionS tudies show that 70% of all delays are related to weather and 60% are caused by convective weather. 1 To guide flow control decisions and identify the strategies to reduce delays, cancellations, and costs during the day of operations in various weather conditions, it is useful to create a delay estimation model and provide accurate delay estimation based on weather information.Efforts have been made to identify the correlation between weather and delay both at the regional and national levels.The most promising concept is to use the Weather-Impacted Traffic Index (WITI), which was first introduced by Callaham et al. 2 Sridhar 3, 4 and Chatterji 5 expanded the concept and built daily delay estimation models by linear regression.Klein 6 developed objective measures of the combined impact of traffic demand and weather on the air traffic system by further combining en route WITI, terminal WITI and queuing delay to form a new metric, the National Airspace System Weather Index.Hansen 7 developed models involving the use of econometric concepts to understand the relationship between observed airline delay and several causal factors, including traffic, airport weather, en route convective weather, and weather forecast accuracy.All of these models are two dimensional, considering only the storm location, not the echo tops.As a result, previous research in delay estimation does not take into account the ability of some aircraft to fly above echo tops.The objective of this paper is to extend the WITI concept by adding aircraft altitude and the storm echo tops.The methodology of WITI generation in Ref. 3-5 is refined and a three dimensional WITI (3D-WITI) is generated based on data from the Corridor Integrated Weather System (CIWS). 8CIWS, developed and operated by MIT Lincoln Laboratory, provides both accurate precipitation and echo tops data.The relationships between CIWS WITI without echo tops information (2D-WITI), 3D-WITI, and Aviation System Performance Metrics (ASPM) delay were studied.The periodic linear models 9, 10 were used to evaluate the performance of the delay estimation.The remainder of the paper is organized as follows.Section II provides the definition of 2D-WITI, 3D-WITI, and delay.Next, the delay estimation models are described in Section III.The delay estimation models and the methods to compute the model parameters are formulated.The results and performance of the models are demonstrated in Section IV.Finally, Section V provides conclusions.
+II. WITI and Delay
+A. 2D-WITIWITI is an indicator of the number of aircraft affected by weather.At given time k, the computation of WITI consists of finding: 1) the weather contours of interest W i (k), 2) the aircraft location T j (k), and 3) if aircraft T j (k) is located inside contour W i (k).It should be noted that in the second step, the aircraft location is based on the air traffic on days unaffected by weather, as described in Ref. 3-5.Considering only the projected positions of aircraft location and storm location, the 2D-WITI is formulated as follows,W IT I 2D (k) = m(k) j=1 n(k) i=1    1 if T j (k) is inside W i (k) 0 if T j (k) is outside W i (k) ,(1)where n(k) is the number of weather contours of interest at time k, and m(k) is the number of aircraft of interest at time k.][5] Here CIWS is used for the WITI computation.CIWS, created by MIT Lincoln Laboratory, provides 2-hour convective forecasts updated every 5 minutes.Although the current CIWS does not cover the entire NAS, the coverage includes the major east airway and most high volume terminal areas.All or most of the Chicago Center (ZAU), New York Center (ZNY), Atlanta Center (ZTL), Houston Center (ZHU), Washington Center (ZDC), Boston Center (ZBW), Cleveland Center (ZOB), and Memphis Center (ZME) are covered by the current CIWS.The CIWS-WITI generation method has been integrated with the Future ATM Concepts Evaluation Tool (FACET). 11,12 gure 1a shows a FACET display with the CIWS weather and the air traffic.The grey rectangular bounding box indicates the CIWS-covered area.The WITI computation involves finding the number of aircraft within the weather contours at a certain level, as formulated in Eq. (1).As an example, in Fig. 1b, an arbitrary level 3 contour is shown in yellow, with a total of five aircraft within the contour.Therefore, the WITI count of the contour is 5.A day is defined as 24 hours starting at 0400 Eastern Standard Time (EST), since most of the aircraft departing on the previous day would have landed before 0400 (EST) and new aircraft are starting to depart after 0400 (EST).The WITI is generated at the sampling rate of 1 minute, as in previous studies. 3The CIWS data are updated every 5 minutes and are considered constant during the 5-minutes interval.
+B. 3D-WITIIn addition to the precipitation weather products, CIWS provides the echo tops information, which indicates where it is safe to fly over the storms.If an aircraft is planning to fly through the area affected by the storm but over the echo tops, it should be able to fly through the area safely, and thus is not affected by the weather.Based on this concept, the definition of WITI is extended by including the echo tops information and the altitudes of the aircraft.The echo tops products used in this study have vertical resolutions of 5,000 feet, up to 65,000 feet.Similar to the WITI defined in Eq. ( 1), the three dimensional WITI (3D-WITI), which considers not only the position of the aircraft but also its altitude, consists of one more element, E j i , which are the echo tops weather contours.The superscript i, which denotes the level of the echo tops, is defined as the vertical height divided by 5000 feet.For example, E 3 i means the i th echo tops contour is at 15,000 feet.There are 14 echo tops products available, E 0 i . . .E 13 i .The 3D-WITI is defined asW IT I 3D (k) = m(k) j=1   n(k) i=1    1 if T j (k) is inside W i (k) 0 if T j (k) is outside W i (k)   .   p(k) i=1    1 if T j (k) is inside E aj (k) i (k) 0 if T j (k) is outside E aj (k) i (k)   ,(2)where n(k) is the number of precipitation weather contours of interest at time k, p(k) is the number of echo tops weather contours of interest at time k, m(k) is the number of flying aircraft of interest at time k, and a j (k) is the altitude level defined as the aircraft altitude divided by 5000 feet rounding to the next integer.For example, if the altitude of aircraft T j (k) is 36000 feet, a j (k) is the next integer of 36000/5000 = 7.2, which is 8.Following the example in Fig. 1b, the echo tops at altitude level 30,000 feet in the same area are shown in Fig. 2a.There are two aircraft, indicated in yellow color, outside the echo tops contours.The aircraft flying outside the contours at the flight level over 30,000 feet means that they are flying over the storms, thus they are not affected by weather.The one in the north is at flight level 36,000 feet and the one in the south is at 34,000 feet, which means both are above the storms and should not contribute to the W IT I 3D .Therefore, the W IT I 3D count for this area is 3.The three dimensional view of the CIWS echo top products is shown in Fig. 2b.It can be seen that the two yellow aircraft are above the echo tops of the storms.Both 2D-WITI and 3D-WITI are processed using the data from June 4, 2007.Figure 3 shows a comparison between the 2D-WITI and the 3D-WITI.The time series values are shown in Fig. 3a.The hourly WITIs are defined as the sum of WITIs in every hour, and are shown in Fig. 3b.The discrepancy between the two suggests that some air traffic affected by the precipitation weather products could fly over the echo tops and would not contribute to delay in the NAS.Further analysis of the delay estimate models will be presented in the next section.in Fig. 4. As illustrated by this figure, high correlation among the three is clearly shown.Note that the ASPM delay in the figure is scaled down by 1/6 in order to have the same level of magnitude as the WITI counts.As a reference, the monthly average correlation coefficient between 2D-WITI and ASPM delay is 0.86.There is no improvement in the monthly average correlation coefficient between 3D-WITI and ASPM over 2D-WITI.However, looking at days where there is a large discrepancy between 2D-WITI and 3D-WITI, 3D-WITI is indeed better correlated with the delay.For example, on June 4, 2007, the 2D-WITI is high at 0900(EST) while 3D-WITI remains low, as shown in Fig. 5. On June 4, the 2D-WITI and ASPM delay has a correlation coefficient of 0.84, while 3D-WITI and ASPM delay has a correlation coefficient of 0.93.This suggests even though there might be many aircraft routes covered by the bad weather, some of them should have no problem flying over the storms as planned.Thus, these aircraft should not contribute to the NAS delay, and this fact is indicated by a lack of a corresponding peak in the ASPM delay plot.correlation of hourly air traffic delay with respect to hourly 2D-WITI and 3D-WITI within a day.Three classes of models are described in this section: 1) a periodic linear (PL) hourly delay model, 2) a periodic finite impulse response (PFIR) hourly delay model, and 3) a periodic linear autoregressive with exogenous inputs (PARX) hourly delay model.
+A. Periodic Linear (PL) Hourly Delay ModelFirst, given the observed daily WITI values for p days, w = [w 1 w 2 . . .w p ] T and the observed aggregate daily delay, d = [d 1 d 2 . . .d p ] T , the linear model for the daily delay can be formulated asd = α w + γ + e,(3)where α and γ are the model coefficients and e is the error estimate.The α and γ can be found by solving the least-square solution of Eq. ( 3).The delay estimate, d, can be expressed asd = α w + γ,(4)Next, as seen in Fig. 4, both the ASPM delay and WITI have a 24-hour period.Instead of using the aggregate daily delay and WITI, the hourly data can be used to build the delay model.The daily delay model can be divided into 24 individual hourly delay models.Given the observed hourly WITI and delay on p days, the WITI and delay data matrices are defined asW =     w 1,1 w 2,    ,(5)where w i,j and d i,j are the hourly WITI and delay at hour i on day j.Assume w h and d h are the h th columns of W and D, which represent the hourly WITI and delay at hour h of the observed days.Note that h = 1, 2, . . ., 24, and it starts at 0400(EST).The delay model at hour h is described asd h = α h w h + γ h + e h ,(6)where α's and γ's can be found by solving the least-square solution of Eq. ( 6).The estimate of the hourly delay dh can then be expressed asdh = α h w h + γ h .(7)The model in Eq. ( 6) and Eq. ( 7) is referred to as the periodic linear hourly delay model.
+B. Periodic Finite Impulse Response (PFIR) Hourly Delay ModelThe periodic linear delay model considers only the relationship between current delay and current WITI.In reality, the delay might be caused by not only the current weather but also the weather hours earlier.Assuming the current delay is correlated with the current WITI and the WITI in the previous hour, the model can be described asd h = α h,0 w h + α h,1 w h-1 + γ h + e h .(8)The α's and γ's can be found by solving the least-square solution of Eq. ( 8).The estimate of hourly delay dh can be formulated asdh = α h,0 w h + α h,1 w h-1 + γ h .(9)The model in Eq. ( 8) and Eq. ( 9) is referred to as the first-order PFIR model with direct feed-through.First-order means that data one time-step earlier was used and the direct feed-through means that current data are used to build the model.More generally, the n th -order PFIR model can be formulated asd h = n k=0 α h,k w h-k + γ h + e h , (10)dh = n k=0 α h,k w h-k + γ h . (11)Note that w h-k is defined as 0 for h ≤ k, which implies that the least-square solutions of α h,k are 0's for h ≤ k.Also, the PL model described in the previous subsection is a special case of the PFIR model when n = 0.
+C. Periodic Linear Autoregressive with Exogenous Inputs (PARX) Hourly Delay ModelAt a given hour h, in addition to the current and past WITI, the past delay might also be available in certain applications such as real-time delay prediction. 13Assuming the current delay is correlated with the current WITI, the WITI in the past n hours, and the delay in the past m hours, the model can be formulated asd h = n k=0 α h,k w h-k + m l=1 β h,k d h-l + γ h + e h ,(12) dh= n k=0 α h,k w h-k + m l=1 β h,k d h-l + γ h ,(13)where α's, β's, and γ's can be found by solving the least-square solution of Eq. ( 12).This model is referred to as the PARX model with order (n, m).Note that the PFIR model in the previous subsection is a subset of PARX model when m = 0. To be more explicit, Eq. ( 12) can be rewritten asd h = w h . . . w h-n d h-1 . . . d h-m 1               α h,0 . . . α h,n β h,1 . . . β h,m γ h               + e h .(14)The Moore-Penrose pseudo-inverse 14 is used to solve the equation.The solution is described asα h,0 . . . α h,n β h,1 . . . β h,m γ h T = w h . . . w h-n d h-1 . . . d h-m 1 † d h ,(15)where [•] † represents the pseudo-inverse of the matrix.
+IV. ResultsThe delay and 3D-WITI data for the month of June, 2007 were used as reference data to build a PARX model, described in Eq. ( 12), with model order (n, m) = (1, 1).In this model, there are a total of 96 model parameters to be identified, including α 1,0 . . .α 24,0 , α 1,1 . . .α 24,1 , β 1,1 . . .β 24,1 , and γ 1 . . .γ 24 .Once the parameters are identified, Eq. ( 13) is used to compute the estimate of the hourly delay on reference days, dh .Figure 6a shows the actual ASPM hourly delay d h versus the estimate of the hourly delay dh on all the reference days.The red line in the figure indicates the perfect estimates.As shown in the figure, all the dots lie around the red line which suggests d h and dh are close.The average daily root-mean-square (RMS) error between d h and dh , or e h , is 1714 minutes, which yields only 5.83% of the average RMS of daily ASPM delay in June, 2007, which is 29403 minutes.Next, a day not in the reference days was selected to evaluate the performance of the delay estimation model.For July 9, 2007, which has total ASPM delay of 324577 minutes, Fig. 6b shows the actual ASPM delay and the estimated delay.The RMS error between the actual ASPM delay and the delay estimate is 1573 minutes, only 5.54% of the RMS of the actual ASPM delay.Furthermore, the PARX models with different order (n, m) were used to evaluate the performance of air traffic delay estimates using 2D-WITI and 3D-WITI.The pair (n, m) is the order of the model, where n is the number of past WITI and m is the number of past delay used in the model.There are different variations of the models.For example, for m = 0, the delay estimates are related to the WITI and do not depend on past values of delay.These models are essentially PFIR models, and n = 0 represents the simple PL model.On the other hand, for n = 0, the delay estimates are only related to the past delays and do not depend on the WITI.The models are periodic autoregressive (PAR) models.The PAR models are used as the baseline to evaluate how much improvement can be achieved with the WITI information.The whole month of data from July, 2007 are used to validate the models.All PL, PFIR, PAR and PARX models using both 2D-WITI and 3D-WITI with different orders were tested.The results are summarized in Table 1 and2.As shown in Table 1, the PFIR models do not perform well because of the lack of past delay information.In Table 2, it shows that the PARX model with order (1, 1) using 3D-WITI is slightly better than the other models.It was noticed that higher order models do not provide better performance for this class of models.The reason might be higher order models tend to over fit the observed data and lose the generality for the validation data.The PARX model using 3D-WITI with order (1, 1) was selected as the best for this class of models.In this case, the average daily RMS error of the model is 1876 minutes, mean absolute error is 1382 minutes, and maximum error is 4762 minutes.It provides a small improvement (about 2%) in delay estimation over other methods.Figure 7 shows the correlation between the actual delay and the optimal estimated delay for each hour in July, 2007.The correlation coefficient between the two is 0.98.
+V. Conclusions and Future WorkIn this paper, a new three-dimensional weather-impacted traffic index was developed and presented.The new index uses the aircraft altitude and the storm echo tops information to discount an aircraft if it can fly over the weather-impacted area safely, thus incurring no delay.Both 2D-WITI and 3D-WITI were computed using CIWS weather product, which provides both accurate precipitation and echo tops weather information.The delay estimation methodology utilizes the hourly resolution of the ASPM data.The indices were used as exogenous inputs for periodic autoregressive models to perform the NAS delay estimation.Various linear hourly models using different combinations of past and current weather and traffic information were examined to determine the optimal delay estimation model.The models were built using traffic and weather data from June 2007, and were validated with the data from July 2007.The recursive models using WITI information outperform models using only delay information.Another result from the study is that using higher order models may not provide more accurate estimates due to overfitting of the data.No clear conclusions can be drawn about the additional benefits of using 3D-WITI information versus 2D-WITI information.The performance of 3D-WITI models need more examination.The result shows that the 3D-WITI provides a small improvement (about 2%) in delay estimation.The reason that using 3D-WITI does not provides significant superior performance over using 2D-WITI might be that the aircraft do not take full advantage of the echo tops information to fly over storm.Further studies, with larger datasets and a better understanding of how echo top information is used by pilots and air traffic controllers may lead to more accurate delay estimates.Accurate delay estimates will benefit the ATM in identifying the strategies to reduce delays, cancelations, and costs during operations in severe weather conditions.Figure 1 .1Figure 1.FACET is running with CIWS weather and air traffic loaded.
+Figure 2 .2Figure 2. FACET is running with CIWS echo tops weather and air traffic loaded.
+Figure 3 .3Figure 3. 2D-WITI and 3D-WITI on June 4, 2007.
+Figure 4 .Figure 5 .45Figure 4. Hourly 2D-WITI, 3D-WITI, and ASPM delay in June, 2007
+Figure 6 .6Figure 6.ASPM delay and estimated delay
+Figure 7 .7Figure 7. Hourly ASPM delay and estimated delay in July, 2007.
+Table 1 .1Validation for the PFIR models with different parameters using July, 2007 data.The numbers are in minutes.Model2D PL2D PFIR3D PL3D PFIR(n,m)(0,0)(1,0)(2,0)(0,0)(1,0)(2,0)RMS error479148014850481447844907Mean absolute error360235673692362935703761Maximum error10861 11011 10830 10940 11050 10971
+Table 2 .2Validation for the PARX models with different parameters using July, 2007 data.The numbers are in minutes.ModelPAR2D PARX3D PARX(n,m)(0,1) (0,2) (1,1) (2,2) (1,1) (2,2)RMS error1920 2050 1895 2086 1876 2070Mean absolute error 1388 1496 1399 1525 1382 1513Maximum error5074 5273 4800 5433 4762 5433
+		
+		
+			
+
+				
+
+
+	
+		Weather Forecasting Accuracy for FAA Traffic Flow Management
+		10.17226/10637
+	
+	
+		Weather Forecasting Accuracy for FAA Traffic Flow Management
+		Washington, DC
+		
+			National Academies Press
+			2003
+		
+		
+			National Research Council
+		
+	
+	National Research Council, Weather Forecasting Accuracy for FAA Traffic Flow Management, The National Academies Press, Washington, DC, 2003.
+
+
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+I. IntroductionD emand for air transportation has grown rapidly in recent years and is expected to grow in the future.In order to ensure smooth air traffic flow and safety in the presence of disruptions caused by convective weather, innovative modeling and design methods are needed in traffic flow management (TFM).One of the main functions of TFM is to predict and resolve demand-capacity imbalances at the sector level to avoid congestion.Thus an accurate sector prediction model that can account for traffic flow uncertainty and weather impact is an essential component of TFM.Efforts have been made in the past few years to perform sector demand predictions.Traditionally, models used in air traffic control and flow management are based on simulating the trajectories of individual aircraft.Deterministic forecasting of sector demand is routinely done within the Enhanced Traffic Management System (ETMS), which relies on the computation of each aircraft's entry and exit times at each sector along the path of flight.Gilbo 1 proposed a regression model for improving aggregate traffic demand prediction in ETMS, acknowledging the uncertainty in the predictions.A more recent TFM simulation tool, the Future ATM Concepts Evaluation Tool (FACET), 2 was used to propagate the trajectories of the proposed flights forward in time and use them to count the number of aircraft in each sector for demand forecasting and establish confidence bounds on the forecasts. 3][6] The objective of this paper is to develop an empirical sector prediction model that accounts for traffic flow uncertainty and weather impact on the prediction for both short-term (less than 30 minutes) and midterm (30 minutes to 2 hours) predictions.Unlike the traditional methods that use trajectory prediction to perform the sector demand prediction, the periodic autoregressive (PAR) model and its variants 7,8 were used to build the prediction model.The class of PAR models consider both the historical traffic flows to capture the mid-term trend, and the flows in the near past to capture the transient response.In addition, a weather component was embedded in the model to reflect weather impact on sector demand.The remainder of the paper is organized as follows.Section II provides the sector demand data used in the research and the description of the weather-free sector demand prediction model.Next, a weather factor is introduced and the prediction model that considers weather is described in Section III.The results and performance of the models are demonstrated in Section IV.Finally, a summary and conclusions are presented in Section V.
+II. Data and Model
+II.A. Sector Demand DataThe air traffic demand data used in this paper are provided by the recorded Aircraft Situation Display to Industry (ASDI) data generated by the Federal Aviation Administration's Enhanced Traffic Management System (ETMS).The ASDI data provide the locations of all aircraft at one-minute intervals.The sector demand, defined as the number of aircraft in each sector at a given time, can be computed using the ASDI data.In this research, the recorded ASDI data were processed using FACET to obtain the sector demand.Since traffic flow management decisions are made by comparing the peak number of aircraft in a sector during a fifteen-minute interval with the sector's Monitor Alert Parameter (MAP) value, the 15-minute peak sector demand, defined as the maximum sector demand every 15 minutes, was used to build the models.Figure 1 shows the sector demand at every minute and the 15-minute peak sector demand at sector ZID93 on September 3, 2007.Note that in the paper, a day is defined as a 24-hour interval starting at 4:00 AM local time since most of the aircraft departing on the previous day would have landed before 4:00 AM.The black line in Fig. 1 represents the sector demand in an one-minute intervals and the blue dots in Fig. 1 represent the 15-minute peak sector demand during a day, denoted as d k , where k = 1 . . .96.The mid-term trend of sector demand on different days can be observed in Fig. 2, which shows the variation of 15-minute peak sector demand in September 2007.In this figure, each horizontal strip represents one day of 15-minute peak sector demand, and each vertical strip represents the peak sector demand at the same time of day during the entire month.As shown in the figure, the horizontal strips on 9/1, 9/8, 9/15, 9/22 and 9/29, which are Saturdays, have lower demands than the others.The blue vertical regions on the left and right show the off-peak traffic in the early morning and the late night.A vertical light blue region at around 12 o'clock divides the sector demand into morning rush left of the region and the afternoon peak right of it.The sector demand prediction model presented in the next section captures these variations in the demand.
+II.B. Demand Prediction ModelAuto-regressive models have been used for short-term hourly air traffic delay prediction. 9,10 his research extends the delay prediction approach to sector demand prediction.For a given day, a 24-hour period, starting at 4:00 AM local time, is divided into 96 fifteen-minute intervals.Given the observed 15-minute peak sector demands for n days, the sector demand data matrix is defined asD =     d 1,1 d 2,1 . . . d 96,1 . . . . . . . . . . . . d 1,n d 2,n . . . d 96,n     ,(1)where d i,j represents the 15-minute peak sector demand at the i th time step on day j.For September 2007, D has a dimension of 30 by 96, and Fig. 2 shows the image of the matrix D. Assuming d k as the k th column of D, the p-step-ahead sector demand model at time step k in the form of a linear regression model is described asd k+p = α k,p d k + β k,p + e k ,(2)where α k,p and β k,p are coefficients that map the sector demand at the k th time step to the (k + p) th time step, and e k is the error of the model.The least-square solution of α k,p and β k,p that minimizes e T k e k in Eq. ( 2) can be written explicitly asαk,p = n i=1 (d k,i -dk )(d k+p,i -dk+p ) n i=1 (d k,i -dk ) 2 , (3) βk,p = dk+p -α k,p dk ,(4)In the model, αk,p and βk,p , identified from the historical data, capture the periodic features during a day, and the observed sector demand d k,m provides the transient information.The model in Eq. ( 2) and Eq. ( 5) is referred to as the periodic auto-regressive (PAR) sector demand prediction model.As an example, peak sector demand data in August 2007 were used to construct the data matrix in Eq. ( 1), and Eq. ( 2) was used to identify the model parameters αk,p and βk,p , where k = 1 . . .96 and p = 1 . . .8 for 1-step-to 8-step-ahead predictions.The 15-minute-ahead peak sector demand on September 3, 2007 was predicted using Eq. ( 5) with p = 1.The result is shown in Fig. 3a.The root-mean-squared (RMS) error between the actual peak sector demand and the 15-minute demand prediction is 1.93.For the 2-hour prediction, the prediction model is solved for p = 8, and the estimates in Eq. ( 5) are generated.The result is shown in Fig. 3b.The RMS error is 2.15.It is noticed that the PAR model yields larger error as the prediction interval increases.This suggests that using a single observation d k,m in Eq. ( 5) contains less information about dk+p,m when p is large.An alternate method to perform the demand prediction is to use the cumulative sum of the past sector demands as an observation, since the sum includes more information than a single observation and has less noise compared with the single peak sector demand.Following the definition of the sector demand matrix D in  Eq. ( 1), where d k is the k th column of D, the cumulative p-step-ahead sector demand model at time step k can be described in terms of the cumulative sum of q past sector demands asd k+p = α k,p k i=k-q+1 d i + β k,p + e k ,(6)where α k,p and β k,p are the coefficients that map the cumulative sector demand at the k th time step to the sector demand at the (k + p) th time step.Once the least-square solution of coefficients αk,p and βk,p are identified, the p-step prediction of the sector demand at the k th time step for a day m, dk+p,m , based on the observed cumulative sector demand,The model in Eq. ( 6) and Eq. ( 7) is referred to as the cumulative periodic auto-regressive (CPAR) sector demand prediction model.During the analysis, it is noticed that the CPAR model using the sum of the all demands in the past (q = k) works best overall.For the example used in the PAR model, the CPAR model with q = 8 has a RMS error of 1.68 for the 15-minute prediction, and 2.00 for the 2-hour prediction, compared with 1.93 and 2.15 respectively for the PAR model.It appears that the CPAR model performs a little better than the PAR model.More analysis is done in Section IV to evaluate this property.
+III. Weather FactorWeather has a big influence on air traffic sector demand and the uncertainty in weather may cause error in the predictions. 5,11 f a severe storm blocks a sector or regions near it, both the sector capacity and demand may drop dramatically. 12,13  weather factor that discounts the weather-free sector demand prediction is derived in this section.In order to model the weather impact on sector demand prediction, an accurate weather forecast product with high update rate is required.In addition, to capture the impact on all low, high, and super high sectors, the storm echo tops information is useful.The weather data used in this paper was provided by the Corridor Integrated Weather System (CIWS). 14CIWS, developed and operated by MIT Lincoln Laboratory, provides both accurate precipitation and echo tops data and is updated every 5 minutes.In addition, CIWS provides convective forecasts at 5-minute intervals up to 2 hours in the future.The weather factor used to discount the sector demand prediction was chosen to be the sector weather index, defined as the percentage of area covered by the storm with precipitation vertically integrated liquidw k = A w k A ,(8)where A is the area of the sector and A w k is the area of the sector covered by storms with the echo tops at or above the lower bound of the sector at time k.Note that if time k is a future time, the weather forecast is used to determined A w k .It is possible to use other definitions of a sector weather index. 12, 13Figure 4a shows a snap shot of the CIWS data for the high altitude sectors at Indianapolis center (ZID) on a severe weather day.The red spots indicate the storms with VIL level 3 and above, and the echo tops at 35,000 ft.As shown in this figure, most of the sector ZID93 is covered by the storm.The sector weather index for ZID93 on August 16, 2007 is shown in the red line in Fig. 4b.Also shown in the figure is the actual sector demand on the same day in blue line.Notice the sector weather index is greater than 30% from 18:00 to 20:00 Eastern Daylight Time (EDT), and clearly the sector demand drops during the same period.Traffic reduction due to weather impact can be modeled in many different ways. 15In this research, the weather-free prediction was first estimated, then the sector weather index was used to adjust the prediction.Assume that the sector demand starts to decay when the sector weather factor exceeds w low , and reaches 0 when the weather factor reaches w high .The sector demand reduction rate is modeled as the power law distribution, 1 -((w k -w low )/(w high -w low )) γ , where γ is the power of the distribution.To reflect the thresholds, the sector weather index in Eq. ( 8) is redefined asw k =      w low if A w k /A ≤ w low A w k /A if w low < A w k /A < w high w high if w high ≤ A w k /A .(9)In order to adjust the weather impact on the sector demand prediction model, the weather forecast is used to compute the predicted sector weather index.Assume at time k, the predicted sector weather index at time k + p is w k+p , the PAR sector demand prediction model in Eq. ( 5) can be rewritten asdk+p,m = 1 - w k+p -w low w high -w low γ (α k,p d k,m + βk,p ), (10)or the CPAR sector demand prediction model in Eq. ( 7) can be rewritten asdk+p,m = 1 - w k+p -w low w high -w low γ (α k,p k i=k-q+1 d i,m + βk,p ). (11)Using the echo tops information provides a more representative weather index, especially for the high sectors.If there are storms with low echo tops located at some high sectors, the weather might have minimal impact on the sector demand.The sector demand and weather index for sector ZID93 on two different days is shown in Fig. 5.Both days have severe storms, but one has high echo tops while the other has low echo tops.The sector demands on severe weather days were compared with the average sector demand on the rest of the days in the same month.In Fig. 5a, the sector demand on August 16, 2007 is lower than the average between 18:00 and 20:00 (EDT) because of the high weather index during the period, as indicated in Fig. 5c.The blue line in Fig. 5c shows the weather index considering the area covered by storms without the echo tops information, and the red line is the weather index considering the echo tops at 35,000ft and above.In this case, the two lines are close.This suggests that there are severe storms in the area and most of the echo tops are higher than the lower bound of sector ZID93.On the other hand, on October 23, 2007, there is no demand reduction compared to the average of the other days in October 2007 during 18:00 and 20:00 (EDT), shown in Fig. 5b, even though there are storms in the sector during the period, as shown in Fig. 5d.The red line in Fig. 5d is substantially lower than the blue line, which means even though there are storms in the sector, most the echo tops are lower than the low boundary of the sector and have minor impact on the sector demand.In the next section, the sector weather index refers the index with the echo tops information.
+IV. ResultsThe sector demands of 25 high and superhigh sectors in ZID were investigated in this research.The major flows of ZID include the arrivals to Philadelphia International Airport (PHL), Ronald Reagan Washington National Airport (DCA), Chicago O'Hare International Airport (ORD), Detroit Metropolitan Wayne County Airport (DTW), and Cleveland-Hopkins International Airport (CLE), the departures from ORD and DTW, the westbound traffic of airway J80 from New York Center (ZNY) and Boston Center (ZBW), and the traffic to New York Terminal Radar Approach Control (N90).The sector demands for the month of August, 2007 were used to build the sector demand prediction PAR and CPAR models, described in Eq. ( 1), Eq. ( 2), and Eq. ( 6) .The time step used in the models is 15 minutes.Once the parameters were identified, Eq. ( 5) and Eq. ( 7) were used to perform the sector demand prediction for the month of September, 2007.Starting  6, were presented.The behavior of both PAR and CPAR models are summarized in Table 1.Even though the performance of the two models are very close, CPAR prediction models perform equal to or better than the PAR models in all the cases with the exceptions of the 15-min prediction at ZID81 (PAR 1.95, CPAR 1.98) and at ZID82 (PAR 1.57, CPAR 1.58).Among the cases, the error in CPAR models is 2.46% smaller than the error in PAR models in average.Also notice the errors of both PAR and CPAR models are not sensitive to the look ahead time.In general, the errors are larger with longer look ahead time, but only slightly.The errors of 120-min prediction is 5.12% larger than the 15-min prediction in average for the PAR models, and 2.87% larger for the CPAR models.Consider all the high and super-high sector in ZID, the results are similar.The errors of the PAR models are between 1.57 and 2.11 in the 15-min prediction, and between 1.64 and 2.24 in the 120-min prediction, while the errors of the CPAR models are between 1.58 and 2.10 in the 15-min prediction, and between 1.61 and 2.15 in the 120-min prediction.The sector demand prediction for bad weather days uses the weather factor described in the previous section to adjust the weather-free prediction, formulated in Eq. ( 9), Eq. ( 10), and Eq. ( 11), with w low = 0, w high = 1, and γ = 1.The days with peak weather factors greater than 30% are considered bad weather days.For the days and sectors tested, there are four cases of severe weather periods: ZID83 on 08/16/07 between 1600-2200 (EDT), ZID93 on 08/16/07 between 1600-2200 (EDT), ZID82 on 08/21/07 between 0600-1400 (EDT), and ZID92 on 08/21/07 between 0800-1400 (EDT), shown in Fig. 7. Since all these cases happened in August 2007, the model is built using data for July 2007.Two types of weather-weighted models are built, one uses the actual weather information and the other uses the forecast weather information.Using the actual weather information to perform sector demand prediction represents the cases with perfect weather forecast.It is used to evaluate the performance of the weather-weighted prediction model and eliminate the error caused by forecast inaccuracy.The average prediction errors of the four severe weather periods in August 2007 are shown in Fig. 8.It is noticed that in all four cases, both the weather-weighted model using actual weather information (red dash line) and the model using forecast weather (green dash-dot line) produce smaller error than the weather-free model (blue solid line).The weather-weighted model using forecast weather performs as well as the model using actual weather when the prediction time is small (less than 30 minutes).However, with longer prediction time (more than 60 minutes), the performance starts to decay and the errors are closer to the weather-free model.As an example, in Fig. 8b, the weather-weighted sector demand prediction model using actual weather information improves the 15-minute prediction over the weather-free model by 36.38%, the 60-minute prediction by 42.92%, and the 120-minute prediction by 40.77%.For the weather-weighted model using forecast weather, the improvement is 34.73% for the 15-minute prediction, reduced to 27.81% for the 60-minute prediction, and down to 7.71% for the 120-minute prediction.This suggests that with longer prediction time, the forecast inaccuracy might effect the performance of the weather-weighted prediction model using forecast weather.
+ZID super high and high sectors
+V. ConclusionA class of auto-regressive models developed for sector delay estimation is used for predicting traffic demand in a sector between 15 minutes and two hours in the future.The PAR and CPAR models capture both the mid-term trend based on the historical data, and the short-term transient response based on the near past observation.For the sectors tested, the errors of CPAR models are 2.46% smaller than the PAR models.The CPAR model provides the demand predictions with an average RMS error between 1.58 and 2.10 in theFigure 1 .Figure 2 .12Figure 1.Sector demand and peak sector demand at sector ZID93 on September 3, 2007
+Figure 3 .3Figure 3. Peak sector demand and predicted peak sector demand on September 3, 2007
+k i=k-q+1 d i,m , can be expressed as dk+p,m = αk,p k i=k-q+1 d i,m + βk,p .
+CIWS at ZID at 19:30 (EDT) on August 16, 2007 The sector demand and weather index of ZID93 on August 16, 2007
+Figure 4 .4Figure 4.The weather data, the sector demand and weather index on a severe weather day
+Weather index on October 23, 2007
+Figure 5 .5Figure 5. Sector demand and weather indices with and without echo tops information on August 16 and October 23, 2007.
+Figure 6 .6Figure 6.Southwest region of superhigh (red) and high sector (blue) in ZID center
+ZID92 on August 21, 2007
+Figure 7 .7Figure 7. Sector weather indices on severe weather days in August 2007.
+Table 1 .1Sector demand prediction error of the PAR and CPAR models in September 2007.The model is built using August 2007 data.The smaller errors in each case are in bold.The unit is the number of aircraft.SectorAverage prediction RMS errorName MAP Model 15-min 30-min 45-min 60-min 75-min 90-min 105-min 120-minZID8117PAR CPAR1.95 1.982.05 2.012.07 2.012.09 2.022.06 2.022.08 2.032.09 2.052.11 2.06ZID8216PAR CPAR1.57 1.581.62 1.581.64 1.581.60 1.581.61 1.591.62 1.591.63 1.601.64 1.61ZID8316PAR CPAR1.63 1.581.67 1.591.67 1.611.70 1.621.71 1.631.71 1.641.71 1.651.72 1.65ZID8416PAR CPAR1.82 1.821.87 1.831.92 1.851.94 1.851.92 1.851.92 1.871.90 1.881.89 1.88ZID9119PAR CPAR2.06 2.042.13 2.052.13 2.062.10 2.062.12 2.072.14 2.082.11 2.092.16 2.09ZID9217PAR CPAR1.68 1.681.76 1.691.73 1.691.72 1.701.71 1.701.75 1.701.74 1.711.74 1.71ZID9319PAR CPAR2.11 2.102.20 2.112.21 2.122.19 2.122.23 2.132.24 2.152.23 2.142.24 2.15ZID9417PAR CPAR1.90 1.901.99 1.911.98 1.911.98 1.921.99 1.921.95 1.921.97 1.931.99 1.93
+		
+		
+			
+15-min prediction, and between 1.61 and 2.15 in the 120-min prediction.The performance of the prediction only decays slightly as the prediction interval is increased from 15-minute to 2-hour in both the PAR and CPAR models, as the error increases 5.12% in PAR models and 2.87% in the CPAR models.To improve the accuracy of sector demand prediction in the presence of severe weather, the paper introduced the concept of weather factor.For severe weather days, the model uses the three-dimensional weather information, considering both storm location and echo tops to form the weather factor and then adjusts the weather-free prediction.The weather-weighted model improves the sector demand prediction by as much as 34.73% for the 15-minute prediction, 27.81% for the 60-minute prediction, and 7.71% for the 120-minute prediction on the days and sectors tested.Unlike traditional trajectory-based sector demand prediction methods which predict the behavior of the National Airspace System adequately for short durations of up to 20 minutes and are vulnerable to weather uncertainties, the weather-weighted periodic auto-regressive models provide a reliable short-to mid-term sector demand prediction which accounts for weather uncertainty.			
+			
+
+				
+
+
+	
+		A New Model to Improve Aggregate Air Traffic Demand Predictions
+		
+			EugeneGilbo
+		
+		
+			ScottSmith
+		
+		10.2514/6.2007-6450
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			Aug 2007
+		
+	
+	Gilbo, E. and Smith, S., "A New Model to Improve Aggregate Air Traffic Demand Predictions," AIAA Guidance, Navigation and Control Conference, Hilton Head, SC, Aug 2007.
+
+
+
+	
+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
+		
+			BanavarSridhar
+		
+		
+			ShonRGrabbe
+		
+		
+			GanoBChatterji
+		
+		
+			KapilSSheth
+		
+		10.2514/atcq.9.1.1
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			9
+			1
+			
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Bilimoria, K., Sridhar, B., Chatterji, G. B., Sheth, K., and Grabbe, S., "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, Vol. 9, No. 1, 2001, pp. 1-20.
+
+
+
+	
+		Methods for Establishing Confidence Bounds on Sector Demand Forecasts
+		
+			GanoChatterji
+		
+		
+			BanavarSridhar
+		
+		
+			KapilSheth
+		
+		
+			DouglasKim
+		
+		
+			DanielMulfinger
+		
+		10.2514/6.2004-5232
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Providence, RI
+		
+			American Institute of Aeronautics and Astronautics
+			August 2004
+		
+	
+	Chatterji, G. B., Sridhar, B., Sheth, K., Kim, D., and Mulfinger, D., "Methods for Establishing Confidence Bounds on Sector Demand Forecasts," AIAA Guidance, Navigation and Control Conference, Providence, RI, August 2004.
+
+
+
+	
+		Compressive Representations of Weather Scenes for Strategic Air Traffic Flow Management
+		
+			JEEvans
+		
+		10.2514/6.2022-4079.vid
+	
+	
+		Europe Air Traffic Management RD Seminar
+		
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Evans, J. E., "Tactical Weather Decision Support to Complement Strategic Traffic Flow Management for Convective Weather," Europe Air Traffic Management RD Seminar , 2001.
+
+
+
+	
+		Measuring Uncertainty in Airspace Demand Predictions for Traffic Flow Management Applications
+		
+			CraigWanke
+		
+		
+			MichaelCallaham
+		
+		
+			DanielGreenbaum
+		
+		
+			AnthonyMasalonis
+		
+		10.2514/6.2003-5708
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Austin, TX
+		
+			American Institute of Aeronautics and Astronautics
+			August 2003
+		
+	
+	Wanke, C. R., Callaham, M. B., Greenbaum, D. P., and Masalonis, A. J., "Measuring Uncertainty in Airspace Demand Predictions for Traffic Flow Management Applications," AIAA Guidance, Navigation and Control Conference, Austin, TX, August 2003.
+
+
+
+	
+		Modeling Traffic Prediction Uncertainty for Traffic Management Decision Support
+		
+			CraigWanke
+		
+		
+			SandeepMulgund
+		
+		
+			DanielGreenbaum
+		
+		
+			LixiaSong
+		
+		10.2514/6.2004-5230
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Providence, RI
+		
+			American Institute of Aeronautics and Astronautics
+			August 2004
+		
+	
+	Wanke, C. R., Mulgund, S., and Song, L., "Modeling Traffic Prediction Uncertainty for Traffic Management Decision Support," AIAA Guidance, Navigation and Control Conference, Providence, RI, August 2004.
+
+
+
+	
+		System Identification: Theory for the User
+		
+			LLjung
+		
+		
+			1999
+			Prentice Hall
+			Englewood Cliffs, NJ, 2nd ed.
+		
+	
+	Ljung, L., System Identification: Theory for the User , Prentice Hall, Englewood Cliffs, NJ, 2nd ed., 1999.
+
+
+
+	
+		Multivariate periodic time series models
+		
+			PhilipHansFranses
+		
+		
+			RichardPaap
+		
+		10.1093/019924202x.003.0005
+	
+	
+		Periodic Time Series Models
+		London, UK
+		
+			Oxford University PressOxford
+			2003
+			
+		
+	
+	Franses, P. and Papp, R., Periodic Time Series Models, Oxford Univ. Press, London, UK, 2003.
+
+
+
+	
+		Short Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index
+		
+			BanavarSridhar
+		
+		
+			NeilChen
+		
+		10.2514/6.2008-7395
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, HI
+		
+			American Institute of Aeronautics and Astronautics
+			Aug 2008
+		
+	
+	Sridhar, B. and Chen, N., "Short Term National Airspace System Delay Prediction Using Weather Impacted Traffic Index," AIAA Guidance, Navigation and Control Conference, AIAA, Honolulu, HI, Aug 2008.
+
+
+
+	
+		Estimation of Air Traffic Delay Using Three Dimensional Weather Information
+		
+			NeilChen
+		
+		
+			BanavarSridhar
+		
+		10.2514/6.2008-8916
+	
+	
+		The 26th Congress of ICAS and 8th AIAA ATIO
+		Anchorage, AK
+		
+			American Institute of Aeronautics and Astronautics
+			Sep 2008. 11
+		
+	
+	Chen, N. and Sridhar, B., "Estimation of Air Traffic Delay Using Three Dimensional Weather Information," The 8th AIAA Aviation Technology, Integration, and Operations Conference, AIAA, Anchorage, AK, Sep 2008. 11
+
+
+
+	
+		Analysis of En Route Sector Demand Error Sources
+		
+			JimmyKrozel
+		
+		
+			DanRosman
+		
+		
+			ShonGrabbe
+		
+		10.2514/6.2002-5016
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Monterey, CA
+		
+			American Institute of Aeronautics and Astronautics
+			August 2002
+		
+	
+	Krozel, J., Rosman, D., and Grabbe, S., "Analysis of En Route Sector Demand Error Sources," AIAA Guidance, Navigation and Control Conference, Monterey, CA, August 2002.
+
+
+
+	
+		Predicting Sector Capacity under Severe Weather Impact for Traffic Flow Management
+		
+			LixiaSong
+		
+		
+			CraigWanke
+		
+		
+			DanielGreenbaum
+		
+		
+			DavidCallner
+		
+		10.2514/6.2007-7887
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			September 2007
+		
+	
+	Song, L., Wanke, C., Greenbaum, D., and Callner, D., "Predicting Sector Capacity under Severe Weather Impact for Traffic Flow Management," The 7th AIAA Aviation Technology, Integration, and Operations Conference, Belfast, Northern Ireland, September 2007.
+
+
+
+	
+		Methodologies of Estimating the Impact of Severe Weather on Airspace Capacity
+		
+			LixiaSong
+		
+		
+			CraigWanke
+		
+		
+			StephenZobell
+		
+		
+			DanielGreenbaum
+		
+		
+			ClaudeJackson
+		
+		10.2514/6.2008-8917
+	
+	
+		The 26th Congress of ICAS and 8th AIAA ATIO
+		Anchorage,AK
+		
+			American Institute of Aeronautics and Astronautics
+			September 2008
+		
+	
+	The 26th Congress of International Council of the Aeronautical Sciences
+	Song, L., Wanke, C., Greenbaum, D., Zobell, S., and Jackson, C., "Methodologies of Estimating the Impact of Severe Weather on Airspace Capacity," The 26th Congress of International Council of the Aeronautical Sciences, Anchorage,AK, September 2008.
+
+
+
+	
+		Description of the Corridor Integrated Weather System (CIWS) Weather Products
+		
+			JEvans
+		
+		
+			DKlingle-Wilson
+		
+		ATC-317
+		
+			Aug 2005
+		
+		
+			MIT Lincoln Laboratory
+		
+	
+	Project Report
+	Evans, J. and Klingle-Wilson, D., "Description of the Corridor Integrated Weather System (CIWS) Weather Products," Project Report ATC-317, MIT Lincoln Laboratory, Aug 2005.
+
+
+
+	
+		Model Estimates of Traffic Reduction in Storm Impacted En Route Airspace
+		
+			BrianMartin
+		
+		10.2514/6.2007-7889
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			September 2007
+		
+	
+	Martin, B., "Model Estimates of Traffic Reduction in Storm Impacted En Route Airspace," The 7th AIAA Aviation Technology, Integration, and Operations Conference, Belfast, Northern Ireland, September 2007.
+
+
+				
+			
+		
+	
diff --git a/file137.txt b/file137.txt
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+		
+I. IntroductionAircraft-induced environmental impact has drawn attention in recent years. 1 The three largest emission impacts include direct emissions of greenhouse gases such as CO 2 , emissions of NOx, and persistent contrails.Contrails are clouds that are visible trails of water vapor made by the exhaust of aircraft engines.Contrails form when a mixture of warm engine exhaust gases and cold ambient air reaches saturation with respect to water, forming liquid drops which quickly freeze.Contrails persist if aircraft are flying in certain atmospheric conditions.Persistent contrails reduce incoming solar radiation and outgoing thermal radiation in a way that accumulates heat. 2 The global mean contrail coverage in 1992 was estimated to double by 2015, and quadruple by 2050 due to predicted increase in air traffic. 3Studies suggest that the environmental impact from persistent contrails are estimated to range from three to four times, 4 to ten times 5 larger than from aviation-induced emissions.To address minimizing environmental impacts due to contrails, methods to reduce aircraft induced persistent contrails have been proposed.Various approaches have been proposed in the past to reduce the persistent contrail formation.The approach based on changing aircraft flight altitudes looks promising.Mannstein 6 proposed a strategy to reduce the climate impact of contrails significantly by only small changes in individual flight altitude.Fichter 7 showed that the global annual mean contrail coverage could be reduced by downshifting the cruise altitude.Williams 8,9 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restriction policy.These restrictions generally imply more fuel burn, thus more emissions, and add congestion to the already crowded airspace at lower altitudes.Sridhar 10 and Chen 11 proposed contrail reduction strategies by altering an aircraft's cruising altitude in a fuel-efficient way.The strategies were designed without increasing congestion in the airspace.However, none of the above strategies take into account the range and duration of an aircraft's flight.The objective of this paper is to evaluate contrail reduction strategies based on an aircraft's flight distance.Contrail reduction strategies have different effects on aircraft with different flight distances.In general, aircraft with longer flight distances cruise at the altitudes where contrails are more likely to form because of cold air temperatures.The concept of the contrail frequency index is used to quantify contrail formation.The strategy of reducing persistent contrail formation is to minimize the contrail frequency index by altering the aircraft's cruising altitude.A user-defined factor was applied to evaluate the tradeoff between contrail reduction and extra CO 2 emissions.A higher tradeoff factor results in more contrail reduction and extra CO 2 emissions.Contrail reduction strategies using different tradeoff factors behave differently for different flight distances.For this analysis, the flights during a day were divided into four groups: flights with flight distances less than 500 miles (short-distance flights), between 500 and 1,000 miles (medium-distance flights), between 1,000 and 1,500 miles (long-distance flights), and more than 1,500 miles (transcontinental flights).The remainder of the paper is organized as follows.Section II provides descriptions of the contrail model, definition of contrail frequency index, and the contrail reduction strategies.Next, Section III shows the results and analysis of contrail reduction strategies applied to different ranges of flights.Finally, Section IV presents a summary and conclusions.
+II. Models and Strategies
+II.A. Contrail Model and Contrail Frequency IndexThis paper follows the contrail models described in Ref. 11.The contrail models use atmospheric temperature and humidity data retrieved from the Rapid Updated Cycle (RUC) data, provided by the National Oceanic and Atmospheric Administration (NOAA).The horizontal resolution in RUC is 13-km with 37 vertical isobaric pressure levels ranging between 100 and 1000 millibar (mb) in 25 mb increments.Since the vertical isobaric pressure levels do not correspond to 2,000 feet increments, linear interpolation was used to convert the RUC data to a vertical range from 26,000 feet to 44,000 feet with increments of 2,000 feet.This range is chosen because it generally is too warm for contrails to form below 26,000 feet and most commercial aircraft fly below 44,000 feet.The 2,000 feet increment is chosen is because in general same direction of flights have a vertical separation range of 2,000 feet due to the standard in Reduced Vertical Separation Minima. 12These modifications result in dividing the U.S. national airspace into a three dimensional grid with 337 elements along the latitude, 451 elements along the longitude, and 10 altitudes ranging from 26,000 feet to 44,000 feet.Contrails form when a mixture of warm engine exhaust gases and cold ambient air reaches saturation with respect to water, forming liquid drops which quickly freeze.Contrails form in the regions of airspace that have ambient Relative Humidity with respect to Water (RHw) greater than a critical value r contr . 13Regions with RHw greater than or equal to 100% are excluded because clouds are already present. 14Contrails can persist when the environmental Relative Humidity with respect to Ice (RHi) is greater than 100%. 15In this paper, contrail favorable regions are defined as the regions of airspace that have r contr ≤ RHw < 100% and RHi ≥ 100%.The contrail frequency index (CFI) is used to quantify the severity of contrail activities and represents the number of aircraft in a volumetric element which meets conditions for persistent contrail formation.Air traffic in the U.S. can be mapped into the same volumetric grid as in the RUC data.The Contrail frequency index is zero for volumetric elements which do not meet the conditions for persistent contrail formation.Precise definitions of contrail frequency index are provided in Ref. 11.
+II.B. Contrail Reduction StrategiesThis paper uses the contrail reduction strategies described in Ref. 11.That strategy for reducing the persistent contrail formations is to minimize contrail frequency index by altering the aircraft's cruising altitude.Note that these altitude changes are subject to the cruise altitude limits of each aircraft.An additional constraint is added such that where an aircraft crosses a sector boundary and causes congestion, it will stay at the original cruise altitude.Additional conditions can be added to satisfy other operational procedures.An Air Route Traffic Control Center (or Center) is divided into sectors horizontally and vertically which is monitored by an air traffic controller to maintain separation between aircraft.The number of aircraft in a sector is kept below a maximum, referred to as the Monitor Alert Parameter (MAP), to keep the controllers workload within acceptable limits. 16Therefore the MAP can be used to define the airspace capacity.The contrail reduction altitude changes will not change the sector counts unless they cross the sector boundaries.To address this operational constraints, the strategies only allow the altitude changes such that the aircraft count in a sector does not exceed the sector capacity after the altitude changes.Consider the traffic situation at Kansas City Center at 8AM Eastern Daylight Time (EDT) on April 23, 2010.Kansas City Center has 15 high sectors and 11 super-high sectors.Among them, sector 31 has the highest sector count during the hour.Sector 31 has a lower bound of 37,000 feet and is on top of sector 28, 29 and 30, shown in Fig. 1.During the hour, altitude 35,000-36,999 feet has been identified as a high contrail area.The contrail reduction strategy suggests to increase the cruise altitude of those aircraft passing through this area by 2,000 feet.The move would reduce the contrail frequency index by 17.Now consider if the move would cause congestion by examining the sector counts and capacities.The aircraft cruise altitude changing from 35,000-36,999 feet to 37,000-38,999 feet would move some aircraft in sector 28, 29 and 30 to sector 31.Sector 28, 29, 30 and 31 have the MAP values of 18, 18, 19 and 21 respectively.Figure 2 shows the MAP values and the sector counts in sector 28, 29, 30 and 31 before and after the altitude changes.The aircraft counts in sector 28, 29 and 30 decrease because some aircraft have been moved up to sector 31; the sector count in sector 31 increases but is still lower than the sector capacity of 21.Thus the contrail reduction altitude changes are applied without exceeding the capacity of the airspace.In addition, contrail reduction altitude changes are only applied when the aircraft enter a new Center.The number of altitude changes is not expected to result in frequent climbs and descents to affect current operations.However, if needed, additional constraints can be imposed on the number of altitude changes.All flights over the United States National Airspace from a 24-hour period on April 23, 2010 were analyzed.The contrail reduction strategies were applied and the results are shown in Fig. 3.The CFIs for a Center before applying the maximum reduction strategy are shown as dark blue bars.When the aircraft altitudes are allowed to alter by 2,000 feet, the center CFIs after reduction are shown as light blue bars.The total CFI reduction among all centers is 62%.When the aircraft altitudes are allowed to alter by 4,000 feet, the total reduction is 88% as indicated as green bars.The strategies in this paper limit the altitude changes to 4,000 feet.Altering cruising altitudes changes the aircraft fuel consumption and emissions.In order to analyze the environmental impact of contrail reduction strategies, fuel consumption and emissions are considered in the strategies.Fuel burn and emissions computations are based on a prototype of the Aviation Environmental Design Tool (AEDT) developed by the Federal Aviation Administration (FAA). 17Considering the relative environmental impact of emissions and contrails, the aircraft altitudes are modified only if the contrail reduction benefits exceed the environmental impact of additional emissions.The strategy uses a user-defined trade-off factor α to determine whether the strategy should apply to an aircraft.It can be interpreted as the equivalent emissions in kg that the user is willing to trade off for a contrail frequency index of 1.In general, higher α would result in more contrail reduction and extra CO 2 emissions.Figure 4 shows the amount of contrail reductions versus extra CO 2 emissions using different α values when the aircraft altitudes are allowed to alter by 4,000 feet on April 23, 2010.In the figure, more contrail reduction takes place from left to right and more CO 2 emissions occurs from bottom to top.At the lower-left point, no reduction strategy (α = 0) is applied.The upper-right point is the maximal reduction strategy (α = ∞).As the value of α increases, the curve moves from lower-left to upper-right.The user-defined trade-off factor α provides a flexible way to trade off between contrail reduction and extra emissions.Better understanding of the trade-offs between contrails and emissions and impact on the climate needs to be developed to fully utilize this class of contrail reduction strategies.
+III. Analysis
+III.A. Contrail Frequency IndexThe relative climate impacts of long haul and short haul air travel were studied previously. 18For long distance flights, the fraction of the flight time spent in the high-thrust take-off and climb-out is smaller than the short distance flights.Therefore, long distance flights are more fuel efficient and generate less CO 2 emissions per unit distance than short distance flights.However, short distance flights generally cruise at lower altitudes where contrails are less likely to form because the temperature is too warm.Therefore, long distance flights create more contrail impact than short distance flights.The combined climate impacts of contrails and CO 2 emissions from long and short distance flights need further investigation.This section evaluates the effect of flight distances on contrail reduction strategies.This paper defines the flight distance as the great circle distance between the origin and destination of a flight plan.The flights during a day are divided into four groups: flights with flight distance less than 500 miles (short-distance flights), between 500 and 1,000 miles (medium-distance flights), between 1,000 and 1,500 miles (long-distance flights), and more than 1,500 miles (transcontinental flights).The reason for dividing flights into such groups is to have comparable total flight distances in each group.Based on the flight data on April 23, 2010, 43% of short-distance flights have cruise altitudes lower than 24, 000 feet, which are not likely to form contrails because of the warm temperature.On the other hand, most medium, long, and transcontinental flights cruise at high altitudes, and less than 2% of flights with flight distance more than 500 miles have cruise altitudes lower than 24, 000 feet.Data from all flights during the month of April 2010 were analyzed, and in that month, April 12, April 19 and April 3 had the highest CFI for all flights during a 24-hour period.Considering the contrail activities of different ranges of flights for these three days, the number of flights, total flight distance, total CFI and CFI per 1000 miles of range are summarized in Table 1.The group of short-distance flights has the most flights but the lowest CFI, less than 5% of the total CFI for all flights.The short-distance flights have only 0.6 to 1 CFI per flight on average, or 2.3 to 3.5 CFI per 1000 miles.Even excluding the flights with a cruise altitude lower than 24, 000 feet, the average CFI per flight is 1.1 to 1.7 and the CFI per 1000 miles is 3.1 to 4.8; they are still lower than the averages in other groups.The group of transcontinental flights has the fewest flights and the greatest CFI, on April 12, among all four groups.However, on April 3 and19, its total CFIs are less than the total of groups of medium-and long-distance flights.For the month of April, transcontinental flights have the greatest CFI on 15 days.It seems that the total CFIs of transcontinental flights are more sensitive to the locations of contrail areas than the other groups, therefore the CFIs for this group has larger variance.The group of medium-distance flights has the greatest total distance.Even though its total CFI is more than in the group of long distance flights, it is consistent that its CFI per flight and CFI per 1000 miles are lower than the CFI in long-distance flights.The groups of long-distance and transcontinental flights have the grestest CFI per flight and CFI per unit distance.However, the CFI per unit distance for the group of transcontinental flights drop below the group of long and medium flights on April 19 and April 3, 2010.It is consistent that longer range of flights has more CFI per flight and, with the exception of the group of transcontinental flights, per unit distance.
+III.B. Contrail Reduction StrategiesThe contrail reduction strategies using different values of trade-off factor α were applied to all four groups. .The group of short-distance flights has a much smaller CFI therefore the tradeoff curves (blue) in the figures are relatively short and are located at the lower left corner.The groups of medium-distance (green curves) and long-distance (red curves) flights have similar trends where the medium-distance flight is on the right because of higher CFI to be reduced.This is true for 29 days in April, with an exception on April 28.It is noticed that the locations of the curves (light blue) for the group of transcontinental flights are not consistent across the three figures.This is also true for the entire month, which suggests that the efficiency of contrail reduction strategies for the group of transcontinental flight has different characteristics than in the other groups.It can be interpreted that the transcontinental flights have longer flight distances and the contrail reduction efficiencies are sensitive to the locations of the contrail areas.Also note that in Fig. 5c, the second dot (α = 10) from the left of the light blue curve (transcontinental flights) has a negative value of extra CO 2 emissions.This indicates that the strategy found a way to reduce both contrails and CO 2 emissions for the transcontinental flights on April 3, 2010.Table 2 summarizes the amount of contrail reductions with different α values for all four groups.The numbers are the CFI reductions and the percentages are over total reductions during the day.The shortdistance flights contribute the least reductions, only 9.6% to 12.6% using different α values on April 12, 7.3% to 8.8% on April 19, and 3.9% to 4.3% on April 3.As described in the previous paragraph, the contrail reduction efficiencies for the group of transcontinental flights are sensitive to the locations of the contrail areas, therefore, there is no obvious trend for the reduction efficiencies compared with other groups.It contributes the most reductions, 36.3%, among all reductions for the maximum reduction strategy on April 12, but the reduction rates decay with smaller α values.Also, for April 3 and 19, the reduction rates are smaller than the groups of medium-and long-distance flights.Among the groups of short-, medium-, and long-distance flights, it is consistent that the group of medium flights contributes the most reduction rate, with 33.3% on April 12, 39.9% on April 19, and 39.5% on April 3 for the maximum reduction strategies.It is noticed that the contribution rates of medium-distance flights increase with smaller α values (smaller α value means less CO 2 emissions).For α = 10, the reduction rate increases to 38.1% on April 12, 43.8% on April 19, and 42.6% on April 3 for the maximum reduction strategies.The strategies are more efficient for transcontinental flights with larger α values and more efficient for short-and medium-distance flights with smaller α values.The percentages of the reductions are similar with different α values for long-distance flights.Table 3 summarizes the CFI per 1,000 miles after the contrail reduction strategies were applied.The CFI per 1,000 miles for medium, long, and transcontinental flights can be reduced, from 9.0, 10.7 and 20.0 to 2.4, 3.5 and 8.1, respectively, on April 12; from 12.5, 16.8 and 12.0 to 2.5, 4.1 and 2.6, respectively, on April 19; from 13.1, 16.4 and 11.1 to 2.5, 3.5 and 1.6, respectively, on April 3, an average reduction of 75%.The reduction rates of CFI per 1,000 miles in medium-distance flights are larger than the rates in long-distance flights but the absolute reduction values are smaller.The contrail reduction performance for the group of transcontinental flights varies with the days selected.There is no obvious trend compared with other groups.
+IV. ConclusionsDifferent concepts of contrail reduction strategies based on range of flights have been analyzed and evaluated.The concept of the contrail frequency index is used to quantify the contrail formations.The proposed strategy for reducing the persistent contrail formations is to minimize the contrail frequency index by altering the aircraft's cruising altitude within 4,000 feet.A user-defined tradeoff factor was used to trade off between contrail reductions and extra CO 2 emissions.A high value of tradeoff factor results in more contrail reduction but more CO 2 emissions.The results from an analysis of a month of data show that the groups of short distance flights (< 500 miles) contributes the least to contrail reduction when the strategy is applied.The contrail reduction performance for the group of transcontinental flights (> 1500 miles) varies with the days selected.Among the groups of short-distance, medium-distance (500 to 1000 miles), and long-distance (1000 to 1500 miles) flights, when the strategy is applied, it is consistent that the group of medium-distance flights contributes the most contrail reduction during a day.The strategies are more efficient for transcontinental flights with larger α values and more efficient for short-and medium-distance flights with smaller α values.The percentages of the reductions are similar with different α values for longdistance flights.For the top three contrail days in April, 2010, the contrail frequency index per 1,000 miles for medium-range, long-range, and transcontinental flights can be reduced by an average of 75%.In general, the short-distance flights are more frequent but contribute least to contrail reduction, therefore the group has the lowest priority when applying the contrail reduction strategy.The group of medium-distance flights has a higher priority if the goal is to achieve maximum contrail reduction, the group of long-distance flights has a higher priority if the goal is to achieve maximum contrail reduction per flight.The characteristics of the group of transcontinental flights vary with weather so the priority of the group needs to be further evaluated based on the locations of the contrail areas during the day.The results provide a starting point for developing operational policies to reduce the impact of aviation on climate based on aircraft flight distances.Figure 1 .1Figure 1.Kansas City Center sector 28, 29, 30 and 31.
+Figure 2 .Figure 3 .23Figure 2. MAP values and sector counts before and after the contrail reduction strategies at 8AM EDT on April 23, 2010.
+Figure 4 .4Figure 4. Contrail reduction versus extra CO2 emissions with different α values for all flights on April 23, 2010.
+Figure 55shows the amount of contrail reductions versus extra CO 2 emissions using different α values for different flight ranges on April 3, 12 and 19, 2010.The strategies limit the aircraft cruise altitude changes to 4,000 feet.In the figures, different colors indicate groups of different flight ranges.Each curve has six dots showing different strategies (right to left: α=∞, 80, 40, 20, 10, 0, where α = ∞ is maximum reduction and α = 0 is no reduction).For example, in Fig 5a, the second from the right dot of the light blue curve indicates that the contrail reduction strategy applied to transcontinental flights with a trade-off factor α = 80.It reduced the CFI on April 12 by around 40,000 with extra CO 2 emissions of about 1,000 metric ton (1,000 kg)
+Figure 5 .5Figure 5. Contrail reduction versus extra CO2 emissions with different α values on April 12, April 19 and April 3, 2010.
+Table 1 .1Summary of contrail activities for different distances of flights.daterangenumber total distanceCFICFICFIof flightsof flights(1000 miles)(total) (per flight) (per 1000 miles)short1321236721279613.5April 12, 2010medium long8096 28645814 337852504 360216.5 12.69 10.7transcontinental195333786742034.520short133653707115280.93.1April 19, 2010medium long8250 27635932 325774249 546539 19.812.5 16.8transcontinental195333994072120.912short9423263760710.62.3April 3, 2010medium long6592 26874794 314362774 515049.5 19.213.1 16.4transcontinental170530423363319.711.1
+Table 2 .2Results of contrail reduction in CFIs with different α values.daterange of flights max reduction α=40 α=10short9.6%10.6% 12.6%April 12, 2010medium long33.3% 20.8%34.9% 38.1% 20.2% 20.9%transcontinental36.3%34.3% 28.3%short7.3%7.7%8.8%April 19,2010medium long39.9% 29.3%41.2% 43.8% 29.9% 29.9%transcontinental23.5%21.2% 17.5%short3.9%4.1%4.3%April 3,2010medium long39.5% 32.3%40.8% 42.6% 32.3% 32.2%transcontinental24.2%22.8% 20.9%
+Table 3 .3Contrail frequency index per 1000 miles after reduction with different α values.daterange of flights no reduction α=10 α=40 max reductionshort3.51.20.50.4April 12, 2010medium long9.0 10.75.7 8.13 4.62.4 3.5transcontinental20.717.710.28.1short3.110.40.3April 19, 2010medium long12.5 16.86.7 113 52.5 4.1transcontinental12.09.94.32.6short2.30.70.30.2April 3, 2010medium long13.1 16.46.4 92.9 4.42.5 3.5transcontinental11.17.42.91.6
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+I. IntroductionAircraft induced environmental impact has drawn attention in recent years. 1 The three largest emission impacts include direct emissions of greenhouse gases such as CO 2 , emissions of NOx, and persistent contrails.Contrails are clouds that are visible trails of water vapor made by the exhaust of aircraft engines.Contrails form when a mixture of warm engine exhaust gases and cold ambient air reaches saturation with respect to water, forming liquid drops which quickly freeze.They persist if the aircraft is flying in certain atmospheric conditions.Persistent contrails reduce incoming solar radiation and outgoing thermal radiation in a way that accumulates heat. 2 The global mean contrail cover in 1992 was estimated to double by 2015, and quadruple by 2050 due to an increase in air traffic. 3Studies suggest that the environmental impact from persistent contrail is estimated to be three to four times, 4 or even ten times 5 larger than the aviation induced emissions.Therefore, methods to reduce aircraft induced persistent contrails are needed to minimize the impact of aviation on climate.Efforts have been made in the past to reduce the persistent contrail formation.Gierens 6 and Noppel 7 reviewed various strategies for contrail avoidance.Mannstein 8 proposed a strategy to reduce the climate impact of contrails significantly by only small changes in individual flight altitude.Campbell 9 presented a methodology to optimally reroute aircraft trajectories to avoid the formation of persistent contrails with the use of mixed integer programming.Both methodologies require onboard contrail detection system and flight rerouting.Fichter 10 showed that the global annual mean contrail coverage could be reduced by downshifting the cruise altitude.Williams 11,12 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restriction policy.These restrictions generally imply more fuel burn, thus more emissions, and add congestion to the already crowded airspace at lower altitudes.The objective of this paper is to develop strategies to reduce persistent contrail formation with consideration to extra emissions and air space congestion.The concept of contrail frequency index is used to quantify the severity of contrail formation.The strategy for reducing persistent contrail formation is to reduce contrail frequency index by altering the aircraft's cruising altitude with minimal increase in emissions.A class of contrail reduction strategies that considers extra emissions is proposed.It provides a flexible way to trade off between contrail reduction and emissions.The results show that the contrail frequency index can be reduced with extra emissions and without adding congestion to airspace.The strategies provide a starting point for developing operational policies to reduce the impact of aviation on climate.The remainder of the paper is organized as follows.Section II provides descriptions of contrail model, definition of contrail frequency index, and the fuel burn and emission models.Next, contrail reduction strategies and the trade-offs between contrail reduction and emissions are described in Section III.Section IV shows the results.Finally, Section V presents a summary and conclusions.
+II. Data and Model
+II.A. Contrail ModelContrails are vapor trails caused by aircraft operating at high altitudes under certain atmospheric conditions.The contrail model in this paper uses atmospheric temperature and humidity data retrieved from the Rapid Updated Cycle (RUC) data, provided by the National Oceanic and Atmospheric Administration (NOAA).The horizontal resolution in RUC is 13-km.RUC data has 37 vertical isobaric pressure levels ranging between 100 and 1000 millibar (mb) in 25 mb increments.Since the vertical isobaric pressure levels do not correspond with 2,000 feet increments, linear interpolation was used to convert the RUC data to a vertical range from 26,000 feet to 44,000 feet with an increment of 2,000 feet.This range is chosen because it generally is too warm for contrails to form below 26,000 feet and most aircraft fly below 44,000 feet.Contrails form when a mixture of warm engine exhaust gases and cold ambient air reaches saturation with respect to water, forming liquid drops which quickly freeze.Contrails form in the regions of airspace that have ambient Relative Humidity with respect to Water (RHw) greater than a critical value r contr . 13Regions with RHw greater than or equal to 100% are excluded because clouds are already present. 14Contrails can persist when the environmental Relative Humidity with respect to Ice (RHi) is greater than 100%. 15In this paper, contrail favorable regions are defined as the regions of airspace that have r contr ≤ RHw < 100% and RHi ≥ 100%.The estimated critical relative humidity for contrail formation at a given temperature T (in Celsius) can be calculated asr contr = G(T -T contr ) + e liq sat (T contr ) e liq sat (T ) ,(1)where e liq sat (T ) is the saturation vapor pressure over water at a given temperature.The estimated threshold temperature for contrail formation at liquid saturation isT contr = -46.46 + 9.43ln(G -0.053) + 0.72ln 2 (G -0.053),(2)whereG = EI H2O C p P Q(1 -η) , (3)EI H2O is the emission index of water vapor (assumed to be 1.25); C p = 1004 (in JKg -1 K -1 ) is the isobaric heat capacity of air, P (in Pa) is the ambient air pressure, = 0.6222 is the ratio of molecular masses of water and dry air, Q = 43 • 10 6 (in JKg -1 ) is the specific combustion heat, and η = 0.3 is the average propulsion efficiency of the jet engine.The value of r contr is computed by Eq (1)-(3) using RUC measurements for RHw and temperatures.RHi is calculated by temperature and relative humidity using the following formula: 16 RHi = RHw • 6.0612e 18.102T /(249.52+T ) 6.1162e 22.577T /(237.78+T ) ,where T is the temperature in Celsius. Figure 1 shows the temperature, RHw, RHi, and contrail favorable regions at 8AM EDT on April 23, 2010 at an altitude of 34,000 feet.
+II.B. Contrail Frequency IndexContrail frequency index (CFI) is used to quantify the severity of contrail activities.This paper uses 13km RUC data instead of the 40km RUC data used in Ref. 17.The modified 13km RUC data divide the U.S. national airspace into a three dimensional grid with 337 elements along the latitude, 451 elements along the longitude, and 10 altitudes ranging from 26,000 feet to 44,000 feet.Air traffic in the U.S. can be mapped into the same volumetric grid.Contrail frequency index is the number of aircraft in a volumetric element which meets conditions for persistent contrail formation.Contrail frequency index is zero for volumetric elements which do not meet the conditions for persistent contrail formation.Precise definitions of contrail frequency index are provided by the following equations.The altitude level index l is defined as l = 1 . . . 10 corresponding to altitudes of 26, 000, 28, 000, . . ., 44, 000 feet.The persistent contrail formation matrix (contrail matrix) at time t at level l is defined asR l t =     r l 1,1,t r l 1,    ,(5)where r l i,j,t is 1 if r contr ≤ RHw < 100% and RHi ≥ 100% at grid (i, j), and 0 if the conditions are not met.The Center contrail frequency indices of twenty U.S. air traffic control centers at time t at level l are defined asC center,l,t = 337 i=1 451 j=1 r l i,j,t a l i,j,t c i,j ,(6)where a l i,j,t is the number of aircraft within RUC 13km grid (i, j) flying closest to altitude level l at time t, and c i,j is 1 when grid (i, j) is inside the center and 0 if not.The twenty U.S. air traffic control centers are listed in Table 1.The aircraft data were provided by the Federal Aviation Administration's Aircraft Situation Display to Industry (ASDI) data.For planning contrail reduction strategies, traffic flow managers need to know potentially high contrail regions in the next few hours.Therefore predicted contrail frequency indices are needed for contrail reduction strategies.Similar to the concept of Weather Impacted Traffic Index (WITI) introduced by Callaham et al. 18 and Sridhar, 19 and the three-dimensional index derived by Chen, 20 predicted contrail frequency index was defined as a convolution of predicted traffic data and forecast of atmospheric conditions.The index consists of the RUC forecast data and the predicted aircraft locations when t is a future time.The Center contrail frequency index can then be rewritten asC center,l,t =    337 i=1 451 j=1 r l i,j,t a l i,j,t c i,j if t <= t now , 337i=1451 j=1 rl i,j,t âl i,j,t c i,j if t > t now ,(7)where t now is the current time, rl i,j is defined in (5) with RUC forecast data, and âl i,j is the predicted number of aircraft within RUC 13km grid (i, j) flying closest to altitude level l at time t.Figure 2 illustrates how contrail frequency index is computed.The aircraft trajectories and contrail formations between 33,000 feet and 34,999 feet for the hour of 8AM EDT on April 23, 2010 are shown in Fig. 2a.An one-minute time interval is used.The blue polygons indicate the contrail favorable regions; grey dots are the aircraft between 33,000 feet and 34,999 feet.When the aircraft enter the blue polygons, contrails would form as indicated by blue dots.The number of blue dots is defined as the contrail frequency index.As shown in Fig. 2b, there are 148 blue dots for the hour in Kansas City Center.Therefore, the center contrail frequency index for Kansas City Center for the hour of 8AM EDT is 148.The total time, due to all aircraft that would form contrails during the hour, is 148 minutes.The Center contrail frequency indices for all 20 US air traffic control Centers at 34,000 feet at 8AM EDT on April 23, 2010 were computed and are shown in Fig. 3.As shown in the figure, Minneapolis Center (ZMP) and Chicago Center (ZAU) have high contrail frequency indices because there are large contrail favorable regions in the Centers and also high density of air traffic, as shown in Fig. 2a.Salt Lake City Center (ZLC) has large contrail favorable regions inside the Center but the contrail frequency index is low because not many aircraft fly through the Center.Contrail frequency index takes both atmospheric and air traffic data and quantifies the contrail activities.It will be used later in developing contrail reduction strategies.
+II.C. Fuel Burn and Emission ModelsThe computations of aircraft fuel burn and emissions are needed in order to study the trade-offs between contrail reductions and aircraft induced emissions.This paper uses the fuel consumption model in Eurocontrols Base of Aircraft Data Revision 3.7 (BADA). 21The air traffic data provide aircraft information including aircraft type, mass, altitude and speed to compute the fuel burn.There are five stages, climb, cruise, descent-idle, descent-approach, and descent-landing that are determined by the aircraft altitude and  speed.Only climb, cruise, and descent-idle models are used in this paper since the other two are used at the low altitudes.For climb stage, the fuel burn is computed using the following equation,F B = SF C • T • ∆t, (8)where F B is the fuel burn in kilograms, SF C (kg/min•kN) is the thrust specific fuel consumption, T is the trust in Newtons, and ∆t is the elapse time in minutes.For cruise, the fuel burn isF B = SF C • T • C f cr • ∆t,(9)where C f cr is the cruise fuel flow factor.For descent-idle, the fuel burn isF B = C f 3 (1 - h C f 4 ),(10)where C f 3 and C f 4 are descent fuel flow coefficients, and h is the altitude in meters.SFC in ( 8) and ( 9) are formulated asJet: SF C = C f 1 (1 + V T AS C f 2 ), Turboprop: SF C = C f 1 (1 - V T AS C f 2 ) • (V T AS /1000),(11)where V T AS is the true air speed in meters per second, and C f 1 and C f 2 are thrust specific fuel consumption coefficients.The thrust in (8) for climb stage is formulated asJet: T climb = C T c,1 (1 - h C T c,2 + C T c,3 • h 2 ), Turboprop: T climb = C T c,1 (1 - h C T c,2 )/V T AS + C T c,3 ,(12)where C T c,1 , C T c,2 and C T c,3 are climb thrust coefficients.For cruise, thrust is set equal to drag.Drag is computed byD = C D • ρ • V 2 T AS • S 2 , (13)where D is the drag in Newtons, C D is the drag coefficient, ρ (kg/m 3 ) is the air density, and S (m 2 ) is the wing reference area.The emission model is based on a prototype of the Aviation Environmental Design Tool (AEDT) developed by the Federal Aviation Administration (FAA). 22Five emissions are computed including CO 2 , SO x , CO, HC and NO x .Emissions of CO 2 and SO x (modeled as SO 2 ) are modeled based on fuel consumption. 23he emissions are computed byE CO2 = 3155 • F B, E SO2 = 0.8 • F B,(14)where E CO2 and E SO2 are emissions of CO 2 and SO 2 in grams, and FB is fuel burn in kilograms.Emissions of CO, HC and NO x are modeled through the use of the Boeing Fuel Flow Method 2 (BFFM2). 24The emissions are determined by aircraft engine type, altitude, speed, and fuel burn and the coefficients in International Civil Aviation Organization (ICAO) emission data bank.In the models, fuel burn is corrected to sea-level reference temperature (273.15K) and pressure (14.696 psi): 3.8 amb exp(0.2MF B c = (F B/δ amb )[θwhere F B c is the corrected fuel flow, P amb is the at-altitude ambient pressure, T amb is the at-altitude ambient temperature, and M is the Mach number.where EICO, EIHC and EIN O x are emission indices of CO, HC and N O x , H is the humidity correction factor, and ω is the specific humidity.The emissions are computed byE CO = EICO • F B, E HC = EIHC • F B, E N Ox = EIN O x • F B,(17)where E CO , E HC and E N Ox are emissions in grams.
+III. Contrail Reduction Strategies
+III.A. Use of contrail frequency indexContrail frequency index (CFI) quantifies the contrail activities.The strategy for reducing the persistent contrail formations is to minimize contrail frequency index by altering the aircraft's cruising altitude.Assume the aircraft at altitude level l in a Center are made to fly at a different level l .Both l and l range from 1 to 10, corresponding to altitudes of 26, 000, 28, 000, . . ., 44, 000 feet.The definition of the contrail frequency index is changed from (6) toC l center,l,t = 337 i=1 451 j=1 r l i,j,t a l i,j,t c i,j ,(18)A contrail frequency index matrix is formed asC center,t =       C 1 1,where the diagonal term C l l,t is the contrail frequency index at level l before changing cruising altitude, and C l l,t is the contrail frequency index when guiding aircraft at level l to level l .The contrail reduction from level l to l is ∆C l l,t = C l l,t -C l l,t .Note that when l > l, not all aircraft have the ability to fly from level l to level l .If altitude level l is higher than an aircraft's maximal flight altitude, it stays at level l and is not counted in C l l,t .In addition, if an aircraft crosses a sector boundary and causes congestion, it stays at level l and does not add to C l l,t .Additional conditions can be added to satisfy other operational procedures.The strategy is to find the altitude that would form least contrails.In other words, find the smallest element in each column of C center,t .If the aircraft are limited to alter ∆l levels, the solution is the smallest elementin [C l-∆l l,t . . . C l l,t . . . C l+∆l l,t] T in each column.The solution is denoted as [ l1 . . .l10 ].Each li means aircraft at flight level i is flying at level li .If li = i, the aircraft at level i do not alter.The total contrail reduction at the given center at time t can be expressed asΣ∆C t = 10 i=1 ∆C li i,t .(21)Consider the traffic situation at Kansas City Center.For ∆l = 2, the CFI matrix at 8AM EDT on April 23, 2010 was computed,C ZKC =                    0 0 0 × × × × × × × 0 0 0 0 × × × × × × 0 0 0 0 0 × × × × × × 0 0 0 0 0 × × × × × × 61 89 148 387 233 × × × × × × 35 102 230 154 83 × × × × × × 104 213 141 65 0 × × × × × × 164 67 22 0 0 × × × × × × 137 17 0 0 × × × × × × × 18 0 0                    , (22)where the elements not used are marked as ×.The center is divided into sectors horizontally and vertically.An air traffic controller monitors traffic in each sector and maintains separation between aircraft.The number of aircraft in a sector is kept below a maximum, referred to as Monitor Alert Parameter (MAP) in the current U.S. air traffic system, to keep the controllers workload within limits. 25The MAP is used to define the airspace capacity.The contrail reduction moves will not change the sector counts unless they cross the sector boundaries.The strategies only allow the moves such that the aircraft count in a sector does not exceed the sector capacity after the moves.In the previous example, Kansas City Center has 15 high sectors and 11 super-high sectors.Among them, sector 31 has the highest sector count during the hour.Sector 31 has a lower bound of 37,000 feet and is on top of sector 28, 29 and 30, shown in Fig. 4. The move from level 6 (35,000-36,999 feet) to level 7 (37,000-38,999 feet) would move some aircraft in sector 28, 29 and 30 to sector 31.Sector 28, 29, 30 and 31 have the MAP values of 18, 18, 19 and 21 respectively.Figure 5 shows the MAP values and the sector counts in sector 28, 29, 30 and 31 before and after the moves.The aircraft counts in sector 28, 29 and 30 decrease because some aircraft have been moved up to sector 31; the sector count in sector 31 increases but is still lower than the sector capacity of 21.Thus the contrail reduction moves are applied without exceeding the capacity of the airspace.The altitudes of the aircraft are changed as they enter a new Center.The number of altitude changes is not expected to result in frequent climb and descents to affect current operations.However, if needed, additional constraints can be imposed on the number of altitude changes.Data from a 24-hour period on April 23, 2010 was analyzed.The contrail reduction strategies were applied and the results are shown in Fig. 6.The center CFIs before reduction are shown in blue bars.When the aircraft altitudes are allowed to alter by 2,000 feet, the center CFIs after reduction are shown in light blue bars.The total reduction among all centers is 62%.When the aircraft altitudes are allowed to alter by 4,000 feet, the total reduction is 88% as indicated in green bars.Since allowing aircraft to alter 4,000 feet would eliminate most of the contrail formation, the strategies in this paper limit the altitude changes to 4,000 feet.
+III.B. Tradeoff between contrails and emissionsAltering cruising altitudes changes the aircraft fuel consumption and emissions.In order to analyze the environmental impact of contrail reduction strategies, fuel consumption and emissions are considered in the strategies.Fuel burn and emissions computations are based on the models described in Sec.II.C. Define E l l,t as the emissions for all aircraft at level l at a given center at time t before contrail reduction, and E l l,t as the total emissions when guiding aircraft from level l to level l .When aircraft change their flying altitude from level l to l , the difference in emissions is∆E l l,t = E l l,t -E l l,t .(23)∆E l l,t < 0 implies emission reduction.Define the emission matrix as∆E center,t =         0 ∆E        .0This matrix helps to study the emissions trade-offs when applying contrail reduction strategies.For the contrail reduction solution of [ l1 . . .l10 ], the change in emissions can be expressed asΣ∆E t = 10 i=1 ∆E li i,t .(25)Consider the same example in the previous subsection and study the trade-offs between contrail reduction and CO 2 emissions.The emission matrix for CO 2 was computed based on the models described in Sec.II.C and is the following:∆E ZKC =                    0 484 1130 × × × × × × × -27 0 531 3562 × × × × × × -41 -31 0 1674 3169 × × × × × × -28 11 0 1417 4542 × × × × × × 55 237 0 2143 3462 × × × × × × 285 1331 0 1683 1042 × × × × × × 961 1237 0 420 2 × × × × × × 434 1892 0 0 0 × × × × × × 70 106 0 0 × × × × × × × 128 0 0                    ,(26)where the elements not used are marked as × and the unit is kilograms.Assuming the environmental impact of the contrail frequency index of 1 is equivalent to CO 2 emissions of 10 kg, the move from level 5 to 4 makes sense because a reduction of 148 in CFI is greater than the impact of additional CO 2 of 1417 kg (148 • 10 -1417 > 0).However, the move from level 6 to 4 is not worth while because the net impact is negative (230 • 10 -4542 < 0).Instead, the move from 6 to 8 is preferred because it has a CFI reduction of 66 with additional CO 2 emissions of 434 kg and reduces the net impact (66 • 10 -434 > 0).Similarly, the move from level 7 to 8 and from 8 to 9 are not preferred because of the net negative impacts.Aircraft at level 7 and 8 are not altered.The new solution can be denoted as [1 2 3 4 4 8 7 8 9 10], resulting in a CFI reduction of 214, with additional CO 2 emissions of 1851 kg.Compared with the maximal reduction strategy, this strategy achieves less contrail reduction, 40% versus 84%, but emits much less CO 2 emissions, 1, 851 kg vs 7, 957 kg (77% less).This example shows that the proposed contrail reduction strategies have the capability to trade off contrail reduction with emissions.Considering the relative environment impact of emissions and contrails, the strategy would move aircraft only if the contrails reduction benefits exceed the environmental impact of additional emissions.The aircraft would be guided from level l to l only if∆C l l,t > 1 α ∆E l l,t ,(27)where ∆C i,t and ∆E l,t are defined in (20) and (23) and α is a user-defined trade-off factor.It can be interpreted as the equivalent emissions in kg that has the same environmental impact as the contrail frequency index of 1.For the maximal contrail reduction strategy, the effect of emissions is ignored.In other words, α = ∞.Also, α = 0 simply means no reduction strategy is applied because (27) will never be true.Higher values of α means more contrail reduction and more emissions (closer to maximal reduction strategy); lower α means less contrail reduction and less emissions (closer to no reduction).In the previous example, α is 10.The appropriate value of α can be determined in two different ways.It is possible to monetize the value of both contrails and emissions as suggested in Ref. 26.Another approach is to consider contrails and emissions as disturbances to the global climate equilibrium and measure their impact as changes to the global mean surface temperature. 27However, both these methods are beyond the scope of this paper and the value of α will be considered as a user-preference weighting factor in the rest of the paper.
+IV. ResultsThis section presents the results of contrail reduction strategies and the trade-offs between contrail reduction and extra emissions over a 24-hour period on April 23, 2010.The 24-hour period starts at 4AM EDT and ends at 4AM the next day.The strategies allow aircraft to move 4,000 feet up or down within a center and use various user-defined α values to trade off between contrail reduction and emissions.This paper focuses on the trade-offs between contrails and CO 2 emissions while other emissions like NOx, SO 2 , HC and CO have a similar trend.Figure 7 shows the hourly variations in contrail reduction and extra emissions with different trade-off factors during a 24-hour period over the entire U.S. In Fig. 7a, the blue line shows the hourly CFI during the day with no reduction strategy applied (α = 0).When reduction strategies are applied, it is consistent that higher α results in lower CFI, meaning more reduction.The maximal reduction strategy (α = ∞), shown in the magenta line, has the lowest CFI at every hour.On the other hand, Fig. 7b shows that higher α results in higher extra CO 2 emissions, and the maximal reduction strategy has the highest CO 2 emissions.The results show that contrails reduction results in extra CO 2 emissions.Looking at the Center level, Fig. 8 shows the daily contrail reduction and extra emissions in twenty U.S. air traffic control centers.The blue bars in Fig. 8a are the daily center contrail frequency index for each Center.It is consistent that for all Centers, higher α values results in more contrail reduction and the maximal reduction strategy achieves most contrail reduction in all twenty Centers.On the other hand, higher α also results in more CO 2 emissions, as shown in Fig. 8b, while the maximal reduction strategy has the most CO 2 emissions.Table 2 summarizes the trade-offs between contrail reduction and extra CO 2 emissions over the entire U.S. on April 23, 2010.On that day, the maximal reduction strategy has an 88% contrail reduction rate with extra CO 2 emissions of 3778 megagram (Mg).A smaller value of α lowers the contrail reduction ratio but has less emissions.For α = 40, the contrail reduction rate is 73% with 2,621 Mg extra CO 2 emissions, 31% less than the emissions in the maximal reduction strategy.If CO 2 has more environmental impact, using α = 10 results in a contrail reduction of 21% with 100 Mg extra CO 2 emissions, 97% less than the emissions in the maximal reduction strategy.As for fuel burn, considering all aircraft flying between 26, 000 feet and 44, 000 feet on a day with large contrail favorable regions, an 80% reduction in contrails can be achieved with around 1% extra fuel.The increase in fuel would be less on a day with smaller contrail favorable regions.The main focus of this paper is to study the trade-offs between contrail reduction and extra emissions.Therefore, the factor of the extra fuel burn is not taken into account in the strategies.Figure 9 shows the contrail reduction versus extra CO 2 emissions with various α values.In the figure, more contrail reduction takes place from left to right and more CO 2 emissions occurs from bottom to top.At the lower-left point, no reduction strategy is applied.The upper-right point is the maximal reduction strategy.As the values of α increases, the curve moves from lower-left to upper-right.The user-defined trade-off factor α provides a flexible way to trade off between contrail reduction and extra emissions.Better understanding of the trade-offs between contrails and emissions and impact on the climate need to beRelative Humidity with respect to ice (d) Contrail favorable regions
+Figure 1 .1Figure 1.Atmospheric data and contrail favorable regions at 34,000 feet at 8AM EDT on April 23, 2010.
+entire U.S. airspace (b) Kansas City Center
+Figure 2 .Figure 3 .23Figure 2. Aircraft trajectories and contrail favorable regions at 8AM EDT on April 23, 2010.
+F B c is used in ICAO emission data bank to determine the reference emission index REIHC, REICO and REIN O x for HC, CO and NO x .The emission indices are computed by EICO = REICO(θ 3.3 amb /δ 1.02 amb ), EIHC = REIHC(θ 3.3 amb /δ 1.02 amb ), EIN O x = REIN O x [exp(H)](δ 1.02 amb /θ 3.3 amb ) 0.5 , H = -19.0(ω-0.0063),
+Figure 4 .4Figure 4. Kansas City Center sector 28, 29, 30 and 31.
+Figure 5 .5Figure 5. MAP values and sector counts before and after the contrail reduction strategies at 8AM EDT on April 23, 2010.
+Figure 6 .6Figure 6.Results of contrail reduction strategies on April 23, 2010.
+Figure 7 .7Figure 7. Hourly contrail reduction and extra CO2 emissions using different trade-off factors on April 23, 2010.
+Figure 9 .9Figure 9. Contrail reduction versus extra CO2 emissions on April 23, 2010.
+Table 1 .1Center index of twenty continental U.S. air traffic control centers.IndexNameIndexName1Seattle Center (ZSE)11Chicago Center (ZAU)2Oakland Center (ZOA)12Indianapolis Center (ZID)3Los Angeles Center (ZLA)13Memphis Center (ZME)4Salt Lake City Center (ZLC)14Cleveland Center (ZOB)5Denver Center (ZDV)15Washington D. C. Center (ZDC)6Albuquerque Center (ZAB)16Atlanta Center (ZTL)7Minneapolis Center (ZMP)17Jacksonville Center (ZJX)8Kansas City Center (ZKC)18Miami Center (ZMA)9Dallas/Fort Worth Center (ZFW)19Boston Center (ZBW)10Houston Center (ZHU)20New York Center (ZNY)
+2 )], δ amb = P amb /14.696, θ amb = (T amb + 273.15)/273.15,
+The diagonal elements of the matrix show the current CFIs at various altitudes.First consider the case if the aircraft are allowed to move one level (2,000 feet) up or down to reduce contrail formation.All the aircraft between 33,000 feet and 34,999 feet (level 5) have a totalCFI of 148 (C ZKC (5, 5) = 148). Moving the aircraft to level 4 will result in zero CFI (C ZKC (4, 5) = 0), areduction of CFI by 148. Other contrail reduction moves include moving aircraft from level 6 to 7 (a CFIreduction of 17), 7 to 8 (a CFI reduction of 74) and 8 to 9 (a CFI reduction of 5). The solution is expressedas [1 2 3 4 4 7 8 9 9 10], resulting in a CFI reduction from 541 to 297, a 45% reduction. If the aircraft areallowed to move two levels up or down, even greater reductions can be achieved. The moves include movingaircraft from level 5 to 4, 6 to 4, 7 to 8 and 8 to 9. The solution is expressed as [1 2 3 4 4 4 8 9 9 10],resulting in a contrail reduction from 541 to 84, an 84% reduction.
+4s shown in the matrix and in(22), moving aircraft from level 5 to 4 results in a CFI reduction of 148 with additional CO 2 emissions of 1,417 kg (∆E45,t = 1417); moving from level 6 to 4 results in a CFI reduction of 230 with additional CO 2 of 4,542 kg; moving from level 7 to 8 results in a CFI reduction of 74 with additional CO 2 of 1892 kg; moving from level 8 to 9 results in a CFI reduction of 5 with additional CO 2 of 106 kg.This solution achieves the most contrail frequency index reduction of 457 with additional CO 2 emissions of 7,957 kg.
+		
+		
+			
+developed to fully utilize this class of contrail reduction strategies.
+V. ConclusionsA class of strategies for reducing persistent contrail formations with the capability to trade off between contrails and emissions has been developed.The concept of contrail frequency index is defined and used to quantify the contrail activities.The strategy of reducing the persistent contrail formations is to minimize the contrail frequency index by altering the aircraft's cruising altitude with consideration to extra emissions.The strategies use a user-defined factor to trade off between contrail reduction and extra emissions.The analysis results show that the contrails can be reduced with extra emissions and without adding congestion to airspace.For the day tested, the results show that the maximal contrail reduction strategy can achieve a contrail reduction of 88%.When a trade-off factor is used, the strategy can still achieve a 73% contrail reduction while emitting 31% less emissions compared to the maximal contrail reduction strategy, or achieve a 21% contrail reduction while only emitting 97% less emissions.The user-defined trade-off factor provides a flexible way to trade off between contrail reduction and extra emissions.Better understanding of the trade-offs between contrails and emissions and impact on the climate need to be developed to fully utilize this class of contrail reduction strategies.The strategies provide a starting point for developing operational policies to reduce the impact of aviation on climate.			
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diff --git a/file139.txt b/file139.txt
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@@ -0,0 +1,660 @@
+
+	
+	
+		
+I. IntroductionAircraft-induced environmental impact has drawn attention in recent years. 1 A recent study estimates that aviation is responsible for 13% of transportation-related fossil fuel consumption and 2% of all anthropogenic carbon dioxide emissions. 2Domestic air traffic is expected to grow at an annual rate of 3.5% over the next 20 years, and the global air traffic is expected to grow more rapidly at an annual rate of 4.8% from 2011 to 2030. 3 To address the aviation environment impacts with the forecast in air traffic growth, various methods have been proposed.The three largest environmental impacts for enroute air traffic include direct emissions of greenhouse gases such as carbon dioxide (CO 2 ), emissions of nitrogen oxides (NO x ), and persistent contrails.CO 2 and NO x emissions are related to fuel burn therefore minimizing fuel consumption results in minimal emission solutions.Various procedures have been proposed in the past to reduce the persistent contrail formation, including promising approaches based on changing aircraft flight altitudes.Mannstein 4 proposed a strategy to reduce the climate impact of contrails significantly by only small changes in individual flight altitude.Williams 5,6 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restriction policies.However, these restrictions generally imply more fuel burn, thus more emissions, and add congestion to the already crowded airspace at lower altitudes.Sridhar, 7 Chen, 8 and Wei 9 proposed contrail reduction strategies by altering an aircraft's cruising altitude in a fuel-efficient way, but these strategies did not address the environmental impact from aircraft emissions.Recently, the Absolute Global Temperature Potential was introduced in Ref. 10 and 11 to study the combined effect of CO 2 emissions and contrail formation on the reduction strategies, and effect of NO x was added in Ref. 12.However, none of the above evaluates both the reduction in environmental cost and the increase in operational costs for the reduction strategies.The idea of placing a financial cost to the impact aircraft operations have on the environment has been used by Virgin America airlines.Virgin America offers passengers the option to pay for carbon-offset based on the length of their flight. 13A methodology can be developed to evaluate a policy that seeks to minimize the environmental impact due to aircraft operations while considering the cost to the airline for invoking such a policy.The objective of this paper is to evaluate the tradeoff between environmental impact reduction and the corresponding operational costs for enroute air traffic.First, a linear climate model was used to convert climate effects of CO 2 emissions and aircraft contrails to changes in Absolute Global Temperature Potential, 14 a metric that measures the mean surface temperature change due to aircraft emissions and persistent contrail formations.NO x is not considered since its effect on the reduction strategy is minor. 12Next, the concept of social cost of carbon 15 and the carbon auction price from California's 2013 cap-and-trade system 16 were used to provide an estimate of the environmental cost of CO 2 , which was used to estimate the cost of contrails.Even though the estimate of the cost is highly uncertain, 17 a suggested value was used and sensitivity analysis was conducted.The environmental impact reduction strategy uses a previously developed fuelefficient contrail reduction strategy 8 to minimize the combined impacts of emissions and contrails.The strategy minimizes the environmental impact by altering the aircraft's cruising altitude while computing the additional fuel burn and emissions.Some policies may consider this strategy to be favorable when the reduction in the combined environmental cost exceeds the increase in operational cost with a certain tradeoff factor.This paper evaluates how the net environmental benefit varies with different decision-making time-horizons, carbon and fuel costs, and atmospheric conditions.Introducing the cost models provides a method to tradeoff environmental cost and operational cost that will result in maximal net environmental benefit.The remainder of the paper is organized as follows.Section II provides descriptions of the linear climate models, the environmental impact reduction strategy, and the environmental and cost models.Next, Section III shows the results and analysis of environmental reduction strategies with various parameters.Finally, Section IV presents a summary and conclusions.
+II. Models and Methods
+II.A. Linear Climate ModelsThe climate response to aviation emission and contrails can be modeled as outputs from a series of linear dynamic systems.The carbon cycle models describe the changes to the CO 2 concentration due to the transport and absorption of CO 2 by the land mass and various ocean layers.The Radiative Forcing (RF) for CO 2 emissions is comprised of a steady-state component and three exponentially decaying components. 18oncentration dynamics of other non-CO 2 greenhouse gases can be described by first order linear systems.Radiative Forcing due to different emissions affects the climate by changing the Earth's global average nearsurface air temperature and the temperature response and energy balance to RF can be modeled using either a first order linear model 19 or a second order linear model. 20ontrails form when a mixture of warm engine exhaust gases and cold ambient air reaches saturation with respect to water, forming liquid drops which quickly freeze.Contrails occur at different regions of the earth and add non-uniform sources of RF to the atmosphere.The latest estimates indicate that contrails caused by aircraft may be causing more climate warming today than all the residual CO 2 emitted by aircraft. 21The net RF for contrails includes the effect of trapping outgoing longwave radiation from the Earth and that of reflecting incoming shortwave radiation from the sun.Energy Forcing (EF) is the net energy flux induced to the atmosphere by a unit length of contrail over its lifetime.Estimates of EF given the RF forcing due to contrails are described in Ref. 22.The lifetime associated with different emissions and contrails varies from a few hours to several hundred years.The impact of certain gases depends on the amount and location of the emission, and the decision-making time horizon, H in years, when the impact is estimated.These variations make it necessary to develop a common yardstick to measure the impact of various gases.Several climate metrics have been developed to assess the impact of the aviation emissions.Using linear climate response models, the Absolute Global Temperature Potential (AGTP) measures the mean surface temperature change because of different aircraft emissions and persistent contrail formations. 14AGTP provides a way to express the combined environmental cost of emissions and contrails as a function of the fuel cost.Only CO 2 emissions are considered in this paper, as the effect of NO x emissions are relatively small compared with CO 2 . 12Assume that the RF due to contrails is independent of the location of the contrails, the near surface temperature change ∆T , in Kelvin (K), for the decision-making time horizon of H years, can be approximated as∆T (H) = ∆T CO2 (H) + ∆T Con (H),(1)where ∆T CO2 (H) is the contribution to AGTP from CO 2 emissions for the time horizon of H years and is a linear function of additional CO 2 emissions, and ∆T Con (H) is the contribution to AGTP from contrails for the time horizon of H years and is a linear function of contrail length.The units of ∆T CO2 and ∆T Con are also in Kelvin.The coefficients of the linear functions depend on the linear models for RF, the specific forcing because of CO 2 , energy forcing because of contrails, energy balance model and the duration of the climate effect horizon. 10 Using the coefficients described in Ref. 12, Eq.( 1) can be rewritten as∆T (H) = α(H)E CO2 + β(H)L Con ,(2)where α(H) is the coefficient of AGTP due to CO 2 for the time horizon of H in K/kg, β(H) is the coefficient of AGTP due to contrails for the time horizon of H in K/km, E CO2 is the amount of CO 2 emissions in kg, and L Con is the contrail length in km.A list of α(H) and β(H), derived from Ref. 12, is shown in Table 1.Notice that the AGTP coefficient for contrails is much larger at shorter time horizons and smaller at longer time horizons, as contrails have more short-term environmental impact; the AGTP coefficient for CO 2 does not change much with different time horizons.Table 1.AGTP coefficients for CO2 and contrails for three different time horizonsTime Horizon H = 25 years H = 50 years H = 100 years α(H), K/kg 6.71×10 -16 5.78×10 -16 5.07×10 -16 β(H), K/km 2.99×10 -14 6.98×10 -15 5.10×10 -15
+II.B. Environmental Impact ReductionPrevious research 3 shows that the aviation environmental effect can be reduced efficiently by only changing the flight cruise altitude.This paper modifies the contrail reduction strategy described in Ref. 8 and uses the approach to reduce AGTP rather than contrails.The strategy divides the U.S. National Airspace System into twenty regions horizontally based on the twenty continental U.S. Air Traffic Control Centers (Centers), and ten levels vertically, from 26,000 feet to 44,000 feet at increments of 2,000 feet.At each hour, the strategy looks at all aircraft cruising in a Center at the same flight level, alters their cruise altitude by -4,000, -2000, +2000, or +4,000 feet, and selects the optimal cruise altitude that provides the minimal ∆T .The strategy also computes the additional fuel burn needed for such a move, and uses a fuel-efficient index, the ratio of the ∆T reduction and the additional fuel burn, to determine the temperature to fuel changes ratio.For example, if (a) moving all the aircraft at a Center up 2,000 feet will burn 1,000 kg more fuel for the climb and the remainder of the flight in the Center, and reduce ∆T by 2 × 10 -10 K, or if (b) moving the aircraft down 2,000 will reduce ∆T by 3 × 10 -10 K but will burn 10,000 kg additional fuel for the descent and the remainder of the flight in the Center, the strategy to minimize the climate impact will choose (b) to move aircraft 2,000 feet lower to achieve a greater reduction in ∆T .However, if the strategy looks at the fuel-efficiency index and only moves aircraft when the fuel-efficient index is greater than 10 -10 K/ 1000 kg, the strategy will choose (a) to move aircraft 2,000 feet higher, even though the ∆T reduction is 10 -10 K less, and the additional fuel burn is 10 times less.Using the different thresholds on the fuel-efficient index allows the strategy to tradeoff fuel burn with ∆T .Note that the strategy is applied to each Center at each  hour independently.Also these altitude changes are subject to the cruise altitude limits of each aircraft.An additional constraint is added such that where an aircraft crosses a sector boundary and causes congestion, it will stay at the original cruise altitude.Figure 1 presents the results from a 24-hour simulation based on historical data on April 19, 2010.The environmental impact reduction strategy, which allows the aircraft cruise altitudes to change in the range of -4,000 to +4,000 feet, was applied to the historical data, and the trade-off between AGTP due to CO 2 emissions, AGTP due to contrails, and total AGTP and additional fuel consumption for the decision-making time horizon of 100 years were summarized in Fig. 1a.The corresponding reduction in contrail length and additional CO 2 emissions are shown in Fig, 1b.In Fig. 1a, the contribution to AGTP from CO 2 emissions, the black line, increases linearly with additional fuel burn.The AGTP due to contrails, the green line, decreases faster at the beginning, and slower with more additional fuel burn.This is because the strategy selected the altitude changes with higher fuel-efficiency index first, resulting in more AGTP reduction with less additional fuel burn at the beginning (left end of the curve); the changes with lower fuel-efficiency index were then selected that slowed down the AGTP reduction rate (right end of the curve).The cumulative AGTP, the blue line, decreases initially with reduction in contribution from contrails and is eventually offset by the increase in contribution from CO2 emissions.The curves show that even if the cost of fuel is not taken into consideration, under certain conditions, reducing contrails beyond a certain level may neither be economical nor good environmental policy.
+II.C. Cost ModelThe United States Government recently concluded a process to develop a range of values representing the monetized damages associated with an incremental increase in CO 2 emissions, commonly referred to as the social cost of carbon. 15These values were used in benefit-cost analyses to assess potential federal regulations.In California, the state has a carbon cap-and-trade system which is the largest of its kind in the U.S. and the second-biggest carbon market in the world behind the European Unions. 16California cites its program as an example for the rest of the world to follow, and plans to use it and other emissions-reduction measures to cut greenhouse-gas pollution to 1990 levels by 2020.The cap-and-trade system recently sold carbon allowances for $13.62 per metric ton.This paper attempts to relate AGTP due to CO 2 emissions and aircraft contrails to the environmental cost in dollar amounts in order to perform a quantitative analysis of the environmental benefit resulting from the environmental impact reduction strategy.Using the social cost of carbon dioxide as an estimate of environmental cost of CO 2 due to warming, the additional contribution to environmental cost from CO 2 emissions, ∆Cost CO2 , can be formulated as∆Cost CO2 = SCC • ∆E CO2 1000 ,(3)where SCC is the social cost of carbon in dollar per metric ton, and ∆E CO2 is the changes in CO 2 emissions in kg.In order to quantify the environmental cost of contrails, the environmental cost of temperature changes, specifically one Kelvin of AGTP, was defined using the SCC and the AGTP coefficient of CO 2 for time horizon H years,ECK = SCC 1000 • α(H) , (4)where ECK is the equivalent environmental cost of temperature change in dollars per Kelvin and α(H) is the AGTP coefficient of CO 2 for the time horizon of H years listed in Table 1.Using the ECK to relate the environmental cost from contrails, ∆Cost H Con , to ∆Cost CO2 assuming that the same ∆T CO2 and ∆T Con have the same environmental cost for the time horizon of H years, ∆Cost H Con can be formulated as∆Cost H Con = ECK • ∆T Con (H) = SCC 1000 • β(H) α(H) • ∆L Con ,(5)where ∆L Con is the change in contrail length, and β(H) is the AGTP coefficient of contrails for the time horizon of H years listed in Table 1.In general, ∆L Con is negative as the strategy is reducing the contrail length and ∆Cost CO2 is positive due to the additional fuel burn.The superscript H in ∆Cost H Con indicates the environment cost due to contrails depends on the decision-making time horizon.The combined environmental cost changes, ∆Cost H Env , from both CO 2 and contrails for time horizon of H years can be written as ∆CostH Env = ∆Cost CO2 + ∆Cost H Con ,(6)All ∆Cost H Env , ∆Cost CO2 , and ∆Cost H Con are in US dollars.Note that ∆Cost H Env is always negative after the environmental impact reduction strategy.The net environmental benefit index, N BI H Env , is defined asN BI H Env = -∆Cost H Env -∆Cost Opr ,(7)where ∆Cost Opr is the additional operational cost of applying the environmental impact reduction strategy.Only the cost of additional fuel burn is considered as additional operational cost in this paper.Note that since ∆Cost H Env is always negative after the environmental impact reduction strategy, the first term in Eq.( 7), -∆Cost H Env , indicates the environmental cost savings.For the same example in the previous subsections, using a social cost of CO 2 of $21 per metric ton suggested by the United States Government 15 as an estimate of the environmental cost of CO 2 , the fuel cost of $4 per gallon, and the fuel density of 0.82 kilogram per liter, the AGTP and additional fuel burn in Fig. 1a were converted into the environmental cost reduction, -∆Cost H Env , and additional operational cost, ∆Cost H Opr , are shown in Fig. 2a.The blue curve shows the environmental cost reduction versus the additional operational cost after the environmental reduction strategy.The black dash line is a straight line with a slope of one.When the blue curve is above the black line, it suggests that the reduction strategy provided a positive net benefit.The net benefit versus the additional operational cost is shown in Fig. 2b.At the apex of the curve, marked as 'x,' that the strategy could provide a positive N BI H=100 Env of around $57, 000, or equivalent to around 2, 700 tons of CO 2 , after applying the reduction strategy at the point that the strategy will burn an additional 1.05 × 10 5 kg fuel for all aircraft.When the blue curve falls below the black line in Fig. 2a and Fig. 2b, it suggests that the additional cost for the strategy exceeded the environmental benefit thus the strategy is not recommended.Introducing the cost model provides a solution to select the fuel-efficiency index described in Section II.B that will result in the most net environmental benefit.
+III. AnalysisThe cost models introduced in the previous section can be used to evaluate the environmental impact reduction strategy with different parameters, including the decision-making time-horizon of environmental impact and the cost estimate of CO 2 , and the fuel cost.The variation due to different days are also shown in this section.The social cost of carbon was used as an estimate of the environmental cost of CO 2 .The social cost of temperature changes, defined in Eq.( 4), was used to relate the environmental cost of contrails to CO 2 .
+III.A. Varying Decision-Making Time-HorizonSince CO 2 emissions and aircraft contrails have different life times, a parameter of decision-making timehorizon H needs to be defined to compute the Absolute Global Temperature Potential and evaluate the  environmental impact.Three different time horizons, 25, 50, and 100 years were considered.Figure 3a shows the environmental cost saving versus the additional operational cost with different time horizons.The social cost of CO 2 at $21 per metric ton was used as an estimate of the environmental cost of CO 2 , the social cost of temperature changes at time horizon 100 years was used to estimate the environmental cost of contrails, and the fuel cost of $4 per gallon was used in this analysis.The blue line in the figure is the same as in Fig. 2a for H = 100, and the green and magenta lines are for H = 50 and H = 25 respectively.As shown in the figure, the magenta line is much higher than the blue and green lines, and also above the black dashed-line all the time.This indicates that shorter time horizon would result in more short-term environmental cost savings for the same operational cost.This is because aircraft contrails have shorter life time than CO 2 so the benefit from contrail reductions is more obvious in a shorter time-horizon.For longer time-horizons, the impact of contrails decays and the relative impact from CO 2 becomes larger.The net environmental benefit for different time-horizons after applying the environmental impact reduction strategy described in Sec.II.B can be seen in Fig. 3b.Same as in Fig. 2a, at H = 100 (blue line), the strategy could result in an net environmental benefit of around $57, 000, or around 2, 700 tons of CO 2 equivalent for all aircraft in the U.S. on April 19, 2010, indicated at the blue 'x' in Fig. 3b.For a shorter time-horizon such as H = 50 (green line), the strategy could result in net environmental benefit of around $129, 000, or around 6, 100 tons of CO 2 equivalent, indicated at the green 'x'.For H = 25 (magenta line), the strategy could result in net environmental benefit of around $1, 421, 000, or around 67, 700 tons of CO 2 equivalent, indicated at the magenta 'x.'It is worth mentioning that the environmental cost saving and net benefit are time-horizon-dependent, meaning a net gain in benefit in a 25-year time horizon might turn into net loss in benefit at 50-or 100-year time horizons because the benefit from reducing contrails decays with the length of the time-horizon.Figure 4 shows how the maximum net benefit decays with time.The upper right magenta 'x' in the figure is the same as the magenta 'x' in Fig. 3b, showing an net environmental benefit of $1,421,000 at H = 25.The benefit decays to -$267, 000 at H = 50 and -$400,000 at H = 100, as the magenta line suggested.If the decision-making time horizon for the reduction strategy is H = 50, the net benefit decays from $129,000 at H = 50 to $6,100 at H = 100 (green line), which happens to be the net benefit for the strategy with decision time horizon of H = 100.This is because the strategy for decision time horizon H = 50 and H = 100 are the same in this case.The strategy may behave differently with different time horizons and the net environmental benefit may also vary.
+III.B. Varying Estimate of the Cost of Carbon DixocideEven though an approximate social cost of CO 2 is suggested, 15 the estimate of the cost is highly uncertain. 17n addition to the suggested price at $21 per ton of CO 2 , a sensitivity analysis was conducted using prices of $5 and $64 suggested in Ref. 15.Another good reference of the carbon cost is the auction price under California's cap-and-trade system in 2013, at $13.62 per metric ton of CO 2 . 16igure 5a is the same as Fig. 3b and is placed here for easier comparison.Figures 5b,5c, and 5d show  the net environmental benefit curves after reduction strategy for three different time horizons with different estimates of CO 2 cost with the fuel cost of $4 per gallon.Note that the scales on y-axis in these figures are different in order to shows the variations of the three curves in each individual plot.The maximum net benefit from the strategy is marked as 'x.'If the 'x' is located at the origin, it means there is no feasible solution to reduce environmental impact given the time horizon and the estimate of CO 2 cost.With higher estimate of CO 2 cost of $65, shown in Fig. 5b, the strategy results in more net benefit compared to that in Fig. 5a.On the other hand, when the estimated cost of CO 2 is small, the environmental benefit was offset by the relatively high operational cost.When the cost is $5, the strategy can only achieve net benefit at the 25-year time horizon, shown in Fig. 5c.Even with the estimate cost of CO 2 at $13.62, the current California auction price, the strategy cannot find a feasible solution for the net environmental benefit for time horizons of 50-and 100-years; the strategy can only achieve net benefit in a 25-year time horizon.In order to achieve more net benefit with a given set of time horizons and estimates of CO 2 costs, the efficiency of the environmental impact reduction strategy needs to be improved.Note that the strategy used in this paper is very conservative.It alters the cruise altitudes for all the aircraft within a Center to certain specified altitudes.The strategy can be improved by using a finer spatial resolution 9 and a resulting increase in net environmental benefit.Increasing the carbon cost or reducing the fuel cost will help the strategy to achieve more net environmental benefit.The net benefit with different estimated costs of CO 2 and fuel costs are shown in Table 2.For the environmental impact reduction strategy used in this paper, the net benefit will turn positive at a CO 2 price of $20 per ton with the fuel cost of $4 per gallon for H = 100, about 47% more than the current California auction price.
+III.C. Variation on Different DaysThe same simulation and analysis were applied to the entire month of April, 2010 based on the historical air traffic and atmospheric data with the estimated environmental cost of CO 2 at $21 and the fuel cost at $4.The daily net environmental benefit for the month with time horizon 25, 50, and 100 years are shown in Fig. 6.The daily net environmental benefits vary on different days mainly because of different atmospheric  conditions.The net benefit with decision-making time horizon of 25 years (magenta bar) are much higher than the net benefit with time horizon of 50 years (green bars) and 100 years (blue bars).The average daily net benefit for the month is $773,000 for H = 25, $102,000 for H = 50, and $63,000 for H = 100.The results show that the environmental impact reduction can achieve net benefit (environmental cost reduction is greater than the operational cost) for all time horizons on all 30 days in April, 2010.The daily total aircraft contrail length is also shown in the figure (green line).The daily contrail length is normalized so that it has the same magnitude as the environmental net cost at H = 25 (magenta bars).It is clear that the daily net benefit for H = 25 is highly correlated with the daily total contrail length; the correlation coefficient is 0.92.It is not surprising as the net benefit of the reduction strategy mainly comes from the reduction in contrail length, and in general more aircraft contrails can be reduced on days with more contrail formations.The correlations are not as high for H = 50 and H = 100.The results show that the environmental impact reduction strategy can reduce environmental cost effectively so that it outweighs the additional operational cost on days with different atmospheric conditions.
+IV. ConclusionsThis paper provides a method to evaluate the tradeoffs between environmental impact and the corresponding operational costs for enroute air traffic.A linear climate model and the concept of social carbon cost and Absolute Global Temperature Change Potential were used to provide an estimate of the aviation environmental costs.An environmental impact reduction strategy was introduced to reduce environmental costs by changing aircraft's cruise altitude while computing additional operational costs.Depending on the specific environmental policy, the strategy is considered favorable when the reduction in environmental costs exceeds the increase in operational costs.It is shown that the reduction strategy can achieve more environmental benefit with shorter decision-making time horizons.The results show at the current suggested social cost of CO 2 at $21 per metric ton and higher, the reduction strategy can achieve net benefits in 25-, 50-, and 100-year time horizons.However, at the recent California carbon auction price of $13.62 per metric ton, the strategy can only achieve net benefit at the 25-and 50-year time horizons.The auction price needs to be about 47% more than the current price in order to see net benefit in 100-year time-horizon.Increasing the efficiency of the strategy or reducing the operational cost would also gain more net benefit.The results also show that the reduction strategy can achieve net environmental benefit on days with different atmospheric conditions, and the daily net benefit for the 25-year time horizon is highly correlated with the daily aircraft contrail formations.This tradeoff study provides guidance to environmental policy that will result in the most net environmental benefit.CO 2 emissions and contrails
+Figure 1 .1Figure 1.AGTP (H=100), CO2 emissions, and contrail length versus additional fuel burn after the environmental reduction strategy for all flights on April 19, 2010.
+Figure 2 .2Figure 2. Environmental cost saving and net benefit for all flights on April 19, 2010.
+Figure 3 .Figure 4 .34Figure 3. Environmental cost saving index and factor with different time horizons for all flights on April 19, 2010.
+Estimate cost of CO 2 =$21 per ton, fuel cost $4 per gallon , US$ Environmental Net Benefit, US$ (b) Estimate cost of CO 2 =$65 per ton, fuel cost $4 per gallon , US$ Environmental Net Benefit, US$ (c) Estimate cost of CO 2 =$5 per ton, fuel cost $4 per gallon , US$ Environmental Net Benefit, US$ (d) Estimate cost of CO 2 =$13.62 per ton, fuel cost $4 per gallon
+Figure 5 .5Figure 5. Net environmental benefit index with different social cost of CO2 for all flights on April 19, 2010.
+Figure 6 .6Figure 6.Daily maximum net benefit with different decision-making time horizon and contrails for all flights in April, 2010.
+Table 2 .2Net environmental benefit after impact reduction strategy for all flights on April 19,2010Estimate of CO 2 costFuel CostH = 25 years H = 50 years H = 100 years$5 per ton$4 per gallon$140,000$0$0$13.62 per ton$4 per gallon$750,000$36,000$0$21 per ton$4 per gallon$1,421,000$129,000$57,000$65 per ton$4 per gallon$6,103,000$826,000$483,000$21 per ton$3 per gallon$1,606,000$162,000$91,000$21 per ton$4 per gallon$1,421,000$129,000$57,000$21 per ton$5 per gallon$1,276,000$95,000$23,000$21 per ton$6 per gallon$1,173,000$61,000$015x 10 5Environmental Net Benefit, US$5 10net benefit (H=25) net benefit (H=50) net benefit (H=100) normalized contrail length0 051015202530day of April, 2010
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+		
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+		
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+			JTownsend
+		
+		
+			JCutcher-Gershenfeld
+		
+		
+			EGreitzer
+		
+		
+			JKerrebrock
+		
+		Report to the United States Congress: Aviation and the Environment, A National Vision, Framework for Goals and Recommended Actions
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+			December 2004
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+	Tech. rep
+	Partnership for AiR Transportation Noise and Emissions Reduction
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+		Impact of Aviation on Climate
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+			GuyPBrasseur
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+			MohanGupta
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+		Bulletin of the American Meteorological Society
+		Bull. Amer. Meteor. Soc.
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+		Call for Papers
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+			BSridhar
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+			NYChen
+		
+		
+			HKNg
+		
+		10.1027/2192-0923/a000067
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+		Aviation Psychology and Applied Human Factors
+		Aviation Psychology and Applied Human Factors
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+		2192-0931
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+			4
+			2
+			
+			June 2013
+			Hogrefe Publishing Group
+			Chicago, IL
+		
+	
+	Tenth USA
+	Sridhar, B., Chen, N. Y., and Ng, H. K., "Energy Efficient Contrail Mitigation Strategies for Reducing the Environmental Impact of Aviation," Tenth USA/Europe Air Traffic Management Research and Development Seminar , Chicago, IL, June 2013.
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+			HermannMannstein
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+		10.1016/j.trd.2005.04.012
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+		Transportation Research Part D: Transport and Environment
+		Transportation Research Part D: Transport and Environment
+		1361-9209
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+			10
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+			September 2005
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+		Reducing the climate change impacts of aviation by restricting cruise altitudes
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+			VictoriaWilliams
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+			RobertBNoland
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+			RalfToumi
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+		10.1016/s1361-9209(02)00013-5
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+	
+		Transportation Research Part D: Transport and Environment
+		Transportation Research Part D: Transport and Environment
+		1361-9209
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+			7
+			6
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+			November 2002
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+	Williams, V., Noland, R. B., and Toumi, R., "Reducing the climate change impacts of aviation by restricting cruise altitudes," Transportation Research. Part D, Transport and environment, Vol. 7, No. 6, November 2002, pp. 451-464.
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+		Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation
+		
+			VictoriaWilliams
+		
+		
+			RobertBNoland
+		
+		10.1016/j.trd.2005.04.003
+	
+	
+		Transportation Research Part D: Transport and Environment
+		Transportation Research Part D: Transport and Environment
+		1361-9209
+		
+			10
+			4
+			
+			July 2005
+			Elsevier BV
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+	Williams, V. and Noland, R. B., "Variability of contrail formation conditions and the implications for policies to reduce the climate impacts of aviation," Transportation Research. Part D, Transport and environment, Vol. 10, No. 4, July 2005, pp. 269-280.
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+		Fuel efficient strategies for reducing contrail formations in United States airspace
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+			BanavarSridhar
+		
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+			NeilYChen
+		
+		10.1109/dasc.2010.5655533
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+	
+		29th Digital Avionics Systems Conference
+		Salt Lake City, UT
+		
+			IEEE
+			October 2010
+		
+	
+	Sridhar, B., Chen, N. Y., and Ng, H. K., "Fuel Efficient Strategies for Reducing Contrail Formations in United State National Air Space," 29th Digital Avionics Systems Conference, Salt Lake City, UT, October 2010.
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+	
+		Tradeoff Between Contrail Reduction and Emissions in United States National Airspace
+		
+			NeilYChen
+		
+		
+			BanavarSridhar
+		
+		
+			HokKNg
+		
+		10.2514/1.c031680
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+		Journal of Aircraft
+		Journal of Aircraft
+		0021-8669
+		1533-3868
+		
+			49
+			5
+			
+			2012
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Chen, N. Y., Sridhar, B., and Ng, H. K., "Tradeoff between Contrail Reduction and Emissions in United States National Airspace," Journal of Aircraft, Vol. 49, No. 5, 2012, pp. 1367-1375.
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+		A Linear Programming Approach to the Development of Contrail Reduction Strategies Satisfying Operationally Feasible Constraints
+		
+			PengWei
+		
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+			BanavarSridhar
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+			NeilChen
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+			DengfengSun
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+		10.2514/6.2012-4754
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+		AIAA Guidance, Navigation, and Control Conference
+		Minneapolis, MN
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+			American Institute of Aeronautics and Astronautics
+			August 2012
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+	
+	Wei, P., Sridhar, B., Chen, N., and Sun, D., "A Linear Programming Approach to the Development of Contrail Reduc- tion Strategies Satisfying Operationally Feasible Constraints," AIAA Guidance, Navigation and Control Conference, AIAA, Minneapolis, MN, August 2012.
+
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+	
+		Integration of Linear Dynamic Emission and Climate Models with Air Traffic Simulations
+		
+			BanavarSridhar
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+		
+			HokNg
+		
+		
+			NeilChen
+		
+		10.2514/6.2012-4756
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+		AIAA Guidance, Navigation, and Control Conference
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+			American Institute of Aeronautics and Astronautics
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+	Sridhar, B., Ng, H., and Chen, N., "Integration of Linear Dynamic Emission and Climate Models with Air Traffic Simulations," AIAA Guidance, Navigation and Control Conference, AIAA, Minneapolis, MN, August 2012.
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+		Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds
+		
+			BanavarSridhar
+		
+		
+			HokNg
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+		
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+I. IntroductionI nterest in the effect of aircraft condensation trails or contrails on climate change has increased in recent years. 1 Contrails form in the wake of aircraft for various reasons but the most important is the emission of water vapor. 2They appear and persist if the aircraft location has certain atmospheric conditions.The global mean contrail cover in 1992 is estimated to double by 2015, and quadruple by 2050 due to the increase in air traffic. 3Studies suggest that the environmental impact from persistent contrails may be three to four times, 4 or even ten times 5 larger than that from aviation induced emissions.Therefore, methods of persistent contrail reduction need to be explored to reduce aircraft induced environmental impact.Efforts have been made in the past years to design strategies for reducing persistent contrail formations.Gierens 6 and Noppel 7 reviewed various strategies for contrail avoidance including changing engine architecture, enhancing airframe and engine integration, using alternate fuels, and modifying traffic flow management procedures.Among the traffic flow management solutions, Mannstein 8 proposed a strategy to reduce the climate impact of contrails significantly by small changes to each aircraft's flight altitude.Campbell 9 presented a mixed integer programming methodology to optimally reroute aircraft to avoid the formation of persistent contrails.Fichter 10 showed that the global annual mean contrail coverage could be reduced by reducing the cruise altitude.Williams 11,12 proposed strategies for contrail reduction by identifying fixed and varying maximum altitude restrictions.Sridhar proposed strategies that minimize the persistent contrail formation by altering aircraft cruising altitudes in a fuel efficient way 13 and proposed methods of contrails avoidance in the presence of winds. 14These methods did not take into account the effect of existing cloud coverage and the presence of severe weather.The objective of this paper is to develop contrail reduction strategies using diverse weather resources.Two weather products were used in this paper, the wind, temperature and humidity forecast provided in the National Oceanic and Atmospheric Administration's Rapid Update Cycle and the weather forecast provided by the FAA's Corridor Integrated Weather System.4][15][16] This paper uses the two weather products in a complementary way to develop contrail reduction strategies in the presence of different weather conditions.The concept of contrail frequency index was used to quantify the severity of contrail formation. 17The contrail frequency index was defined as the number of aircraft flying through regions that would form contrails during a period of time.When the weather resource indicated that there were already clouds formed in the regions, the index was discounted.The indices are used to identify the air traffic control centers and altitudes with high potential contrail formation in the next few hours such that different strategies can be used to alter the aircraft cruising altitudes to reduce the contrail formation in the entire airspace.When severe weather presents, the feasibility of the strategies will be evaluated using the weather information.The proposed contrail reduction strategies are suitable for reducing the contrail formation in the entire airspace with the presence of clouds and severe weather conditions.The remainder of the paper is organized as follows.Section II provides the descriptions of weather and contrail weather models.Section III demonstrates the correlations between contrail and cloud coverage regions.Section IV derives the contrail frequency index using different weather resources and its usage for contrail reduction strategies.Finally, a summary and conclusions are presented in Section V.
+II. Models
+A. Weather ModelThis paper uses the FAA's Corridor Integrated Weather System (CIWS) weather model. 18CIWS provides accurate, low-latency, high-resolution three-dimensional (3D) weather information and forecasts for up to two hours.It has a temporal resolution of two and a half minutes and a spatial resolution of one kilometer by one kilometer.The CIWS 3D weather depiction is composed of two main product types: precipitation, or vertically integrated liquid (VIL), and echo tops.Figure 1a shows the CIWS precipitation information at 8AM eastern daylight time (EDT) on April 23, 2010.The colors indicate the VIL level, where blue is VIL level 1 and 2, light blue is 3, yellow is 4, and red is 5 and 6.VIL level 1 and above indicate the presence of precipitations and were used to identify the clouds coverage regions.In addition, pilots would generally penetrate VIL level less than 3 and avoid regions with VIL level 3 and above. 19IWS also provides echo tops information, which indicate the storm heights.Echo tops were used to provide three dimensional weather information.It is assumed that the storm is having impacts from ground level up to the height of the storm tops.
+B. Contrail ModelContrails are vapor trails caused by aircraft operating at high altitudes under certain atmospheric conditions.The contrail model in this paper uses atmospheric temperature and humidity data retrieved from the Rapid Updated Cycle (RUC) data, provided by the National Oceanic and Atmospheric Administration (NOAA).The horizontal resolution in RUC is 13-km.RUC data has 37 vertical isobaric pressure levels ranging between 100 and 1000 millibar (mb) in 25 mb increments.Since the vertical isobaric pressure levels do not correspond with 2,000 feet increments, linear interpolation was used to convert the RUC data to a vertical range from 26,000 feet to 44,000 feet with an increment of 2,000 feet.The range is chosen because it generally is too warm to form contrails below 26,000 feet and most aircraft fly below 44,000 feet.Contrails form when a mixture of warm engine exhaust gases and cold ambient air reaches saturation with respect to water, forming liquid drops which quickly freeze.Contrails form in the regions of airspace that have ambient Relative Humidity with respect to Water (RHw) greater than a critical value r contr . 20Regions with RHw greater than or equal to 100% are excluded because clouds are already formed. 21Contrails can persist when the environmental Relative Humidity with respect to Ice (RHi) is greater than 100%. 22In this paper, contrail favorable regions are defined as the regions of airspace that have r contr ≤ RHw < 100% and RHi ≥ 100%.The estimated critical relative humidity for contrails formation at a given temperature T (in Celsius) can be calculated asr contr = G(T -T contr ) + e liq sat (T contr ) e liq sat (T ) ,(1)where e liq sat (T ) is the saturation vapor pressure over water at a given temperature.The estimated threshold temperature for contrails formation at liquid saturation isT contr = -46.46 + 9.43ln(G -0.053) + 0.72ln 2 (G -0.053),(2)whereG = EI H2O C p P Q(1 -η) ,(3)EI H2O is the emission index of water vapor (assumed to be 1.25); C p = 1004 (in JKg -1 K -1 ) is the isobaric heat capacity of air, P (in Pa) is the ambient air pressure, = 0.6222 is the ratio of molecular masses of water and dry air, Q = 43 × 10 6 (in JKg -1 ) is the specific combustion heat, and η = 0.3 is the average propulsion efficiency of the jet engine.The value of r contr is computed by Eq (1)-(3) using RUC measurements for RHw and temperatures.RHi is calculated by temperature and relative humidity using the following formula:RHi = RHw × 6.0612e 18.102T /(249.52+T ) 6.1162e 22.577T /(237.78+T ) , (4)where T is the temperature in Celsius. Figure 1b shows the contrail favorable regions at 8AM EDT on April 23, 2010 at an altitude of 34,000 feet.
+III. AnalysisContrail favorable regions can be computed and predicted using the weather forecasts.One might ask what if there are already clouds in regions favorable for contrail formation.In that case, it does not matter if contrails are formed or not since the incoming and outgoing radiations are reduced anyway.That is, aircraft should be able to fly through these regions without causing negative environmental impact.On the other hand, if the weather condition is too severe to safely fly through, aircraft should avoid the regions even though doing so would reduce the contrail formations.In order to take into account these effects, precipitation prediction is used to modify the contrail models.To observe the correlations between contrail favorable and precipitation (VIL level 1 and above) regions, the two regions in Fig. 1 were overlapped and shown in Fig. 2. In the figure, the green polygons indicates the regions of CIWS precipitation and the grey contours are the contrail favorable regions over twenty U.S. air traffic control centers.As shown in the figure, the locations of contrail favorable and CIWS precipitation are correlated.In fact, there are 48% of CIWS precipitation regions overlapped with contrail favorable regions.Therefore, the effect should not be ignored.The average ratios of CIWS precipitation overlapped with contrail favorable regions at different altitudes over the year of 2010 are shown in Fig. 4. The coverage ratios vary with altitudes.The maximum coverage ratio at each month ranges from 19.0% (November, 34,000 feet) to 45% (January, 34,000 feet).It is noted that in summer the coverage ratios are larger at higher altitude (July and August at 40,000 feet), while the ratios are higher at lower altitude in winter (January and December at 34,000 feet).The observation can be interpreted as contrail favorable regions are larger at higher altitude in summer because of the warm temperature at lower altitude.In July and August, there are no contrail favorable regions formed at 26,000 and 28,000 feet.Since there are overlapped regions between the two that can not be ignored, the development of contrail formation analysis and reduction strategies should take into account the effect of precipitation.Another observation is that the contrail reduction strategies should be more efficient in winter than in summer since the contrail favorable regions in summer happen more frequently at high altitude (40,000 feet and above) where fewer aircraft are flying.The contrail formation analysis and reduction strategies in the presence of different weather conditions are described in the next section.
+IV. Methodologies
+A. Contrail Frequency IndexThis paper modifies the contrail frequency index (CFI) defined in Ref. 17 by adding the component of cloud coverage information and using 13km RUC data instead of 40km RUC data.The altitude level index, l, is defined as l = 1 . . . 10 corresponding to altitudes of 26, 000, 30, 000, . . ., 44, 000 feet.The potential persistent contrail formation matrix (contrail matrix) at time t at level l is defined asR l t =     r l 1,1,t r l 1,where r l i,j,t is 1 if r contr ≤ RHw < 100% and RHi ≥ 100% at grid (i, j), and 0 if not.The Center contrail frequency indices of twenty U.S. air traffic control centers at time t at level l are defined asCF I center,l,t = 337 i=1 451 j=1 r l i,j,t a l i,j,t c i,j ,(6)where a l i,j,t is the number of aircraft within RUC 13km grid (i, j) flying closest to altitude level l at time t, and c i,j is 1 when grid (i, j) is inside the center and 0 if not.The twenty U.S. air traffic control centers are listed in Table 1.Contrail frequency index is used to quantify the severity of contrail activities.For planning for contrail reduction stretegies, traffic flow managers need to know the potential high contrail regions in the next few hours.Therefore predicted contrail frequency index is needed for contrail reduction strategies.Similar to the concept of Weather Impacted Traffic Index (WITI) introduced by Callaham et al. 23 and Sridhar, 24 and the three-dimensional index derived by Chen, 15 predicted contrail frequency index was defined as a convolution of predicted traffic data and forecast of atmospheric conditions.The index consists of the RUC forecast data and the predicted aircraft locations when t is a future time.The Center contrail frequency index can then be rewritten asCF I center,l,t =    337 i=1 451 j=1 r l i,j,t a l i,j,t c i,j if t <= t now , 337i=1451 j=1 rl i,j,t âl i,j,t c i,j if t > t now ,(7)where t now is the current time, rl i,j is defined in Eq. ( 5) with RUC forecast data, and âl i,j is the predicted number of aircraft within RUC 13km grid (i, j) flying closest to altitude level l at time t.Based on the assumption that there are already clouds in regions with VIL level 1 and above, CFI should be discounted when the contrail favorable and CIWS VIL level 1 and above regions overlapped.However, regions with VIL level 3 and above are consider severe storms and pilots would try to avoid those regions.Contrail favorable regions are considered as soft constraint areas, meaning it is fine to fly through, but better to avoid for reducing the environmental impact.Severe weather regions are considered as hard constraint areas that pilots would try to avoid.In order to incorporate these constraints in the contrail reduction strategies, the VIL level matrix that maps the CIWS data to 13km RUC grid is defined asW l t =     w l 1,    ,(8)where w l i,j,t is the CIWS VIL level if the echo top at grid (i, j) is higher than altitude level l at time t.w l i,j,t is zero if the echo top at grid (i, j) is lower than altitude lvl l.The r i,j,t , l in Eq (5) will be set to zero if w l i,j,t is 1 or 2. This matrix will later be used to evaluate the feasibility of the contrail reduction strategies.As an example, the Center contrail frequency indices with and without weather information at 34,000 feet at 8AM EDT on April 23, 2010 were computed and are shown in Fig. 5.The blue bars are the CFI without weather information.When the contrail favorable regions are covered by CIWS VIL level 1 or 2, the CFI was discounted, as represented by the green bars.For ZMP, ZKC, and ZAU centers, the CFIs without weather information are 212, 148 and 237 respectively, while the CFIs without weather information are 188, 98 and 174 respectively.The CFI differences in these centers are more significant as there are more regions with VIL 1 and 2 overlapped with contrail favorable regions (see Fig. 2).Figure 6 shows the aircraft trajectories and contrail formations in Kansas City Center.In the figures, the grey polygon indicates the contrail favorable regions.Blue dots are the flying aircraft between 33,001 feet and 35000 feet.When the aircraft enter the grey polygon, contrails would form, as indicated by grey dot.The number of grey dots in Fig. 6a are the CFI without weather information, which is 148.The green polygon in Fig. 6b are the regions with CIWS VIL level 1 and 2. When an aircraft flies through a grey polygon (contrail favorable regions) overlapped with a green polygon, the aircraft is already covered by clouds therefore it should not be counted toward CFI.There are total of 50 grey dots that are inside green polygons, bringing down the CFI with weather information to 98.In order to determine if it is feasible to perform the contrail reduction strategies, the Weather Severity Index (WSI) is defined as the number of aircraft that would be impacted by the severe weather in a future time, formulated asW SI center,l,t = 337 i=1 451 j=1 ŵl i,j,t âl i,j,t ,(9)where ŵl i,j is defined in Eq. ( 8) with CIWS forecast data, and âl i,j predicted number of aircraft within RUC 13km grid (i, j) flying closest to altitude level l at time t.Note that the WSI is the same concept with WITI in Ref. 24 and is used to determine the feasibility of applying the proposed contrail reduction strategies.
+B. Contrail Reduction StrategiesThis paper extends the contrail reduction strategies developed in Ref. 13  and 2, as described in the previous subsection.Second is to use the weather severity index as an indicator to decide whether the strategy is feasible or not.Contrail frequency indices are used to quantify the severity of contrail formation.The strategy for reducing the persistent contrail formations is to minimize contrail frequency index by altering the aircraft's cruising altitude.Assume the aircraft at altitude level l at a center are made to fly a different level l .The contrail frequency index changes toCF I l center,l,t = 337 i=1 451 j=1 r l i,j,t a l i,j,t c i,j ,(10)A contrail frequency index matrix is formed asCFI center,t =       CF I 1 1,t CF Iwhere the diagonal term CF I l l,t is the contrail frequency index at level l before contrail reduction, and CF I l l,t is the contrail frequency index when guiding aircraft at level l to aircraft at level l .The contrail reduction is ∆CF I l l,t = CF I l l,t -CF I l l,t .Note that when l > l, not all aircraft have the ability to fly from level l to level l .If altitude level l is higher than an aircraft's maximal flight altitude, it stays at level l and is not counted in CF I l l,t .In addition, if an aircraft crosses a sector boundary and causes congestion, it stays at level l and does not add to CF I l l,t .The strategy is to find the smallest element in each column of CFI center,t .If the aircraft are limited to alter ∆l levels, the solution is the smallest element in [CF I l-∆l l,t . . .CF I l l,t . . .CF I l+∆l l,t] T in each column.The solution is denoted as [l 1 . . .l 11 ].Each l i means aircraft at flight level i is flying at level l i .If l i = i, the aircraft at level i did not alter.In order to determine whether the contrail reduction strategy is feasible in the presence of severe weather, a weather severity matrix is used, defined asWSI center,t =       W SI 1 1,t W SIThe increase of W SI if aircraft flying at level l are altered to level l is ∆W SI l l,t = W SI l l,t -W SI l l,t .The value helps to determine if it is feasible to alter the aircraft cruising altitude from level l to level l .If ∆W SI l l,t ≤ 0,that means it is even safer to fly at level l than at l, therefore the contrail reduction move can be applied.If ∆W SI l l,t > ε, where ε is a threshold value, the contrail reduction move might not be feasible because of the increase of weather impact.Further analysis is needed to determine the value of ε.As an example, the CFI matrix for Kansas City Center at 8AM EDT on April 23, 2010 was computed,CFI ZKC =                    0 0 0 × × × × × × × 0 0 0 0 × × × × × × 0 0 0 0 0 × × × × × × 0 0 0 0 0 × × × × × × 29 47 98 230 100 × × × × × × 17 51 124 39 32 × × × × × × 39 101 23 16 0 × × × × × × 91 18 15 0 0 × × × × × × 14 6 0 0 × × × × × × × 7 0 0                    . (16)Assume that the aircraft are allowed to move one level (2,000 feet) up or down to reduce contrails formation.All the aircraft between 33,001 feet and 35000 feet (level 5) have the total CFI of 98 (CFI ZKC (5, 5) = 98).Moving the aircraft to level 4 will result in 0 in CFI (CFI ZKC (4, 5) = 0), a 98 reduction.Other contrail reduction moves include moving aircraft from level 6 to 7, 7 to 8 and 8 to 9. The solution is expressed as [1 1 1 1 4 7 8 9 9 10], resulting in a contrail reduction from 260 to 125, a 51.9% reduction.If the aircraft are allowed to move two levels up or down, it can achieve more reduction.The moves include moving aircraft from level 5 to 4, 6 to 4, 7 to 9 and 8 to 9. The solution is expressed as [1 1 1 1 4 4 9 9 9 10], resulting in a contrail reduction from 260 to 20, a 92.3% reduction.To evaluate the impact of severe weather, WSI matrix was computed,WSI ZKC =                    1 2 3 × × × × × × × 1 2 3 3 × × × × × × 1 2 3 3 0 × × × × × × 2 3 3 0 12 × × × × × × 3 2 0 4 1 × × × × × × 2 0 4 1 0 × × × × × × 0 0 1 0 0 × × × × × × 0 1 0 0 0 × × × × × × 1 0 0 0 × × × × × × × 0 0 0                    . (17)For the case that allows aircraft to move two level up or down, the move from level 6 to level 4 results in a CFI reduction of 124 with WSI increased by 8 (WSI ZKC (4, 6) -WSI ZKC (6, 6) = 8).If the threshold ε is set to 10, the move is still feasible even with an increase of weather impact.If the threshold ε is set to 0, the move is not feasible.Instead, the aircraft in level 6 will be moved to level 8, resulting in a CFI reduction from 124 to 91 and a WSI reduction of 4. The contrail reduction strategies described in this section incorporates the weather data so that they are feasible in the presence of severe weather.Data from 24-hour period on April 23, 2010 were analyzed.The Center contrail frequency indices at all altitudes, with and without CIWS percipication information, are shown in Fig. 7.For the day, the difference of CFI with and without CIWS precipitation information among all centers are 14%.As shown in the figure, it has more impact in the centers including Denver (ZDV), Minneapolis (ZMP), Kansas City (ZKC), Chicago (ZAU) and Memphis Center (ZME).The effect of cloud coverage on computing contrail frequency index is significant and should not be ignored.Next, the contrail reduction strategies were applied using the CFI with weather information and WSI threshold ε = 0 .The results are shown in Fig. 8.The center CFIs with weather information before reduction are shown in green bars.When the aircraft are allowed to alter 2,000 feet, the center CFIs after reduction are shown in light green bars.The total reduction among all centers is 63.7%.When the aircraft are allowed to alter 4,000 feet, the total reduction is 92.6%.
+V. ConclusionsThis paper describes contrail reduction strategies using different weather resources.Two weather products were used in this paper, Rapid Update Cycle for the wind, temperature and humidity forecast and Corridor Integrated Weather System for the weather forecast.The two weather products are used in a complementary way to develop contrail reduction strategies.Analysis shows that the contrail favorable regions are correlated with regions with precipitation.During the month of April 2010, there are 53% of CIWS VIL level 1 and above overlapped with contrail favorable regions.For the day tested, adding precipitation weather information reduces the contrail frequency index by 14%.The strategies use the concept of contrail frequency index with weather information to achieve maxima contrail reduction while avoiding severe weather regions.The contrail reductions are achieved by changing the aircraft pre-departure cruising altitudes that would form less contrails.For the day tested, the strategies achieve a 63.7% contrail reduction taking into account the weather impact if the aircraft are allowed to change their cruising altitude 2,000 feet up or down.When the aircraft are allowed to move 4,000 feet up or down, it has a 92.6% reduction.The proposed contrail reduction strategies are suitable for reducing contrail formation in the entire airspace with the presence of different weather conditions.Figure 1 .1Figure 1.CIWS weather and contrail regions at 8AM EDT on April 23, 2010.
+Figure 2 .2Figure 2. Contrail favorable and CIWS precipitation regions overlapped at 8AM EDT on April 23, 2010.
+Figure 33Figure3shows the correlations between CIWS precipitation and contrail favorable regions in April 2010.The blue line indicates the size of the CIWS precipitation, defined as the number of grid points in 13km RUC 451 × 337 grid with CIWS VIL level 1 and above.The green line is the number of grid points that are overlapped with contrail favorable regions.During the month, there are 53% of CIWS VIL level 1 and above overlapped with contrail favorable regions.The yearly data of 2010 have the similar trend.
+Figure 3 .3Figure 3. CIWS precipitation regions overlapped with contrail favorable regions in April 2010.
+Figure 4 .4Figure 4. Overlap ratio between CIWS precipitation and contrail favorable regions in 2010.
+Figure 6 .6Figure 6.CIWS precipitation and contrail regions at 8AM EDT on April 23, 2010.
+Figure 7 .7Figure 7.Total contrail frequency index with and without weather information on April 23, 2010.
+Figure 8 .8Figure 8. Results of contrail reduction strategies on April 23, 2010.
+Table 1 .1Center index of twenty continental U.S. air traffic control centers.IndexNameIndexName1Seattle Center (ZSE)11Chicago Center (ZAU)2Oakland Center (ZOA)12Indianapolis Center (ZID)3Los Angeles Center (ZLA)13Memphis Center (ZME)4Salt Lake City Center (ZLC)14Cleveland Center (ZOB)5Denver Center (ZDV)15Washington D. C. Center (ZDC)6Albuquerque Center (ZAB)16Atlanta Center (ZTL)7Minneapolis Center (ZMP)17Jacksonville Center (ZJX)8Kansas City Center (ZKC)18Miami Center (ZMA)9Dallas/Fort Worth Center (ZFW)19Boston Center (ZBW)10Houston Center (ZHU)20New York Center (ZNY)
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+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, HI
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+			American Institute of Aeronautics and Astronautics
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+		29th Digital Avionics Systems Conference
+		Salt Lake City, UT
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+			IEEE
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+		Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds
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+			BanavarSridhar
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+			HokNg
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+			NeilChen
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+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
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+			American Institute of Aeronautics and Astronautics
+			September 2010
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+	Sridhar, B., Ng, H. K., and Chen, N. Y., "Aircraft Trajectory Optimization and Contrails Avoidance in the Presence of Winds," The 10th AIAA Aviation Technology, Integration, and Operations Conference, AIAA-2010-9139, AIAA, Fort Worth, TX, September 2010.
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+		Estimation of Air Traffic Delay Using Three Dimensional Weather Information
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+		The 26th Congress of ICAS and 8th AIAA ATIO
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+			American Institute of Aeronautics and Astronautics
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+			MRobinson
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+			2008
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+		
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+		AIAA Guidance, Navigation, and Control Conference
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+			American Institute of Aeronautics and Astronautics
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+	
+	Chen, N. Y., Sridhar, B., and Ng, H. K., "Prediction and Use of Contrail Frequency Index for Contrail Reduction Strategies," AIAA Guidance, Navigation, and Control Conference, Toronto, Ontario, August 2010.
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+		Description of the Corridor Integrated Weather System (CIWS) Weather Products
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+			2006
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+			RabindraPalikonda
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+	
+		Meteorologische Zeitschrift
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+		0941-2948
+		
+			14
+			4
+			
+			June-July 2003
+			Schweizerbart
+			Friedrichshafen at Lake Constance, Germany
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+	
+		Assessing NAS Performance: Normalizing for the Effects of Weather
+		
+			MBCallaham
+		
+		
+			JSDearmon
+		
+		
+			ACooper
+		
+		
+			JHGoodfriend
+		
+		
+			DMoch-Mooney
+		
+		
+			GSolomos
+		
+	
+	
+		4th USA/Europe Air Traffic Management R&D Symposium
+		Santa Fe, NM
+		
+			December 2001
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+	
+	Callaham, M. B., DeArmon, J. S., Cooper, A., Goodfriend, J. H., Moch-Mooney, D., and Solomos, G., "Assessing NAS Performance: Normalizing for the Effects of Weather," 4th USA/Europe Air Traffic Management R&D Symposium, Santa Fe, NM, December 2001.
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+		Relationship Between Weather, Traffic and Delay Based on Empirical Methods
+		
+			BanavarSridhar
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+		
+			SeanSwei
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+		10.2514/6.2006-7760
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+		6th AIAA Aviation Technology, Integration and Operations Conference (ATIO)
+		Wichita, KS
+		
+			American Institute of Aeronautics and Astronautics
+			September 2006
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+	Sridhar, B. and Swei, S., "Relationship between Weather, Traffic and Delay Based on Empirical Methods," 6th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Wichita, KS, September 2006.
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+I. INTRODUCTIONThis paper describes NASA's ATD-2 Phase 3 prototype capability, its initial demonstration in the North Texas, and its expected benefits.The Phase 3 concept expands on Phases 1 and 2 of the Integrated Arrival, Departure, and Surface (IADS) traffic management system and incorporates the use of TOS to identify alternative departure routes when flights are predicted to incur surface delay due to demand/capacity imbalances.
+A. BackgroundThe National Aeronautics and Space Administration (NASA) has collaborated with the Federal Aviation Administration (FAA) and industry partners to investigate and test IADS technologies over the last decade [1,2,3,4,5].For Phases 1 and 2 of the ATD-2 field demonstration, NASA developed a prototype Surface Metering Program (SMP) for the FAA's Terminal Flight Data Manager (TFDM) [6,7].This system has been used since 2018 by the American Airlines ramp and Air Traffic Control (ATC) Tower personnel at Charlotte Douglas International Airport (KCLT) with positive results [8].From November, 29 2017 to April, 30 2020, 25,748 departures (3.8% of departures) were held at the gate for an average of 5.9 minutes.Gate holds were estimated to have saved about 2,883,410 pounds of fuel and reduced CO2 emissions by 8,880,901 pounds, the equivalent of 66,038 urban trees [9].The maturation of the Phase 2 system which enables a strategic SMP [10], was based on a series of human-in-the-loop simulations [11], users' feedback [12,13], as well as refinements of the scheduler's takeoff, pushback and taxi times [14].The technologies and lessons learned have been transferred to the FAA.The FAA plans to replace the ATD-2 IADS system in CLT with TFDM by late 2021 and has started the process of developing new requirements for its future implementations.The goal for Phase 3 was to develop and operationally test a capability to support the management of departure demand/capacity imbalances in a multi-airport airspace.The Dallas-Fort Worth TRACON (D10) metroplex was chosen due to its high traffic demand and frequent delays due to inclement weather.This airspace includes two major hub airports, Dallas Love Field (KDAL) and Dallas-Fort Worth (KDFW), where both Southwest Airlines and American Airlines, NASA's ATD-2 field demonstration partners, have a large presence.The desire to incorporate TOS operations in the ATD-2 Phase 3 capability and evaluation stemmed from both industry partners' support and early analyses.In recent years, the concept of TOS has gained recognition by the FAA and the airline industry as a potentially useful resource to help ATC manage flowconstrained areas.Use cases of TOS outside of the Collaborative Trajectory Option Program (CTOP) were investigated by The MITRE Corporation, with the FAA and Collaborative Decision Making (CDM) groups [15].In addition, NASA's early benefit analysis indicated that rerouting departure flights on alternative routes, when deemed beneficial, may result in delay reductions for both rerouted flights and subsequent flights.The initial concept received strong support from field demonstration partners, the airline industry, the CDM Flow Evaluation Team (FET), the Surface CDM Team (SCT) group, and the FAA NextGen organization (FAA/ANG).
+B. Trajectory Option Set (TOS)In current-day operations, Flight Operators (FOs) can file only one flight plan per flight.While airline dispatchers may consider various routes before filing a flight plan, ATC assume that pilots will fly the filed route.This assumption is reasonable when flights are not subjected to surface or airborne delay.When demand/capacity imbalances occur, however, both the FOs and the ATCs may be looking for opportunities to offload demand to reduce congestion and flight delay.A TOS consists of a set of preferred routes, each of which has an associated Relative Trajectory Cost (RTC).The RTC is a weighted time that accounts for the cost of flying additional miles.Adding alternative TOS routes to the filed plans may provide opportunities to compare routes and determine when flights could depart or arrive earlier and benefit from a reroute.This is analogous to when drivers look up alternative routes on a map application to assess if other routes would enable them to arrive at their destination sooner.This determination requires the ability to predict when benefits (delay savings) outweigh the cost (RTC) of flying an alternative versus the filed route (net savings) in real-time.This is, in essence, what the Phase 3 capability attempts to provide for departure flights to both the FOs and ATC in a tactical manner.The concept of TOS has been integrated in FAA's Traffic Flow Management System (TFMS).It is a key component of the CTOP to help ATC to strategically manage flows in constrained areas.CTOP analyzes demand at a constrained area and determines when flights could be routed outside of constrained areas based on TOS submitted no later than 45 min prior to departure.Flights that cannot be rerouted would receive an Expect Departure Clearance Time (EDCT).TOS has also been integrated in Pre-Departure ReRoute (PDRR) and Airborne ReRoute (ABRR) capabilities.Both PDRR and ABRR would enable Traffic Management Coordinators (TMCs) to amend flight plans, based on submitted TOS, via the Route Amendment Dialog (RAD).However, to this date, CTOP has been mainly used for analysis purposes, and TOS has not been used operationally.There are three types of potential TOS scenario that we considered for the development of Phase 3:1) Departure Demand/Capacity Imbalances: Excess demand and/or reduced capacity may result in surface departure delay for the flight's filed route, but not necessarily for alternative routes.Under normal circumstances, the D10 airspace has enough route capacity to absorb the departure demand from both major airports and the surrounding airports.D10 has four terminal gates (North, East, South, and West), with four departure routes each (for a total of 16 departure routes).When convective weather occurs in the region, routes may be closed and/or increased space between departures may be required, i.e., Miles-in-Trail (MIT) between departures.The most common restriction compresses the capacity from four routes down to one with 10 MIT.In this situation, both KDFW and KDAL airports are requested to space departures, and as a result, flights frequently incur surface delays.While departures to the East gate may be delayed, departures to the North or South gates may not be.Therefore, opportunities for flights to depart earlier on a north or south alternative route may provide advantageous delay savings despite the cost of flying a longer route.In this scenario, rerouting flights would effectively off load demand and reduce delay to the East gate as well, as shown in Fig. 1.In addition to terminal restrictions, different taxi times and demand loads across runways may also provide options for flights to take off earlier and thus may contribute to potential delays savings.2) Arrival Demand/Capacity Imbalances: Arrival flights may also experience airborne delay due to excess demand or reduced capacity at the arrival metering fixes or runways.Recent HITL simulations, using a research version of CTOP, assessed the potential benefits of using TOS to strategically offload arrivals across routes into Time-Based Flow Management (TBFM)'s arrival metering.Results indicated that delays could be reduced while maintaining throughput at the destination [16].This scenario was not addressed in Phase 3.3) En-route Demand/Capacity Imbalances: Special Use Airspace (SUA), Aircraft Hazard Areas (AHA), and weather events may constrain airspace and create demand/capacity imbalances, as well.The Air Traffic Control System Command Center (ATCSCC, or DCC) may issue Traffic Management Initiatives (TMIs), such as Airspace Flow Programs (AFPs), or route advisories to manage flows.AFPs may be used to reduce the demand near constrained airspaces.Flights that are subject to AFPs receive an EDCT and are usually delayed.The flights that are delayed are those that cross metering arcs set near the constrained airspaces.Under this type of TMI, TOS may be used to look for alternative routes that would not cross metering arcs and would therefore enable flights to depart earlier.Mandatory route advisories may also be issued to manage the flow of traffic away from constrained areas onto specific protected route segments.In this case, comparing TOS routes to the protected segments may help to identify which TOS routes may be available.There may be other constraints, such as predicted arrival time, flight duty time limitations, or airport curfews, that may be considered by the FOs.These may act as compounding factors to the demand/capacity imbalance scenario described above.
+II. CONCEPT OVERVIEWAt a minimum, the tactical identification of beneficial alternative TOS routes depends on the ability to consider constraints on available resources, to predict real-time delay savings benefits, and for the FOs and ATC to coordinate the submission and the approval of flight reroutes.The Phase 3 concept can be summarized as consisting of four essential processes: 1) creation of a TOS, 2) estimation of delay, 3) determination of candidate TOS routes, and 4) submission and approval of TOS reroutes.These four processes are described at a high-level below.1) Creation of a TOS: A TOS service generates a TOS, that is a set of alternative routes, for every departure flight.It computes an RTC for each TOS route.In this early prototype and evaluation, Coded Departure Routes (CDRs) were used as pre-defined TOS routes.CDRs are full-route procedures that are published, and therefore accessible to both ATC and the FO.Using CDRs also enables the system to continuously assess the impact of restrictions and delays on these routes.2) Delay estimation: A scheduling service provides offtimes and delay estimates for both the filed route, and each TOS route, every 10 seconds.This is based on demand and capacity predictions at the runways and at the terminal boundaries, and delays that are imposed on the surface.The scheduler then predicts delay savings for each TOS route, as well as aggregate delay saving for subsequent flights that would not need to be rerouted themselves.Lastly, real-time metrics indicate the probability that a delay savings will occur based on the accuracy of the previous system predictions and the expected size of the delay savings.3) Determination of Candidate TOS Route: A TOS service identifies when and which TOS routes may be beneficial to fly.It compares the TOS route RTC (cost) with predicted delay savings (benefits).When the delay savings exceeds the RTC (net savings), the system identifies the TOS route as a candidate for reroute.
+4) Submission and Approval of TOS reroute:A Graphical User Interface (GUI) enables the FO to view and submit one or more TOS routes for a flight, and enables ATC to view and approve a reroute of a flight on a TOS route.Audio/visual alerts are available to ATC users when a TOS route is submitted and to FO users when a TOS route is approved by ATC.The scheduler updates the estimated schedule and predicted delays for all flights after each TOS reroute approval.Dispatch and pilots concur on the new flight route.The ATC tower updates the flight plan in the FAA's legacy system and clears the pilots on the new flight route.In the following sections, the technology, the GUI and how the system is used are described in more depth.
+III. PHASE-3 CAPABILITY
+A. ATD-2 Phase 3 Built Upon Phase 1 & 2 CapabilitiesThe Phase 3 TOS capability was built upon the Phase 1 and 2 single airport IADS traffic management system.The purpose of the single airport system is to provide accurate prediction of future surface demand/capacity imbalances, to propose SMPs, and to provide gate hold advisories to reduce excess taxi time for departures [6,7,10].The scheduling capability used in Phases 1 and 2 is fully compatible with the Phase 3 TOS capability.However, it is worth noting that the Phase 3 prototype is being evaluated on its own in the North Texas metroplex.Another precursor of the Phase 3 TOS prototype was the Tactical Departure Scheduling -Terminal software that was developed to provide a terminal-wide scheduling system that would support the management of demand/capacity at the terminal boundary [5].Some of the terminal capability has been integrated in the Phase 3 system described below.
+B. Traffic Management Inititiatives (TMI) ServiceThis service parses TMI restrictions from across the NAS and provides the constraints needed by the scheduler service to schedule takeoff times and predict delays at the runways and crossing times at the terminal boundaries.It also identifies constraints on the CDRs used by the TOS service to determine when CDRs are available in the flight's TOS.The TMI service parses restrictions that are available in the TFM Flow data via the System Wide Information Management (SWIM).The system accounts for Ground Stops, EDCTs from Ground Delay Programs (GDPs) and AFPs, Approval Request / Call-for-Releases (APREQ/CFRs), as well as terminal fix closures and MITs.TMCs at the Fort Worth Air Route Traffic Control Center (ZFW) agreed to enter terminal restrictions imposed on the D10 TRACON in the National Traffic Management Log (NTML).These entries were standardized so that the ATD-2 system could parse them reliably.Both fix closures and MITs are entered in the NTML's MIT restriction tab.Alternative fixes to the closed fixes are entered in a qualifier field.The TMI service assesses whether CDR routes are impacted by the DCC's route advisories that mandate traffic to fly specific protected route segments.The system compares the CDRs with the protected segments and filters out excluded CDR routes from the flight's TOS accordingly.
+C. Trajectory Option Set (TOS) ServiceFor this first TOS prototype and evaluation, field demonstration partners agreed to use CDRs in the flights' TOS.These pre-defined routes provide full procedures from KDFW and KDAL to over 95% of their destinations.Examples of CDRs to La Guardia Airport (KLGA) are shown in Fig. 2. Some CDRs were removed from the database, including, CDRs to international destinations and those that could only be used when arrival traffic demand is light at a specific D10 arrival gate.There are several advantages to using CDRs as static TOS routes.First, they are known by ATCs and FOs, and thus do not require the FO to submit a TOS to ATC via SWIM.This enables the TOS service to compare the CDRs to the filed routes on a constant basis.Second, ATC can use the CDR alphanumerical codes to expeditiously amend the flight plan in the ATC legacy system.Third, the CDR codes may also be used by the ATC Tower to relay the new route to the fight crew without having to go through a full route readout, provided a Letter of Agreement to that effect is in place.The TOS service determines TOS states.The eligibility state indicates whether a route is available and if it is beneficial for a reroute.The eligibility states are: Potential, Candidate, Excluded, and Expired.The coordination state enables communication between the FO and ATC about the TOS routes.The coordination states are: FO Submitted, ATC Approved, Reroute Filed, ATC Excluded, and FO Excluded.Before the TOS Service determines if any TOS route is a candidate for a reroute, the service first determines if any CDRs are available.This information is provided by the TMI service.CDRs may be impacted by DCC mandatory reroutes, terminal fix closures, or be excluded due to APREQs, EDCTs or Ground Stops.In these instances, the routes will be labeled "ATC Excluded."Note that the FOs can also exclude their own flights.The service then compares the TOS route delay savings to its RTC.Predicted delay savings are provided by the scheduler service.When delay savings rise above the RTC (net savings), the route state is changed from the "Potential" state to "Candidate" (for reroute).Flights' TOS routes are initially shown as in a "Potential" state until they become a "Candidate."The TOS states are updated every 10 seconds.The RTC of a TOS route is a weighted travel time (in minutes).It is computed by multiplying the difference between mileage of the TOS route and the filed route by a cost factor, divided by the filed speed.The cost factor accounts for the FO's additional cost to operate in the air versus on the surface, or other business priorities.As an example, the FOs' costs could be based on types of aircraft, destinations, and time of day.The FOs' cost factors are static and accessed by the TOS service.A configurable minimum RTC is also included.This sets a threshold for the minimum delays savings that is deemed beneficial.A future system may consider cost factor parameters to be dynamic and managed by the FOs.Fig. 2 shows an example of additional nautical miles and ranges of RTC values for two CDRs via the North terminal gates (in blue and orange) compared to the commonly filed route to KLGA via the East gate (in green).In this example, the routes to the north add about 57 to 60nm, and depending on cost factors, the RTC values could range from a weighted time of about 8 min to 19 min, depending on the variability of FOs' cost factors.The concept includes a buffering feature that prevents the eligibility state to change from "Potential" to "Candidate" too frequently when the delay savings value is close to the RTC value.Thresholds can be set under and above the RTC value to allow for some fluctuations.When the thresholds are set, the state doesn't change unless the delay savings crosses the threshold.So far, the scheduler has proven stable enough to not use these buffers.The TOS service identifies a "top" route amongst the flight's TOS.The top route is the route with the highest predicted net savings, that is, the difference between predicted delay savings and the RTC.The top route is displayed in a TOS Table .Users can also view each TOS route for a particular flight in that flight's TOS Menu.When the FO determines that a flight would benefit from a reroute, the FO submits the route in the ATD-2 GUI.The route coordination state then changes to "FO Submitted."When the ATC Tower determines that they can approve the reroute, they approve the route in the GUI.The route coordination state then changes to "ATC Approved."With this prototype, no data is sent to the TFMS.The ATC Tower enters the CDR code in the FAA legacy system to amend the flight plan.Once the flight plan is updated in SWIM, the ATD-2 system will detect the change and update the coordination state to "Reroute Filed" on the GUI.Note that the system allows the FO to submit any route at any time, whether the route is in a "Potential" or "Candidate" state.Indeed, there may be circumstances when the FO may want a flight to take off earlier on an alternative route despite the RTC value, for example, to accommodate other time constraints.Lastly, the prototype includes the option that TOS routes can be set to expire.A Required Minimum Notification Time (RMNT) can be tailored to certain flight events.For example, this type of threshold could be used by ATC or FO personnel to prevent TOS submissions to occur after certain events, such as pushback.This feature has not been used by the field demonstration partners, yet.
+D. Scheduling ServiceThe scheduling service computes predicted delay savings and provides opportunities to use TOS in a tactical manner.It uses multiple algorithms to provide: Below is a high-level description of the Phase 3 scheduler's capability.More details are provided in [14,17].The Phase 3 scheduler computes Estimated Takeoff Times (ETOTs) based on various inputs and constraints: 1) the surface scheduler that provides surface transit times, runway predictions, aircraft movement detections and position of traffic on the surface; 2) the tower TMC's runway utilization entered on the ATD-2 GUI; 3) FOs' estimated departure times, such as the Earliest Off-Block Times (EOBTs) that are used to predict the Undelayed Takeoff Times (UTOTs)-the earliest times flights could depart independent of other flights and separation applied at the runway; 4) Terminal restrictions from the TMI service which may require combining departures over certain fixes when other fixes are closed, as well as increased spacing for MIT compliance; and lastly 5) the competing demand between major airports and engine types over certain departure fixes that may result in terminal delay that is passed back to the departure runways.The best estimate of a flight's takeoff time on the filed route is the ETOT.Predicted total delay on the filed route is computed as the difference between the ETOT and the UTOT.In addition, the scheduler computes what-if ETOTs for all TOS routes for all flights.It assumes the same UTOT on the filed route but then computes a new ETOT for each specific TOS route.Because the restrictions and the demand may vary across the different routes and runways, it is possible that the ETOT for an alternative route may differ from the ETOT for the filed route.The difference between the two ETOTs is then used to compute delay savings.The following factors may influence delay savings: 1) when a terminal restriction may be in effect on the filed route, but not on the alternative route, 2) when departures on the alternative route depart from a different runway that has a lower demand than the filed route, and 3) when departures on an alternative route have a shorter taxi time.Fig. 3 provides an example of AAL1560's UTOT, filed route ETOT and a TOS route ETOT.In this example, the ETOT of filed route indicates a delay of 13 minutes (middle timeline).However, the ETOT for the southern TOS route (right timeline) is as early at the UTOT (left timeline) thus providing a delay saving of 13 minutes.Note that the delay savings accounts for different UTOTs at different runways, as well as when flights push from the gate (out events).This helps to account for accrued delays, in case route submissions and reroute decisions are made after pushback.Typically, an increase in accuracy is observed after an aircraft pushes back and gets closer to the runway.However, the closer the flight gets to the runway, the fewer the opportunities there will be for departing earlier on an alternative route, and the higher the workload for the FO and ATC to reroute the flight.Lastly, the scheduling service provides a probability (%) that the flight's delay savings for the "top" route will occur.Essentially, it compares the likelihood of a flight's ETOT with previous Actual Takeoff Times (ATOTs) from historical data.The difference between the probability of an ETOT and the distribution of ATOTs then yields a probability for various delay values.The method to calculate this probability is described in detail in [17].The probability can indicate how likely a delay savings is going to be higher than zero, or higher than the RTC value.When compared to the RTC value, the probability typically reaches 50% or higher when a TOS route becomes candidate which is when the delay savings is estimated to be higher than the RTC.The probability is influenced by 1) the accuracy of estimates (the higher the accuracy, the higher the probability), and 2) the size of the delay savings (the larger the size, the higher the probability).In addition to individual delay savings estimates, the scheduling service also provides what-if estimates of aggregated delay savings for other flights, up to 60 minutes, behind a given flight if the given flight were rerouted on its top TOS route.The aggregated delay savings is a sum of the delay savings for other flights plus the delay savings for the given rerouted flight.The aggregate delay savings is computed at the following levels: for the air carrier (e. g.SWA), for the major's fleet (e. g., the American Airlines Group operates with several sub air carriers), for each airport, and for the metroplex (KDFW and KDAL).Consider the following example: Flight ABC123's filed route is under a 10 MIT restriction to the terminal East gate.It shows a delay savings of 10 minutes on TOS route #1.There are 10 flights under the same MIT restriction that are predicted to take off behind ABC123 at the same airport.Each of the 10 have an additional one minute of delay, due to the increased spacing to comply with the MIT.Four of those belong to the same air carrier.An additional three of those belong to the same fleet.There are five other flights from another airport that are also impacted by the MIT.In this example, rerouting ABC123 on the TOS route #1 would actually delay two flights from the same fleet by a minute each, because the rerouted flight would be predicted to move in front of these 2 flights.The breakdown of the aggregate delays is shown in Table 1.
+IV. GRAPHICAL USER INTERFACEThe Phase 3 GUI, called Metroplex Planner, provides key display elements such as, Timelines, Maps, and Tables.Many of these elements are part of the Surface Trajectory Based Operations (STBO) GUI that was developed for Phases 1 and 2 -See [6,7].Modifications to the GUI for Phase 3 are highlighted in the sections below.
+A. TOS TablesTwo new tables were created for TOS information as shown in Fig 4 .The TOS Departure Table (TDT) lists flights' filed route data, the "top" TOS route data, and the TOS states.Users can set various relevant information to be displayed in the TDT.Fig. 4 shows examples of flight's filed route, equipage, TMI information, ETOT, delay, plus the top route's individual and aggregate delay savings, the probability of delay savings, and the TOS eligibility and coordination states.An editable scratchpad field was added to allow users to make notes for themselves and to communicate with NASA researchers.Several TDTs can be stacked on top of each other.Using filters, tables can be set up by TOS states, flight events, and time or value thresholds, thus enabling an ad-hoc organization of flights.For example, one table may list flights with candidate routes, and another may list the flights with submitted and approved routes.The other table is the TOS Flight Menu (FM).It lists all the routes for a specific flight as shown in the bottom of Fig. 4. The filed route is in the first row, and in the following rows are the CDRs that are available.Each of these rows indicates the route procedure, the mileage, additional mileage compared to the filed route, the RTC, as well as the ETOT and estimated delay.In the TOS Flight Menu shown in Fig. 4, the CDRs via the North gate show earlier ETOTs than the filed route and delay savings above the RTC.In Fig. 4 the FM for AAL2334 lists two candidate routes via the North gate with 45min of delay savings on each route.On the TDT display above, the top route is the CDR DFWPHLJ3.This is due to its lowest additional mileage of 54nm (as seen under "Add nm" in the FM).The ETOT of this route has a probability of 86% of delay savings above the RTC value.The aggregate delay savings are 54min for 27 flights (2min per flight) at the air carrier level, 59min at the fleet level, 68min at the airport level, and 91min for both airports.Both tables can be used to submit or approve TOS routes.To do so, an FO user right-clicks on a flight route and selects which TOS route to submit.The FO can submit more than one route per flight.Once the FO submits one, or more, routes, the coordination state of the route changes to "FO Submitted."To approve a reroute, the ATC user right-clicks on the flight route and approves the route.This will update the Coordination State to "ATC Approved."The FO has the ability to undo a submission until ATC approves a submitted route.ATC can also undo an approval, as needed.Optional pop-up windows can be set to alert ATC users when a route has been submitted or to alert the FO users when a route has been approved.Measures were taken to protect any sensitive information of the FOs.Each operator had their own criteria to compute the RTC's cost factor and minimum RTC values.Each FO can see only their own flight and TOS information in the TOS tables, but can see all the traffic on the timeline and map on their respective clients.However, ZFW, KDFW and KDAL ATC facilities can see both FOs' flights and TOS information across airports.
+B. TimelineThe purpose of the Timeline is to present the likely time of departure or arrival to a reference point, such as a runway or a fix.Datablocks provide flight-related information such as delay, restrictions, aircraft type, destinations, origin, runway, and parking gates.In Phase 3, timelines were upgraded to show the ETOT of the flights' filed route, the predicted delay, as well as two new letter indicators.The letter D indicates that the aircraft is equipped with Controller Pilot Data Link Communications-Departure Clearance (CPDLC-DCL).The letter T, highlighted in white, indicates that a TOS route has been submitted for a flight, and the letter T, highlighted in green, indicates that the TOS route has been approved by ATC.Both the FO and ATC can submit and approve routes by right-clicking on a flight's datatag.The users can also bring up the Flight's TOS Menu from there.
+C. MapThe Map provides a planview of D10 arrival and departure traffic, as well as airspace elements, such as airports, fix names The color indicates the level of restriction on the CDRs.When the color is green, the CDRs are available to all possible destinations.The CDR is shown as either "Potential" or "Candidate" state in the flight's TOS.When the color is red, the CDRs are not available to any destination.In this case, the CDR is shown as "ATC Excluded" state in the flight's TOS.When the color is yellow, some CDRs are not available to specific destinations.The CDR is shown as "ATC Excluded" state in the flight's TOS to these destinations only.The user can hover over the indicator to see which destinations are effectively excluded.
+D. Management of TOS OperationsA TOS Operation tab and a DCC Route Advisory tab were added to the Traffic Management Menu in the GUI.The TOS Operation tab provides ways for the center's TM Unit to turn TOS operation ON or OFF, as well as to manage the exclusions of destinations or CDR routes in the flights' TOS as shown in Fig. 6.The upper left corner of Fig. 6 (highlighted in red) shows where the TM Unit can activate or deactivate TOS operations.When TOS operations are active, the FO can submit TOS routes, and ATC Tower can approve them.When TOS operations are  active, a TOS icon (not shown in the document) changes from gray to orange to provide situation awareness.The top section of Fig. 6 (highlighted in blue) shows where the TM Unit can enter destinations that are restricted from TOS rerouting.That is when none of the CDRs are available to these destinations.The TMI service automatically provides destinations under TMIs such as those with EDCTs under GDPs, and APREQ/CFRs.However, the parsing of the TMIs may not include all destinations that need to be excluded.The main section in Fig. 6 displays CDR indicators by cardinal direction.The CDRs listed under each direction are the most frequently used for reroutes via adjacent terminal gates.For instance, flights that are filed over the East gate may use the 1N or 1S CDR via the North and South gates respectively as alternate routes.The TMI Service will determine when certain CDRs are excluded to certain destinations in the DCC mandatory routes.These destinations are then listed next to the given CDR indicators.For example, in Fig. 6, the CDRs ending in 1N are excluded in the flights' TOS to EWR, JFK, and LGA (highlighted in green).The user can manually add or subtract destinations, as needed.
+V. ATD-2 PHASE 3 PROCESS FLOW AND AGREEMENTS
+A. Phase 3 -Process FlowThe Metroplex Planner was designed to be adaptable to various decision processes.It can help either the FOs or ATC to identify flights that would benefit from being rerouted.Below is the approximate process flow developed by the FOs and ATC after a period of shadow evaluation, early tests, and discussion amongst field demonstration partners in 2019.
+1)The en route ZFW TM Unit determines terminal restrictions and makes entries in NTML.This unit also decides if TOS operations could be initiated based on the traffic situation and workload involved in the terminal airspace and airports.2) The FO's ATC coordinator coordinates with dispatch about potential flights that could be rerouted.They discuss whether flights could be rerouted based on fuel and equipment.They may decide to include CDR information in the flight plan release for pilots.3) The FO's ATC coordinator monitors the TOS Table .As the flight approaches 30min before pushback, a TOS route may be inspected for weather and wind information.If dispatch and the ATC coordinator concur, the TOS route is submitted on the ATD-2 GUI.
+4)The ATC Tower verifies that there is an opportunity to reroute the flight on the alternative route.The ATC may approve the reroute and coordinate with Clearance Delivery or Ground Control, depending on which position is controlling the flight at the moment.The flight plan is amended in the Flight Data Input/Output system.5) The FO's ATC coordinator may alert dispatch when ATC approves the reroute on the ATD-2 interface.Dispatch may see that the flight plan amendment has been updated on the FO's system.Dispatch is then legally required to concur with the pilots on the new route and the amount of fuel on board.
+6)The ATC Tower clears the pilots on the new route.The clearance may be carried via CPDLC-DCL or VHF.CPDLC-DCL allows the Tower to send the amendment via datalink to flights that indicated this capability in the flight plan.VHF requires a full route readout.Some airlines may have a Letters Of Agreement with ATCT to use the CDR code to relay the new route, in the absence of CPDLC-DCL equipment.
+7)The pilots eventually enter, or select, the new route in the Flight Management System, verify fuel and recompute weight and balances on the new departure procedure.
+B. Phase 3 -Procedural AgreementsAgreements were made to facilitate the above process.These may not be applicable to other airspaces.The ZFW TM Unit agreed to make standardized NMTL entries.Restriction durations are up to two hours, but these may be updated as needed, based on evolving conditions and weather predictions.The ZFW TM Unit determines if TOS submissions and approval are acceptable, based on workload and constraints in the airspace.However, the ATC Towers approve the reroutes, since they were in control of the flights 45 min prior to departure time.FO submissions are treated as reroute requests and are approved by the ATC Towers, as able.It was agreed that ATC Towers don't need to approve or reject each of the TOS reroute requests and instead, don't act upon a TOS submission if they are not able to evaluate the request due to higher priority duties.This, however, has not happened yet.The system allows the FO to submit one or more TOS routes, regardless of its "Potential" or "Candidate" state.If several are submitted, ATC picks the route they deem beneficial and can approve.However, in practice, the FO has submitted only one alternative route per flight, so far.Both the FO and ATC have preferred to submit and approve a TOS reroute before pushback to reduce workload on the pilots and the controllers.There is a mutual desire to avoid amending the flight plan once the flight is past the spot.Note that so far in North Texas, the onus has been on the FO to submit TOS routes when reroutes are deemed beneficial for a flight.The Phase 3 system supports that approach which seems to work particularly well when multiple routes are available, and the demand on alternative routes is not saturated.This may not be the case in other circumstances when ATC is constrained to impose reroutes, such as during Severe Weather Avoidance Plan (SWAP) events.
+VI. BENEFITS
+A. Individual and Aggregate Delay SavingsThe Phase 3 system calculates predicted delay savings for both individual benefits (for the rerouted flights) and aggregate (system-wide) benefits (for multiple subsequent flights).These predicted benefits may weigh differently in the decisions made by the FOs or by ATC.The combination of individual and system-wide benefits can result in the following:1) High individual delay savings, and high aggregate delay savings: This is the "no-brainer" case when the cost of rerouting the flight provides both individual and system-wide benefits, which may or may not span across air carriers and airports.2) Low individual delay savings, and low aggregate delay savings: This is the nominal scenario where demand is not exceeding capacity, and no delay savings can be produced.3) High individual delay savings, and low aggregate delay savings: In this case, rerouting the flight may result in delaying other subsequent flights.An aggregate savings value lower than the individual value indicates that delay may increase for some flights, since the aggregate delay savings includes the individual delay savings as well.
+4) Low individual delay savings, and high aggregate delay savings:In this case, rerouting the flight may not result in individual delay savings, but in aggregate delay savings for the air carrier, the fleet, for the airport, or for the metroplex.When the aggregate delay savings benefits the air carrier or the fleet, the FO may consider rerouting 'low-impact' flights.ATC may have different criteria for selecting flights to reroute.The aggregate delay savings computation may be used to justify rerouting a particular flight.The operational use of the system should provide additional insights in how the individual and aggregate delay savings weigh in the decisions to submit and approve reroutes.A future system could leverage both individual and aggregate delay savings, as well as other inherent costs to determine possible outcomes when several flights are considered for a reroute, or to provide recommendations on which flights would provide targeted benefits.Such a capability could be designed to support both FOs or ATC priorities.
+B. Stormy 2019 and 2020 EvaluationsAn initial TOS prototype was deployed at the end of Spring 2019 to AAL and SWA ATC Coordinators at their respective Operation Centers, to the TMC positions at ZFW, D10, and to KDFW and KDAL ATC Towers (there is no TMC position at KDAL).After a period of training and shadow observations, the system was tested with users for four-hour periods over several days in the summer of 2019.On a few occasions, the weather resulted in restrictions and delay savings and candidate routes were observed.Several TOS routes were submitted, and flights were rerouted on the TOS routes.This provided opportunities to evaluate the system, and identify additional features to be added, such as aggregate delay savings and the probability of delay savings.A field test was initially planned during the stormy season of 2020, which usually takes place from late May to early September.However, the health-related measures implemented to contain the COVID-19 pandemic resulted in a significant reduction of air traffic.By May 2020, the combined average daily departures at KDFW and KDAL had dropped to 47% of January 2020's average (1226/day average in January to 572/day in May).Departure demand was reduced from eight to three banks at KDFW.In addition to traffic, both FO and ATC staffing was reduced.By the end of June, neither the FO nor the ATC had effectively used the system.However, inclement weather developed as expected.Severe weather took place on several days which resulted in fix closures, MITs, and some departure delays, despite reduced demand.The TOS system indicated candidate TOS routes on several occasions.Below is one example to illustrate possible benefits in the absence of actual reroutes.On May 15 th , fix closures restricted the East gates.Traffic from two departure routes were combined over one, with one other departure route remaining unimpacted.That level of restriction lasted for 90min (21:30 to 01:00 UTC) and coincided with the evening departure bank at KDFW (the first OUT occurred at 23:25 and the last OFF occurred at 01:30).During that period of time, the TOS system indicated that seven flights had candidate TOS routes.No flights were actually rerouted during this period.Therefore, realized aggregate delay savings are not reported here.Because rerouting flights impact the ETOTs and delay savings of subsequent flights, and without knowing how many flights could be effectively rerouted, we opted to show the potential benefits for only one flight in that period of time.In the example below, the flight was the first in a bank that met the criterion of having a candidate TOS route at pushback (OUT).The example flight was AAL1446 from KDFW to KTUL.Normally, flights to KTUL are filed over the North gate.However, due to weather, this flight was filed on the FORCK route via the East gate and was expected to depart from the East runway.When it pushed from the gate at 23:25, its ETOT was 23:44 with a predicted delay of three minutes at Runway 17R.At the same time, an alternate route to the West gate from Runway 18L had an earlier ETOT of 23:35 with no predicted delay at the runway.Due to differences in taxi times and delay at the runway, the system indicated a delay savings of about nine minutes and a positive net time savings on the TOS route.The flight's probability of delay savings above its RTC was about 60%.The aggregate delay savings was projected to be about 32min at the air carrier level (AAL), 60min at the fleet level (AAL plus ENY, ASH, and SKW), 63min at the airport level (all airlines at KDFW), and 63min at the metroplex level (both KDFW and KDAL).It is worth noting that in this example, the predicted delay savings on the TOS route was based upon an advantageous taxi time and the absence of delay at a different departure runway.Also, our decision to pick the first flight in the bank with delay savings greater than the RTC may result in larger aggregate delay savings than if the flight was later in the bank.The delay savings in this example may, or may not, reflect the FO's own criteria to make a TOS submission.Lastly, the FOs may have additional considerations and metrics that are not yet being accounted for in the ATD-2 system.
+C. Operational BenefitsThe integration of TOS in FO and ATC operations has been shown to provide many advantages.Below are identified operational benefits from the use of the Phase 3 capability.These may be worth considering for the development of future systems:1) Real-time predictions: Estimates of demand, off-time, delay, delay savings, and likelihood of delay savings could support traffic flow management decisions such as load balancing.These estimates could also help identify other sources of delay.2) Integration of TMIs: It is critical to account for the impact of TMI restrictions when calculating predicted delay, alternative routes, and destinations.Considering TMIs helps to determine when flights are eligible to submit a TOS.3) Information on which flights are eligible for viable alternative TOS routes: This information helps ATC to identify whether flights are able to fly alternative routes, based on criteria such as equipment and fuel.4) Information on when alternative TOS routes are beneficial for reroute: This helps to inform FOs and ATC rerouting decisions, based on agreed-upon benefit criteria.5) More efficient workflow: TOS information helps to increase ATC and FO's awareness of reroute opportunities and decisions.It also reduces the coordination effort between them.6) Reduction of risks/costs: Avoiding departure delay could help to minimize costs related to flights returning to the parking gate to refuel, to offload passengers to avoid tarmac rules, or to avoid crew Flight Duty Time limitations.Reducing delay may help FOs with passenger, crew, and aircraft connectivity at destinations, as well as help ATC with maintaining throughput.
+VII. CONCLUSIONTOS has been identified as a resource to meet NextGen objectives by the Industry and FAA.Among those objectives are better communications via data exchange across the airspace system and its users, fuel savings, a reduction in emissions and reduced congestion.The ATD-2 Phase 3 system enables both the FOs and ATC to tactically identify reroute opportunities for departure flights impacted for departure fights impacted by demand/capacity imbalances.The Phase 3 field evaluation has helped to identify uses cases and operational benefits.The remainder of the field evaluation will provide opportunities to analyze benefits, identify new areas of development as well document lessons learned.In the future, evaluating TOS opportunities in other airspace configurations and traffic demands, as well as efforts to integrate TOS information and predictive engines into the FO's own flight planning system and into the FAA's TFMS and TFDM systems may be considered.Fig. 1 .1Fig. 1.Alternative TOS departure routes to offload demand and reduce delay.
+Fig. 2 .2Fig. 2. RTC values and CDRs from KDAL, KDFW to KLGA.The green line is the CDR route via the East gate that is commonly filed, and the blue and orange lines are two CDRs via the North gate.
+Fig. 3 .3Fig. 3. Example of filed route UTOT and ETOT, and TOS route ETOT and delay savings for AAL1560.
+Fig. 4 .4Fig. 4. Top: Example of a TOS Departure Table (image shown in two parts: left and right halves) which shows two flights with candidate TOS routes.Bottom: Example of a TOS Flight Menu which shows the filed route and each alternative TOS route for AAL2334.
+and radar video maps.New information about restrictions at the terminal boundary and on the CDRs were added to the Metroplex Planner Map.Terminal restrictions impacting departure fixes, such as fix closures and MITs are now indicated next to the fix name, and restrictions on CDRs are located on the outskirts of the D10 airspace.An example is shown in Fig. 5.In Fig. 5, Fix ZERLU (ZER) is displayed in gray.This indicates that the fix is closed."ZER" is however displayed next to the adjacent fix, TRRCH (TRR).This indicates that flights bound to ZERLU are being routed over the alternate fix TRRCH.The number 10 in front of the fix indicates that there is a 10 MIT in place over TRRCH.In Fig. 5, color-coded indicators of CDR availability in the D10 airspace are shown, grouped by Departure Fix.These indicators are based on the last two alphanumeric characters of each CDR.Many CDRs have the same route name since many use the same Standard Instrument Departure (SID) routes exiting in the D10 airspace into the ZFW airspace.For instance, numerous CDRs ending in 1N fly over the AKUNA (AKU) fix.
+Fig. 5 .5Fig. 5. Map with Departure Fixes, TRACON radar video map, and TOS Route availability labels.
+Fig. 6 .6Fig. 6.Metroplex Planner TOS Operation Tab.
+TABLE I .IAGGR.DELAY SAVINGS (IN MIN) IF FLIGHT ABC123 DEPARTED ON TOS ROUTE #1
+Aggregate Levels Delay Values Added Together (min) Total Flight Delay Savings Flights With Reduced Delay Flights With Increased Delay Aggregate Delay SavingsAir carrier-10-40-14Fleet-10-72-15Airport-10-102-18Metroplex-10-152-23
+		
+		
+
+			
+ACKNOWLEDGMENTNone of the concept, prototype development, and field evaluation would have been possible without the commitment and support of our field demonstration partners in North Texas, as well as the CDM Stakeholders Group (CSG), FET and SCT groups, and the FAA/ANG.
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+IntroductionA concept of advanced terminal airspace area operations is part of the Next Generation Air Transportation System (NextGen) efforts to accommodate expected increase in the demand and provide a higher level of throughput at the airports and within en route airspace [1,2,3].Scheduling optimization problems in dense terminal airspace area operations are drawing interest [4], since the increase of airspace capacity is becoming more important to accommodate large air traffic flows in that environment.The use of scheduling algorithms more efficient than the traditional FCFS approach is a way to increase a way to increase throughput and efficiencies in congested terminal airspace compared to the FCFS scheduler.On the other hand, it is known that the route topologies in the extended terminal airspace play an important role in the scheduling performance.Efficient scheduling and route assignment directly affect important performance metrics such as runway delays, throughput, fuel efficiency, and robustness to uncertainties in operations.However, despite the realization of the importance of the route topology, few extensive studies of the problem have been reported.The purpose of this paper is: 1) to investigate optimal and efficient scheduling algorithms for a dense terminal airspace operation that yield better performance compared to the traditional FCFS approach, and 2) to design an optimal route structure for the extended terminal area.A scheduling algorithm based on MILP has been successfully used in airport surface management due to its ability to optimize both the arrival/departure sequence and their scheduling [5].However, due to the expensive computational cost attributed to the branch and bound search algorithm, only a limited number of flights, typically less than 40, were allowed in the scheduling.The application of MILP-based scheduling algorithms to the dense terminal airspace operation has been less effective.Consideration of hundreds of flights in the span of a couple of hours typical of the dense terminal airspace operation dramatically increases the size of the MILP formulation and the corresponding computation burden easily exceeds the available computing resources.In a previous study, a heuristics-based MILP formulation was introduced in order to reduce computational burden and applied it to metroplex operations during their busiest operations [6].However, the cost for the branch and bound search is still expensive though not as prohibitive as in the original MILP formulation.The combination of GA-based heuristics and a Linear Programming (LP) method was proposed by Capozzi et al. [8] This is the method that is adopted in the current work.It explicitly separates the search for the optimum binary variables of route assignment and aircraft sequencing from the solution procedure for the continuous scheduling variables.GA-based MILP optimization scheme is able to find the optimal solution in significantly less computational time on the example problem considered.A central focus of the current work is the design of an optimal route topology in the extended terminal area using the GA-based MILP scheduler.This work will also use with flight trajectory uncertainty which is different from the previous work that held an unrealistic premise that detailed flight intent information including the transit times is known a priori.This would assume that one snapshot of the planning is sufficient to predict the scheduling performance.However, this is not true in the real-time simulation where uncertainties are present in flight trajectories and the schedules of the following flights are dynamically updated later in time based on the schedules of the previous flights.In order to overcome the above issues, we developed a dynamic planner framework that periodically updates the flight schedules to handle uncertainties.The dynamic planner consists of separate modules: a planner and a simulator.Uncertainty is implemented in the trajectory model of the simulator to account for aircraft arrival time errors.The planner adds an extra separation buffer at the scheduling points to cope with these inter-arrival errors.As a practical application of the heuristics-based, stochastic schedulers to dense terminal airspace simulation, a design of an optimal route structure in the extended terminal airspace area is carried out.Key design parameters are the number of merge points and their locations.First, we construct five distinct route structures with various merge topologies.The scheduling performance is evaluated for each topology using the stochastic FCFS heuristics-based scheduler and a dynamic planner framework is applied to each topology in order to validate the predicted scheduling performance.It is demonstrated that the largest separation amount required at all scheduling points is a dominant factor in scheduling performance.Finally, more generalized airspace topologies are considered.To investigate the sensitivities of the merge topology to the uncertainty modeling, three types of uncertainty distributions are considered: a constant, linear and quartic increment of the uncertainty per unit route length.The comparison of the corresponding scheduling results show that: 1) there exist optimal merge locations in the extended terminal airspace area, 2) the optimal merge locations tend to be positioned where large uncertainties are present so that pilot's control efforts reduce the local uncertainties at the control point.The rest of this paper is organized as follows: First, the basic formulation of the original MILP algorithms is explained, and the objectives and constraints for our route assignment and scheduling problem are defined.Second, a number of heuristics are introduced into the original MILP formulation as ways to reduce computational cost.Third, the dynamic planner framework with uncertainty modeling and extra separation buffers are detailed.Finally, a design of optimal route structure in terms of the merge topologies is discussed and followed by conclusions and future work.
+Problem FormulationWe apply a MILP-based formulation to the scheduling problem in the extended terminal airspace area, from the entry fixes to runway, and only the arrival portion of the scheduling will be considered.The plan consists of a time-constrained route for each aircraft in the demand set, with STA specifications at control points along each route, as dictated by separation rules, such that the resulting movement plan for all aircraft in the demand set is conflict-free.
+Basic Mixed Integer Linear ProgrammingA MILP scheduler has advantages in the scheduling problems over the traditional FCFS scheduler [9].The design variables can be freely chosen specifically to a problem as forms of binary variables and continuous variables.However, the computational cost of a branch and bound search algorithm is expensive and its scalability with the problem size is rather poor and a number of heuristics to reduce the computational burden will be explained in later sections.
+Initial Demand SetLet F, R, and P define a set of flights, available routes, and scheduling points, respectively, and N F , N R , and N P are the total number of flights, routes, and scheduling points in the demand set.F = {f j | 1 ≤ j ≤ N F } = {f 1 , f 2 , ..., f N F } R = {r j | 1 ≤ j ≤ N R } = {r 1 , r 2 , ..., r N R } P = {r j | 1 ≤ j ≤ N R } = {p 1 , p 2 , ...p N P }We further presume the existence of functions that select the subset of routes from R that are feasible for a given flight f ∈ F and the subset of points from P that are feasible for a given route r ∈ R.R f = {r k | all possible routes that flight (1) f can fly, k ∈ {1, 2, ..., N R (f )}} P r = {p k | ordered set of scheduling points (2)on the route r, k ∈ {1, 2, ..., N P (r)}}where N R (f ) is the number of all routes that flight f can fly, and N P (r) is the number of all the scheduling points on route r.Then, it can be easily inferred that a set of total routes and the points are the superset of R f and P r , respectively, and the relation is represented as follows:R = f ∈F R f and P = r∈R P r
+Decision VariablesGiven the notation in the previous section, the decision variables of interest for this problem can be defined:• A f,r -A binary route assignment variable that takes on the value of 1 if flight f is assigned to the route r and zero otherwise.• T f,r,p -A continuous variable representing the time that flight f is scheduled to cross the scheduling point p on route r, where f ∈ F, r ∈ R f , and p ∈ P r .• S f,f ,r,r ,p -A binary variable that takes on the value of 1 if flight f on route r is sequenced prior to flight f on route r at shared scheduled point p, where f ∈ F, r ∈ R f , r ∈ R f , and p ∈ P r ∩ P r = ∅.
+Objective and ConstraintsFor the purposes of this paper, the objective function is defined so as to minimize the total time required for all flights to reach the end of their route, i.e., the runway threshold:J = f ∈F r∈R f A f,r T f,r,p F(3)The problem constraints are as follows:• Assignment to Only One Route.Each flight can be assigned to one and only one route.• Crossing Time at Initial and Final Point.If a flight is assigned to a given route, then its start time on that route must be no earlier than the earliest feasible time on that route, T E f,r , and no later than the latest feasible time, T F f,r .• Ordering Constraint at Potentially Shared Scheduling Points.For each pair of flights and each pair of route assignment options that share a common scheduling point, the order in which the flights are sequenced at the common point must be uniquely specified.• Separation Constraint.At each common scheduling point, successive aircraft must be separated by a minimum time that is potentially intersection-dependent.• Transit Time Constraints.In order to be physically realizable, the travel time between scheduling points must be greater than the minimum possible travel time and should be bounded by the maximum "delayability" of the flight between the scheduling points.
+Heuristics-Based SchedulersOne of the drawbacks of the MILP-based scheduler is its prohibitive computational cost.However, observation of the branch and bound search procedure indicates a large portion of search time is spent on evaluating unrealistic combinations of route assignment and sequencing.Heuristics in the route assignment and the queuing can help eliminate some of the unrealistic search efforts and reduce the computational burden.In our study, we tried two types of heuristics.First, heuristics based on the FCFS scheduling behavior can eliminate a number of binary decision variables in the original MILP formulation.Second, the branch and bound search algorithm can be replaced by GA-based heuristics.
+Heuristics-Based Mixed Integer Linear ProgrammingBased on the observations of the FCFS scheduling strategy, assumptions are made on sequencing along certain route segments, such as merge portions.The details of the following heuristics were explained in our previous work on the metroplex operations and only brief summaries are listed here:• Precedence constraint heuristic.Sequences along the common segments of the routes do not change and follow the queuing from the upstream of the merging segments.• Windowing heuristics.A sequence change is not allowed for a pair with their earliest crossing times at the entry fixes separated by more than a certain amount, i.e., windowing value.Using windowing heuristics, resulting schedules are planned locally and subsequently corresponding to this value.• FCFS heuristics.The ordering at all scheduling points can be predetermined based on unimpeded transit times to a specific scheduling point, a runway or a entry fix.This strategy is equivalent to the FCFS scheduling ideas and the computational cost benefits are maximum.A scheduling performance almost equivalent to that of the original MILP formulation was obtained at only a fraction of the computation cost of the original MILP formulation.However, the cost for the branch and bound search is still expensive though not as prohibitive as in the original MILP formulation.
+Heuristics of Genetic AlgorithmsA GA is a stochastic search method widely used in numerous optimization areas and has ad-vantages in handling both discrete and continuous design variables [10].GAs are simple in mathematical formulation but are typically expensive in computation due to their stochastic search procedure.The idea of adopting GAs in the scheduling problem for determining the binary decision variables in lieu of the branch and bound searches has been suggested and used in surface management work [7] and metroplex operations [8].Once the binary variables of the route assignment and the sequencing are determined through GAs, computation of the STAs is carried out by an LP solver.Since the LP is very efficient in computation and takes less than a couple of seconds to schedule hundreds of flights, the idea of combining GAs with pure LP procedure is favored for the dense terminal airspace simulations.Advantages of the GA-based MILP planner include:• It allows for the solution to be seeded with a good initial guess, based on heuristics.• All individual candidate solutions are, by definition, feasible solutions -thus a usable solution is available at all times.• The solution tends to improve with computational time.• It naturally handles windowing heuristics.
+Individual Candidate Solution RepresentationEach individual candidate solution, or "an individual" in short, consists of two vectors: an assignment vector and a sequence vector.The length of each vector is equivalent to the number of flights in the demand set.Each element of the assignment vector represents a possible route assignment for a flight in the demand set.Given the structure of the route, a sequence of the scheduling points in the route is prescribed, and the sequence vector is defined at each scheduling point as a possible sequence of the flights passing through that point.Route assignments and the queuing are random based on the stochastic nature of the GAs.A constraint of no passing in the sequence vector along the common route segment is enforced to further reduce the computational cost.
+MutationTo maintain the diversity of the individual from one generation to the next, mutations are carried out at a specified probability in each generation to both vectors of route assignment and sequence.Due to the coupling between the assignment map and sequence map held within each individual, the mutation operators are applied sequentially.The assignment mutation simply consists of randomly replacing the route assigned for an individual with another value from the feasible set of routes for the flight.Then, the sequence map for each individual is updated based on the mutated route assignment map.A sequence mutation is applied to each scheduling point at a given route assignment.Swapping sequence is determined via probability.If a random number sample is less than the specified probability of mutating sequence, then the number of swaps are chosen from a (0, N max ), where N max is the maximum number of swaps.A random pair of flights that contain this scheduling point in their assigned routes swap their ordering relative to the current ordering.
+Fitness and SelectionFitness of each individual is defined as an objective function value and evaluated by solving the pure LP problem implied by its route assignment and sequence.The definition of the objectives and the imposition of the constraints are equivalent to those of the MILP formulation: earliest transit time limit, lower and upper bounds of the transit time via the specified speed controllability, and separation requirements specific to the aircraft type at the scheduling points.Once each individual in the population has been assigned a fitness, the selection of individuals to form the basis of the next generation is performed using a simple tournament selection scheme.A tournament scheme finds the best-performing P/2 number of individuals, where P is the size of the population, and those are selected as the basis of the next generation.These P/2 number of individuals are then mutated to form the population of size P to be evaluated.This cycle of fitnessselection-mutation is repeated until a specified number of generations are completed.
+Comparison of Computation Time and OptimalityThe optimality and the computation times are compared for the schedulers that were described in previous sections.The terminal route structure tested for comparisons is a binary route topology with double merges: 4 route options and 8 scheduling points (4 entry fixes, 2 merge points and 1 runway).The route topology is shown in Figure 1 with the entry fixes of WP 31 through WP 34 and the runway of WP 0 .The number of flights varies from 6 to 100.Computation times with respect to varying number of flights and the average delays of 8 flights are compared in Table 1.The expensive computational costs of the original MILP scheduler and the FCFS heuristics-based MILP scheduler prohibited computations for more than 8 flights and 40 flights, respectively and their values are represented as N/A in Table 1.Although the FCFS heuristics allows scheduling up to 40 flights, the computation time is not still satisfactory for the dense terminal airspace simulation.However, the speed-up of the computation time for the GAbased MILP planner is considerable even with 100 flights.It is observed from the additional scheduling of a larger number of flights using GA-based MILP planner that it is able to handle hundreds of flights in a few minutes.Therefore, it is concluded that the GA-based MILP scheduler is faster than the others and is more appropriate for the scheduling problem in the dense terminal airspace operation.
+Dynamic Planner FrameworkThe schedulers described in the previous section are deterministic.The transit time of any route segment was a function of aircraft type and the speed profile only, and the uncertainties along the routes were not considered in the planning.Even if the uncertainties are taken into account in the planner, the aforementioned schedulers are based on the premise that the uncertainties of each aircraft are known prior to the planning along any route segments.However, the STAs in the realtime simulation should be updated in a dynamic manner corresponding to the varying situations of weather, wind and off-nominal scenarios.A key to the realistic scheduling is a dynamic update of the STAs in a real-time trajectory model in consideration of the uncertainties in flight simulation.A dynamic planner framework is developed in our study by interactively integrating the trajectory simulation and the schedule planning for STA update.The framework consists of two components:• Simulator: This module is responsible for advancing time.It manages the creation of targets at specified location and time, and constructs the demand snapshot at a given instance of time.The module delegates to a trajectory model that handles the actual movement of flights along their most recent plan and blends motion between successive plans.Uncertainties in the transit time along the route segment and their propagations are implemented in the simulation module, which will be explained in later section.• Planner: This module is responsible for con-structing conflict-free plans for each aircraft in a given demand set.Each motion plan consists of a sequence of waypoints with an associated STA.Although any type of the scheduler can be used in the dynamic planner framework, the GA-based MILP scheduler is integrated into the current framework.Furthermore, to take into account the uncertainties in the trajectory simulation, an extra buffer additional to the desired separation is added to mitigate effect of uncertainties so that the resulting schedule remains conflictfree.The details of the additional buffer are discussed in later section.A specified amount of controllability is allowed in speed profile to maximize the scheduling performance and is implemented in both the planner and the trajectory model simulator.
+Trajectory Model SimulationFlight simulation of the trajectory model is made via subsequent communications with the planner.First, the dynamic planner starts from the pre-planning of the initial demand set.Given the speed profile and the ETA of each aircraft to the first schedule point, the initial STAs are computed by the scheduler at all scheduling points on the assigned route.The STAs are predicted such that they satisfy the constraints of the transit time bounds on the route segments and the desired separation at the scheduling points.The trajectory model periodically computes the distance from the current position to the next scheduling point.For our work, the update period is 60 seconds.With distance to the next scheduling point and the STA predicted by the planner, the target speed is calculated and checked whether it is bounded by the speed range specified in the original speed profile.Once the target speed is determined, then the trajectory simulator advances the flights by an update period.After the simulation, the aircraft position is recalculated and the earliest time to the scheduling point is updated.A subsequent planning cycle updates the STAs based on the most recent simulation results .This cycle of simulation and planning is iterated and advanced in time by the update period until all the flights arrive at the runway.An example of the cycles of simulation and planning is shown in Figure 2. Given a simple route structure shown in Figure 2(a), the STAs at the scheduling points of "Waypoint1" and "Run-way1" are updated at each update period of 60 seconds.Their convergence history is plotted in Figure 2
+ControllabilityTo delay or expedite an aircraft on its way to the next scheduling point, the controllability on any route segment is modeled such that it allows ±10 % speed variation.The corresponding transit time bounds are computed.This controllability is derived from the statistics of the aircraft flying with modern avionics and the onboard precision system [11].
+Uncertainty Modeling and PropagationAccurate prediction of uncertainties along an entire aircraft's trajectory is not trivial.It is a complicated function of space and time, which requires precise understanding of where and how much of the uncertainties are present and how they affect individual aircraft operations.However, the uncertainties in the runway arrival times are quantifiable from the statistics of the runway arrival times observed in a given duration of time.We can model the aircraft arrival time prediction errors at the runway by a normal distribution with the timeinvariant mean and standard deviation values.On the other hand, the uncertainty at the intermediate control points and route-merge points require a mathematical model of the uncertainty propagation mechanism along the routes.A simple linearized form is introduced in our simulation module: variance at each scheduling point is assumed to be proportional to the variance of the runway arrival time prediction errors when there are no control effort in between the scheduling point and runway.Uncertainty amount at any point on the route is scaled by the ratio of the intermediate route segment length to the entire route length from the entry fix to the runway.This presumes that the uncertainties grow longer along the longer routes since the flight is associated with longer transit time without control efforts.Based on the central limit theorem, we assume that the position error of aircraft at the scheduling point is approximately normally distributed.Then, the corresponding inter-arrival error of any pair is also normally distributed, and the mean and variance of inter-arrival errors are estimated by the following basic relationships: if X and Y are independent random variables that are normally distributed, then X + Y is also normally distributed, i.e., if X ∼ N (µ, σ 2 ) and Y ∼ N (ν, τ 2 ) and X and Y are independent, then aX + b ∼ N (aµ + b, a 2 σ 2 ) and X + Y ∼ N (µ + ν, σ 2 + τ 2 ).The means and the variances of the X and Y are the µ and ν, and σ and τ , respectively.The validity of the above relations holds best when the independence of two variables, X and Y , is relatively well guaranteed.The inter-arrival error of a pair of aircraft is a complicated function of many factors such as precision errors in navigation and weather including wind.We assume for simplicity in our trajectory simulation that the wind effect during the traffic simulation is rather constant.Direction and magnitude of the wind are relatively non-changing on each aircraft throughout the whole simulation, then we can treat the wind effect as a constant that is freely addable / subtractable to/from the standard deviation of the position error of each aircraft at all scheduling points, and the above relation holds relatively well.
+Additional Separation Buffer due to UncertaintiesAircraft arrival time errors and the corresponding inter-arrival error in a pair are likely to cause violations of the desired separation.In order to ensure desired separation is maintained in spite of arrival time error, extra buffers are added to the original desired separation [12].The amount of the additional buffers is determined from the probability of the inter-arrival errors and its normal distribution shown in Figure 3.If we choose a value, Z, for an additional buffer such that the cumulative probability corresponding to Z coincides with a specified confidence level, 90% for the current work, then we can say that the separation requirement in any pair will be satisfied under uncertainty with 90% confidence and be violated with 10% tol- erance.A brief graphical explanation is shown in Figure 3.This sets the additional buffer value as 1.645σ, where σ is the standard deviation of the inter-arrival error distribution.€ f 1 , σ 1 € f 2 , σ 2The above can be expressed mathematically as follows.Standard deviations of the arrival time errors of a leader and a follower in a given pair are denoted as σ leader and σ follower , respectively as in Figure 4.The amounts are scaled from runway standard deviation in proportion to the ratio of the local route segment to the entire route from runway.Assuming the probability of the position errors of a leader and a follower are independent of each other, the standard deviation of the interarrival error is assumed to be √ σ leader + σ follower .A corresponding additional buffer is set as 1.645 σ 2 leader + σ 2 follower for a 90 % confidence interval based on the normal distribution of the inter-arrival errors.A simple mathematical formulation of total amount of buffers is expressed in following Equation.
+Application: Optimal Route Structure Under UncertaintyA practical application of the developed schedulers and the dynamic planner framework is shown in this section.A design of an optimal route structure under uncertainty in the extended terminal airspace area is carried out to improve scheduling performance and, thus, to best utilize the limited airspace resources.First, key parameters for a route structure design are identified.A total of five example route structures are constructed with varying design parameters.A static FCFS heuristics-based MILP planner is used to analyze the scheduling performance of each notional route structure, but the results are validated by using the dynamic planner framework.Based on the analyses of the scheduling performance of the notional route structures, more general cases of various merge topologies are considered subsequently.
+ParameterizationA route structure consists of such parameters as the location and number of entry fixes, runways and merge points as well as route segment lengths.A demand set is also critical in scheduling performance.The demand set defines relevant flight information including the total number of flights, arrival time at entry to the route structure, and traffic duration time.In fact, the scheduling performance is very sensitive to the demand set.A fully saturated demand set, i.e., one which has no periods of low demands, is used to isolate the scheduling performance from the effects of the route structure alone.A fully saturated traffic flow is consistent with dense terminal airspace operation whereby demand exceeds capacity for extended periods of time.In this way, the runway capacity is always exhausted and the number of runways becomes no longer a parameter of the airspace topology.An entry fix topology, i.e., the total number of entry fixes and their locations, is also assumed to be given to facilitate the fully saturated traffic flow.Thus, the main parameter in our design study is the merge point topology, i.e., the location and number of the merge points.
+Numerical Test IFirst, a numerical experiment is performed on five route structures having different topologies with varying numbers and locations of the merge points.Figures 5 and6 have a single merge point whereas Figures 7 through 9 have two merge points.The locations of the merge points are moved in order to vary the ratios of route segment lengths in a given topology and thus vary the uncertainty distribution along the route.These topologies can also be defined by the parameters of the route segment length and the merge angle between two routes.For example, Figure 7 through 9 can be defined by varying the parameters of a, b, c, α, and β as shown in Figure 1, where a, b and c represent the route segment length, and α and β represent angles between two merging routes.A FCFS heuristics-based MILP is used for computing the scheduling performance of each topology.In the cost comparison of the schedulers explained in previous sections, a demand set of 80 flights is not trivial in scheduling even for the heuristicsbased MILP scheduler.Total computation time grows very quickly, especially for a stochastic case where hundreds or thousands of Monte Carlo simulations are performed.Thus, for this numerical experiment, the route is pre-assigned for each aircraft and the orders at the merge points are predetermined based on the the unimpeded transit times from the entry fix to the runway.For a stochastic scenario, an uncertainty model is directly implemented in the scheduler, and we do not employ a dynamic planner for this preliminary numerical experiment.However, a more realistic validation of these five airspace topologies is carried out by the dynamic planner and analysis results will be shown in the following section.Initially, a total of 80 flights pass through the entry fixes equally divided into four streams and follow their pre-assigned routes during a short period of 100 seconds.This short duration time ensures fully saturated air traffic.Four types of weight class categories are used: "heavy", "B757", "large", and "small".A majority of aircraft in our demand set, more than 90%, belong to the "large" type, indicative of today's operations.The total amount of separation required at each scheduling point is the sum of the minimum desired separation and the extra additional buffer to mitigate uncertainty.The quantification of this amount is done using the formulation of Equation 4. The amount of the desired separation is based on the weight classes of the leader and the follower in a given pair, and the values are specified in Table 2. Airspeed is assumed to be linearly decreasing along the routes in the extended terminal airspace area and its value at each scheduling point is summarized at the tables in Figure 5 through 9.Airspeeds at the entry fixes are fixed at 250 kts.Additionally, the desired separation of the entry fix was 5 nmi to model the separation required for en route airspace.Previously collected data indicates that a standard deviation of uncertainty (in the aircraft arrival time errors) at the entry fix is found to be approximately 30 seconds.The additional uncertanty-related separation buffer required at the entry fix in order to mitigate this uncertainty is calculated in a similar way for the merge points and runway.Given the prescribed uncertainty and its corresponding separation buffer, the FCFS heuristicsbased MILP scheduler simulates both the deterministic and stochastic scenarios for all airspace topologies shown in Figure 5 through 9.A total of 500 Monte Carlo simulations were carried out for the stochastic simulations.The separation buffer and total separation are computed for each case and summarized in the tables in Figure 5 through 9.The resulting average delays are shown in Table 3.The average delay is calculated as a difference between unimpeded transit time and actual transit time.The value of average delay is an artifact of the fully saturated traffic scenario.The key result is the performance improvement.First, for the deterministic scenarios where no consideration of uncertainty is made, the third column in Table 3   the double merge cases, differences in the average delays among all cases are very minor, less than 1%.This can be explained from the formulation in Equation 4, where the amount of total separation is solely a function of airspeed alone when there is no uncertainty.As airspeed gradually decreases towards the runway, the runway always requires the biggest separation of all the scheduling points.This makes the scheduling performance largely insensitive to the particular upstream route structure.The controllability of each aircraft does not affect the scheduling performance either, as most of the aircraft have to absorb delays propagated from the preceding aircraft.Second, for the stochastic scenarios, an improvement of 6.7 % is shown in Case 5, when comparing the single merge and the double merge topologies.Uncertainty creates perturbations in the transit times, and some of the aircraft can exploit their controllability to fill the gaps in a pair created by the uncertainty.The results from both deterministic and stochastic cases in Figure 5 and 6 are interesting and informative.The actual, average spacings between all pairs are extracted from the scheduling results for both cases and shown in blue in tables in Figure 5 and6.A careful comparison of five cases of the resultant average spacings and the required separation indicates that the average spacing at each scheduling point is dominated by the largest separation required along the route.This is shown in red in tables in Figure 5 and6.This observation suggests that in a fully saturated traffic flow, the traffic becomes steady with acceleration and deceleration adjusted by the controllability that is allowed in each route segment and incurs approximately the same amount of spacings in any pairs.
+Validation Using Dynamic PlannerThe scheduling results in Section of Numerical Test I are validated using the dynamic planner framework.A GA-based MILP is employed for the planner of the framework for a comparison with the FCFS heuristics-based MILP.Unlike the static planner used in Section of Numerical Test I, a dynamic planner involves iterative interactions between the planner and the simulator as time advances.An update period of 60 seconds was used in our validation.In each dynamic planning cycle, the simulator tries to track the STAs provided by the planner at all scheduling points, and the planner creates new schedules as a result of updated aircraft positions and ETAs from the simulator.A realistic demand set is critical in the dynamic planner so that the trajectory model can be simulated based on the operationally reasonable and realistic schedule plans.For this reason, rather than using fully saturated traffic as in the Numerical Test I, initially well-separated traffic flows, by 5 nmi in any pairs with random deviation ranging from -20% to +20%, are used for the dynamic planner.This results in hour-long traffic flows for the same number of flights.Also the initial departing point for all aircraft is fixed at about 100 nmi away from the runway and is almost aligned with the freeze horizon.The validation results are summarized in Table 4.An average delay value is, again, defined as a difference between the unimpeded transit time and the actual transit time from the entry fix to the runway.Compared to the values in Table 3 which used a fully saturated traffic flow, average delay values become more reasonable with the maximum value less than five minutes.It should be noted that the dynamic planner is more computation-intensive than the static scheduling planner, as the specified plan update period cannot be set too large in a real-time simulation.A wall-clock CPU time per dynamic planning takes about an hour with 60 seconds update period.The dynamic planning requires hundreds of cycles of simulation and planning for aircraft to travel the entire route of 100 nmi in length.Thus, the computation time of the dynamic planner for the stochastic case becomes very expensive with Monte Carlo simulation.The results for the stochastic cases shown in Table 4 are obtained from only 100 Monte Carlo simulations.More simulations are planned as part of the future work.It can be concluded from the average delay results shown in Table 4 that Case 5 is the bestperforming airspace topology and it has a performance improvement, compared to Case 1, of approximately 50% in the deterministic case and approximately 30% in the stochastic scenario.Although the sample size of the Monte Carlo simulations in the stochastic scenarios is not big enough to make the conclusion more trustworthy, the standard deviation of the 100 Monte Carlo samples are as little as 15 seconds resulting in 5% tolerance that is far less than our percentile performance improvement.It should also be noted that for a less saturated traffic flow, an improvement in the scheduling performance is more dramatic compared to the 6.7% in Table 3.This can be explained by the fact that in fully saturated traffic flow, all the aircraft quickly exhaust their controllability and are assigned their slowest speed profile.That is not the case with less saturated air traffic, and the benefits from the controllability are more dramatic in this case.It should also be noted that the same trend of the static planning in Numerical Test I is shown in the validation results: an airspace topology with small separation requirements at the scheduling points is more favorable in the scheduling performance.
+Numerical Test IIBased on the results in the previous sections that the scheduling performance is heavily dependent on the amount of maximum separation at the scheduling points, a simple numerical experiment is carried out to investigate more general variations of the airspace topology.Relationships among the component separation amounts at the scheduling points are analyzed when the merge points move around in the extended terminal airspace area.The airspace topology is simplified to a circle with 40 nmi radius as shown in Figure 10.The points, WP1 and WP2, represent the merge locations that can move circumferentially at a relatively constant radial distance away from the runway, and the routes can be merged at these locations.Radial distances, x and x + y, are also allowed to vary within radius bounds: 0 < x < R and 0 < y < R -x, where R is the radial distance from the runway to the entry fix.The movement of the merge points, WP1 and WP2, are shown in Figures 10(a) and 10(c), and the corresponding example airspace topologies are plotted in Figures 10(b) and 10(d), respectively.As WP1 and WP2 move about in the extended terminal airspace area, the entire route length in a particular airspace topology from the entry fix to the runway and the corresponding transit time may change.However, any large variations were excluded in the transit times by locating the entry fix such that the the entire route from the runway to the entry fix does not deviate much from the original radial lines.For the airspace topology with a single merge as in Figures 10(a) and 10(b), total separation amounts at the runway and the WP1, are computed from Equation 4 and plotted as solid lines with symbols in Figure 11(a).The two dashed lines represent the desired separation amount using the nominal speed profile, and the uncertaintyrelated, extra separation buffers are plotted as dotted lines.The traces of the maximum separation with respect to the varying merge locations are shown in Figure 11(b).Figure 11(b) implies that there exists an optimal merge location somewhere around 5 or 6 nmi away from the runway.The case of double merges inside the extended airspace area as in Figures 10(c) and 10(d) shows similar trend as the single merge case.At the given downstream merge point location, x, which can move from the runway to the entry fix, the upstream merge location, y, also traverses between downstream merge point and the entry fix.The plot of the maximum separation buffer of all scheduling points, i.e., runway, downstream and upstream merge points, are almost identical to the plot of Figure 11(b) and is omitted in the paper; how-   ever, this indicates that the separation at the runway typically requires the biggest amount and in other cases the downstream merge point requires a larger buffer than the upstream merge point.The exact optimal locations of merge points are less meaningful in our analysis, as they can vary depending on the underlying airspeed profile, uncertainty quantification and propagation model, and the assumed additional separation buffer due to uncertainty.Possible changes to the optimal merge locations can be inferred from Figure 11(a).Desired separation represented as the dashed lines in blue and red are rather non-changing as these are solely determined by the airspeed topology alone, and the assumption of monotonically decreasing airspeed in the extended terminal airspace area is reasonable.On the other hand, the modeling of uncertainty and its propagation in the extended terminal airspace area is still an area of active research that requires extensive studies on how the uncertainties are distributed or propagated along the routes.How temporal or spatial deviation from its predicted trajectory behavior is quantified over time and distance and translated into the time-based scheduler is a difficult and yet very important topic in the scheduling and real-time simulations.
+Various Uncertainty Propagation ModelsA brief sensitivity study of the optimal merge locations is carried out with respect to the different models of uncertainty distribution and propagation.The results in previous sections assumed that the increment of the uncertainty per unit route segment is constant and the resultant uncertainty is linearly proportional to the route segment length.The plot of constant uncertainty increment and the corresponding optimal merge location is plotted in Figure 12.The maximum of the two solid lines with the symbols represents the traces of the largest separation amount of all the scheduling points.The x-axis represents the location of the downstream merge point.Once the downstream merge point is located at the predicted optimum point, the separation amount at the upper merge point appear to be smaller than the one at the downstream merge point.On the other hand, if we put more weights in the uncertainty towards the entry fixes, different optimal merge locations are predicted.An assumption of a linear increment as in Figure 13 moves the optimal merge location slightly towards the entry fix, about 10 nmi away from the runway.If we put more weights in the entry fix boundary area as in Figure 14, at a quartic increase rate for example, the optimal merge location falls in the regions farther from the runway, about 23 nmi away.From these simple models of the uncertainty quantification and propagation, it is concluded that the optimal location of the merge point is where a large amount of uncertainties are present, and the merge point plays a role in reducing the uncertainties in that region by enforcing the pilot's control efforts to meet the suggested STA at that point.
+ConclusionsMILP-based optimization algorithms were used in our scheduling and route assignment problem, and a number of heuristics were introduced into the original formulation to keep its computational cost realizable in the dense terminal airspace operations.FCFS-heuristics and GA-based heuristics were adopted to reduce the computational burden, and the resultant computational costs and scheduling results were compared with the original MILP solutions.The GA-based MILP planner appears to be very efficient without loss of optimality.To take into account uncertainty propagation in the route structure, a dynamic planner framework is developed.The dynamic planner consists of the modules of the planner and the trajectory simulator.The STAs are updated in a dynamic manner via the interaction of the planner and the simulator throughout the whole simulation.An uncertainty model is implemented in the trajectory model of the simulator and the extra separation buffers are added at the scheduling points in the planner.As a practical application of the proposed schedulers, an investigation of the optimal route structure under uncertainties in the extended terminal airspace was carried out.A constant uncertainty increment was assumed along the route, ensuring the uncertainty amount grows linearly in proportion to the route segment length.After analyzing the airspace topologies with varying merge topologies, a route structure having the least maximum separation at the scheduling points has shown the best scheduling performance.These results were validated with a dynamic planner framework with a more reasonable demand set.Finally, airspace topologies with various uncertainty distribution models were tested: a constant, linear and quartic unitincrement of the uncertainty along the route segment.It is shown that the optimal merge point should be positioned to bound the growth of the uncertainty-related separation buffer such that the maximum total separation at any point along the route is minimized.This fact indicates that there exists a likely optimal merge topology.The optimal merge topology is still tuned for a of uncertainty propagation models while the exact topology did vary.Figure 1 .1Figure 1.Example route topology
+Trajectories of STAs and Actual Time of Arrival (ATA): STAs in solid lines and ATA as the last point of the line.
+Figure 2 .2Figure 2. Example of dynamic planner framework
+(b), and STA updates are shown by the triangles.Unlike the static planner, the STA values are updated at each update period and finally coincide with the Actual Time of Arrival (ATA) values at the scheduling points.The speeds along the routes change correspondingly in each update period.
+Figure 3 .3Figure 3. Probability distribution of position
+Figure 4 .4Figure 4. Uncertainties of position errors in a pair at the merge point (σ1=σ leader and σ2=σ follower .)
+Figure 10 .10Figure 10.Simplified airspace topology of the exterminal area with varying merge point locations
+Figure 12.Constant distribution of uncertainty
+Table 1 .1Computation times (in seconds) to schedule different number of flightsNumberAverageof67840100 delayfights(8 flights)MILP6.798.3 1258.4 N/A N/A 8.23MILP +FCFS2.133.2 419.3 2040 N/A 8.38heuristicsMILP +GA≤ 1.0 1.02.223.4 50.0 8.58heuristics
+t sep tot = t sep desired + t sep σ=d sep desired V+ 1.645 σ 2 leader + σ 2 follower ,where V is airspeed and t sep tot represents totalamount of separation requirement. Terms oft sep desired , d sep desired and t sep σ represent a desiredseparation in time, a desired separation in dis-tance, and an uncertainty-related, additional buffer,respectively.
+Table 2 .2Desired separation (nmi)leader PP P P P follower heavy B757 large small P P P Pheavy4555B7573334large3334small3333
+Table 3 .3Predicted average delays (seconds).AveragePerformanceAveragePerformanceTopology Casesdelayimprovementdelayimprovement(Deterministic) (w.r.t Case 1)(Stochastic) (w.r.t Case 1)singleCase 13363.41.0 %3834.81.0 %mergeCase 23370.8-0.2 %3918.2-2.1 %doubleCase 33348.10.45 %3571.66.8 %mergeCase 43343.60.6 %3564.87.0 %Case 53352.00.3 %3579.76.7 %
+Table 4 .4Average delays (seconds) validated by dynamic planner framework for stochastic case.Topology Cases Update DeterministicPerformanceUpdate StochasticPerformancecyclesscenarioimprovementcyclesscenarioimprovement(w.r.t. Case 1)(w.r.t. Case 1)singleCase 1304249.11.0 %350291.01.0 %mergeCase 2257235.05.6 %329283.02.7 %doubleCase 3228107.2656.9 %294228.221.6 %mergeCase 4232108.3156.5 %295213.526.6 %Case 5247109.1756.0%294199.331.0 %
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+IntroductionGround-based flight simulation has a variety of aeronautics applications such as training, research and development, and accident investigations.Safety and cost savings relative to flight test are the most appealing virtues of using groundbased flight simulation.With the advanced technology in digital computing and image generation, the realism and fidelity of today's flight simulators have improved significantly from the old blue box of the thirties.However, the effectiveness of ground-based flight simulation is difficult to determine.Simulation may be physically similar to flight, e.g., same cockpit layout, control force feel, and tasks.But the fundamental human/machine interaction, specifically in visual-motion interactions, is often very different.In the extreme, the specific force cues are missing from fixed'base flight simulators.Motion-based flight simulators do provide onset specific force cues but can have visual-motion cueing conflicts due to limited travel.Pilots, therefore, must adjust their strategy in using the simulation cues to perform the given tasks.Since humans are adaptive and optimizing in nature, unless these characteristics can be quantified, the effectiveness of flight simulation, e.g., transfer of training and transfer of handling qualities results, with respect to simulator missions cannot be predicted.The presumption is that, if one can develop a comprehensive understanding of how pilots perceive aircraft states and task parameters from available simulation cues, and how they process and react to that information in given tasks, an analytical methodology can be developed to characterize that behavior process.It may then be used to interpolate and extrapolate results learned from ground-based flight simulations.Thus the effectiveness issue can be determined.This paper reviews several critical elements associated with ground-based flight simulation's visual and motion cues that are most influential to the human/machine interface.The objectives are to summarize significant results from past studies and to identify future research directions for determining ground-based flight simulation effectiveness.
+Visual Cues. Visual cues are the single most important simulation cues in all ground-based flight simulators for determining the orientation and position of the simulated aircraft.From the out-the-window (OTW) scene, instruments, and perhaps a Head-Up Display (HUD) and/or Head-Down Display (HDD), pilots observe the simulated aircraft states to develop appropriate actions to perform the tasks.
+Transport DelayTransport delay has been a critical factor in visual cueing perception.The delay reflects how fast the image generator or displays can present the simulated aircraft's response due to pilot's control inputs.Time delay has been found to have significant effects on pilot workload in several studies. 1 ' 2 FAA Advisory Circulars have suggested no more than 150 msec delay for transport flight simulators, 3 and 100 msec delay for helicopter flight simulators 4 where delay is defined as the time interval taken from the control input to change in the OTW scene.The current technology has improved the transport delay contributed by the image generator alone to under 50 msec (e.g., about 50 msec for E&S ESIG 4530 5 and about 25 msec for SGI Onyx 6 ).Time delay due to integration steps in the real-time digital computer, i.e., from accelerations to rates, and rates to displacement, has also improved significantly due to using predictive algorithms 7 and faster real-time computer processors.The technology allows modern simulators to easily meet those recommended criteria.
+Visual ResolutionThe ability to distinguish and recognize an object or a target from OTW is primarily dependent on the contrast and resolution of the displayed objects and targets.Level of contrast depends on the display system technology, e.g., collimation through lens, and projection through light valves.The limiting factor for resolution is the number of polygons that image generators can generate, and the performance and efficiency of all visual system components in the pipeline.The resolution requirement also depends on the distance (range) and flight tasks.Brown 8 showed a process to determine the required resolution for a TA-4J in an aerial combat maneuver.Larsen 9 used Johnson's Criteria, 10 which are dependent on task level, i.e., detection, shape orientation, shape recognition, and detail recognition, to develop the required resolution in line pairs for an air combat training.Polygon count, though convenient, is not a good measurement of the resolution nor provides a good comparison between systems since each manufacturer has its own polygon definition.A recommended measurement common in industry is to use the Modulation Transfer Function (MTF) 11 which combines the contrast and resolution as a single parameter to determine the entire display pipeline performance, 12 i.e., from image generator to display.Therefore, a logical recommendation to quantify the display system resolution performance is to develop a standard test pattern and measuring procedure, and then use MTF as an objective measurement.
+Scene ContentOut-the-window scene content plays an important role in pilot's perception in estimating position, attitude, and their rate of change.Lintern, 13 in a simulation bombing training study with 42 student pilots, compared results in dusk condition with limited scene features and from day light with extensive scene features.He found that scene content produced significant effects in pilots' bombing error performance.Lintern has also found that training effectiveness improves with increases in visual scene detail. 13" 15 In a separate bombing study with 32 pilot subjects, Lintern 16 found that scene content, i.e., landscape vs. grid pattern, has a significant effect on pilots pitch control performance and transfer of training, all in favor of the landscape case.The shape of objects and application of texture also play significant roles.Kleiss,17 in his discussions of visual scene properties for low-altitude flight, found that change of global optic flow rate and change of optical edge rate are useful for perceiving change in speed.In a visual environment at a speed of 600 knots and 150 ft above ground with 21 A-10 pilots, DeMaio 18 found objects are effective for estimating altitude.He suggests that a density of about 12 to 15 objects per square mile is necessary and sufficient for maintaining altitude.The same study also finds equivalent cueing effectiveness can be provided by a two-dimensional texture pattern.Kellogg 19 in his investigation with 10 experienced C-130 pilots found that texture had a significant and positive effect in centerline positioning and altitude control in an assault landing task.That conclusion is consistent with findings from DeMaio 18 and Kraft. 20ditional studies have been recommended by DeMaio to develop better understanding of what types of texture patterns contribute to effective altitude cueing.Kleiss indicates variations in terrain shape and object size or spacing are important parameters for the simulator designer, and suggests further investigation to determine level of ' terrain resolution requirements.Visual Field-Of-View (FOV1 The effectiveness of FOV is a very practical issue for ground-based flight simulators.For realism purposes, one would naturally keep the visual cueing environment as close to the simulated aircraft as possible, i.e., wide FOV for most of simulated aircraft.From the visual self-motion perspective, peripheral vision is also important. 21However, wide FOV can be an expensive proposition.It typically demands a high cost in image generation systems and monitors even if added weight and space are not issues.In a single roll degree-of-freedom (DOF), Moriarty 22 has shown peripheral vision has significant effects in a compensatory tracking task when subjects using a sidestick to control higher order control element dynamics (~k/s 3 ).With peripheral vision, results showed that pilots were able to provide more phase lead in the frequency range below the crossover frequency. 23In the same study, however, he did not find peripheral vision had a significant effect when a lower order control element (~k/s 2 ) was used.This suggests that wide FOV may have significant benefit when the simulated aircraft dynamic characteristics become higher in order.A review of the effectiveness of wide FOV in multiple degrees-of-freedom flight simulations has produced mixed results.Several studies 13 ' 19 ' 24 " 27 have been identified which cover a range of tasks and types of aircraft.These investigations all have used a large number of test subjects and used statistical analysis to determine the significance of their results, as summarized in Table 1.As shown, results from the same flight simulator differ as tasks and test subjects varied which suggest more systematic investigation in determining the effectiveness of FOV is required.
+Man/Machine InteractionEffectiveness of motion vs. no-motion in ground-based flight simulations is a heatly debated issue.Platform motion has been shown to improve pilot-vehicle performance when compared with fixed-based flight simulators.Using a roll attitude stabilization task in hover, Stapleford 28 found that motion cues increased pilot phase lead and led to higher pilot crossover frequency and gain.In a dogfight scenario investigating the effects of motion vs. no-motion, Jex 29 found that under the full motion case test subjects were able to provide more phase lead at low American Institute of Aeronautics and Astronautics frequency which helped avoid drifts and overshoots in target tracking, and to provide higher gain (a factor of 1.6 over nomotion condition) in disturbance rejection.These results support the applications of motion platforms in groundbased flight simulations.For training effectiveness, however, no significant transfer of training due to motion was found in several military studies 25 ' 27 even though motion cues were found to have significant effects to improve pilots performance in some measurements and tasks. 27A comprehensive understanding of man/machine interaction involving visual and motion cues is therefore required to determine the effectiveness of the ground-based flight simulator.
+PsychophysicsIn fixed-based simulators, even without a motion device, visual cues generate self-induced motion.The self-motion is dependent on the peripheral visi&n, spatial frequency, and background of the scene. 30The approximate frequency response of the visually induced motion bears a first order characteristic which falls off at 0.1 Hz. 21This indicates a significant delay in integrating the acceleration to rate and/or position to perform the task if the acceleration information is solely derived from visual cues.To determine the simulation cueing effects one approach is to develop a structured model such that pilot/vehicle interaction can be analyzed.It is desired that a closed-loop mathematical structure can represent pilot's physical interaction with controls, simulation cues, and the task.A representative structure developed from manual flight control concepts is shown in Figure I. 31 If each key element in this closed-loop structure can be characterized and quantified, the complicated man/machine interface with simulation cues in ground-based flight simulations may be explained analytically.The human's motion sensing mechanism primarily comes from vestibular system, and proprioceptive feedback via organ, limbs, and surface pressure.Gum 32 discussed these sensing devices characteristics and developed mathematical models for each sensing mechanism.Peters 33 did a summary review on both angular and translational motion sensing studies in 1969, followed by another extensive review by Zacharias 34 in 1978.Both reviews identified a wide range of studies and results in specific human sensory characteristics and modeling.Most of the results, however, have been found in a single degree-of-freedom only.The established understanding indicates that angular rates are sensed by semicircular canals in the vestibular system, 34 ' 35 low-frequency linear accelerations are sensed by the otoliths, and highfrequency linear accelerations are sensed by other tactile mechanisms, including the neck muscles and receptor in a pilot's seat-of-pants. 34" 36 A clear and brief summary including block diagrams of key motion sensory characteristics models is presented by Schroeder 36 in his 1999 report.Threshold is one of the nonlinear human sensing characteristics of particular interest since it is directly related to the time delay in sensing the onset acceleration and the perception of smoothness of motion cues.Table 2 summarizes findings from several representative investigations. 34" 35 ' 37 ' 38 The range of variations reflects empirical effects due to different test subjects, test apparatus, and methods.In addition, as a common practice in motionbased flight simulators, low frequency longitudinal and lateral accelerations are generated by tilting the platform, e.g., a x = g sin0.The translational acceleration threshold, therefore, has an effect on angular tilt threshold.Similarly, the angular rate threshold also has a direct impact on the tilting motion which may lead to a conflict with visual perception and a sensation of vertigo due to pilot sensing uncommanded rotational cues.A lot of work has been done in this area but knowledge of human sensing characteristics is still incomplete.Understanding of otolith characteristics is limited to the longitudinal DOF only.The tactile model needs more refinement and validation.Angular motion sensing characteristics are mainly developed from pure rotational motion alone.Data have shown significant angular rate threshold increases when translational motion is added which suggests there is a dependency in angular motion sensing characteristics on otolith sensing. 35Most importantly, most of the past works are done in single DOF.The need to develop an integrated cueing model for multiple DOF as recommended by Zacharias 34 still exists.
+Pilot ModelingWith pilot-in-the-loop ground-based flight simulation, a feedback loop is formed with the pilot closing the control loop with a task using the perceived simulated aircraft response via visual and motion cues.The goal is to utilize a structured approach for human characteristics and behavior to determine the effectiveness of given flight simulation cues.If such loop structure and simulation feedback cueing characteristics can be identified, criteria can then be developed to determine and predict the simulation effectiveness based on the missions.McRuer 23 investigated such a logical approach by formulating a pilot model based on plant characteristics in a tracking task with fixed-based flight simulations.One important aspect from his investigation was developing a crossover model, which relates the operator (pilot) and controlled element (simulated aircraft) transfer characteristics in the frequency domain.This model has been widely used among the researchers and investigators with its key parameters, crossover frequency and phase margin, to measure pilot's response due to specific variations in a closed-loop system.One specific application using the pilot crossover characteristics to determine the simulation cueing effectiveness with a closed-loop structural pilot model is by American Institute of Aeronautics and Astronautics Hess. 39In a series of studies, Hess investigated a single loop maneuver, i.e., vertical (bob-up and bob-down), and a multi-loop maneuver, i.e., roll-lateral (a sidestep), by comparing simulator data and flight test data from an Army UH-60 Black Hawk. 40n a closed-loop system representation, a structural pilot model was developed based on psychophysics characteristics that included central nervous system and neuromuscular inner loop modeling, and a procedure using pilot crossover parameters to determine the loop closure performance was developed to determine simulation fidelity.This approach shows promise, but has not been fully validated.Another approach in analyzing and determining simulation cueing effectiveness is through application of optimal control theory. 41Levision and Junker 42 investigated a structured closed-loop model which applied bank angle error and roll acceleration in a cost function for a roll tracking task and a disturbance rejection task.They found that motion cues were much more effective in the disturbance task than in the tracking task, and led to significant increase in gain-crossover frequency of pilots.This is consistent with findings from Stapleford 28 and Jex. 29In addition, to check the general application of the model, a typical set of pilot parameter values were chosen and remained fixed, which included adding control rate to the cost function, to be tested in eight different test conditions.The model results showed good agreement with experimental measures, i.e., RMS tracking error.In the same investigation, efforts were made to include vestibular sensor dynamics to determine the significance of the sensory characteristics in the disturbance rejection task.The results did not find significant differences compared with the simple informational representation.Structured pilot model approaches have shown promise in providing analytical ways of characterizing and estimating man/machine interaction with simulation cues.The findings, however, have been limited to small samples of control tasks and limited degrees-of-freedom.The interaction between the visual cues and motion cues are not fully understood.
+Motion Cueing CriteriaMotion cues have been shown to improve pilot performance.False cues due to limited motion travel, however, could have severe impact on the effectiveness of the motion. 43It should be noted that motion cues are a combination of the motion system dynamics and the motion drive algorithms, i.e., washout filters.Therefore, the characteristics of both must be considered in evaluating motion cueing effectiveness.For motion system dynamics, AGARD-AR-144 44 has identified five key system characteristics.They are: excursion limits for single DOF, describing function, linearity and acceleration noise, hysteresis, and dynamic threshold.However, no objective performance criteria were recommended.FAA AC 120-63" proposes a minimum describing function requirement in the frequency domain for helicopter simulators, Figure 2, and is supported by an investigation using a 20-ft sidestep with motion cues fully matching the visual cues. 5Logically, the linearity and acceleration noise criteria can be developed from the human's motion sensing threshold.To determine the motion cueing fidelity requirement due to washout filter applications, Sinacori 45 first developed criteria using the magnitude and phase of motion cues at 1 rad/sec for angular rate and specific force, Figure 3, to correlate with pilots' subjective perception of motion cues in an "S" maneuver at 60 knots with a high performance helicopter simulation.High, Medium, or Low motion fidelity region was established based on motion sensation relative to visual flight (as perceived through the use of the visual display).Jex 46 developed a lateral washout filter criterion, also shown in Figure 3, based on four pilots comments using an air-toair gunnery type evasive maneuver and a roll washout filter of s/(s+0.4).Schroeder 36 refined Sinacori's criterion based on his work in yaw and vertical motion DOF with helicopter tasks.White 47 takes a different approach in defining motion fidelity criterion that is dependent on the magnitude of false specific force cues, Figure 4.This approach is justified based on human motion sensory threshold characteristics.There are two specific motion drive components that typically are overlooked by simulator operators but have significant effects in cueing conflict with visual cues.One relates to translational motion relative to the angular motion, and the other is the tilting.Translational travel that is required to fully coordinate with roll and pitch angular motion is normally heavily attenuated due to available travel.The resulting specific force false cue has been found to significantly affect pilots' perception of motion and their workload. 48A roll-lateral coordination criterion 49 was developed independently for this specific cueing application from a sidestep task.Tilting is another visual-motion cueing conflict that bears a significant effect.Usually, low frequency longitudinal and lateral specific force cues are generated by tilting the cab as discussed previously.Excessive angular rate can easily lead to severe visual-motion cueing conflict.A rate limit tied to human angular-rate sensing threshold is recommended.The criteria being reviewed provide some guidelines to the flight simulation community that may affect the effectiveness of motion-based flight simulators.However, these criteria are developed from limited empirical data with selected tasks, and from single DOF and two degrees-offreedom investigations.Extending the investigation into multiple degrees-of-freedom, and developing correlation with visual cueing parameters, e.g., FOV and delay, and pilot crossover characteristics, which are simulated aircraft dynamics and task dependent, are recommended.
+American Institute of Aeronautics and Astronautics
+SummaryA brief summary is presented as follows, Transport delay: Modern technology can meet current FAA specifications.Visual resolution: Guideline for required visual resolution relative to task level exists.Developing a universal procedure to measure the visual resolution is recommended.Scene content: Scene content has significant effects in transfer of training and pilot performance.Future studies in texture patterns, terrain shape, and object size and spacing are recommended.Field-of-view:Large FOV has been shown to have significant effects with higher-order simulated aircraft dynamics.Results from various transfer of training studies were mixed.More empirical data with a range of tasks and simulated aircraft characteristics are recommended to establish the FOV effect.Psychophysics:Human angular motion sensing characteristics have been established.Translational motion sensing characteristics from the otoliths are limited to the longitudinal DOF.The tactile model needs refinement and validation.Future studies in interaction between multiple sensing mechanisms and integrated cueing model in multiple degrees-of-freedom are recommended.Pilot modeling: The existing approaches to determine simulation effectiveness in limited DOF studies have shown promises.More empirical data from a variety of tasks, simulated aircraft, and visual and motion cueing conditions are recommended to improve the modeling techniques and to validate the approach.Motion cueing criteria: Developing a more comprehensive motion system dynamic specification is recommended.More empirical data to support the established motion fidelity criteria and expand the criteria to multiple degrees-of -freedom are recommended.
+Concluding RemarksThis review covers only a small but important part of issues related to ground-based flight simulation effectiveness.Extensive work has been done and quite a bit knowledge has been gained in past decades yet few definite answers are offered to determine the effectiveness of the simulation.The statement reflects limited knowledge in man/machine interaction using simulation cues and suggests additional research is required.In addition to preceding recommendations and summarized future work, additional recommendations are presented for future research.(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
+(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s) 1 Sponsoring Organization.
+(c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s) 1 Sponsoring Organization.
+FigureFigure 2 .2Figure I.A representative man-in-the-loop manual flight control structure
+Figure 3. Recommended motion fidelity criterion
+Figure 4 .4Figure 4.The permissible values of nonlinear distortion (Reference 47)
+Table 2 .235mmary of motion sensing threshold Hosman 38 sine wave, 1-14 rad/sec Zaichik et al35, sine wave, 0.5 -8 rad/sec 8 American Institute of Aeronautics and Astronautics1. A more organized effort in following recommendationssuggested by past investigators and researchers to fill inthe blanks.2. A universal test procedure that documents the keysimulation cueing characteristics and effects thatinclude, but not limited to, simulated aircraft, visual
+			American Institute of Aeronautics and Astronautics
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+Table 1. Summary of Field-of-View (FOV) effectiveness resultsIrish 24 Nataupsky 25 Kellogg 19 Dixon 26 Westa 27 Lintern 13 Advanced Simulator for Pilot Training (ASPT), T-37 Summary: Used three levels of FOV, 36°V.Found FOV to be significant i controlled approach, and 360°			
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diff --git a/file144.txt b/file144.txt
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--- /dev/null
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@@ -0,0 +1,211 @@
+
+	
+	
+		
+INTRODUCTIONIn 2015, NASA Ames Research Center conducted the "Big Data, Data Analytics, and Net-enabled ATM and Airspace Operations Project" to identify air traffic management (ATM) functions that can benefit from networked, net-enabled, and/or cloud-based architecture.The objective of this effort was to investigate methodologies that could reduce duplication; reduce cost of operations and upgrades related to air traffic management, airline operations, and flight operations; and provide data analytics.Additionally, potential "fee for service" mechanisms for funding the development and operation of a solution application were to be considered.A subject matter experts (SME) team with experience in ATM, airline operations, airport operations, and big data architectures was assembled to tackle this challenge.Five topics were developed by the team related to the National Airspace System (NAS) performance, they are i) Weather Impacts to Aviation, ii) Big Data and Modeling Infrastructure, iii) Integrated Gate Turnaround Management, iv) Flight Operations Control Management System, and v) Departure and Arrival Management.After a team review, the Integrated Gate Turnaround Management (IGTM) was selected to proceed with a prototype demonstration with the following guidelines.• Use of simulation in the ATM environment • Focus on airline side and ramp operations o Desire to leverage data to manage the uncertainties in nominal and non-nominal, and o Provide performance monitoring and collaborative decision tools to promote integrated gate turnaround operations to meet the on-tie and/or predictable performance upon arrival and departure at the terminal
+CONCEPT AND BENEFITS OF THE INTEGRATED GATE TURNAROUND MANAGEMENTThe concept of Integrated Gate Turnaround Management (IGTM) is to leverage i) the data analytics technologies with multiple historical database and live data to establish bounds of uncertainties of dependent parameters associated with NAS performance, [1,2] ii) a distributed data network shared by stakeholders, [3] and iii) collaborative decision tools [4,5] to optimize the arrival and departure performance at an airport through en route, terminal, and the gate.The IGTM concept is shown in Figure 1.By providing predictable traffic performance information under nominal and off-nominal conditions, e.g., adverse weather, high/low traffic density, runway condition due to rain and/or ice, available flight crew, available ground crew, ground stop orders, and number of transient passengers, collaborative decision tools can be developed for individual stakeholders to optimize the performance under an integrated environment.This coordinated optimization will prevent localized optimization that could lead to system-wide delay.
+THE IGRM PROTOTYPEThe IGTM prototype was defined for the service space starting from the final metering fix upon arrival at an airport to takeoff or wheel-off.This will allow the prototype to be integrated with automation tools for terminal and en route airspace operations for future applications.The IGTM prototype covers the flight and ground operations from approach to runway, landing and wheels-down, to taxi-in from the active movement area to the in-bound transfer spot for handover by ATC to ramp control, lastly, to taxi in the ramp area to arrive at the assigned gate.The aircraft, once parked at the gate, would then undergo gate turnaround operations after which the aircraft reverses the process with gate pushback, taxi-out to the out-bound transfer spot from ramp control to ATC, taxi-out to the departure queue, and ending at takeoff to meet the departure slot time.This complex process involves the controlling authorities and decision stakeholders which includes the ATC ground controllers, that have authority for the airport movement area, and the airport/airline ramp controllers, that have authority for the airport non-movement area.The airline operators, airline dispatchers, pilots and ground handling personnel who determine the readiness of the airplane are also stakeholders in this process.The system architecture of the IGTM prototype is shown in Figure 2. The prototype has eight major components, listed below.Descriptions of these components are discussed in following paragraphs.1. IGTM Model/Controller, which controls the IGTM simulation and collaborative decision tools.  5. SOSS JMS Service Adaptor, to translate data between the SOSS and the IGTM Controller.6. IGTM Graphic User Interface (GUI) including the Dashboard, which provides the interface to the stakeholders.7. ActiveMQ, an open source broker with a Java Messaging Service (JMS), provides the communications network for servicing the data exchanges.8. Mongo server or MongoDB, an open source database server for the IGTM prototype.
+IGTM Host System/Launch ScriptsThe IGTM prototype is entirely written in JAVA and works with products, which are OS agnostic and can run on Linux, Windows or a MAC system.The prototype is a collection of 8 processes (See Figure 2, excluding the scenario configuration) that can be configured through XML to run on any number of systems.Scenario configuration is completed pre-simulation, by loading the desired simulated case files into MongoDB.The prototype was designed to allow any number of GUI/Dashboards users from multiple stakeholders and Model/Controllers exchange messages through the ActiveMQ Bus.By modifying the host, port, database and transport prefix in the various XML files one can reconfigure the process home system.Mongo itself can be configured to run on a clustered system solely by making changes in its XML files and the configuration files of the Model/Controller/BAI and the AOC components.These modifications can be made to increase the overall application performance.Modifications to the ActiveMQ configuration can increase the number of throughput channels and the volume of traffic allowed to pass through each channel.The launch, configuration, and control of the IGTM processes were accomplished with several Linux Bash scripts, which were called from a master script.All major events are captured in log files and are visible in the standard output.Data collection for the events are captured on the MongoDB server.
+IGTM Model/ControllerThe IGTM Model/Controller, or Controller for short, is an event driven process.The Controller used the data from AOC App, which provided the airline scheduled data, BAI App, which provided the estimated event performance (in this demonstration the mean and standard deviation, σ, of a specified event performance from historical data was used), SOSS for actual flight event data, and commands from the GUI, which provides inputs from the stakeholders for collaborative decision making or gaming on a specific event.The events modeled in the prototype are listed below.
+i.Final metering fix ii.Wheels Down (or touchdown) iii.Inbound spot iv.Gate Arrival/Parked v.Gate turnaround (based on completion of all the gate activities) vi.Push back vii.Outbound spot viii.Departure queue ix.Wheels Up (Takeoff)For each flight, three sets of time performance related data, Scheduled Time, Estimated Time, and Actual Time, are required for each event as shown in Figure 3. Scheduled event time, T i_Scheduled , for an Event i as listed above for individual flights was drawing from the AOC App.Actual event time, T i_Actual , came from the Surface Operations Simulator and Scheduler (SOSS).Estimated event completion time, T i_Estimated was calculated based on the mean and standard deviation (σ) from the BAI App on a specific event as shown in Equation 1.T Estimated_i = T Estimated_i-1 + t Estimated_i-1_to_i(1) where T is the simulation time, e.g., in zulu in seconds i denotes an event, e.g., arrival at the gate i-1 denotes a previous event, e.g., inbound spot t is the duration rom Event i-1 to Event iThe time to take from Event i-1 to Event i is defined in Equation 2.t Estimated_i-1_to_i = Size of the Event/Rate of the Event (2)An example for the Inbound Spot Time, "Size of the Event" would be the distance from the touchdown spot to the inbound spot, and the "Rate of the Event" would be the average (or mean) taxi speed.Distance from the touchdown spot to the inbound spot is given by SOSS, and the average taxi speed is provided by the BAI App.At the gate, the "Turnaround Time," where applicable, determines the "Pushback Time" in Figure 3, is dictated or triggered by the completion of following gate activities i.Deplaning of passengers ii.Baggage unloading iii.Fueling iv.Cabin services v.Catering services vi.Baggage loading vii.Flight crew availability viii.Cabin crew availability ix.Maintenance x.
+Boarding of passengersFor the gate turnaround events, Equation 1 applies to all these events with the same three sets of time performance, i.e., Scheduled, Actual, and Estimated.In these instances, the Deplaning Time was determined by the number of passengers (Size of the Event), and the deplane rate (Rate of the Event).Number of passengers is given by the AOC App, and the average deplane rate is provided by the BAI App.For the turnaround time at the gate, assumptions of critical paths were developed as shown in Table 1.Times for critical paths were calculated, and the estimated turnaround time was determined by the critical path that took the longest time.Additional time delay due to door-close to brake-release, late arrival of the flight crew, and/or cabin crew, and time to receive the clearance for a pushback were included in the turnaround time to determine the Estimated pushback time.OR# OR# OR# OR# OR# OR# OR# Σ# Σ# Σ# Σ# Σ# Σ# OR# TOD# Time# Touchdown# Time# Arrival# Time# Pushback# Time# Outbound## Spot#Time# Departure# Slot#Time# Takeoff# Time# Actual# TOD# Time# Touchdown# Time# Arrival# Time# Pushback# Time# Outbound## Spot#Time# Departure# Slot#Time# Takeoff# Time# Es?mated#by# Computa?on# Descent# Time# Es?mated#by# Analysis# Ramp# Time# TOD# Time# Touchdown# Time# Arrival# Time# Pushback# Time# Outbound## Spot#Time# Departure# Slot#Time# Takeoff# Time# Scheduled# Turnaround# Time# Schedule# Op?miza?on# Normal# Clearance# Op?mal# Clearance# Taxi# Time# Taxi# Time# Normal# Delay# Op?mal# Clearance# OR# Σ# Inbound# Spot#Time# Inbound# Spot#Time# Taxi#In# Time# Inbound## Spot#Time# FMF# Time# FMF# Time# FMF# Time# FMF#-#Final#Metering#Fix#
+Collaborative Decision ToolsOne of the key concepts of the IGTM is applying collaborative decision-making among stakeholders to gain the integrated NAS performance improvements than the optimization of a local event.Thus, the effects of a single decision-making must be propagated through the NAS beyond the local decision-making domain, and promote the coordinated or collaborative decision-making on scheduling the events.For the IGTM prototype, simple uses cases were developed to demonstrate the tools to mitigate unexpected early and late arrival situations.
+Airline Operational Control Center (AOC) AppThe AOC App was developed to simulate the data from Airline Operational Control Center (AOC), which consisted of scheduled flight time information at the Final Metering Fix (FMF), wheels-down (touchdown), inbound spot, arrival at the gate, pushback, outbound spot, and wheels-up (takeoff).The AOC data were developed based on the arrivals and departures at Terminal A of Ft.Worth International Airport (DFW).For the prototype, all the AOC data were stored on the Mongo Server to be accessed by the AOC App.
+BAI AppThe Big Data/Analytics Input (BAI) App simulates the applications of the Big Data and Analytics technologies, which develop predictable results based on the past and current NAS performance according to selected decision trees through a User Interface (UI).The prototype was developed to demonstrate the potential benefits of the BAI data through the UI.For the IGTM prototype, arrival and departure time of flights in December 2013 at Terminal A of DFW were analyzed based on data from the NASA data warehouse.[6] The mean and standard deviation of the turnaround time were identified by analyzing Actual gate-in and gate-out time at each gate per type of airplanes.Estimated time events based on predicted results, in this case, the mean and standard deviation from historical data, were generated for each events defined in the IGTM Controller.For example, "Rate of Event" such as the flight speed between the FMF and the touchdown was defined by SOSS of a specific type of airplane, e.g.B737-800, with a specified speed variation.Rate of Events for gate turnaround, e.g., passenger deplaning, cabin services, and boarding, were obtained from References [7,8].BAI data were also stored on the Mongo Server to be accessed by the BAI App.
+Surface Operations Simulator and Scheduler (SOSS)SOSS is a NASA developed Fast-Time simulator, which was used to simulate flights arriving and departing from Terminal A of DFW, with arrivals on runway 17C and departures on 17R.SOSS was connected through a Java Messaging Service (JMS) Interface which provided the Actual events and the simulated clock time.
+SOSS AdapterThe SOSS Adapter is the fast-simulation translator, while the SOSS component is the Fast-simulation message emitter/consumer that reads/writes in scenario data from files.The SOSS Adapter translates messages from a proprietary socket connection structured data stream and converts each packet into JSON or Serialize Data, which is then transported over the ActiveMQ message bus.
+IGTM GUIThe Control Panel of the IGTM GUI is shown in the top of Figure 4, which allows the user to configure the environment to observe flight data associated with the Airport, Terminal, Spot, and Departure Runway.The Control Panel also provides the user with Collaborative Decision Making (CDM) use case options.The Dashboard, which displays flight status and predicted time events, is shown in the bottom of Figure 4.The color code on the right describes the estimated bounds based on the standard deviation derived by the BAI.A light Green of an event represents a likelihood of an event would be completed within one standard deviation of the scheduled event time or about a 68 percent of successful probability.A light Yellow indicates the event would be completed between 1 and 2 standard deviations or about a 95 percent of successful probability.A Red indicates the event would be completed greater than 2 standard deviations or there is only an about 5 percent of chance to meet the scheduled time.The purpose of the color code is to give users a direct implication of the success rate of completing a given event under the uncertainties the BAI data are generated.Therefore, performance bounds can be established based on the dependencies among performance parameters and specified uncertainties.For gate turnaround performance at the gate, a Gate Status display can be selected by the users from the Dashboard for a given flight as shown in Figure 5.The display shows the same color code topology and the standard deviation (σ or Sigma) in minutes.Users can then associated the color code to the time objectively.
+ActiveMQA messsage 'event' can be defined as "a change in state."In the IGTM application events are as simple as 'an aircraft touches down', 'the aircraft reaches the gate', 'another aircraft has all baggage removed," or various other changes in an aircraft's state of location or activity as shown in Figure 3 in the Gate-Turn Model.IGTM software components handle numerous events.An ATM's system architecture may treat this state change as an event whose   Gate""""A16"PushBack DeNicing Outbound2Taxi2SpotDeparture2Slot TakeNoff occurrence can be made known to other processes within the application architecture.From a formal perspective, what is produced, published, propagated, detected, or consumed is a (typically asynchronous) message called the event notification, and not the event itself, which is the state change that triggered the message emission.Events do not 'travel', they just occur.However, the term event is often used metonymically to denote the notification message itself, which may lead to some confusion.This architectural pattern was used in the design and implementation for transmitting events among loosely, coupled software components and services.The IGTM event-driven system consists of event emitters/agents, event consumers/sinks, and event channels.These channels, or collection of channels, are also referred to as the message bus.The emitters have the responsibility to detect, gather, and transfer events.Event emitters are unaware of a consumer of events, when a consumer does exist the event emitters do not know how the event is used or further processed.Sinks have the responsibility of applying a reaction as soon as an event is presented.These emitter/consumers components are the Dashboard, Controller, Big Data Analytics, AOC, and the SOSS Adapter.The Apache ActiveMQ broker fulfills the event channel role.Event channels are conduits in which events are transmitted from event emitters to event consumers.The knowledge of the correct distribution of events is exclusively present within the event channel.The physical implementation of event channels can be based on traditional components such as message-oriented middleware or point-to-point communication.The selection of ActiveMQ as the messaging broker conduit was based on its ease of configuration, its support in other third party API and its ability to handle various types of payload data.One such data payload type is JSON.In the purposes of IGTM the ActiveMQ message bus use was to simulate the NEMS/SWIM message bus (also ActiveMQ).
+Mongo Server or MongoDBNoSQL databases have emerged in recent years to provide the performance, scalability, and flexibility required of modern applications.This new wave of databases is much better suited for Big Data applications and agile software development practices than its relational counterparts.Mongo was selected because it is one of the leaders in the NoSQL arena and that it couples with other application frameworks.The framework selected for rapid application development was SpringFramework.NoSQL databases offer many benefits, including:• Flexible Data Model.Unlike relational databases, NoSQL databases easily store and combine any type of data, both structured and unstructured as JSON.• Elastic Scalability.MongoNoSQL databases scale out on low cost, commodity hardware, allowing for almost unlimited growth.• High Performance.NoSQL databases are built for great performance, measured in terms of both throughput and latency.These advantages account for the growing popularity of NoSQL databases, and specifically MongoDB.MongoDB stands apart from its peers with its Nexus Architecture that incorporates the strengths of relational databases along with the innovations of NoSQL.MongoDB is the only NoSQL options, which offer an expressive query language, strong consistency, and secondary indexes.IGTM chose MongoDB for this reason and that it mates easily to changes the data model and to not have to tinker with the data layer code.Mongo does all the work for you.Mongo also makes it easily possible to work with many of the Big Data Analytic tools such as Tableau, JasperSoft, OpenRefine, Knime, NodeXL, Import.io and others because of the simplicity of the Mongo Query language.
+USE CASESTwo use cases, i.e., early arrival with gate conflict and gate recovery of a late arrival, of the IGTM prototype were demonstrated.Figure 6 shows a CDM display of gate conflict due to an early arrival.The IGTM Controller would identify available gates, which could be available for the estimated arrival time and required turnaround time if applicable, and display on the Gate Availability display.This will allow the user to identify and select an available option, which may require coordination among stakeholders, with minimum time lost at the tarmac, cost, and ground crew resources.The second use case was to demonstrate a speed-up gate turnaround in order to meet the Scheduled pushback time due to a late arrival.The IGTM Controller would identify the most critical path among all critical paths and allow the user to adjust the time performance within the available resources or methods based on AOC data or BAI data.The user used the Gate-Turnaround Management display as shown in Figure 7 to speed up the Passenger Deplane rate, Passenger Boarding rate, and increase the number of cabin service (or cleaning) crew, typical methods to recovering lost time at the gate.In this case, the Estimated turnaround time was reduced from original 52 minutes to 43 minutes.
+CONCLUDING REMARKSThe IGTM prototype demonstrated the concept and benefit of technologies that provide a stream of real-time analytics combined with historic archived data that bound the uncertainties in a gate turnaround NAS operational space.NAS stakeholders can share the flight information, resources, and time management tools through a common messaging network service to coordinately improve the NAS performance under the nominal and off-nominal conditions.The prototype also offers a modular design to incorporate additional Big Data and Analytics products to support future ATM research.2. AOC App, to simulate the Airline Operational Control Center 3. BAI App, to simulate the Big Data/Analytics interface and provide the analytical data.4. Surface Operations Simulator and Scheduler (SOSS), to simulate live traffic data.
+FigureFigure 1.The IGTM concept
+Figure 3 .3Figure 3. Data flow of the IGTM events
+Figure4Figure4.The IGTM prototype's Control Panel
+FigureFigure 6.A Gate Availability display for gate conflict mitigation
+)be)modified)by)users On)the)critical)path If)the)entry)is)greater)than)the)maximum)assigned)valued)as)described)in)Appendix)D,)set)the)value)to)the)maximum)and) set)the)background)to)RED
+Table 1 . Critical paths during the gate turnaround events1Critical PathsSequence of Events (from left to right)1Deplaning of passengersCabin servicesBoarding of passengers2Baggage unloadingBaggage loading3Catering services4Deplaning of passengersFuelingBoarding of passengers5Maintenance
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+ACKNOWLEDGEMENTSThis work was funded by a NASA Big Data, Data Analytics, and Net-enabled ATM and Airspace Operations Project under Contract NNA15AB05C.Authors wishes to thank Deepak Kulkarni and Yao Wang of NASA Ames Research Center for their technical guidance and advice.Authors wish to thank industrial team, which include Joe Burns of XCELAR Inc., Steve Koczo and Arlen Breiholz of Rockwell-Collins, Henry Smith and Warren Qualley of Harris Corp., Randall Ho of the IBM Software Group Federal, Bruce Sawhill of the NextGen Aero Sciences, and Ben DeCosta of DeCosta Consulting LLC, for the concept development.Authors also wish to thank John Walker and Darrell Wooten of the SAIC software development group for their support in developing the IGTM prototype.
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+He has been working on a wide range of flight simulations, including fixed-wing and rotorcraft, and NextGen projects as well as unmanned aircraft systems.He has a Master Degree in Aeronautics and Astronautics from Stanford University, a Master Degree in Mechanical Engineering from Oregon State University, and a Bachelor Degree of Science in Industrial Engineering from Chung Yuan University. is the technical lead for NASA's FutureFlight Central facility and the software manager of the Airspace Traffic Generator system.Carla currently works in the Aerospace Simulation Research and Development Branch at Ames Research Center and has been supporting air traffic management simulation research for over 18 years, covering the en route, TRACON, and airport domains.Her prior research experiences include high-fidelity rotorcraft research simulations.She has a B.S. degree in Mechanical Engineering from California State University at Chico.Chachad has 36 years of engineering experience with real-time flight simulations, and in structural analysis and design.Mr. Chachad has an MBA from San Jose State University, an MS in Civil Engineering from the University of Rhode Island, and a BS in Technology for Civil Engineering from the Indian Institute of Technology.
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+I. Introductionate Turnaround Performance Management, i.e., aircraft arrival at the gate, off-loading, servicing, reloading of passengers, baggage and cargo, door-closing, and pushback (referred to as Gate Turnaround), plays a key role in the National Airspace System (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon pushback from the gate and subsequent takeoff.The time the aircraft spends at the gate in preparation to meet the planned departure time is influenced by many factors; and some having considerable uncertainties.Principal factors affecting gate turnaround include: weather, early or late aircraft arrivals, time spent disembarking/boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services, ground handling, traffic density on the ramp, availability of movement areas for taxiin and taxi-out, deicing, and departure queue management for takeoff.Missing the scheduled pushback time can produce a delayed departure that can cause sufficient schedule deviation to potentially cause a schedule disturbance throughout the flight.Large delays can ripple into an airline operator's schedule for other aircraft in their fleet.In contrast to its importance, the gate turnaround process is not managed well in today's operations and does not effectively make use of technologies that provide enhanced data and modeling of gate operations.Gate Turnaround requires multiple participants, who may not be under a single jurisdiction.Gate turnaround, i.e., door closing time, falls under individual airlines' responsibility, and is managed by multiple organizations within the airline.Gate pushback, on the other hand, is under the responsibility of either a Ramp or Ground controller.There is no coordination on pushback times between the airlines and ground control.Gate arrival and departure times are not fully integrated with surface and terminal area NAS automation and frequently take place in an ad-hoc, first-comefirst-served manner.To address these issues, NASA's Spot and Runway Departure Advisory (SARDA) 1 has been developing tower controller advisory tools to improve the flow of surface traffic.Additional work has been done to applying machine learning techniques in taxi-out time prediction 2 to improve the takeoff time performance.FAA has also identified Traffic Flow Data Manager (TFDM) 3 to provide better predictive and collaborative decision-support tools to the stakeholders such that more informed tactical decisions can be made to improve the surface traffic under uncertainties.The Big Data/Analytics technologies 4 could offer additional benefits by providing predictive models extracted from historical data according to specific set of uncertainty parameters, which are potentially well suited for the complicated gate turnaround environment.The Integrated Gate Turnaround Management (IGTM) concept was developed under the NASA "Big Data Analytics, and Net-enabled ATM and Airspace Operations Project" to identify air traffic management (ATM) functions that can benefit from networked, net-enabled, and/or cloud-based architecture.The project team assembled to tackle this challenge included subject matter experts in ATM, airline operations, airport operations, and in big data management architectures.The IGTM concept is therefore focused on the NAS performance in an integrated service space at an airport terminal or terminals by leveraging following technologies to improve the traffic throughput performance at the gate in meeting the on time performance:1) Data analytics technologies with multiple historical databases and live data to establish bounds of uncertainties of dependent parameters associated with NAS performance, 4,5,6 2) A distributed data network shared by stakeholders, 7 3) Collaborative decision tools, 8,9 for stakeholders to optimize the arrival and departure performance at an airport through en route, terminal, and the gate.
+II. Concept of Operations (CONOPS) for the IGTMThe IGTM concept is developed based on the methodology shown in Figure 1.By analyzing historical data and identifying the dependency of uncertainties in NAS performance parameters or patterns, the adaptive analytics will develop performance models as functions of independent parameters, such as: weather, traffic density, time of the day, day of the week or year, type of airplane, and origin and/or destination of the flight.With these models, IGTM can develop descriptive, predictive, and prescriptive information for a given flight's service performance based on the current states of the NAS, either nominal or off-nominal, and deliver scheduled, estimated (or predicted), and current (or actual) status of all flights within the IGTM's operational space at a given airport to all stakeholders via a user interface.These stakeholders include Air Traffic Control (ATC) ground controllers, ramp controllers, airport operators, and airline personnel, which includes dispatchers, flight and cabin crews, gate agents, and those responsible for fueling, catering, baggage/cargo handling, maintenance, and aircraft parking and pushback.The adaptive analytics analysis will continuously check the patterns developed from the historical data and compare with the live data, then make adjustments if significant deviation in the trend is developed from the process.IGTM will also provide collaborative decision tools by applying the performance models to allow stakeholders to enhance NAS performance collaboratively through efficient surface movement, gate turnaround, and pushback under the nominal and off-nominal operational conditions.To maximize the IGTM's benefits, integration with the en route and terminal automation tools, such as Traffic Flow Management System (TFMS), 10 Terminal Flight Data Manager (TFDM), 3 and Center TRACON Automation System (CTAS) 11 as well as surface sequencing optimization tool such as the NASA developed Spot and Runway Departure Advisor (SARDA) 1 is expected.
+A. Predict System Status with UncertaintiesThe IGTM provides a unified picture of the schedule status of each aircraft and all of the processes and support functions that are required for the flights to depart on schedule.This tracking begins while the aircraft is en route with an estimation of its ability to meet its scheduled time from the top of descent.At the same time, the system tracks the schedule performance of each element of the airport facilities that are required for on-time gate turn around and takeoff of the aircraft.These factors include:• Geography of the airport surface The IGTM will use live data and historical performance models from the Big Data/Analytics, Figure 1, under similar conditions to forecast the times of aircraft touchdown, arrival at the in-bound spot, arrival at the gate, gate pushback, arrival at the outbound spot, and takeoff.It also provides a detailed snapshot of expected schedule performance of each operation involved in gate turn around.In order to accomplish its mission, the IGTM uses a combination of information including detailed geography of the airport surface, characteristics of various aircraft models, actual and planned passenger, baggage, and fuel loads, assignments of gates, aircraft crews, and ground facilities along with schedule and current performance of with respect to the schedule.In addition, the IGTM collects performance data over time building a model of the average performance of various entities.This historical information allows the IGTM to more accurately estimate schedule performance of each flight as a function of the aircraft, ambient conditions, and required services.Each airline user has a detailed operational view of the schedules affecting each of its aircraft.Ramp controllers, service providers, and other aircraft operators see a summary view that protects proprietary information but provides summary schedule information that enables each stakeholder to plan for smooth operations.
+B. Network CommunicationsIGTM requires data content and/or message exchanges among NAS and non-NAS operational services as well as collaborative decision communications among the stakeholders.Figure 2 presents notional system architecture for sharing IGTM information among NAS and non-NAS stakeholders.Communications under the System-Wide Information Management (SWIM) 7 and data messaging exchange will be an ideal application to support the IGTM communication functions.This diagram depicts the programs-of-record that produce/publish information for stakeholder consumption, historical data sources, stakeholders, and interface for IGTM value-added services (i.e.user-interface).It should be noted that it is assumed that Enterprise Service Buses (ESBs)/Interfaces are in place to consume and/or produce information for a particular stakeholder domain (Airline, Airport, etc.).Consumed content is securely accessed, filtered per stakeholder requirements, and distributed within that stakeholder domain as determined by stakeholder operations.Again, the method of access will be determined by whether the information source or stakeholder is considered internal to NAS operations or external.
+C. Collaborative Decision ToolsThe IGTM will provide collaborative decision tools to allow stakeholders to manage nominal and off-nominal events with inherent uncertainties, such as late or early arrival, adverse weather conditions (e.g., limited visibility and thunderstorm, runway condition due to rain, snow, and/or ice), congestion at the surface, and flight/cabin/ground crew availability.A notional decision tree is shown in Figure 3.The collaborative decision tools will allow stakeholders to collectively examine available options and resources in order to deal with operational issues, such as mitigating gate conflicts upon arrival, achieving an optimal pushback time upon departure, and optimizing the departure queuing sequence by utilizing descriptive, predictive, and prescriptive information from the Big Data/Analytics.In short, the tool will enable stakeholders to better meet the scheduled or planned time of arrival and departure, or, if need be, to delay or cancel a flight.
+III. The IGTM System's RequirementsThe IGTM must first evaluate current gate-turnaround operations and identify the primary independent parameters and their uncertainties that have impact on the NAS performance according to a given ATC ConOps on the surface and airlines' operational procedures at the airport terminal.The IGTM then identifies information resources that can be leveraged from Big Data and/or Net-enabled ATM data, and utilize analytics to develop performance models based on statistically significant patterns.With these performance models, descriptive, predictive, and prescriptive information about the current state of the NAS can be delivered to stakeholders.Thereby improving predictability to addressing uncertainties in a coordinated decision making process leading to improved NAS performance.As shown in Figure 1, the Big Data/Analytics hosts the big data warehouse, reads in live ATM data, executes analytics analysis (adaptively) according to scenarios, and generate and deliver statistical performance data to the IGTM module.The Big Data/Analytics software needs to process large volumes of live data, which include flight tracks, ground tracks, schedule changes, weather, and runway configuration changes, and deliver the results with minimum latency.Industry has shown a data processing performance of 12 million messages per second with results returned in 120 msec. 6It is yet to be determined if this level of data processing performance is sufficient to support the mass live and historical data analysis in the IGTM application.Inputs to the Big Data/Analytics: 1) Historical data, which includes flight plans, performance data (delays at arrival), and weather data 2) Performance parameter query from the IGTM Outputs of the Big Data/Analytics 3) Statistical performance of queried performance parameters 4) Live data sources, which include traffic, flight plans, and weather
+A. Systems Engineering ApproachA systems engineering approach was developed to identify independent parameters and functional requirements according to the IGTM system architecture.Figures 456show the operational research approach in identifying the events and activities required during the approach, at the gate, and upon the departure at an airport respectively.Table 1 shows a further breakdown to independent parameters associated with representative events to be managed within the IGTM operational space.These events and independent parameters will define requirements for: Big Data, Analytics, collaborative decision tools, and network communications to support the IGTM concept for the gate operations.
+B. Data SourcesThe IGTM products and decision tools rely on data fusion from historical data and live data as shown in Figure 1.Representative historical data sources available from NASA's ATM data warehouse 12 for ATC-NAS services are listed in Table 2. Data for non-NAS services specifically related to airplane turnaround performance at the gate, such as: passenger deplaning rate, passenger boarding rate, cabin service time, catering service time, fueling time, baggage/cargo unloading rate, baggage/cargo loading rate, maintenance time per type of services, flight/cabin crew availability rate, and ground crew availability rate, will be needed from airlines.Additional non-NAS services data will be needed from airport operators.These include data for runway configurations, runway construction, snow plowing rate, and anti-icing facilities and processing rate.
+IV. The IGTM PrototypeAn IGTM prototype 13 was developed to evaluate the modeling and simulation system architecture of the IGTM concept and demonstrate use cases for mitigating off-nominal conditions.Figure 7 shows a fast-time IGTM simulation system architecture using NASA developed Surface Operations Simulator and Scheduler (SOSS) 14 to simulate live arrival and departure traffic as well as surface traffic to and from the gates at the Dallas Fort Worth International Airport.Use cases were developed using the collaborative decision making tools to mitigate the uncertainties associated with early and late arrival of gate operations in meeting the on time pushback performance.Under this system architecture, the prototype is suitable to support future research and development topics.1) Evaluate data processing and throughput performance requirements in applying Big Data/Analytics according to volume and type of the data in the IGTM application space.2) Identify issues in ATM applications associated with developing modeling patterns from historical data.3) Investigate and evaluate different adaptive analytics methods.4) Integrate with surface automation tools, such as SARDA, to streamline the on time performance optimization including the gate operations.The'IGTM' System'5) Develop data sharing protocol among stakeholders to ensure integrity, security, and safe use of the data.6) Develop collaborative decision making tools for mitigating uncertainties to improve the on time performance.
+V. Concluding RemarksAn IGTM concept was developed to leverage the potential descriptive, predictive, and prescriptive capabilities as well as adaptively adjust the performance models derived from the Big Data/Analytics to improve the surface and gate management at the airport.The concept also leverages using a distributed communication network to exchange ATC NAS and non-ATC NAS service data to promote the NAS performance optimization across jurisdiction of traffic flow control authorities.Collaborative decision tools are also a key component of the concept to promote total system performance optimization via a local solution through coordination among stakeholders.Finally, a prototype was developed to evaluate the system architecture as well as systems' requirements to support such a concept.Figure 1 .1Figure 1.An Integrated Gate Turnaround Management methodology
+Figure 2 .Figure 323Figure 2. A notational IGTM system architecture
+Figure 4 .4Figure 4. Flow of events for arrival flights
+Figure 5 .5Figure 5. Flow of events for turnaround flights at the gate
+Figure 6 .6Figure 6.Flow of events for departing flights
+Figure 7 .7Figure 7. IGTM prototype's system architecture
+• Projected roll-out and taxi time • Traffic congestion at the in-bound spot • Availability of the gate (i.e., is the previous flight leaving on schedule?)• Availability of the baggage/cargo unloading crew • Availability of the baggage/cargo loading crew and the baggage/cargo itself• Catering service availability • Fueling service availability • Availability of technicians and materials for any anticipated maintenance • Deicing service availability • Snow plowing service availability • Projected out-bound taxi time
+Table 1 . Independent parameters for Big Data analytics lwc1Delay at ramp=f(snow accumulation rate or rain rate)Dealy at rwy exit=f(aircraft type, lwc, snow accumulation rate or rain rate)Taxi speed=f(RVR, lwc, snow accumulation rate or rain rate, separation distance)Taxi-in time=f(taxi speed, separation distance, rwy exit, gate assignment)GateTime to unload passenger=f(aircraft type, # passengers)Unload baggage delay=f(thunderstorm, lightning , snow accumulation)Time to unload_baggage=f(aircraft type, # passengers, # of special need passengers, # of crew, equipment)Load baggage delay=f(thunderstorm, lightning, snow accumulation)Time to load baggage=f(aircraft type, # passengers, # of crew, equipment)Feuling delay=f(thunderstorm, lightning, snow accumulation)Time of fueling=f(aircraft type, fuel load)Time of cleaning service=f(aircraft type, # of cleaning crew)Time of catering=f(# passenger)Time of maintenance=f(aircraft type, year, type of issues, facilities)Time to board passenger=f(aircraft type, # passengers, # of special need passenger, # gate agent)Time to get clearance=f(throughput)Time to get to deicing facility=f(taxi_speed, distance to deicing pad)Taxi speed=f(RVR, separation distance)Time of deicing=f(lwc, snow accumulation rate, aircraft type, type of fluid)Departure Taxi out time=f(taxi speed, separation distance, departure rwy, gate assignment)Taxi speed=f(RVR, lwc, snow accumulation rate or rain rate, separation distance)Separation distance=f(RVR, lwc, snow accumulation rate or rain rate)Take Off Rate=f(lwc, snow accumulation rate or rain rate): liquid water content RVR: restricted visual range rwy: runway Phase Analytics Functions Arrival Top of descent to touchdown time=f(aircraft type, equipage, separation distance, wind speed, wind direction) Separation distance=f(RVR, equipage) Roll out time=f(aircraft type, lwc, snow or rain accumulation rate)Landing rate=f(aircraft type, lwc, snow accumulation rate or rain rate)
+Table 2 . Potential historical data sources2
+			Downloaded by NASA AMES RESEARCH CENTER on August 18, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3909
+			A"NoSQL"" database" Simulated"live"traffic"data" Downloaded by NASA AMES RESEARCH CENTER on August 18, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3909
+		
+		
+
+			
+AcknowledgmentsThis work was funded by a NASA Big Data, Data Analytics, and Net-enabled ATM and Airspace Operations Project under Contract NNA15AB05C.Authors wish to thank Parimal Kopardekar, Deepak Kulkarni and Yao Wang of NASA Ames Research Center for their technical guidance and advice.Authors wish to thank the industry SME team, which includes Joe Burns of XCELAR Inc., Steve Koczo and Arlen Breiholz of Rockwell-Collins, Henry Smith and Warren Qualley of Harris Corp., Randall Ho of the IBM Software Group Federal, Bruce Sawhill of the NextGen Aero Sciences, and Ben DeCosta of DeCosta Consulting LLC, for the concept development.Authors also wish to thank Carla Ingram, John Walker and Darrell Wooten of the SAIC software development group and Doug Ahlquist of Metis Technology Solutions for their support in developing the IGTM prototype.
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+between the visual and motion cues, the results also suggest that visual delay compensation had little or no effect on pilots '  within the physical displacement constraints, i.e. the angular and translational limits.Therefore, the washout filters must be tuned to deliver consistent onset accelerations that complement the cues perceived by the pilot from other simulated devices.To establish a direct correlation between simulation fidelity and handling qualities, Reference 1 suggests a criteria based on washout gains and phase characteristics as a measurement of motion cueing fidelity.Reference 2 follows the same frequency response approach and develops a 30 degree phase distortion criteria to compare perceived simulation cues for handling qualities evaluations.These were also the guidelines applied in developing the motion configurations for this experiment.Reference 3 suggests that many motion cue errors are introduced in flight simulation due to physical constraints of motion platforms.Of all the motion cues perceived by the pilot, there is a fundamental element that is directly dependent on the kinetically cross-coupled motion system dynamic characteristics.This is a result of rigid body induced linear accelerations due to angular motion.Both Reference 4 and 5 indicate that translational accelerations sensed by the pilot are from the vestibular system and tactile mechanisms in the body.Due to the nature of human organ characteristics, lower frequency motion perception is sensed by the vestibular system and higher frequency motion perception is sensed by pressure from the pilot tactile mechanisms.Therefore, when the pilot station is not at the rotational center of the motion platform, an element of translational accelerations, i.e. induced linear accelerations, will be sensed by pilots due to angular motion.Induced linear accelerations are generally compensated in motion commands by assuming that the cross-coupled motion axis responses are the same.However, if the dynamic characteristics of two cross-coupled motion axes are not the same, or caused by different motion washout filter characteristics, discrepancies will be presented to the pilot and have an impact on the simulation fidelity.The objective of this experiment was to study the effect of phase differences between two kinetically cross-coupled motion axes and to determine if a requirement that defines acceptable phase discrepancy between the cross-coupled motion axes is necessary for ground based flight simulators.
+Description of the ExperimentFor a motion simulator where the pilot center of gravity is not at the rotational center of the motion platform, the specific force vector that is sensed by the pilot is governed by equation 1.a ps = a mp -The accelerations produced by the motion system at the pilot station, a m p, is defined by equation 2.»mp = a rc + r + to m X r + co m X (co m X r)+ 2 co m X r (2)The position vector of the pilot, r, is fixed relative to the rotational center, i.e. r and r arc zero.By assuming the rotational rate of the simulator cockpit, oo m , is relatively small, equation 2 can be simplified to a mp *rc X r(3)The second term at the right hand side of equation 3 is the induced linear acceleration due to rotational motion, and the effect of this term is generally compensated in the motion commands such that this motion-platform-dependent term is not presented to the pilot.Similar reason also applies to the second term in equation 1 in compensating for the gravity component as a function of cab attitude.Therefore, the simulator translational and angular motion commands a rc_cmd and to s_cmd we defined as:a rc _cmd = W t (s) -apiiot -W a (s) -cb p i lot X r + W a (s).T mc .g (4) ct>m_cmd = w a( s ) '(5)But the actual simulator responses from translational and angular motion commands are determined by the individual motion axis dynamic characteristics, given by, Tt(s)-a rc _cmd T a (s)-a> m cm( i (6) (7)Therefore, the perceived motion cues in kinetically crosscoupled axes are dependent on the dynamic characteristics of both the washout filters and the motion hardware.If the overall dynamic characteristics, such as the phase characteristics of the lateral and roll motion responses, are not the same, then pilots will be subjected to erroneous linear acceleration cueing.
+Math Model DescriptionA mathematical model in stability derivative form was developed to represent the dynamic characteristics of a rate command helicopter^ that is fully decoupled.The equations of motion are defined by equation 8 and 9. u w q .6..00 .[Slat]Ur J (9)
+Motion SystemThe VMS, as shown in Fig. 2, is a six degree-of-freedom motion platform that permits large excursions in the vertical and lateral axes.The vertical motion axis is driven by eight mechanically coupled 150-horsepower direct-current servomotors as outlined in Ref. 9. The lateral axis is driven by four 40-horsepower direct-current servomotors.Roll, pitch, yaw, and longitudinal are driven by four independent hydraulic systems with 2400 psi hydraulic pressure.The motion system's roll and lateral dynamic characteristics were tuned to three configurations for this experiment to study the effect of the phase difference between the two cross-coupled motion axes.The lateral accelerations due to yaw motion were not present due to the fact that the pilot longitudinal e.g.position was near to the gimbals rotational center for this experiment.Three motion configurations were developed by using the visual system's 60 msec time delay as a reference.The VMS visual and motion system responses were fitted in a form defined by equation 10 which consists a linear transfer function, H(s), and a time delay, T.P(s) = H(s) (10)The characteristics of the visual system and the roll and lateral motion systems, P(s), of the three motion configurations and their equivalent time delays are shown in
+Motion Washout FiltersThe VMS motion drive logic is shown in Fig. 6.Washout filters are applied to translational and rotational pilot station accelerations after being transferred to the inertial frame to keep the simulator within the physical travel limits.Turn coordination and induced acceleration compensation keep the cross-coupled motion commands in accordance to pilot position states relative to the rotational center.A low pass filter is used to tilt the cab in supplementing linear motion cueing at low frequency.For the experiment, two motion washout configurations as shown in Fig. 7, were developed for the hover task to investigate the phase difference effect on pilots' handling qualities.The high fidelity configuration was developed to keep the phase of roll and lateral washout filters the same, i.e. <t>(W a (s)) = <)>(Wt(s)), and to keep both angular and translational motion cueing within the high fidelity region according to Ref. 1.The mixed fidelity configuration represented a case investigated in Ref. 10.For the sidestep task, the motion washout filters were configured as shown in Fig. 8. Again, the washout filter frequencies for roll and lateral axes were chosen to have the same phase characteristics.The dynamic characteristics of the rotorcraft and perceived visual and motion cueing under each motion washout and motion dynamic configuration (with the visual time delay of 60 msec) are shown in Fig. 9 to 11.The acceptable fidelity range for the high fidelity washout configuration, based on the 30 degree phase distortion criteria from Ref. 2, is summarized in Table 3 for all three motion configurations.The acceptable fidelity range is defined as the frequency spectrum where the phase difference between perceived visual cueing and motion cueing is less than 30 degrees.The acceptable fidelity range for the same group of motion configurations but with a visual compensation of 60 msec are shown in the same table to present the effect of the improved pilot perceived model response.As shown with the visual compensation, the pilot perceived an improved roll model response from a bandwidth of 4.5 rad/sec to 10 rad/sec as defined by the math model.However, phase improvement in the visual cueing alone would also increase the discrepancy between perceived visual and motion responses.As a result, a more restricted lateral-directional acceptable fidelity range over the frequency spectrum was developed.
+TasksTwo low speed tasks, Hover and Sidestep, were developed following the guidelines from ADS-33D under the no wind condition.Portions of the task procedures were modified to match the procedures developed in Ref. 2.For the Hover task, the pilot was positioned at an angle with respect to the designated hover point, outlined in Fig.12. The helicopter was initialized at 15 ft altitude.The pilot was asked to translate to a hover position over the desired hover point, with a ground speed of 6 kts, while maintaining the altitude.The desired hover point was defined by a hover target with a sight to indicate lateral position and height cues and a color-coded wall at a 45 degree angle to define longitudinal position cues.The transition to the hover point was to be achieved in one smooth maneuver, i.e. a smooth acceleration command followed by a smooth deceleration command.Creeping up to the final position was not allowed.The time for the pilot to stabilize at the desired hover point, from initiation of deceleration control input, was 15 seconds.Once in a stabilized hover, the pilot was asked to maintain hover position for 30 seconds.Rotorcraft deviations were measured from the desired hover point to determine pilots performance with respect to specified performance criteria, as given in Table 4.For the Sidestep task, starting from a stabilized hover with the longitudinal axis of the rotorcraft oriented 90 degrees to the runway, as shown in Fig. 13, the pilot was asked to initiate a rapid and aggressive lateral translation, with a bank angle of at least 20 degrees, holding altitude constant with power.When the rotorcraft achieved a lateral velocity within 5 knots of the maximum allowable lateral airspeed, 30 knots, the pilot immediately initiated an aggressive deceleration to hover at constant altitude.The peak bank angle during deceleration was kept to at least 20 degrees, and occurred just before the rotorcraft came to a stop.Longitudinal and vertical position deviations were measured against the desired performance criteria, as shown in Table 4.The visual data base was developed to provide visual cues for each task.Pylons and walls were color-coded such that the pilot could easily identify desirable and adequate performance envelopes.At the end of each task, the pilot was asked to give a handling qualities rating (HQR) based on the Cooper-Harper scale of Ref. 11.A modified sidestep task was developed during the experiment to better reveal the significance of phase characteristics of the model-to-motion response.A closed loop task was added at the end of the sidestep maneuver by asking the pilot to hover before a designated pylon, with the same desirable performance criteria defined as before.Due to time limitations, only one pilot examined the modified sidestep task, and no pilot HQR was taken.
+ResultsThe effects of kinetically cross-coupled motion dynamics were analyzed by studying HQRs and comments.Pilot control stick response and task performance data were also evaluated.The summary of the results are as follows:Hover with high fidelity washout configuration Pilot HQRs for three motion configurations are shown in Fig. 14.In comparing the first two motion configurations, i.e. matched cueing response (MCI) versus delayed lateral motion (MC2) two pilots, A and B, noted coordinated rolllateral motion cueing which allowed them make accurate lateral inputs and pay more attention to longitudinal position control under the matched cueing configuration.Pilot B rated the matched cueing configuration better than the delayed lateral motion configuration.Pilot A felt that the matched cueing case (MCI) provided more solid motion cueing relative to the visual response, which reduced his physical and mental workload from that of the lagged lateral case.The increase in physical workload is strongly supported by the representative pilot lateral stick power spectral density (PSD) plot, given in Fig. 15, and the time trace of the pilot stick motion during the position-holding part of the task, Fig. 16.The power spectral density is the normalized energy distribution across the frequency spectrum.These data clearly showed that pilot workload associated with the lateral controller was reduced significantly across the frequency spectrum in the matched visual and motion cueing configuration.However, according to pilot A, the noted improvements in roll-lateral motion cueing response did not outweigh the required workload to hold the longitudinal position.Pilot C felt that both configurations required moderate pilot compensation to meet satisfactory performance criteria.He also felt that the delayed lateral motion had a slight advantage in pilot workload over the well matched case.For the delayed lateral motion configuration, jerkiness was among the common comments shared by all pilots.The third motion configuration, motion lagged visual, was rated by two pilots, A and B. Pilot A rated this configuration worse than the matched cueing case and pilot B rated these two configurations with the same rating.Since the phase characteristics of the roll and lateral motion axes were the same in both configurations, the difference in pilot ratings could only result from the pilots' cueing preference, i.e. between the visual cueing and the motion cueing.Pilot A noted that some motion cues were lagging while pilot B noted that visual and motion cues were in harmony.A summary of pilot lateral control mean-square-value (cp^) and pilot cutoff frequency (ffl c ) from PSDs developed by using CIFER, Ref. 12, is shown in Table 5.The pilot cutoff frequency approach is developed in Ref. 13 to compare pilot response characteristics under both flight and simulation conditions.By assuming a first order pilot response model, pilot cutoff frequency is defined as the frequency at half the power point of the total power spectral density of the given pilot control input, i.e. (p c ^ / q>t 0 tal = 0.5.The mean-square-value of the control with respect to the frequency spectra from 0 to oof, tpcof > is equal to the total area under the PSD plot and is defined by equation 11, where 655 contains the control power content as a function of frequency.Table 5 shows that under the matched cueing case, the total energy of the lateral control stick input consistently stays low among pilots in comparison with the other two mismatched conditions, which show comparable pilot cutoff frequencies.Standard deviations of longitudinal and lateral position holding errors are given in Table 6.This table shows that pilots were able to maintain about the same level of performance regardless the test configurations, i.e. the change of motion parameters appeared to only affect the workload.The longitudinal position cues were provided by the colorcoded wall on the side window when in the stabilized hover position.Nonetheless, it did not provide an adequate range cueing sensitivity.This visual cueing deficiency combined with poorly coordinated pitch and surge dynamic characteristics with respect to visual cueing, Fig. 17, kept pilots' workload high in keeping longitudinal position within the satisfactory performance criteria, and made it more difficult in achieving Level I handling qualities performance.
+Hover with mixed fidelity washout configurationPilot HQRs are shown in Fig. 14.The mixed fidelity motion configuration had a deviation in washout frequency between roll and lateral, 0.1 and 0.6 rad/sec respectively versus 0.3 for both axis in the high fidelity motion washout configuration.The washout gain on the lateral axis was also reduced from 0.9 to 0.4 in the mixed fidelity washout configuration.Roll washout gain was kept the same as the high fidelity washout case.The perceived roll and lateral motion cueing discrepancies as shown in Fig. 18 to 20, are much more significant at the low frequency range than in the high fidelity washout configuration.For pilot B and C, who evaluated these tasks, both felt that the matched case had much better coordinated motion cueing than the other two cases.The pilot comments were very similar to those in the high fidelity motion configuration.The workload for the matched configuration again showed reduced lateral control energy by both pilots, as given in Fig. 21.A summary of pilot cutoff frequency is shown in Table 5.It is noted that from the PSD data, and pilot comments, that there is no significant difference between the high fidelity and mixed fidelity motion configurations.The large phase discrepancy between roll and lateral motion at low frequency did not have a significant effect on pilot workload, or on performance.The phase discrepancy effect in high frequency, however, had a definite effect on pilot workload.Pilot B's HQR was consistent with the result from Ref. 10.Pilot A evaluated all three motion configurations in mixed fidelity configuration.However, his data was contaminated with an incorrect washout filter setup.Therefore, no conclusion can be drawn to confirm the consistency between the experiments.
+SidestepPilot HQRs for the sidestep task are shown in Fig. 22.There is no clear trend to indicate the effect of cross-coupled motion dynamic response.The results for this task were hampered by a lack of range cues when the pilot proceeded to a hover stop.The lack of longitudinal position information, lightly damped pitch motion characteristics, and visualmotion phase discrepancies again led to an appreciable amount of pilot effort in stabilizing the helicopter within desirable performance criteria.For the modified closed-loop sidestep task, only one pilot data point was taken to evaluate two motion configurations, i.e. the matched cueing and delayed lateral motion cases, without taking any HQR.The time traces of the control stick and position error from deceleration to a stabilized hover are shown in Fig. 23.The power spectrum of the lateral stick is shown in Fig. 24.The power spectral density of lateral stick and the pilot cutoff frequency are shown in Table 5.The PSD did not show any significant differences between the two motion configurations.However, pilot A commented that overall control felt solid without any overshoot tendency in the matched cueing configuration.Desirable performance was easily achieved.With lagged lateral motion, however, it was harder to stabilize, and there was a tendency to overshoot.This is shown in the position error time trace, given in Fig. 23.The motion in the latter configuration "felt jerky and artificial".It also required at least moderate pilot compensation to achieve desired performance, which would be a Level 2 handling qualities rating.
+Visual DelayHQRs from pilot A and B with visual delay compensation turned on and off are shown in Fig. 25.From both pilots' HQR on two washout configurations and three motion dynamic configurations, there is no significant difference in their ratings with and without the visual delay.This result suggests that the improved model bandwidth response by removing the visual delay from the system was offset by the phase discrepancy between visual and motion cueing.Cueing discrepancies over the acceptable frequency range (Table 3) requires the pilot to mentally cross check the overall sensed model response, which meant increased pilot workload.The 30 degree phase distortion criteria provides a credible rationale for such a result.
+ConclusionsA piloted motion based handling qualities flight simulation experiment was conducted to evaluate the significance of kinetically cross-coupled motion dynamic characteristics.Roll and lateral motion dynamic characteristics were perturbed for both precision hover and sidestep tasks.Visual delay and visual compensation were also evaluated under the same test conditions.From pilot workload data, the phase characteristics of crosscoupled roll and lateral motion cueing has a significant effect on overall handling qualities of given tasks.Therefore, a requirement on cross-coupled motion axes phase characteristics with respect to visual response is strongly recommended to ensure the fidelity of flight simulation.The data from this experiment suggest that the roll dynamic response from motion cueing should at least match the visual response.The phase lag in lateral motion response with respect to the roll motion response should not be larger than 40 msec.Further investigations are required to define the specific phase criteria associated with the cross-coupled motion dynamic characteristics.Visual delay compensation theoretically improves the simulation visual cueing responses, which should lead to better control bandwidth responses as well.Under the given test conditions, no noticeable pilot HQR or task performance improvement was found.That leads to the conclusion that the model response improvement made by visual cueing alone must be lost in the discrepancy between visual cueing and motion cueing.However, without the visual delay compensation, the vehicle's response characteristics is effectively reduced due to the inherited time delay in the digital flight simulation.
+0.0Table 1.Damping characteristics and control sensitivityX u (I/sec) /-w (I/sec) M q (1/sec) Lp (I/sec) Y v (I/sec) N r (I/sec) -0.7 -4.3-10.5 -0.12 -2.0(ft/sec^/in) (rad/sec 2 /in) (rad/sec 2 /in) (rad/sec 2 /in) -9.873 O45 L8 004< -> £• 4 o o rt •} ^ V) cd «5 2 Pilot a A 0 B AC • • data from ref. 10 ,-a. ..-o & -A 'qn o -^2* -A _ _ _ s^ _ _ ^ _ _ _ o -cr A X • i i i i i i 1 MC1MC2MC3 MC1MC2MC3High fidelity Mixed fidelityTable 2 .2The equivalent time delay is defined as a pure time delay that matches the phase response of P(s) between . 1 to 10 rad/sec.The frequency responses of these three motion configurations, i.e. acceleration output versus acceleration input, are shown in Fig.3, 4, and 5.The first motion configuration, MCI, the matched visual and roll and lateral motion cueing, was developed such that both roll and lateral motion dynamic phase responses matched the visual phase response.The second motion configuration, MC2, delayed lateral motion, was developed to keep the roll axis phase response in phase with the visual system, but to delay the lateral axis phase response by 40 msec.The third motion configuration, MC3, delayed roll and lateral motion, was designed to keep the phase response of both roll and lateral motion axes 40 msec behind the visual response.The first configuration represents the best phase match of both visual response and roll-lateral motion response as perceived by the pilot.Dynamic response for each configuration was tuned to have a satisfactory phase response up to 10 rad/sec.
+Figure 3 .Figure 4 .Figure 6 .Figure 11 .34611Figure 3. Matched visual, and roll and lateral motion configuration, MCI
+FigureFigure 16 .Figure 17 .Figure 19 .Figure 23 .Figure 25 .1617192325Figure 14.Pilot HQRs for hover task
+handling qualities ratings under the given test conditions.command,rad/sec 2 roll motion acceleration response, rad/sec 2 helicopter model pitch rate, body axis, rad/sec helicopter pitch angular acceleration, rad/sec 2 pitch acceleration motion command, rad/sec 2 pitch motion acceleration response, rad/sec 2 helicopter model yaw rate, body axis, rad/sec helicopter yaw angular acceleration, rad/sec2P(s)a linear representation of visual and motionresponse with time delayPcmdp mqqposition vector of the pilot station with respectto simulator RC, ftrelative velocity vector of the pilot station withrespect to simulator RC, ft/secNOMENCLATURErelative pilot station linear acceleration withT a (s) T m L mcrespect to simulator RC, ft/sec 2 transfer function of angular motion axis direction cosine matrix from inertial to body axes of the simulator, n.d. direction cosine matrix from inertial to simulator body axes attitude excluding the component used for low frequency linear specific force, n.d.8 C $lat ^lon 8 r <) > 'Pcof 2pilot collective stick input, in. Pil ot lateral stick input, in. pilot longitudinal stick input, in. rudder pedal input, in. roll attitude, rad mean-square-value over the specified frequency spectrum, n.d.T t (s) utransfer function of translational motion axis helicopter model translational velocity, x-body axis, ft/sec helicopter model translational velocity, y-body axis, ft/sec9 T ro m d) mpitch attitude, rad fitted time delay for visual and motion response, sec simulator angular rate vector, rad/sec, simulator angular acceleration vector, rad/sec 2W a (s) Wj(s) x e X u y e y c g Y vhelicopter model translational velocity, z-body axis, ft/sec transfer function of angular washout filter transfer function of translational washout filter longitudinal position error for hover and sidestep tasks, ft longitudinal damping coefficient, I/sec lateral position error for hover task, ftcbpilot ci) m _ cm( j simulator angular acceleration command vector, helicopter angular acceleration vector, rad/sec 2 rad/sec 2 a mp linear accelerations generated by the motion simulator at the pilot station, ft/sec 2 3p S total linear accelerations sensed by the pilot, ft/sec 2 a pilol helicopter pilot station acceleration vector, ft/sec 2a rcsimulator rotational center (RC) accelerationa rc_cmdvector, ft/sec 2 simulator rotational center (RC) accelerationIntroductioncommand vector, ft/sec 2ggravitational vector, ft/sec 2Motion simulators are widely used in handling qualitiesH(s)fitted linear transfer function of visual andresearch and flight training. These applications depend onmotion response without the time delayonset accelerations produced by the motion platform in combination with cues presented to the pilot from visual displays, control force feel, audio effects, and instrumentation displays. The fidelity of the onset accelerations is subject to the modeled aircraft dynamic characteristics, motion system's dynamic characteristics,LSlat Lp M5lon Mq N5rr °U control power, rad/sec 2 /in. roll damping coefficient, 1/scc pitch control power, rad/sec 2 /in. pitch damping coefficient, I/sec yaw control power, rad/sec 2 /in.motion control algorithms, and displacement constraints.N ryaw damping coefficient, I/secphelicopter model roll rate, body axis, rad/secphelicopter roll angular acceleration, rad/sec 2r °U acceleration motion For ground based motion simulators, this presents quite a challenge, because the displacement constraints dominate the motion fidelity issue.Washout filters are generally used in motion control logic to generate initial onset accelerations
+Table 2 .2Fitted VMS visual and roll-lateral motion response model, and equivalent time delayFitted model response, P(s)Equivalent timedelay, msecMotionVisuale -0.060s60configuration1. Well matched visual andRoll77 -9 s+80P -0.05?,s65motionLateral2.39(152.4)(s 2 +12s+94)_ n ni<;68(s 2 +21s+225)(s 2 +16.2s+164.5) &2. Delayed lateral motionRoll77 -9 s+80" P -0.052s65Lateral152.4.nnis108s 2 + 16.2 s + 164. 5 C3. Delayed roll-lateral motionRoll19 ' 75 s+20P -0.072s107Lateral1 CO A ij^A-001s108s 2 +16.2s+164.5 C
+Table 3 .3Acceptable simulation fidelity range for high fidelity washout filter configuration, rad/secWith visual delayWith visual compensationMotionRoll axisLateral axisRoll axisLateral axisconfigurationMin MaxMin MaxMin MaxMin Max1. matched visual and0.8 >60 0.817 0.750.75roll-lateral motion2. delayed lateral motion0.8>600.87.80.7590.7553. delayed roll and lateral0.88.50.87.80.7550.755motion
+Table 4 .4Task performance criteriaPositionAltitudeHeadingTime toTaskTolerance (ft) D ATolerance (ft) D ATolerance (deg) D AComplete (sec) D AHover±3±8±2 ± 4± 5±10<15<30Sidestep±20±50±10 ±15±10±15Table 5. Pilot lateral stick power spectrum density and pilot cut-off frequencyfor hover taskHigh fidelity washoutMixed fidelity washoutModified sidestepPilot Motion configuration -? ______________________(rad/sec) Q*(Poof 2«c (rad/sec)(Pcof 20)c (rad/sec)1. matched visual0.0042.10.481.7and roll-lateralmotion2. delayed lateral0.0131.70.881.4motion3. delayed roll and0.0152.4lateral motion1 . matched visual0.0172.10.0052.2and roll-lateralmotionB2. delayed lateral0.0552.40.0592.1motion3. delayed roll and0.0651.80.0371.9lateral motion1 . matched visual0.042.50.062.7and roll-lateralmotion2. delayed lateral0.0512.40.0373.3motion3. delayed roll and0.0554.1lateral motion
+Table 6 .6Hover perfonnance data with high fidelity washout configurationLongitudinal position error (ft)Lateral position error(ft)Pilot Motion configuration Average CTMax Min Average aMax Min1. matched visual-0.21 0.64 1.26 -1.08 0.10.44 1.27 -0.68and roll-lateralmotionA 2. delayed lateral0.80.89 2.53 -1.12 0.24 0.41 1.26 -0.79motion3. delayed roll and0.90.88 2.53 -1.4 0.32 0.47 1.26 -0.80_____lateral motion_______________________________________1. matched visual0.03 1.34 2.04 -3.13 -0.04 0.71 1.0 -2.2and roll-lateralmotionB 2. delayed lateral0.04 1.39 3.1 -2.42 -0.3 0.67 1.35 -1.32motion3. delayed roll and0.93 1.55 3.1 -3.1 0.810.67 2.4 -0.54______lateral motion________________________________________1. matched visual07T1L02 2.67 -1.72 ^035OA60.66 -1.57and roll-lateralmotionC 2. delayed lateral0.17 0.98 1.26 -2.47 -0.03 0.57 1.15 -1.51motion3. delayed roll andlateral motion
+		
+		
+			
+
+				
+
+
+	
+		The Determination of Some Requirements for a Helicopter Flight Research Simulation Facility
+		
+			JBSinacori
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+		STI-TR-1097-1
+		
+			September 1977
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+	Contractor Report
+	Sinacori, J. B..: The Determination of Some Requirements for a Helicopter Flight Research Simulation Facility. Contractor Report STI-TR-1097-1, September 1977.
+
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+	
+		Simulation evaluation of the effects of time delay and motion on rotorcraft handling qualities
+		
+			DavidMitchell
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+			RogerHoh
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+		
+			AdolphAtencio, Jr.
+		
+		
+			DavidKey
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+		10.2514/6.1991-2892
+	
+	
+		18th Atmospheric Flight Mechanics Conference
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+			American Institute of Aeronautics and Astronautics
+			Jan. 1993
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+	Mitchell, D. G.; and Hart, D. C.: Effects of Simulator Motion and Visual Characteristics on Rotorcraft Handling Qualities Evaluations. American Helicopter Society Conference on Piloting Vertical Flight Aircraft, Jan. 1993.
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+		Motion washout filter tuning - Rules and requirements (expert systems flight simulators)
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+			PeterGrant
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+			LloydReid
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+		10.2514/6.1995-3408
+		AFHRL-TR-72-54
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+		Flight Simulation Technologies Conference
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+			American Institute of Aeronautics and Astronautics
+			August 1995. May 1995. June 1973
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+	Proceedings of the AGARD Flight Vehicle Integration Panel Symposium on Flight Simulation
+	Grant, P. R.; and Reid, Lloyd D: Motion Washout Filter Tuning: Rules and Requirements. AIAA Flight Simulation Technologies Conference, August 1995. ^Schroeder, J. A.; and Johnson, W. W.: Yaw Motion Cues in Helicopter Simulation. Proceedings of the AGARD Flight Vehicle Integration Panel Symposium on Flight Simulation, May 1995. ^Gum, D. R.: Modeling of the Human Force and Motion-Sensing Mechanisms. Air Force Human Resources Laboratory, AFHRL-TR-72-54, June 1973.
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+		A Piloted Simulation of Helicopter Air Combat to Investigate Effects of Variations in Selected Performance and Control Response Characteristics
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+		^Danek, G. L.: Vertical Motion Simulator Familiarization Guide. NASA TM-103923
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+	Lewis, M. S..; Mansur, M. H..; Chen, R. T. N.: A Piloted Simulation of Helicopter Air Combat to Investigate Effects of Variations in Selected Performance and Control Response Characteristics. NASA TM-89438, 1987. 'Aeronautical Design Standard, Handling Qualities Requirements for Military Rotorcraft. ADS-33D, July 1994. ^McFarland, R. E.: Transport Delay Compensation for Computer-Generated Imagery Systems.. NASA TM 100084, Jan. 1988. ^Danek, G. L.: Vertical Motion Simulator Familiarization Guide. NASA TM-103923, May 1993. 10
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+		A Simulation Investigation of Motion Cueing and Visual Time Delay Effects on Two Helicopter Tasks
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+		The Use of Pilot Rating in the Evaluation of Aircraft Handling Qualities
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+		Frequency-Response Method for Rotorcraft System Identification: Flight Applications to BO 105 Coupled Rotor/Fuselage Dynamics
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+			MavisGCauffman
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+		10.4050/jahs.37.3
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+		Journal of the American Helicopter Society
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+		2161-6027
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+IntroductionLow cost alternatives to traditional motion platforms have been sought to provide motion cues in ground-based flight simulators to meet mission objectives.One method that has been shown to be effective is the dynamic seat, which provides high-frequency/low-amplitude motions at the pilot station.Subjectively, high frequency vibration cues provide familiar cockpit oscillations due to structure, rotor dynamics, and airspeed for a helicopter flight simulation.Objectively, the limited onset cues may aid the pilot to develop similar control strategies in meeting mission requirements.Previous studies 1 ' 2 have shown that there are benefits in using limited-travel vibration devices in helicopter simulations, especially as a training device.White 1 found there was a significant difference in collective activity in a bob-up task using an idealized helicopter simulation with and without a g-seat.The g-seat had two independent actuators in heave degree-of-freedom (DOF) and was mounted on a three DOF motion platform, i.e., heave, pitch, and roll.The cockpit had a field-of-view (FOV) of 48 degrees in azimuth and 36 degrees in elevation.White also reports that pilots were more consistent in maintaining a linear relationship between collective activity and time to impact in a hurdle task with the g-seat.Pilot comments in this study gave preference to the use of the g-seat.Greig 2 investigated the effectiveness of a multi-axis dynamic seat in the simulation of a Lynx helicopter on the Large Motion System (LMS) at UK's Defence Research Agency Advanced Flight Simulator (AFS).The dynamic seat had 5 independent hydraulic actuators to produce three DOF motion in heave, surge, and sway.The LMS has five DOF, i.e., heave, sway, roll, pitch, and yaw, and a FOV of +/-63 degrees in azimuth and 24 degrees in elevation.The study found that subjective pilot ratings and comments favor the use of a dynamic seat in the five tasks evaluated, i.e., sidestep, quick hop, lateral jinking, spot turn, and NoE course.The Joint Shipboard Helicopter Integration Process (JSHEP), a Navy program sponsored by the Office of the Secretary of Defense, was initiated to investigate the minimum groundbased simulation requirements to develop the launch and recovery operational envelope.Among many JSHIP investigation objectives, a multi-axis dynamic seat, Figure 1, that was similar to Greig's investigation was one of the simulation cueing devices evaluated.For this purpose, a UH-60 Black Hawk motion-based flight simulation experiment was developed at NASA Ames Research Center's Vertical Motion Simulator (VMS), Figure 2, using six ADS-33D 3 maneuvers.The JSHIP simulator cockpit has a FOV of 220 degrees in azimuth and 70 degrees in elevation.Four different motion cueing levels were chosen to investigate the effects of the dynamic seat.The effectiveness of the dynamic seat was then determined by comparing pilots' workload, the perceived vehicle performance, and task performance in six selected maneuvers.
+Experiment Description
+Math ModelA high fidelity mathematical model of the UH-60A Black Hawk known as Gen Hel 4 was used in the investigation.The real-time simulation had a frame rate of 100 Hz.In hover and low speed, the Black Hawk was configured to have an augmented angular rate command system, and the collective controlled vertical acceleration.The angular rate frequency responses at hover generated by a handling qualities analysis program, CIFER 35 , are shown in Figure 3, and the heave control response is shown in Figure 4.
+Motion CueingFour levels of motion cueing were developed to investigate the effects of the dynamic seat.They are: I.The 3-DOF dynamic seat: Uses all three DOF of the dynamic seat, i.e., heave, sway, and surge.The dynamic seat provided high frequency heave and lateral vibrations, onset cues for heave, sway, and surge, and sustained sway and surge motion cues.II.Hexapod-like travel: The VMS was driven by adaptive motion drive algorithms developed for a hexapod motion system 6 ' 7 with six 60-inch stroke actuators.III.Hexapod-like travel plus dynamic seat with only heave mode: The VMS was driven the same way as Level II.The dynamic seat was activated in heave DOF only as a seat shaker to provide the vertical vibration cues.IV.Large motion travel plus 2-DOF dynamic seat: Full VMS travel was utilized to achieve the best possible motion fidelity.VMS was driven by the standard classical motion drive algorithms.The dynamic seat was activated in two DOF, i.e., heave and sway, to supplement the large motion travel with high frequency vibration cues.The dynamic seat commands, which provided sustained surge and sway components, were disabled.Level I motion represents a low-cost option in providing motion cues.Level II represents a motion cueing fidelity that is common to the training community.With the addition of a seat shaker feature, any difference between Level II and IE could be attributed directly to the effect of high frequency heave vibration.Level IV represents the best possible ground-based motion cueing fidelity by using the full translational travel envelope of the VMS.Displacement, rate, and acceleration limits of the VMS and a hexapod-like system are shown in Table 1.The smallamplitude frequency responses of the VMS are plotted against the FAA Advisory Circular 120-63 8 motion specifications as shown in Figure 5.The motion fidelity according to Ref. 9 for all six DOF is shown in Figure 6.Another important motion fidelity factor, the lateral translational motion relative to simulator roll motion, to maintain the proper specific force direction, is low for the hexapod-like case (Level n and III), and is high for the large motion case (Level IV), according to Ref. 10.
+Motion Cueing -Dynamic SeatA multi-axis dynamic seat 11 provided by the Army Apache Training Command was integrated in one of the VMS's inter-changeable cabs.The dynamic seat has four independent actuators to provide three DOF of motion, i.e., heave, sway surge, and.The performance of each actuator is shown in Table 2.The small-amplitude frequency responses of the four actuators are shown in Figure 7.The high frequency heave vibration cues were generated by the seat pan and driven directly according to four per rev of the UH-60 rotor rpm, i.e., at 17 Hz.According to pilot comments, one per rev high frequency lateral vibration cues were added to the back pad to mimic the UH-60 cockpit vibration characteristics during flight.The magnitude of heave vibrations was adjusted based on the Bob-Up/Bob-Down flight test data.The dynamic seat's gains and frequency content were adjusted to match the power-spectral density of the vertical acceleration sensor response taken from the flight test as shown in Figure 8.The onset cues in heave due to pilot control inputs and/or flight conditions have four components, which are translational lift, collective, normal acceleration, and airspeed.The translational lift provides the vibrations due to the change in inflow orientation between the forward and aft portions of the rotor disk in the speed range between 20 and 30 knots.Sustained sway acceleration cues were developed by moving the back pad laterally as a function of pilot-station lateral accelerations.Onset lateral acceleration cues were generated by feeding roll angular acceleration and the high frequency component of lateral acceleration to drive the back pad in lateral motion.Sustained deceleration was generated by moving the back pad forward and the seat pan downward synchronously.Sustained acceleration was developed by moving the back pad aft and the seat pan upward together.Onset longitudinal acceleration cues were generated by feeding pitch angular acceleration and the high frequency component of longitudinal acceleration to drive the back pad fore and aft.
+Visual CueingThe cockpit, as shown in Figure 9, with a wide field-of-view (FOV) display system, producing 220 degrees in azimuth and 70 degrees in elevation, was specially designed and developed for the JSHIP experiment.The primary image generation system is a five-channel E&S ESIG 4530 system operating at 60 Hz with a transport delay measured at 60 msec.The projection system used a projector-mirror design with five BARCO projectors.A high resolution LHA visual model, LHA-5 USS Peleliu, was used for all test maneuvers.The model consists of 3000 textured polygons and employs 4 levels-of-detail.An E&S 3-Dimensional (3D) sea wave model provided additional wave dynamics relative to wave heights and period.
+Aural CueingThe simulator cab had a stereo sound system with six speakers and one sub-woofer around the pilot to provide high quality aural cues that included main rotor, tail rotor, engine, American Institute of Aeronautics and Astronautics transmission, air, and landing gear as functions of collective control and flight conditions.Sound cues were evaluated by UH-60 pilots and were found to be representative of the UH-60 in test tasks evaluated.
+Task DescriptionSix maneuvers modified from ADS-33D for shipboard operations were evaluated in the investigation.They were Acceleration/Deceleration, Bob-up/Bob-down, Hover, Pirouette, Sidestep, and Vertical Landing.Descriptions of maneuvers and performance criteria are presented in Ref. 12. Four experienced Army test pilots participated in this evaluation.An additional test was done fixed-base with the dynamic seat on and off using a modified Bob-Up/Bob-Down maneuver to evaluate the effectiveness of the dynamic seat independent of platform motion.Instead making a Bob-Down maneuver immediately after a brief stabilization at the top, pilots were instructed to maintain stabilization for at least 10 seconds before initiating a Bob-Down.Three UH-60 pilots (two NASA and one Army) participated in this test.
+Results
+Subjective EvaluationsHandling Qualities Rating (HQR) 13 results for the six ADS-33D maneuvers are shown in Figure 10.Results from the 3-DOF dynamic seat, Level I, compare well with the large motion plus 2-DOF dynamic seat, Level IV, except Acceleration/Deceleration and Sidestep, where maneuvers in surge and sway DOF are more dominant.Heave vibration cues do improve the HQR for most of the maneuvers when comparing Level III motion with Level II motion.HQR results for the fixed-base Bob-Up/Bob-Down task with the dynamic seat on and off are shown in Figure 11.A Motion Fidelity Scale 9 (MFS), as shown in Table 3, was used to subjectively determine consistency between perceived visual cues and motion cues.MFS results with the seat on and off are also shown in Figure 11.
+Objective Performance DataObjective performance data were analyzed for two test maneuvers, i.e., Bob-Up/Bob-Down, and Vertical Landing.Both maneuvers emphasized the vertical DOF, which was relevant to VMS large motion and the dynamic seat's primary motion cueing characteristic, i.e., heave.In the Bob-Up/Bob-Down task, the simulated Black Hawk's altitude offset at the lower hover position was analyzed to investigate the pilot's altitude stabilization performance after the bob-down.Maximum descent speed was also analyzed to investigate the pilot's vertical speed control relative to the bob-down task.Both results are shown in Table 4.In the Vertical Landing task, the pilot's landing spot offset in longitudinal and lateral directions were analyzed as well as the maximum descent speed.Results are shown in Table 5.In the fixed-base Bob-Up/Bob-Down test, the simulated Black Hawk's altitude offset at the lower hover position and the maximum descent speed with and without the use of the dynamic seat are shown in Table 6.Power spectral density (PSD) of the collective and pilot's cut-off frequency were analyzed to characterize the pilot's inner-loop response that was related to work load and the task.The PSD directly reflects pilot control magnitude in the frequency domain.The cut-off frequency is defined as a measure of the pilot's control activity bandwidth.When the aircraft's bandwidth exceeds the task bandwidth, the pilot cut-off frequency approaches the pilot crossover frequency and gives a good approximation of the task bandwidth. 14he purpose of using these measurements was to investigate the motion cueing effects in pilot control strategy and aggressiveness.Studies have shown that improved motion fidelity has led to increases in pilot's gain and crossover frequency. 15' 16 Consequently, higher pilot gain leads to lower control PSD.Average Root-Mean-Square (RMS) of the collective PSD and average pilot cut-off frequencies for four different levels of motion cueing conditions are shown in Table 4 for the Bob-Up/Bob-Down maneuver and in Table 5 for the Vertical Landing.Average RMS of the collective PSD, and pilot's cut-off frequency of the fixed-base Bob-Up/Bob-Down test are shown in Table 6.
+DiscussionSubjective Data -HQR As shown in Figure 10, according to the average HQRs, Level IV motion shows the best match with the flight test data among all six ADS-33 maneuvers.Level ffl also shows good results when compared with the flight test data.The differences between Level EH and IV are minimal.Overall, pilots gave good marks to Level IV on motion cueing fidelity, citing that there was no negative cueing and that the realism was good.Level I motion shows a good match in mean HQR with the flight test data in Hover and Vertical Landing tasks.In another vertical DOF task, Bob-Up/Bob-Down, the dynamic seat also fares well relative to the flight data with a mean HQR difference of 0.25 (A L _ I/Flight =0.25).Level I has the worst mean HQR in Acceleration/Deceleration (A L .IAnight =0.85) and Sidestep (A^^g^ =0.5) tasks, which may be attributed to the lack of motion travel in those two DOF.Level I also has the largest standard deviation in Pirouette (a L .!=1.29), Sidestep (0^=1.0), and Vertical Landing (ar =0.63).The widespread ratings suggest there is an inconsistency in pilots' determination in their workload and vehicle performance relative to the task.Some pilots commented that using the back pad to provide sustained sway cues was unnatural because only the upper body moved.Level II motion shows a poor match in mean HQR relative to the flight test data (A L .II/FHght >0.5) for Acceleration/Deceleration (A L _ H/FIight =0.65), Hover (A L ., I/Fliglu =0.75), and Sidestep (A L _ Il/FUglu =0.8).Level II has the largest standard deviation in Acceleration/Deceleration (a L _ n =0.96), and Bob-Up/Bob-Down (a L .n =1.15).Level III improves the mean HQR relative to flight test data in Acceleration/Deceleration (A L _ III/LII =0.25), Hover (A L .III/LII =0.75), Sidestep (A L .III/L .n =0.68), and Vertical Landing (A L _ m/L-ii =0.25) tasks.Level HI matches very well with the flight test's mean HQR in Bob-Up/Bob-Down (A L .III/Flight =0.17), Hover (A L .IiyFlight =0), Sidestep (A L _ III/Flight =0.12), and Vertical Landing (A L _ in/FUght =0.25).The results suggest that there is a benefit of having the high frequency heave vibration in a motion platform.Level IV, the large motion travel and the 2 DOF dynamic seat, matches well with the mean HQR from the flight test in Acceleration/Deceleration (A L _ IV/Flight =0.2), Bob-Up/Bob-Down (A L .IV/Flight =0), hover (A L _ IV/Flight =0.29), and Vertical Landing (A L .rv/Flight =0.14).
+Subjective Data -Fixed-BaseFrom Figure 11, with the dynamic seat on, the mean HQR of the Bob-Up/Bob-Down task improves by 0.5 relative to the seat-off condition.The standard deviation of the mean HQR with the seat on (o>seat-on=0-71) is also smaller than with the seat off (a Seat _on= 1.325).Both results indicate an improvement in pilots' workload and their determination of the vehicle performance when the dynamic seat was on.Motion Fidelity Scale results in Figure 11 show that pilots were less objectionable to the cueing differences between the flight response perceived from visual and the motion cues when the dynamic seat was on.All three pilots found the onset cues were helpful and recommended the use of the seat for the Bob-Up/Bob-Down task.Two of the pilots recommended the use of the vibration cues.Objective Data -Bob-Up/Bob-Down From Table 4, the average altitude stabilization error at the lower hover position after a bob-down for all four motion cueing levels are very similar and are well within the satisfactory performance criterion, i.e., +/-3 ft, for the task.Level IV motion has the smallest standard deviation (a L _ IV =0.33 ft), but differences are relatively small.There is little difference in average maximum descent speed among the four motion cueing levels.Level I motion and Level II motion, however, have larger standard deviations, i.e., 2.82 ft/sec and 3.17 ft/sec respectively, which indicates pilots were not as consistent in their vertical speed control.The mean standard deviations for the other two motion cueing conditions are 0.86 ft/sec for Level III andl.08 ft/sec for Level IV.There is very little difference in average collective RMS and pilot cut-off frequency in this task.With platform motion on, i.e., Level II, III, and IV, the data show a trend with lower collective RMS and higher pilot cut-off frequency as the motion cueing fidelity increases from Level II to IV.This trend is consistent with the concept that pilot's gain and crossover frequency increases as the motion cueing fidelity improves.The increased pilot gain subsequently leads to lower control RMS.The 3-DOF dynamic seat, Level I, however, has the lowest collective RMS and a pilot cut-off frequency higher than the two hexapod motion cueing conditions which contradicts the trend.One possible explanation could be found in the pilot comments where all pilots explicitly indicated that they relied more on visual cues such as the superstructure to judge the translational rate when platform motion was absent.
+Objective Data -Vertical LandingFrom Table 5, landing spot offsets in longitudinal and lateral directions for all four motion-cueing levels are similar.No obvious trends could be found.Only Level IV motion had an average longitudinal offset that was within the satisfactory performance criterion, i.e., +/-1 ft.There is an obvious trend in the average maximum descent speed, where the maximum descent speed decreases as the motion fidelity increases from Level I through Level IV.This result is consistent with the finding from a PIO study 17 and shows pilots are more conscious of the descent speed as the motion fidelity improves.The difference in average collective RMS and pilot cut-off frequency was relatively small among the four motion cueing levels.The large motion travel plus 2-DOF dynamic seat, Level IV, had the least average collective RMS and the pilot cut-off frequency suggests pilots might be easing off the collective due to pronounced vertical speed cues.The small standard deviations under the Level IV motion, i.e., 0.02 inch for collective RMS, 0.01 rad/sec in pilot cut-off frequency, and 0.77 ft/sec in the maximum descent speed, suggest pilots were more consistent in controlling the vertical speed in Level IV than in the other three levels.Objective Data -Fixed-Base From Table 6, the altitude error when stabilizing after the bob-down for the Bob-Up/Bob-Down task is improved when the dynamic seat is on, 1.12 ft vs. 1.52 ft when the dynamic seat is off.There is little difference in the other three objective measurements, which suggests the dynamic seat helps in improving realism of the Bob-Up/Bob-Down task and the task performance, but not pilots' perception of the vertical speed and their control activities.Frequency (rad/sec) Seat pan (heave) displacement response -------Fore-and-aft (surge) displacement response ----------Lateral (sway) displacement response ------Bucket (not used) displacement responsec)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
+Figure 1 .Figure 2 .Figure 3 .Figure 5 .Figure 6 .12356Figure 1. 3 degree-of-freedom (heave, surge, and sway) dynamic seat
+Figure 7 . 2 Figure 9 .729Figure 7. Dynamic seat actuator frequency response
+Table 1 .1VMS and Hexapod-Like operational limitsAxisDisplacementVelocityAccelerationVMSHexapod-LikeRoll±18±18±40±115Pitch±18±18±40±115Yaw±24±24±40±115Longitudinal±4±4±4±10Lateral±20±4±8±16Vertical±303.3 up/ 2.5 down±16±24All numbers, units ft, deg, sec
+Table 2 .2System limits of the Dynamic SeatSeat-Pan (heave)Back-Pad (sway)Back-Pad (Surge)Bucket (heave)Displacement± 0.59 inch± 0.59 inch± 0.59 inch± 0.59 inchVelocity± 2.4 in/sec± 2.4 in/sec±0.8 in/sec± 2.4 in/secAcceleration± 39.4 in/sec 2± 39.4 in/sec 2± 39.4 in/sec 2± 39.4 in/sec 2
+Table 3 .3Motion fidelity scaleDescriptionScoreHigh FidelityMotion sensations are not noticeably different1from those of visual flightMedium FidelityMotion sensations are noticeably different from2those of visual flight, but not objectionableLow FidelityMotion sensations are noticeably different from those3of visual flight and objectionableTable 4. Objective data for Bob-Up/Bob-Down taskBob-Up/Bob-Down3-DOF dynamic seat (Level I)Hexapod like only (Level II)Hexapod like + seat shakerLarge motion + 2-DOF dynamic(Level III)seat (Level IV)Altitude errorAverage1.451.361.11.41(lower hover1 standard0.570.460.640.33position),., ftdeviationMaximumAverage-10.83-11.47-11.87-11.68descent speed,1 standard2.823.170.861.08ft/secdeviationRoot-Mean-Average0.3720.4450.4350.415Square,1 standard0.100.120.080.12Collective,deviationinchesPilot cut-offAverage1.341.281.261.37frequency, rad/secdeviation 1 standard0.290.260.170.31American Institute of Aeronautics and Astronauticsc)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
+Table 5 .5Objective data for the Vertical Landing taskVertical Landing3-DOF dynamicHexapod likeHexapod like +Large motion +seat (Level I)only (Level II)seat shaker2-DOF dynamic(Level III)seat (Level IV)Landing spotAverage1.271.11.350.54offset,1 standard1.191.040.540.34longitudinal, ftdeviationLanding spotAverage1.051.181.191.29offset, lateral, ft1 standard0.740.80.731.33deviationMaximumAverage-4.77-4.55-3.72-2.87descent speed, ft/secdeviation 1 standard2.432.331.580.77Root-Mean-Average0.70.750.620.63Square,1 standard0.20.110.220.02Collective,deviationinchesPilot cut-offAverage0.930.9250.910.83frequency,1 standard0.160.150.080.01rad/secdeviation
+Table 6 .6Objective data for a Bob-Up/Bob-Down task in fixed-baseBob-Up/Bob-DownDynamic SeatDynamic Seat(Fixed-Base)OnOffAltitude errorAverage1.121.52(lower hover1 standard0.470.32position), ftdeviationMaximumAverage-13.30-13.69descent speed,1 standard3.323.19ft/secdeviationRoot-Mean-Average0.620.58Square,1 standard0.20.23Collective,deviationinchesPilot cut-offAverage1.221.20frequency,1 standard0.180.19rad/secdeviationAmerican Institute of Aeronautics and Astronautics
+		
+		
+			
+ConclusionsThere are benefits to use the dynamic seat in ground-based flight simulations.However, dynamic seat alone may not be adequate to meet certain mission requirements.Addition of high frequency heave vibrations to the hexapodlike system has positive effects both subjectively and objectively.Large motion travel with the 2-DOF dynamic seat has the closest representation of the flight.			
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+			
+			May-June 2000
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Schroeder, J.A.; and Chung, W.: "Simulator Platform Motion Effects on Pilot-Induced Oscillation Prediction," Journal of Guidance, Control, and dynamics, May-June 2000, Vol. 23, No. 3, p438-444.
+
+
+
+	
+		Subject and Author Indexes of Technical Papers Published in the AIAA Journals, Progress in Astronautics and Aeronautics, and Astronautics & Aeronautics in 1974
+		10.2514/3.49613
+	
+	
+		AIAA Journal
+		AIAA Journal
+		0001-1452
+		1533-385X
+		
+			12
+			12
+			
+			
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization.
+
+
+				
+			
+		
+	
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+I. Nomenclature
+II. IntroductionWhile flying simulated vehicles, pilots adapt to different stimuli provided in a simulator, e.g., out-the-window visual, audio, motion, and hand-controller force feedback, depending on the task or maneuver.Timely motion feedback through the motion platform, as well as feedback from the force-feel system, can provide lead compensation in closed-loop control tasks and improve handling qualities and task performance [1][2][3][4][5].Force-feel characteristics, such as the breakout, dead-band, damping, force gradient, and inertia of the controller all play an important role in the handling qualities of a (simulated) rotorcraft.Previous research focused on cyclic inceptor force-feel characteristics for improved handling qualities for both passive and active controllers [6][7][8].Different from previous investigations, this study investigated haptic cues that are missing in fixed-base flight simulators that could contribute to tactile feedback pilots would have experienced otherwise in real flight.Specifically, this study focused on the inertial forces and moments a cyclic inceptor experiences due to the aircraft's motion that are either missing completely in fixed-base flight simulators, or being attenuated due to the application of washout filters in motion-based simulators.In an experiment conducted in the Vertical Motion Simulator (VMS) at NASA Ames Research Center, the effects on task performance, control behavior, and handling qualities ratings were investigated when restoring these reaction forces in fixed-base flight simulators.The paper is structured as follows: Section III mathematically derives the control force compensation missing in fixed-base simulation.Section IV provides an overview of the experiment setup, including verification of the dynamic models, and the experimental hypotheses.The results are presented in Section V and discussed in Section VI.Finally, conclusions are provided in Section VII.
+III. Control Force CompensationThe inertial force from the dynamics of the simulated rotorcraft's lateral acceleration, a y , and roll angular acceleration, p b , Fig. 1a, are translated through the center control stick to the pilot as shown in Fig. 1b for the lateral and roll degrees-of-freedom.The equations of motion of the lateral stick response due to the simulated rotorcraft's lateral and roll angular accelerations are described by Eqs. 1 and 2 as a function of control force-feel system damping, ζ y , and force gradient, k y .I xx φ a y = -m c a y r c -ζ y φ a y -k y φ a y(1)I xx φ pbd = -I xx p b -ζ y φ pbd -k y φ pbd(2)The lateral stick displacements, due to lateral accelerations, a y , and angular accelerations, p b , are defined by Eqs. 3 and 4, respectively.Φ a y = - m c r c I xx a y / s 2 + ζ y I xx s + k y I xx(3)Φ pbd = -p b / s 2 + ζ y I xx s + k y I xx(4)The total compensation for the lateral control, Φ c , or what is missing in a fixed-based simulation is the sum of these two dynamic components as shown in Eq. (5).Φ c can be added to the control trim position to move the lateral stick in addition to pilot control inputs to simulate the stick force response due to the inertial force and moment from the rotorcraft's dynamics.Φ c = Φ a y + Φ pbd(5)
+IV. Experiment SetupTo investigate if control force compensation affects pilot control behavior and performance, a two degrees-of-freedom (DOF) lateral side-step task was developed in the VMS with the aim to compare results from no-motion conditions with and without control force compensation.The results were further compared with data from a near one-to-one motion condition, and a medium-fidelity motion condition representing motion found in typical hexapod motion simulators.
+A. Rotorcraft DynamicsA simulated linear 2-DOF rotorcraft model was adopted from previous work investigating the effects of roll-lateral motion on pilot performance [3].The simulated rotorcraft model is provided in Eqs.6 and 7.φ = -4.5 φ + 1.7δ lat (6) v = g sin φ(7)A second rotorcraft model, a full nonlinear UH-60 model [9] with a heavier force gradient of the lateral stick, was also used to investigate if there is a different effect from control force compensation depending on the controlled dynamics.For the UH-60, the Stability Augmentation System (SAS) was turned off purposely with an intent to force pilots to maintain close-loop stability.A comparison of the agility of the two rotorcraft models is shown in Fig. 2.
+B. Control Force-Feel SystemTo determine the mass and moment of inertia of the control stick in Eqs.1-5, a pull test was conducted by setting the damping of the stick to zero and releasing the stick at its maximum lateral travel limit.The force gradient of the linear model was set to 0.6 lbf/in as in a previous test [3].The resulting undamped natural frequency of the stick, ω n , was measured at 1.194 Hz (or 7.5 rad/s).Using Eq. ( 1), with zero external force and zero damping based on a 25 inches rotational reference center for the particular control loader system, the moment of the inertia of the lateral stick was calculated from Eqs. 8 to be 2577 lbm-in about the lateral stick's rotational center.The effective damping ratio of the stick would come out to be 0.85.With these properties, the stick's lateral characteristics would be acceptable according to Ref. [8].A check of the pull-release response of the measured moment inertia vs. the lateral stick response from the simulator is shown in Fig. 3.ω n = k y /I xx or I xx = k y /ω 2 n (8)The product of mass and the rotational center to the center of c.g. of the stick, m c r c in Eq. (3), was estimated through an experimental process since it was difficult to disassemble the roll rotational assembly from the control loader system.The VMS was configured with near one-for-one motion by setting the second-order high-pass washout filter frequencies for both the roll and lateral degree of freedom (DOF) to 0.001 rad/s with a unity gain.A doublet of lateral acceleration was commanded to drive the motion system.By comparing the stick displacement response and the compensation model, m c r c , is estimated to be 52 lb min 2 .With this estimate, the resulted lateral stick displacement compensation shows a good match to the simulator response as shown in Fig. 4. The compensation due to a roll angular acceleration also shows a good match as shown in the same figure.The lateral stick's control-force feel for the UH-60 had a breakout force of 1 lbf, a damping ratio of 0.722, and a force gradient of 1 lbf/in, which resulted an undamped natural frequency of 9.7 rad/s.This would also be acceptable according to Ref. [8].
+C. TaskA 20-foot side-step task was developed to investigate the effect of the control force compensation to the stick due to the lateral accelerations and roll angular accelerations in ground-based flight simulators.Altitude hold, heading hold, as well as forward position hold were assumed to be active to limit the DOFs to roll and lateral only as defined by the model described in Eqs. 1 and 2.All test participants were briefed on the test procedures and task performance criteria prior to taking the test.Test participants were instructed to initiate a smooth lateral input toward the station-keeping position 20 feet to the right in 5 seconds (or time-to-target) for desired performance, and 7 seconds for adequate performance.Once the simulated rotorcraft was visually within the desired station-keeping region, test participants were directed to maintain the station-keeping position in a light disturbance for 10 seconds.Desired, adequate, and not-adequate performance criteria for the station-keeping phase of the task were given to the test participants as shown in Fig. 5.
+D. ConditionsTo investigate the effect of inertial stick force compensation and its interaction with aircraft dynamics, the experiment had two independent variables: motion fidelity with four levels (high-fidelity, medium-fidelity, low-fidelity, and low-fidelity with compensation) and rotorcraft model with two levels (linear and UH-60 dynamics).The experiment had a full-factorial design resulting in eight experimental conditions.The motion configurations were plotted against the modified Sinacori motion fidelity criteria [3] are shown in Fig. 6.The motion gain for the high-fidelity configuration was reduced from unity to 0.8 to alleviate excessive lateral accelerations from the UH-60 model.The test matrix is shown in Table 1.
+E. Participants and ProceduresSeven pilots participated in the experiment.All had extensive rotorcraft flying experience.Every pilot received an extensive briefing and safety-walk-around before the start of the experiment.The original experiment was divided into two sessions.Each session tested the experimental conditions for one rotorcraft model.Training was provided to all test participants at the beginning of each session.Six simulated runs of each test configurations were given to test participants in random order.However, due to an error in the implementation of the control force compensation, the low-fidelity conditions were repeated by the seven test participants several weeks after the first trial of testing.During the second trial of testing, the low-fidelity conditions were presented randomly in two sessions as well.The two motion conditions were not repeated.
+F. ApparatusThe VMS, Fig. 7a, with its large motion envelope provides the realistic cueing environment necessary for performing handling qualities studies, has an operational lateral travel of 30 feet.The simulator was positioned to the left-side of the
+G. Dependent MeasuresThe experiment considered two subjective handling qualities ratings (HQR) [10] as dependent measures: the HQR rating during the transnational phase of the task (HQR tr ans ) and the HQR rating during the station keeping phase (HQR sk ).These ratings were collected in separate runs for conditions 1, 3, and 4 only at the end of the experiment.The HQR were collected only for the linear model.The following eight objective performance variables were considered as dependent measures: the time-to-target, t g ; the root mean square (RMS) of pilots' control inputs, RMS u ; the bandwidth of the control inputs, ω c f [11]; the station keeping score, SK; the RMS of the lateral position and velocity during the station-keeping phase, RMS ye and RMS v ; and the RMS of the roll angle and rate during the station-keeping phase, RMS φ and RMS p respectively.The time-to-target, t g , was the time between two button presses on the center stick.Pilots pressed the event button the first time when they were ready to start transitioning to the hover target.They pressed the button a second time when they felt they were within the desired hover bound and would likely stay in the desired area.Both ω c f and RMS u were calculated for the entire run, i.e., for both the translation and the station-keeping phases.The station-keeping score, SK, ranged from 1 to 3. A score of 1 was given to desired performance achieved during the station-keep phase of the task, 2 was given to adequate performance, and 3 was given to not-adequate performance (Fig. 5).The remaining objective measures (RMS ye , RMS v , RMS φ , and RMS p ) were all calculated for the station-keeping phase only.The lateral position error, RMS ye , was calculated relative to the center of the hover-target board (Fig. 5).
+H. HypothesesThe following hypotheses were formulated based on the characteristics of the control force compensation as derived in Section III and the controlled dynamics presented in Section IV.A: H1: Since the control force compensation in the fixed-base condition is simulating the inertial force and moment feedback due to the motion of the simulated aircraft, it was expected that pilots performing the task with compensation would control more similarly to performing the task under medium-fidelity or high-fidelity motion compared to performing the task without compensation.This would be visible in ω c f and RMS u .H2: As the control force compensation provides lead information similar to motion feedback, albeit less efficient, it was expected that the compensation would result in improved task performance compared to the fixed-base condition without compensation, i.e., a shorter time-to-target t g ; smaller RMS of the lateral position error RMS ye , roll angle RMS φ , lateral velocity RMS v , and roll rate RMS p during the station-keeping task; and a better station-keeping score SK.H3: Since the higher force gradient of the UH-60 model would result in a smaller magnitude of force compensation at the stick, it was hypothesized that pilots controlling the UH-60 model would benefit less from the control force compensation as compared to the linear model, i.e., the performance improvement would be less for the UH-60 model compared to the linear model and control behavior would be more similar to the condition without compensation.H4: It was expected that both high-fidelity and medium-fidelity motion would still provide larger improvements in pilot performance compared to both low-fidelity conditions, as motion provides faster lead information as compared to haptic feedback.Therefore, performance in the low-fidelity condition with compensation was expected to lie between performance in the medium-fidelity and low-fidelity-without-compensation conditions.
+V. ResultsIn this section, the main results of the experiment and the associated data analysis are presented.The seven continuous dependent measures under consideration are: t g , RMS u , ω c f , RMS ye , RMS v , RMS φ , RMS p .The three ordinary variables considered are the performance score SK and the HQR rating for the translation and station keeping phases, respectively.Table 2 and Table 3 present the means and standard deviations of the dependent measures for each condition with the exception of the HQR ratings, which deviated strongly from normality.Table 4 provides the means of the data collapsed over the rotorcraft models for the same dependent measures.
+A. Ordinal Variables
+HQR RatingsThe participants were asked to subjectively evaluate the handling qualities of the linear model using the Cooper Harper rating.They were asked to rate the translation and station keeping phase of the task separately (HQR tr ans and HQR sk , respectively).The assigned scores ranged from 1 to 10, with 1 indicating the best handling characteristics and 10 the worst [10].The pilot responses are shown in Figs.8a and8b.The Jonckheere trend test [12] was used to test the hypothesis that high-fidelity motion would receive the lowest scores, followed by the fixed-base condition with compensation, followed by the fixed base condition without compensation, in this order.The results of the test show a non-significant trend: JT = 57, p = 0.1304.Furthermore, a generalized linear model based on Generalized Estimating Equations (GEE based on logistic regression) was fit to the data, using   the Gaussian family and as cluster variable the participant ID.The planned contrasts for the model compared the high fidelity case against the low-fidelity ones and the low-fidelity cases with and without compensation against each other.For the variable HQR tr ans , the first contrast shows that there is no significant difference between high-fidelity and (combined) low-fidelity conditions.The same holds for the difference between the low fidelity with compensation and without.For the variable HQR sk , the first contrast shows that there is no significant difference between high-fidelity and (combined) low-fidelity conditions.The same hold for the difference between the low-fidelity with compensation and without.The full test results are shown in Table 5.The means and standard errors of the aggregated data are summarized in Table 6.
+Station-Keeping Score, SKThe station-keeping score, or SK, ranged from 1 to 3. A score of 1 indicates desired performance during the station-keep phase of the task, 2 indicates adequate performance, and 3 indicates not-adequate performance as shown in Fig. 5.The data, aggregated over motion-fidelity and aircraft-model variables, are shown in histograms in Fig. 9. Since the data are ordinal and highly non-normal a generalized linear model based on Generalized Estimating Equations (GEE based on logistic regression) was fit to the data, using the Poisson family with logarithmic link function and as cluster variable the participant id.The summary of the statistical results derived from the model are shown in Table 7.The rotorcraft model suggestively affected the score; b = -0.077,χ 2 = 3.308, p = 0.069.The mean SK rating for the UH-60 model (M = 1.25) was lower than for the linear model (M = 1.35).
+B. Continuous VariablesFor the continuous variables linear regression models were used for hypothesis testing, having as independent within-subject variables the simulation fidelity (Fidelity) and aircraft model (Model).First, a repeated-measures Analysis of variance (ANOVA) was conducted on the continuous variables.Unfortunately, most fidelity conditions violated the assumption of homogeneity of variances of the residuals.For this reason, a linear mixed-effect model was fit for all the continuous variables since it does not assume homogeneity of variances of the residuals and can account for the residual dependency by using the pilot ID as a random factor [13]. Adding pilot ID as the random factor significantly improved every model that is discussed further.To compare specific conditions without correcting for multiple comparisons, orthogonal constants where used [14].The orthogonal contrasts considered were the same for all the conditions: 1) High fidelity vs. others compares the mean of the high-fidelity against the aggregate mean of the all the other conditions.2) Medium fidelity vs. low compares the mean of the medium-fidelity condition against the aggregate mean of both the low-fidelity conditions.3) Low fidelity with vs. without compensation compares the mean of the low-fidelity condition with and without compensation.The analysis of variance test results are summarized in Table 8.The overall effect sizes are tabulated in Table 9 to    Table 14, and represented graphically in Figs. 10 and11.
+Time-to-Target, t gThe error-bar plot of the time-to-target for each of the conditions can be seen in Fig. 10a.The summary of the linear mixed effect model is provided in Table 9.The different aircraft models did not have a significant effect on the time-to-target.The time-to-target was significantly lower between the medium-fidelity (M = 4.38) and the low-fidelity conditions (M = 4.6 s); b = -0.115(SE = 0.048), t(29.3)= -2.401,p = 0.023.
+Cut-Off Frequency, ω c fThe error-bar plot Of the control input cut-off frequency for each of the conditions can be seen in Fig. 10b.The summary of the linear mixed-effect model is shown in Table 10.There was a significant interaction effect between the rotorcraft model type and the high-fidelity vs. other contrast; b = -0.137(SE = 0.044), t(18) = -3.146,p = 0.006.As can be seen in Fig. 10b at low-and medium-fidelity conditions, the cut-off frequency for the UH-60 was systematically higher than the one for the linear model while for the high fidelity case this difference disappeared.The cut-off frequency was significantly higher with high-fidelity compared to the other conditions; b = 0.417 (SE = 0.045), t(28, 22) = 9.336, p < 0.001.Moreover, the cut-off frequency for the low-fidelity condition with compensation (M = 2.71 rad/s) was significantly higher than the one for the low fidelity condition without compensation (M = 2.46 rad/s); b = 0.231 (SE = 0.109), t(18) = -3.146,p = 0.044.
+RMS Control Input, RMS uThe error-bar plot of time-to-target for each of the conditions is depicted in Fig. 10c.The summary of the linear mixed effect model is shown in Table 11.There was a significant interaction between the aircraft model and the mediumvs.low-fidelity conditions; b = -0.057(SE = 0.023), t(18) = -2.466,p = 0.024.Even in presence of an interaction, the overall mean of the RMS u for the linear model (M = 0.529) was significantly higher than the one for the UH-60 (M = 0.436); b = -0.093(SE = 0.034), t(6) = -2.744,p = 0.034.The RMS u for the high-fidelity case (M = 0.37) is lower than the one for the medium (M = 0.44) and low fidelity cases (M = 0.56), although the effect is not significant, p = 0.055.
+RMS Lateral Position Error, RMS yeThe error-bar plot of the RMS lateral position error for each of the conditions can be seen in Fig. 11a.The summary of the linear mixed effect model is provided in Table 12.The RMS ye for the linear model (M = 1.66) was significantly higher than the RMS ye for the UH-60 model (M = 1.33); b = -0.311(SE = 0.038), t(5.94) = -3.2,p = 0.019.Furthermore, the statistical analysis reveals that the mean RMS ye of low fidelity with compensation (M = 1.45) was significantly lower than the one of the same fidelity but without compensation (M = 1.7); b = -0.0252(SE = 0.094), t(35.62)= -2.691,p = 0.011.Indeed, for the UH-60 the means for the low fidelity case with (M = 1.429) and without compensation (M = 1.434) were quite similar.On the other hand, for the linear case the means for the low fidelity case with (M = 1.465) and without compensation (M = 1.970) showed a difference.
+B. SummaryThe rotorcraft model introduced significant differences in the dependent measures.These differences were probably caused by the fact that the linear model had a responsive first-order rate response, while the UH-60 model had a somewhat unsteady rate command response by purposely turning off the SAS to force pilots to stay in the loop to expose the effect of the controller compensation.This, however, might unexpectedly have led to pilots staying low-gain during the station-keeping phase.In addition, the higher force gradient of the control inceptor in the UH-60 resulted in smaller force compensations relative to the linear model with a lower force gradient.With the combination of these two factors, the benefit of having the lead provided by the force compensation was found to be significantly reduced with the UH-60 model as observed by comparing the control input cut-off frequency and lateral position error between the low-fidelity conditions with and without the force compensation.The contrast comparing the high-fidelity condition with the rest of the fidelity conditions, and its interactions showed significant differences in the following variables: ω c f , RMS u , RMS v , RMS φ .Pilots had a higher control input cut-off frequency for the motion conditions.This might be a result of the enhanced lead information motion provides as shown by previous studies [1][2][3][4][5].In this case, the simulation cueing feedback from the visual and motion were consistent with little to no phase error between them.Even though not significant, the lower RMS u for motion conditions compared to the conditions without motion could suggest a lower control activity with motion.Furthermore, motion resulted in lower values for RMS v and RMS φ , indicating that pilots could more easily stabilize the aircraft with motion.The medium-fidelity motion condition introduced significant differences compared to both low-fidelity conditions for t g and RMS u .The time-to-target t g is lower for the medium-fidelity motion condition, even though t g for the high-fidelity condition is not significantly different from the rest.Since the time-to-target was measuring the time of a 20-feet sidestep to the hover target, which involved taking out the lateral velocity before stabilizing and hovering, the medium-fidelity motion was able to provide the comfort for pilots to generate a larger bank, or lateral acceleration, to get to the hover target and with the right amount of damping needed to stabilize the simulator via the motion feedback.The RMS control input RMS u was also different for the medium-fidelity motion condition compared to the other conditions due to an interaction with the aircraft model: for the UH-60, RMS u was significantly lower than in the low-fidelity cases, while for the linear model it was comparable to the low-fidelity conditions.This could have been caused by the UH-60's unsteady rate command system allowing motion feedback to provide the damping in the pilot's inner-loop control behavior.The comparison between the low-fidelity condition with and without compensation found significant differences for the following variables: ω c f , RMS ye , RMS v , RMS φ .Even though the interaction effect is not significant (p = 0.07), the compensation algorithm seems to mostly increase the cutoff frequency for the linear model leaving ω c f almost unchanged across the two conditions for the UH-60 model.A significant interaction effect was found for RMS ye and RMS v : when pilots controlled the linear model, the RMS of the lateral deviation and velocity were significantly lower for the condition with compensation.On the other hand, for conditions with the UH-60 model, RMS ye and RMS v were almost unaffected across the two conditions.This is likely due to the fact that the UH-60 had an unsteady rate command response as shown in Fig. 2, resulting in pilots remaining low-gain during the station keeping phase for both low-fidelity conditions.In addition, the UH-60 had a higher force-gradient, which reduced the magnitude of the force compensation relative to the linear model.Overall, the RMS of the force compensation from all pilots was 0.155 lb f for the linear model, and 0.107 lb f for the UH-60.Another interaction effect was found for RMS φ : the RMS of the roll angle was significantly lower for the condition with compensation and the linear model.On the contrary, RMS φ was almost unaffected between the two conditions for the UH-60.This interaction was most likely caused by the same factors as the interaction for RMSE ye and RMS v .It is apparent that the UH-60 model's response and the force gradient of the lateral stick affected pilots' approach to the task.In hindsight, a different approach to the test matrix, e.g., by using the linear model only, but varying the force gradient, might have provided more insight into the relation of pilot control behavior and task performance with motion force feedback.
+VII. ConclusionsTo investigate if control force compensation affects pilot control behavior and performance, pilots performed a two degrees-of-freedom lateral side-step task in the VMS under four different motion configurations (high-fidelity, medium-fidelity, low-fidelity, and low-fidelity with compensation) and with two simulated rotorcraft models (linear and UH-60 dynamics).By comparing pilots' control behavior and task performance between conditions, several conclusions could be drawn.The inertial control force compensation introduced significant differences in some of the dependentφ, φ, φ = Roll attitude, rate, and acceleration of simulated rotorcraft φ a y , φ a y , φ a y = Roll attitude, rate, and acceleration of the lateral stick due to inertia effect of lateral acceleration Φ a y = Lateral stick displacement compensation due to lateral acceleration Φ c = Combined lateral stick displacement compensation due to inertial force and torque φ pbd , φ pbd , φ pbd = Roll attitude, rate, and acceleration of the lateral stick due to inertia effect of roll angular acceleration Φ pbd = Lateral stick displacement compensation due to roll angular acceleration ω c f = Cut-off frequency of pilot's lateral stick input ω n = Natural frequency of the lateral stick ζ y = Damping ratio of the lateral stick a y = Lateral acceleration at the lateral stick's pivot point of the simulated rotorcraft in body frame g = Gravitational acceleration constant I xx = Moment of inertia about the pivot point of the lateral stick about the roll rotational axis k y = Force gradient of the lateral stick l c = Length of the lateral stick m c = Mass of the lateral stick p or p b = Roll angular rate of the simulated rotorcraft p b = Roll angular acceleration of the simulated rotorcraft r c = Distance between the pivot point of the stick and the c.g. of the lateral stick RMS = Root mean square SK = Station-keeping score sk = Station-keeping phase of the task t g = Time-to-target in the translation phase of the task trans = Translational phase of the task u = Lateral stick input v = Lateral velocity of the simulated rotorcraft v = Lateral acceleration of the simulated rotorcraft ye = Lateral position error
+( a )Fig. 1a1Fig. 1 Inertial forces acted on the stick due to simulated rotorcraft's dynamics.
+Fig. 2 Fig. 323Fig. 2 Roll rate responses from both models from a lateral stick doublet.
+Fig. 44Fig. 4 Verification of the control force compensation for the lateral stick displacements.
+Fig. 5 Fig. 656Fig. 5 Task performance criteria via a hover-target board in the out-the-window visual scene.
+Fig. 77Fig. 7 The Vertical Motion Simulator at NASA Ames Research Center.
+( a )Fig. 8 HQRa8Fig. 8 HQR rating given for the translation and station keeping phase of the task.
+Fig. 9 Station9Fig. 9 Station Keeping scores.
+Fig. 1010Fig. 10 Time to target and control input depended measures.
+RMS roll attitude.(d) RMS roll rate.
+Fig. 1111Fig. 11 Lateral and roll performance depended measures.
+Table 11Experimental conditions.Condition Rotorcraft ModelMotion Fidelity1high2 3linear modelmedium low4low + compensation5high6 7UH-60medium low8low + compensation
+Table 2 Mean of the dependent measures.2RMS ye RMS v RMS φ RMS pModel Fidelity ω c f Linear High t g SK RMS u 4.779 1.333 0.401 4.0721.5441.4013.1380.104UH-60 High4.476 1.2140.334 3.9991.1691.2404.6320.102Linear Medium4.369 1.4050.547 2.4501.6921.6894.6030.140UH-60 Medium4.392 1.2860.332 3.0721.3941.3755.1100.113Linear Low+Comp 4.729 1.2620.577 2.6141.4651.5635.0500.184UH-60 Low+Comp 4.462 1.2380.547 2.8091.4291.4896.1930.296Linear Low4.698 1.4290.591 2.1531.9701.9595.9810.195UH-60 Low4.502 1.2860.532 2.7581.4341.4766.1850.195
+Table 3 Standard deviation of the dependent measures.3RMS ye RMS v RMS φ RMS pModel Fidelity ω c f Linear High t g SK RMS u 0.736 0.473 0.104 0.6980.5870.4850.8550.027UH-60 High0.617 0.4110.131 0.6250.4150.3780.9080.042Linear Medium0.500 0.5380.479 0.6190.8500.6431.9400.064UH-60 Medium0.445 0.4530.150 0.7500.6530.5651.4780.053Linear Low+Comp 0.567 0.4920.403 0.5130.5920.8313.7600.148UH-60 Low+Comp 0.556 0.4800.492 0.6090.9040.7592.8610.619Linear Low0.984 0.5420.416 0.4351.0180.9644.0950.143UH-60 Low0.513 0.4530.488 0.6780.6860.8603.4950.181
+Table 4 Mean aggregate value for fidelity.4Fidelity t High 4.63 1.270.37 4.041.361.323.890.10Medium4.38 1.350.44 2.761.541.534.860.13Low+Comp 4.60 1.250.56 2.711.451.535.620.24Low4.60 1.360.56 2.461.701.726.080.19g SK RMS u ω c f RMS ye RMS v RMS φ RMS p
+Table 5 HQR statistical test results.5HQR tr ansHQR skEstimate Std.err Waldp Estimate Std.err WaldpHigh Fidelity vs. Low0.0480.135 0.124 0.725-0.0240.198 0.014 0.904Low Fidelity Without vs. With Comp0.2860.256 1.244 0.2650.2140.273 0.618 0.432
+Table 6 Summary of aggregated HQR means and standard errors.6HQR tr ansHQR skFidelityMean Std.err Mean Std.errHigh2.143 0.3402.571 0.571Low2.285 0.3592.857 0.459Low+Comp. 1.714 0.4202.428 0.369
+Table 7 Summary of statistical results for the station keeping score.7Estimate Std.err Waldp= significant (p ≤ 0.05)
+Table 8 Type III Analysis of Variance Table with Satterthwaite's method for the Multilevel Models8MeasureModelFidelityModel × FidelitydfFpdfFpdfFpt g1,6 1.9056 0.2167 3, 18 1.29210.3075 3, 18 1.4894 0.2511RMS u1,67.528 0.0336 3, 182.0150.148 3, 182.1640.128ω c f1, 66.318 0.0457 3, 1827.59 < 0.001 3, 185.0130.011RMS ye1,610.23 0.0186 3,182.2860.113 3,181.3520.289RMS v1, 611.90.014 3, 181.6360.216 3, 182.4150.100RMS φ1, 6 14.3650.009 3, 183.3880.041 3, 184.4890.016RMS p1,60.5930.471 3, 184.326 0.01836 3, 181.4690.256= significant (p ≤ 0.05)
+Table 9 Summary of the mixed effect model fitted for the time-to-target.9Estimate Std. Errordf t valuep
+Table 10 Summary of the mixed effect model fitted to the cut-off frequency.10Estimate Std. Errordf t valuep
+Table 11 Summary of the mixed effect model fitted to the RMS of the control input.11Estimate Std. Errordf t valuep
+Table 12 Summary of the mixed effect model fitted for the RMS of the lateral position error.12Estimate Std. Errordf t valuep
+		
+		
+
+			
+measures, mainly for the linear model.The control input cutoff frequency was higher, the station keeping score was better, and the RMS of the lateral error was lower with compensation when no motion was present.The RMS of the lateral velocity was marginally significantly lower.Therefore, control force compensation allowed for pilot control behavior and performance more similar to that under high-or medium-fidelity motion to some extent only.Considering all performance variables, we conclude that the control force compensation did not increase overall task performance considering both rotorcraft models at the same time.For the UH-60, the unsteady roll rate command response might have affected pilots' approach to the task and led to a low-gain control technique.In addition, the higher force gradient in the lateral stick for the UH-60 resulted in less inertial force compensation.As a result, the control force compensation only had a minimal effect on pilots' control behavior and task performance for the UH-60 model.This suggests that the control force compensation has limited benefits for controllers that have higher stiffness.Finally, high-fidelity and medium-fidelity motion did not always provide significant improvements in pilot performance compared to the low-fidelity conditions with and without compensation.
+			
+
+			
+RMS Lateral Velocity, RMS vThe error-bar plot of the RMS lateral velocity for each of the conditions can be seen in Fig. 11b.The summary of the linear mixed effect model is shown in Table 13.RMS v for the UH-60 (M = 1.39) was significantly lower than the RMS v of the linear model (M = 1.65); b = -0.258(SE = 0.075), t(6) = -3.450,p = 0.014.Furthermore, the RMS v was significantly lower for the high-fidelity condition compared to the rest of the conditions; b = -0.084(SE = 0.04), t(25.198)= -2.087,p = 0.047.There was a significant interaction between the model type and the low fidelity conditions; b = 0.205 (SE = 0.082), t(18) = 2.500, p = 0.022.The biggest effect of the force compensation algorithm is observed for the linear model.
+RMS Roll Attitude, RMS φThe error-bar plot of the RMS roll attitude for each of the conditions can be seen in Fig. 11c.The summary of the linear mixed effect model is provided in Table 14.There was a significant interaction between the model type in the contrast of the high-fidelity condition against the other conditions; b = 0.219 (SE = 0.0), t(20.53)= 2.738, p = 0.014.Furthermore the model significantly interacted between the low-fidelity condition with (for the linear model M = 5.05 and for the UH-60 M = 6.19)) and without compensation (for the linear model M = 5.98 and for the UH-60 M = 6.185); b = 0.470 (SE = 0.196), t(18) = 2.394, p = 0.028.
+VI. DiscussionThe purpose of this study was to investigate if control force compensation affects pilot control behavior and performance.Therefore, the main goal was comparing the low-fidelity condition and without compensation to assess the benefit, if any, of the force feedback algorithm.The high-and medium-fidelity conditions were used as baselines for the low-fidelity conditions.Due to an error in the implementation of the force compensation, the low-fidelity conditions were repeated by the seven pilots in a second experiment session several weeks after the first session.The low-fidelity conditions without compensation (conditions 3 and 7 in Table 1) were identical between the two sessions and were used to verify that pilots performed similarly between the two sessions and assured that session was not a significant confounding variable.Comparing all dependent measures for these conditions between the two sessions revealed that pilots performed the same overall, minimizing the chances of session having a significant effect on the results.
+A. HypothesesThe hypotheses provided in Section IV.H were accepted/rejected as follows: H1: The cutoff frequency ω c f was significantly higher in the low-fidelity condition with compensation compared to without compensation for both model types and closer to that in the medium-fidelity condition.However, this trend was not detected in the RMS of the control signal RMS u , which was similar for all low-fidelity conditions.The null hypothesis can not be fully rejected and it must be concluded that control behavior with force compensation is only more similar to that with high-or medium-fidelity motion in some respects.H2: The compensation did not lead to an improvement in all task performance measures.Only RMS ye were significantly lower with compensation.RMS v was marginally significantly lower.We fail to reject the null hypothesis and we conclude that the control force compensation did not increase overall task performance considering both rotorcraft models at the same time.H3: Taking into account all the RMS performance parameters in Table 2, there were little differences between the compensation and no compensation data for the UH-60.Results did not reveal a clear benefit of the force compensation with the UH-60 in low-fidelity conditions.No differences were noted in the lateral position error, lateral velocity, and roll attitude.Results showed that the RMS control input for the UH-60 was significantly lower than that of the linear model, indicating the unsteady roll rate response led to pilots staying in low-gain during the station-keeping phase.In addition, the higher force gradient for the UH-60 resulted in less control compensation relative to the linear model.This resulted in many significant interaction effects between the model and the force compensation, suggesting the null hypothesis can be rejected.H4: Only high-fidelity motion had a much higher control input cut-off frequency than all other test conditions.Pilots performing the task under high-fidelity motion also achieved a significantly lower roll attitude and lateral velocity, which suggested pilots were able to use the motion feedback to quickly damp out the roll attitude that commanded the lateral accelerations.The RMS of control input was significantly lower in the medium-fidelity condition than in both low-fidelity conditions.The null hypothesis can not be fully rejected based on these results, i.e., high-fidelity and medium-fidelity motion do not always provide larger improvements in pilot performance compared to both low-fidelity conditions.			
+			
+
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+	
+	
+		
+I. Introduction
+Aflights to runways at an airport is a critical function that influences all aspects of airport operations and performance.These assignments, made by air traffic controllers, indicate the runway on which a flight must land or take off from.In this paper, we describe our research developing data-driven machine learning (ML) models to predict runway assignments for arriving and departing flights for various airports in the U.S.While the precise roles and responsibilities vary for arriving and departing flights, air traffic controllers in general have tremendous expertise in making these runway assignments.They apply a heuristic based on a multitude of factors including at least direction of flight, airport configuration (i.e., which runways are available for arrivals and departures), configuration of nearby airports (e.g., in the same metroplex region), aircraft size and engine type, noise and other environmental regulations, and traffic volumes.They also consider requests from flight crews for specific runways due to preference or operational necessity.Furthermore, the rules applying to each of these factors may evolve over time as new procedures or routes are developed.This heuristic is clearly complex and learned by controllers over years of site-specific training and experience.Some prior research such as Isaacson et al. [1] has attempted to use observational studies on Subject Matter Experts (SMEs) to capture the decision heuristics that controllers employ to assign runways.Likewise, the development of a similar knowledge base has taken place on the ATD-2 project [2] (of which this research is part), and against which the results of the ML-based approach described here will be compared.The ATD-2 expert-driven approach consists of decision trees.For example, the decision tree may have a path such that all flights with a jet engine, headed for the BLECO fix, while the airport is operating in "South Normal" configuration, will be assigned to depart from runway 18L.The challenge with expert-driven approaches like these is that they are expensive and difficult to construct, given the requirement for access to personnel with specialized and timely knowledge, and the highly-nuanced differences in the heuristics that each individual might apply.Our approach in this research is to leverage the vast amounts of data on airspace and airport operations, in conjunction with modern ML techniques, to predict these runway assignments with performance nearly equivalent to that which is achievable through the expert-driven approach.Whereas research has been conducted to apply machine learning to various prediction problems relating to aircraft and airport operations (e.g., predicting airport configurations, landing times, taxi out times), relatively little attention has been paid to predicting runway assignments.However, two recent works do cover the problem in an international context.In [3], the author developed a predictive model for runway assignments using support vector machines (SVM) for Amsterdam Schiphol Airport.Likewise, in [4], the authors developed a neural network based ML approach to predict the runway to be used by arrivals into Tokyo International Airport.Our work builds on this prior research by employing a more generic framework that can be easily applied to multiple airports, by focusing on deployment to a real-time system, and by exploring different ML approaches and metrics to understand model performance.Rather than focusing on the problem of predicting runway assignments, considerable previous research has addressed on the problem of optimizing runway assignments (and sequence of runway use) to achieve various objectives.For example, Berge et al [5] jointly optimize the runway assignment and sequencing decisions.Likewise, see Lohr et al [6] for work that jointly optimizes airport configurations and runway assignments.An example with a longer time scale is [7], in which the authors planned a longer-term runway assignment strategy to achieve various delay, environmental, and safety related objectives.This distinction between prescription (i.e., optimization) and prediction is critical in justifying the approach that we have developed.Although previous research on optimizing runway assignments and utilization has demonstrated potential benefits (e.g., greater runway throughput, shorter taxi distances), these approaches in general make significant assumptions about the ease with which such changes could be implemented.Our approach assumes that air traffic controllers will continue to use their considerable expertise and available automation tools, and we seek to capture (through machine learning) the runway assignment heuristics that exist within this structure.This ML model will enable us to make detailed forecasts of runway utilization several hours in advance of the actual operation.This distinction is essential to understand the broader context of the research as part of an ML-powered shadow system to help evaluate the its ability to match the performance of the the expert-driven approaches currently used in the ATD-2 system.In the remainder of this paper, we describe our modeling approach, the data used for our study, results and discussion, and finally conclusions and directions for continued investigation.
+II. Modeling ApproachIn this section, we describe the approach developed to train ML models that imitate the decision heuristics used by air traffic controllers outlined in the previous section.The modeling approach developed here encompasses several elements, each of which is described in greater detail in the following subsections:• Requirements: Several requirements drove our decision-making process in developing these ML models.• Target: The target value is the runway identifier actually used by the flight in the historical data.• Features: Based on literature review, brainstorming, and consultation with available SMEs, identify, explore, and compute relevant input features.This section also describes imputation and encoding strategies used for each feature.• Building the Dataset: Several steps are described in this section that are used to translate from a list of features to a rectangular dataset that can be used by one of the machine learning algorithms we have evaluated.• Machine learning algorithms: This section describes applying machine learning algorithms to the full prepared training dataset to create models.
+A. Modeling RequirementsImportant requirements informed the development of these models.First, it was of crucial importance that the models developed as part of this research be suitable for deployment in a system processing live data.Thus, they must use features that can be readily computed at runtime.Further, they must have a full suite of imputers to handle inevitably missing data, or alternatively, must have a well-defined filtering approach to ensure that non-imputed features are never passed to the model as nulls.Finally, their query time must be reasonable to support processing large batches of flights at regular intervals.This real-time support was necessary because these runway prediction models are part of a suite of ML models to be assembled into a shadow system to evaluate against the performance of the legacy ATD-2 systems.Second, an additional objective in training these models is that the process used is highly data-driven, in that it does not require the maintenance of significant adaptation data (i.e., site-specific geometric or procedural information).Achieving this objective allows the models to be generated and updated rapidly to cover dozens of airports across the U.S., provided that standard data formats are employed.Finally, the models needed to be trained (and served in real-time) using data from the ATD-2 Fuser [8], a data processing and fusion system that is designed to handle many different data feeds and formats simultaneously, generating a standard relational format.
+B. TargetThe target value for these models is the actual runway on which each flight operated.For this effort, we derived these values from surveillance data (Airport Surface Detection Equipment, Model X [ASDE-X] or Traffic Flow Management System [TFMS]) and runway adaptation from the National Flight Data Center (NFDC) data [9] to ensure consistency and reliability of these crucial data elements.When surveillance located a flight within a runway polygon and with physics consistent with aircraft landing, it was straightforward to determine that the flight operated on that runway at a certain time.When this is not the case, which is relatively rare at many large airports, we used the airborne surveillance data from TFMS to infer which runway was used and at what time.This was accomplished with an approach developed by Robert Kille [10], under funding by NASA.
+C. FeaturesThe set of features used in training the arrival and departure runway assignment models to date is relatively simple, with considerable overlap between the two models.Table 1 summarizes which features are included in each model.Descriptions of each feature follow the table.As with any ML modeling workflow, this is an area of iterative exploration, and we believe that additional features may improve the predictive power of each model.
+Aircraft Engine ClassThis categorical value (handled via one-hot encoding) indicates whether a flight has a jet, turboprop, or propeller engine.Discussions with SMEs and insight from previous ATD-2 work indicated that this was an important discriminator in runway assignments.For example, some runways are reserved exclusively for propeller-driven aircraft, due to the significant differences in their takeoff and landing performance.Missing values for this feature are imputed, by using the most frequently observed value in the training dataset.For most training datasets, this will be jet engine, as jets comprise the vast majority of operations at airports for which this model is relevant.
+Wake Turbulence CategoryThis categorical value (handled via one-hot encoding) indicates the impact of the wake vortex induced by the flight.This roughly correlates with the size of the aircraft, and implies the separation required between operations on the same runway.As with engine class, the wake vortex category was identified during discussions with SMEs and in previous ATD-2 work.For example, at some airports, the largest aircraft may be restricted to using certain runways due to available length for takeoffs or geometric constraints.For all analysis, FAA RECAT wake vortex categories [11] are used.Missing values for this feature are imputed, by using the most frequently observed value in the training dataset to replace missing values.Typically there is only one dominant weight class, making this imputation insignificant.
+Planned FixAs part of filing a flight plan or through communication with air traffic controllers, a flight operator provides some indication about their path through the terminal area and which fix they plan to use to transition into / out of the terminal area.The fix name is generally available through the data feeds used to build training datasets, and is handled in the model through one-hot encoding.Observations with missing values are not used in training models.According to discussions with SMEs, for both arrivals and departures, this planned fix, in conjunction with the current airport configuration, provides significant information about which runway will be used.
+Flight Plan FiledThis boolean indicates whether a flight plan has been filed by the flight operator.The flight plan provides the planned fix, as described in the previous section.However, before the flight plan is actually filed, the FAA automation systems will provide a 'default' fix value for most flights.Missing values are replaced with a false value.By including this indicator, the model is able to differentiate between these filed and default values, and learn the circumstances under which each provides valuable information.
+Airport ConfigurationAirport configuration lists which runways are being used for arrivals and for departures at a specific instant in time.Runways may be in both lists; in this case, they are known as dual-use runways.The distinct combinations of these lists form the set of configurations available for the runway assignment models.Neither in reality, nor in the runway assignment models, do the airport configurations totally constrain which runways may be assigned, however they strongly influence this.This value could be provided by either a data feed that provides live updates (e.g., Digital Automatic Terminal Information Service (D-ATIS)), or a predictive model for airport configurations (e.g., [12]).For model training, the D-ATIS data indicating the actual configuration at the time of the operation was used.This ensures that the model is not biased by errors from another model.In future work, this assumption should be revisited.Regardless of the source, a custom encoder (in place of one-hotting) is used to translate the airport configuration to values usable by the model.The custom encoder, for an airport with runways, creates 2 columns, one for each combination of either 'arrival' or 'departure' and runway name.We hypothesize that this encoding strategy should help the model learn from similar configurations (e.g., add / remove a single runway) in a way that considering the name alone would not.Missing values are extremely unlikely for this data source, as we assume that the previous configuration continues until a new one is explicitly provided by the data feed.However, should a missing value be encountered (e.g., at the beginning of a time period), those observations are not used to train models.
+Time since Airport Configuration ChangedAs there may also be a relationship between runway assignments and the amount of time that the airport has been operating in the current airport configuration, we include this duration in seconds as a feature in the model.The logic for this feature is that the relationship between configuration and runway assignment policies may be more flexible when a configuration is newly in place, e.g., as a result of aircraft already lined up for specific runways.
+Time until Estimated OperationAnother feature that may influence the runway assignment policy is the time expected until a flight operates on the runway.For example, there may be less certainty about the policy to be applied when this lookahead is very long.This lookahead is computed as the difference between the time at which the prediction is being made, and an estimated operation time.For departures, this estimated operation time is the Earliest Off Block Time (EOBT) value provided by the airline (if unavailable, other estimates from FAA systems).For arrivals, this value is one of several landing time estimates provided by FAA systems.Which of these estimates to use at each instant is a research question unto itself, and an approach for selecting this 'best' landing time is described in [13].
+TBFM-assigned RunwayOne additional feature included in the arrival runway model is the runway assignment generated by the FAA's Time Based Flow Management (TBFM) system [14].One of the many features of this decision support system is the capability to predict runway assignments for arriving flights.However, the accuracy of these predictions varies significantly between TBFM systems used in different terminal areas.Under the proper conditions, this TBFM-assigned runway may be sufficiently accurate to match the performance of an ML model, as it is the product of an expensive and lengthy process of codifying the controllers' runway assignment heuristics into adaptation data.In other situations, this value may simply indicate a flow direction for the airport, without a specific runway identifier.In any case, the value of training runway assignment models for each airport individually allows the ML model to learn the value of this data as a feature and use it accordingly.These data are provided as categories, encoded in the model as one-hot features.Missing values for this feature are imputed by filling with a constant value of UNKN, creating a new category.
+D. Building the DatasetSeveral steps are required to build a rectangular dataset that an ML algorithm can use to train a model.These steps are primarily mechanical, but are described in the interest of promoting reproducible research.It is critical to recognize that the various features listed in the previous section are available at different instants in time, and are updated at different rates.Through the use of the ATD-2 Fuser, described in the requirements section, the state of each flight is readily available by carrying forward values from previous messages.However, even with this approach, data are only available at the instants at which messages were received from various automation systems.To create a more uniform dataset from which to sample (e.g., to avoid bias induced by certain kinds of flights producing more messages), this approach of carrying values forward was extended further by creating a uniformly-spaced sequence of lookahead (i.e., time until estimated operation) values against which the raw dataset was joined.An example is shown by converting the data in Table 2 to that in Table 3.This is clearly a large dataset.Assuming a medium-sized airport with 500 flights/day, a four-hour lookahead period with a one-minute update rate, and six months of training data, there are 21.6 million rows of data available.For some algorithms and computational setups, this volume may introduce difficulties, so lower sampling rates may be necessary than are traditionally employed.Once a 'uniform' dataset of this nature has been constructed, some rows may be filtered out for having unsuitable data.First, we check that the target values (i.e., runway names) for each flight fall into the set of known runway identifiers for the airport being studied.After exploratory data analysis, we identified several features that seemed unwise to impute.As a result, these features were identified as core for training a model and making predictions.Thus, any observation with a missing value for a core feature was not used in model training.Because these rules are logged with the trained model itself, they are applied during real-time operations.Any flight in the real-time environment that fails any of these rules (which would not have been used to train the model in the first place) is assigned a default runway.Based on our analysis and discussion with SMEs, the following features are considered core, and so any rows with a missing value are discarded:• Planned fix: could be missing if FAA automation systems malfunction, or data is lost• Airport configuration: could be missing if FAA automation systems malfunction, or data is lost • Time since airport configuration changed: follows mechanically with airport configuration • Time until estimated operation: could be missing if all relevant FAA systems are not providing valid data
+E. Machine Learning AlgorithmsSeveral ML algorithms have been evaluated thus far in training models for the runway assignment problem.As formulated, this problem is a multinomial classification problem, for which many algorithms currently exist.As will be highlighted in the results, we have thus far used the classic logistic regression [15] available through scikit-learn [16], and the more recently developed and very popular XGBoost [17].Cross-validation and hyper-parameter tuning for each approach has been evaluated, but results are not included in this paper.Initial analysis of those processes indicated limited improvement in performance metrics as compared to the default parameters available in their implementations.
+III. Results and DiscussionUsing the features and algorithms described in previous sections, with the Kedro framework [18], we have developed a modeling process that can be easily replicated for any airport for which data are available in the suitable format.A similar series of pipelines for data query, data engineering, and model training / evaluation have been developed for both the arrival and departure runway prediction problems.There is considerable overlap, and code re-use, between the two pipelines.In this section, we first present an overview of the data used in this study, and then a description of various performance metrics of the models trained for various U.S. airports.
+A. DataAppropriate data are essential to conducting research using ML.For this work, we leveraged the Fuser technology previously developed for the ATD-2 project.To generate the datasets used in this research, the Fuser was configured to consume data from the following FAA data feeds: TFM Flight [19], TBFM [14], and STDDS ASDE-X / SMES [20].The details of each of these data feeds is beyond the scope of this paper, but they provide comprehensive detail about the planned and actual operation of each flight in the U.S. from gate to gate.The results presented here are based on models trained and evaluated on data from April 25, 2020 through December 31, 2020.Because of the large size of this dataset, as illustrated in the previous section, just 5% of the dataset is used for training, and 5% for evaluation.These values are approximate because the 5% value for training actually represents the fraction of individual flights, rather than rows.If we were to sample only rows without considering the panel nature of the data, then rows for the same flight might end up in both the training and evaluation samples.After removing the training sample, exactly 5% of the remaining rows are selected as the evaluation dataset.Note that both datasets contain a variety of lookahead values, and both are sampled randomly.It is essential to acknowledge the role of the pandemic in the data used for this research.Flight operations were reduced by an enormous amount at the start of the pandemic, and this is reflected in the data.Were these models simply trained for the purposes of writing this paper, then this drastic change in the data would present few problems, as we would simply use pre-pandemic (e.g., 2018-2019) data to represent normal levels of air traffic.However, this step change in the data creates challenging problems deploying a model trained on pre-pandemic data, and achieving comparable performance using live data.Deploying trained ML models to operate as part of a shadow system for the ATD-2 project was a key requirement of this research, as described earlier.As a result, many of the results presented in this section rely on models trained using data from during the pandemic.This was done to maximize the likelihood that when the models were deployed (still during reduced traffic levels) the conditions used in training the models would be similar to those present.
+B. Arrival Runway Assignment ModelsArrival runway assignment prediction models have been trained for a variety of airports.An initial summary of model accuracy is shown in Table 4.These results reflect the performance of the model trained using XGBoost on the evaluation sample, using the features listed in previous sections.Note that these accuracy metrics include observations that are sampled from a variety of lookahead values.This initial summary of model performance demonstrates that the models are performing at a reasonable accuracy level, given the relative complexity of each airport (e.g., KDFW has more runways used for arrivals than KEWR, so we should expect more uncertainty).However, these results should also be evaluated in a variety of other dimensions, as outlined below.
+Performance of Different AlgorithmsModels were trained using Logistic Regression for some airports.The performance of these models is compared against the XGBoost models in Table 5.The same training and evaluation samples were used for each algorithm.From these results, it is clear that the XGBoost models perform significantly better than those trained using Logistic Regression.Although there may be interesting explanations and insight related to the relative performance of these algorithms, to help achieve the objectives of this research, we are satisfied to identify the superior performance of the XGBoost models and use them going forward.
+Comparison to Expert-Driven approachAs described in the introduction, the legacy ATD-2 system used at several airports has decision trees for predicting runway assignments developed through data analysis and interviews with SMEs.In Table 6, we compare the accuracy of the XGBoost models with that of the expert-driven models.In this comparison, the accuracy metrics for the XGBoost models presented earlier are repeated, but the metrics for the legacy ATD-2 systems are sampled at the fix crossing event, where the accuracy should be highest.There are two interesting trends in this comparison between the ML model results and the expert-driven models.First, the performance of the expert-driven model at KCLT exceeds that of the ML model, reflecting how well-tuned the legacy ATD-2 system is at that facility.In contrast, for the Dallas-area airports, the ML model is able to achieve superior performance.The important difference between KCLT and KDFW is that, during the pandemic, KCLT mostly continued operating in the same fashion (albeit with reduced traffic) while at KDFW, several operational changes were implemented (e.g., arrival runway closed for maintenance).The decision trees in the legacy system were not updated to reflect these operational changes.This highlights the advantage of using an ML approach that is early to update relative to SME informed decision trees.
+Confusion MatricesThe ways in which each model might make incorrect predictions is also of interest.One way to evaluate these incorrect predictions is through the use of a confusion matrix.In Figure 1, the relatively simply confusion matrix for KDAL is shown.Rather than show counts (which would be large numbers) the fraction of the evaluation dataset in each cell is shown as a percentage.Warmer colors (e.g., yellow) indicate a larger fraction of the dataset, while cooler colors (e.g., blue) indicate a smaller fraction.It is clear that the bulk of the observations fall on the diagonal, in which the model correctly predicts the arrival runway.However, an interesting effect for KDAL is that there is a significant amount of incorrect predictions on a parallel runway (e.g., predict 13L, land 13R).In some sense, these incorrect predictions are not as bad as those for which the "flow" direction of the airport is incorrect (e.g., 13 vs 31).
+Fig. 1 KDAL Confusion MatrixIn Figure 2, the confusion matrix for KCLT is shown.This matrix exhibits a sort of block diagonal structure, with blocks for the two directions of the primary group of runways (i.e., 18 and 36).In line with the high accuracy for KCLT, the cells with the highest fraction of observations are along the true diagonal.
+Fig. 2 KCLT Confusion MatrixWe have generalized this important notion about an incorrect prediction to a parallel runway being less bad than an incorrect prediction to a non-parallel runway.Table 7 shows the fraction of rows from the evaluation dataset for which the prediction was incorrect, but for which the numeric portion of the runway identifier (i.e., the direction) matched the true runway used.Only airports with some parallel runways are included in these data.From these data, it is clear that some airports have a more flexible utilization strategy for parallel runways than other airports (e.g., KDFW vs KEWR).In future work, we hope to improve these incorrect predictions to parallel runways by identifying features that may indicate such a balancing strategy is in use, and leverage the "less bad" nature of this incorrect prediction in the model training itself.
+Evolution of Predictions over TimeIn previous analysis sections, results have been evaluated together across all lookahead times.In some ways, this is quite a fair evaluation strategy, because much of the data used in making these predictions is static (excepting the TBFM-assigned runway).However, it is also important to acknowledge and evaluate the dynamic nature of these models.To that end, Figures 3 and4 depict the accuracy of models for KCLT and KJFK over time, as flights approach and land at the airport.These two airports make an interesting contrast, as the accuracy for KJFK is relatively constant, while the accuracy for KCLT is steadily increasing.This likely reflects differences in the flexibility of each airport to make changes to arrival runway assignment planning as flights progress.8.These results reflect the performance of the model trained using XGBoost on the evaluation sample, using the features listed in previous sections.Note that these accuracy metrics include observations that are sampled from a variety of lookahead values.The accuracy levels are slightly higher for these departure runway models than for the arrival runway models, indicating that the decision heuristic employed by the controllers is better able to be captured (e.g., is more consistent) than for the arrival runway prediction problem.However, these results should also be evaluated in a variety of other dimensions, as outlined below.
+Performance of Different AlgorithmsModels were trained using Logistic Regression for some airports.The performance of these models is compared against the XGBoost models in Table 9.The same training and evaluation samples were used for each algorithm.From these results, it is clear that the XGBoost models perform significantly better than those trained using Logistic Regression.For the purposes of this research, we are not concerned about the root cause of this differential, just the trend that XGBoost seems to produce better-performing models.Some preliminary results indicated that there was potential for tuning the hyperparameters of the Logistic Regression models to achieve better performance, but still not at
+Comparison to Expert-Driven approachAs described in the introduction, the legacy ATD-2 system used at several airports has decision trees for predicting runway assignments developed through data analysis and interviews with SMEs.In Table 10, we compare the accuracy of the XGBoost models with that of the expert-driven models.In this comparison, the accuracy metrics for the XGBoost models presented earlier are repeated, but the metrics for the legacy ATD-2 systems are sampled at the pushback event, where the accuracy should be highest.There are two interesting trends in this comparison between the ML model results and the expert-driven models.First, the performance of the expert-driven model at KCLT exceeds that of the ML model, reflecting how well-tuned the legacy ATD-2 system is at that facility.In contrast, for the Dallas-area airports, the ML model is able to achieve superior performance.The important difference between KCLT and KDFW is that, during the pandemic, KCLT mostly continued operating in the same fashion (albeit with reduced traffic) while at KDFW, several operational changes were implemented.These operational changes did not result in updates to the decision trees used in the legacy system.
+Confusion MatricesIn Figure 5, the relatively simply confusion matrix for KLGA is shown, and in Figure 6, the confusion matrix for KDFW is shown.
+Fig. 5 KLGA Confusion MatrixAgain the fraction of the evaluation dataset in each cell is shown as a percentage.It is clear that the bulk of the observations fall on the diagonal for each airport, for which the model correctly predicting the departure runway.From this confusion matrix, is is also clear that the model does face some confusion about the use of the diagonal runways at KDFW (i.e., the 13 and 31 runways).There are also several off-diagonal blocks corresponding to incorrect predictions at KDFW when these runways were (or were not) expected to be used.This is clearly an area where some additional features may improve model performance, since there is clearly some strategy in the controllers' runway assignment decisions to use those diagonal runways (e.g., GA versus airline flights, parking stand location).We have generalized this important notion about an incorrect prediction to a parallel runway being less bad than an incorrect prediction to a non-parallel runway.Table 11 shows the fraction of rows from the evaluation dataset for which the prediction was incorrect, but for which the numeric portion of the runway identifier (i.e., the direction) matched the true runway used.Only airports with some parallel runways are included in these data.From these data, two trends are clear.First, the Dallas-area airports continue to exhibit greater flexibility in runway utilization, as was observed for arrivals.Second, and perhaps more importantly, the value of this metric across all airports is lower than it was for arrivals.This suggests that departure runway assignments are more predictable (as reflected in the higher accuracy metrics) than arrival runway assignments.This is an important finding, because the primary focus of the experiments being conducted in the ATD-2 project is planning runway utilization for departures.
+Evolution of Predictions over TimeIn previous analysis sections, results have been evaluated together across all lookahead times.In some ways, this is quite a fair evaluation strategy, because much of the data used in making these predictions is static (excepting the TBFM-assigned runway).However, it is also important to acknowledge and evaluate the dynamic nature of these models.To that end, Figures 7 and8 depict the accuracy of models for KDAL and KIAH over time, as flights approach and land at the airport.These two airports are simply examples of consistent behavior we observe across airports: there are
+Use of Different Time PeriodsFinally, we present results demonstrating that our use of data from 2020 (during the pandemic) yielded models of similar quality (in aggregate) to models trained on data from earlier periods.To make this comparison, we trained a model on the same time period (April 25 through December 31) from 2019 for KDFW.This airport was selected for comparison because the degradation of ATD-2 decision tree model accuracy apparent in Tables 6 and10 suggested operational changes.The data in Table 12 compares the performance of the two models trained on different time periods, and evaluated using samples taken from their "own" year of data.Additional classification metrics besides accuracy (as shown previously) are presented, including precision, recall, and area under ROC curve (AUC).The results from this comparison suggest that the XGBoost algorithm is able to produce a model of equivalent quality using either data from a "normal" time period, or from the pandemic time period.Or, put another way, the problem of assigning flights to departure runways was equally predictable with the same features during each distinct period, even if those relationships may have changed somewhat.
+IV. Conclusion and Ongoing WorkIn this paper, we have described our work training ML models to predict arrival and departure runway assignments.This work shows initial promise for learning the heuristics used by controllers to assign flights to runways when landing or departing.Models have relatively high accuracy, likely high enough to support the use cases for which they are being evaluated on the ATD-2 project.The overall approach will enable broad deployment across a wide variety of U.S. airports using a standardized approach and dataset.In concert with models to predict other aspects of NAS operations, we believe this data-driven machine learning approach will enable rapid testing and deployment of advanced prediction and decision-support tools.Fig. 3 KJFK3Fig. 3 KJFK Model Accuracy over Time Fig. 4 KCLT Model Accuracy over Time
+Fig. 66Fig. 6 KDFW Confusion Matrix
+Fig. 7 KDAL7Fig. 7 KDAL Model Accuracy over Time
+
+
+
+Table 1 List of modeling features Feature Arrivals Departures1Aircraft engine classxxWake turbulence categoryxxPlanned fixxxFlight plan filedxxAirport configurationxxTime since airport configuration changedxxTime until estimated operationxxTBFM-assigned runwayx
+Table 2 Sample Data from ATD-2 Fuser2FlightLookahead (mins) Other FeaturesABC12374.3[set1]ABC12355.1[set2]ABC12331.9[set3]ABC12312.8[set4]
+Table 3 Cleaned, Carried-forward Sample Data3FlightLookahead (mins) Other FeaturesABC12374[set1]ABC12373[set1]ABC123...[set1]ABC12355[set2]ABC12354[set2]ABC123...[set2]ABC12331[set3]ABC12330[set3]ABC123...[set3]ABC12312[set4]ABC12311[set4]ABC123...[set4]ABC1230[set4]
+Table 4 Arrival Runway Accuracy Metrics for Various Airports Airport XGBoost Classifier Accuracy4KDFW0.618KDAL0.726KCLT0.777KIAH0.699KJFK0.765KEWR0.939KLGA0.973KPHL0.791KBOS0.915
+Table 5 Comparison of Different Algorithms for Arrival Runway Prediction Airport XGBoost Classifier Accuracy Logistic Regression Accuracy5KDFW0.6180.375KDAL0.7260.495KCLT0.7770.266KIAH0.6990.250KJFK0.7650.527KEWR0.9390.567KPHL0.7910.431
+Table 6 Comparison of ML and Expert-Driven Approaches for Arrival Runway Prediction Airport XGBoost Classifier Accuracy ATD-2 Decision Tree Accuracy6KDFW0.6180.524KDAL0.7260.578KCLT0.7770.911
+Table 7 Incorrect Prediction to Parallel Runways Airport Fraction Observations Incorrect, but on Parallel Runway7KDFW0.266KDAL0.227KCLT0.194KIAH0.229KJFK0.167KEWR0.041KPHL0.095KBOS0.012
+Table 8 Departure Runway Accuracy Metrics for Various Airports Airport XGBoost Classifier Accuracy8KDFW0.821KDAL0.813KCLT0.886KIAH0.797KJFK0.932KEWR0.971KLGA0.977KPHL0.902KBOS0.894
+Table 9 Comparison of Different Algorithms for Departure Runway Prediction Airport XGBoost Classifier Accuracy Logistic Regression Accuracy9KDFW0.8210.548KDAL0.8130.839KCLT0.8860.314KIAH0.7970.657KJFK0.9320.497KEWR0.9710.583a level comparable with even the stock implementation of the XGBoost classifier.
+Table 10 Comparison of ML and Expert-Driven Approaches for Departure Runway Prediction Airport XGBoost Classifier Accuracy ATD-2 Decision Tree Accuracy10KDFW0.8210.828KDAL0.8130.654KCLT0.8860.950
+Table 11 Incorrect Prediction to Parallel Runways Airport Fraction Observations Incorrect, but on Parallel Runway11in accuracy leading up to the pushback event, because there are relatively few changes in the features input to the model leading up to pushback.KDFW0.121KDAL0.170KCLT0.081KIAH0.034KJFK0.017KEWR0.014KPHL0.071KBOS0.018relatively few changes
+Table 12 Comparison of Departure Runway Models from 2019 and 2020 Metric 2020 Model 2019 Model12Accuracy0.8210.851Misclassification to parallel runway0.1210.111Precision0.8240.841Recall0.8210.851AUC0.9210.913
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+	
+		XGBoost
+		
+			TianqiChen
+		
+		
+			CarlosGuestrin
+		
+		10.1145/2939672.2939785
+		
+	
+	
+		Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
+		the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningNew York, NY, USA
+		
+			ACM
+			2016
+			
+		
+	
+	Chen, T., and Guestrin, C., "XGBoost: A Scalable Tree Boosting System," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA, 2016, p. 785-794. https://doi.org/10.1145/2939672.2939785.
+
+
+
+	
+		
+			LBălan
+		
+		
+			)Kiyo
+		
+		
+			KKDeriabin
+		
+		
+			DHoang
+		
+		
+			LIvaniuk
+		
+		
+			ADada
+		
+		
+			YDatta
+		
+		
+			DPatel
+		
+		
+			ZWrigley
+		
+		
+			GDanov
+		
+		
+			IStichbury
+		
+		
+			JKhan
+		
+		
+			NTsaousis
+		
+		
+			NTheisen
+		
+		
+			MWalker
+		
+		
+			WNguyen
+		
+		
+			TWestenra
+		
+		
+			RCarvalho
+		
+		
+			LTrevisani
+		
+		
+			MDBertoli
+		
+		
+			SMawjee
+		
+		
+			SNijholt
+		
+		
+			BVukolov
+		
+		
+			DFischer
+		
+		
+			KVijaykumar
+		
+		
+			YMinami
+		
+		10.5281/zenodo.4336685
+		quantumblacklabs/kedro: 0.17.0
+		
+		bru5, and dr3s
+		
+			Dec. 2020
+		
+	
+	Bălan, L., (Kiyo), K. K., Deriabin, D., Hoang, L., Ivaniuk, A., Dada, Y., Datta, D., Patel, Z., Wrigley, G., Danov, I., Stichbury, J., Khan, N., Tsaousis, N., Theisen, M., Walker, W., Nguyen, T., Westenra, R., Carvalho, L., Trevisani, M. D., Bertoli, S., Mawjee, S., sasaki takeru, Nijholt, B., Vukolov, D., Fischer, K., Vijaykumar, Minami, Y., bru5, and dr3s, "quantumblacklabs/kedro: 0.17.0," , Dec. 2020. https://doi.org/10.5281/zenodo.4336685, URL https://doi.org/10.5281/zenodo.4336685.
+
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+		Practice for Application of Federal Aviation Administration (FAA) Federal Aviation Regulations Part 21 Requirements to Unmanned Aircraft Systems (UAS)
+		10.1520/f2505-07
+		
+		
+			null
+			ASTM International
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+	TFMData Service
+	Federal Aviation Administration, "TFMData Service," , ????. URL https://cdm.fly.faa.gov/?page_id=2288.
+
+
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+	
+		Distributing net-enabled federal aviation administration (FAA) weather data
+		
+			MarkSimons
+		
+		10.1109/icnsurv.2008.4559189
+		
+	
+	
+		2008 Integrated Communications, Navigation and Surveillance Conference
+		
+			IEEE
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+		
+	
+	Federal Aviation Administration, "SWIM Terminal Data Distribution System (STDDS)," , ????. URL https://www.faa.gov/air_ traffic/technology/swim/stdds/.
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+				
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+LIST OF FIGURESThe parameters that determine the coupling of operations across a broad range of conditions and airspaces, and underlying issues and factors that drive metroplex operational complexity and practices, are not well understood.Understanding these parameters is critical to enabling the full runway infrastructure benefit of a metroplex in order to meet the demands anticipated by major metropolitan areas.An understanding of current metroplex operations and managementparticularly the underlying issues and factors that drive metroplex operational complexity and practices-is essential to the development of approaches to coordinate operations effectively among increasingly coupled airports in the Next-Generation Air Transportation System (NextGen).The objectives of this project were:• to identify the issues and constraints that dictate current practices (dependencies and interactions between metroplex airports) and are likely to extend to NextGen concepts• to characterize the impact the introduction of NextGen concepts and capabilities will have on metroplex operations• to investigate alternative concepts for significantly increasing capacity in high-demand metropolitan areas.This document is the final report of this research effort.The research objective of this project was to develop a deeper understanding of the constraints on metroplex operations that reduce capacity and to use this understanding to develop and evaluate new metroplex design and operational techniques to increase capacity in high-demand metropolitan areas.This increase in capacity is essential to enable the National Airspace System (NAS) to accommodate the air traffic demand projected in the NextGen time frame.This accommodation will require research in not only airport operations and procedures but also highdensity terminal airspace operations and procedures.The specific task objectives were as follows:• Identify the dependencies and interactions among metroplex airports that affect metroplex operations.• Develop a classification scheme for metroplex dependencies.• Determine the impact that the introduction of NextGen concepts and capabilities will have on metroplex operations.• Investigate new and innovative methods for significantly increasing the capacity of metropolitan airspace and airports.To achieve the research objectives, a comprehensive research approach was developed and implemented.The research project started with the literature review, which focused on three areas:• The state of the art for metroplex operations today• The concepts and capabilities relevant to future metroplex operations• The identification of candidate metroplex sites for site-survey study that would followWith the identified candidate sites, a series of comprehensive metroplex site surveys were conducted.The goals of the site surveys were:• To develop a thorough understanding of the metroplex issues and constraints through studying real-world examples• To catalog the traffic flow dependencies and interactions at each site • To document the best practices at each facility to handle metroplex issues, constraints, and dependencies• To collect information about planned future developments relevant to the metroplex problem at each siteThe next task process, characterization of metroplex operations, used information and data collected through the literature review and site surveys.Qualitative analyses and internal subject domain expert evaluations were employed to develop classifications of metroplex issues and airspace interdependencies.Quantitative metrics were developed to measure the intensity and types of interactions between metroplex airports and specific traffic flows.The knowledge achieved through the previous three task processes led to metroplex concept analysis and the development of an experiment plan.Practices to handle traffic interdependencies and traffic coordination were abstracted into the temporal-spatial displacement concept on which an evaluation framework was based and developed.The existing NextGen concepts were carefully reviewed and compared against the temporal-spatial concept to identify the most relevant concepts.Based on this principle, new concepts were also proposed to close any gaps in forming innovative solutions for significantly increasing the capacity of, and improving the efficiency for, metroplex operations.The experiment plan was then developed to form control parameters that reflect the end effects of various concepts studied in lieu of modeling any specific concepts.Two separate metroplex experiments were formed to first test the basic concepts with a Generic Metroplex model, and then develop solutions and verify the solution with a model of the New York TRACON (N90) and surrounding metropolitan area-the most complex metroplex in the NAS.The Generic Metroplex experiment was based mainly on a linked-node queueing-process model that can be reconfigured to test different metroplex airspace designs and traffic coordination techniques.The general strategy for the experiment was to first simulate currentday conditions, and then test increasingly higher levels of metroplex technologies defined by control parameters developed in the previous task process, the Metroplex Concept Analysis.The outcome of this task is the quantitative assessment of the impact of NextGen and newly proposed concepts.The last task process, analyses and documentation, is the process of analyzing experiment results and reviewing all research outcomes achieved.The outcome is this metroplex final report and recommendations of metroplex technologies and further metroplex research that is needed to achieve the ultimate goal of mitigating metroplex interactions.Through this research, the Georgia Institute of Technology, Atlanta, Georgia, (GaTech) Metroplex team systematically studied the parameters that determine the coupling and inefficiencies in metroplex operations; developed a framework to evaluate concepts and capabilities that manage the coupling of metroplex operations; and conducted the initial simulations to evaluate the impact of down-selected technological capabilities to identify the most promising concepts.These tasks highlight key findings of this research, and details of these research results are documented in this report.Additional information is also available in the individual reports and papers described in appendix B.The GaTech team discovered from a thorough literature review that, although certain aspects of the metroplex problem have been touched on by various previous studies, there has been no systematic research in the interdependencies among arrival and departure operations.A close inspection of interdependencies and interactions among metroplex airports suggests that they can be divided into two fundamental types.The first can be categorized as preexisting conditions, while the second can be categorized as the air-traffic-control (ATC) response to those preexisting conditions.The difference between these two types is that different measures can be taken to counter the same set of preexisting conditions, or dependencies, as illustrated by the metroplex site-survey findings.Of course, there are some measures that may be taken at different sites to serve similar purposes.Through some of the measures, such as segregated routing, traffic flows within a metroplex may operate independently.However, the dependencies between airspace would still be there-there is a price to pay to segregate the flow.It is thus important to maintain the distinction between the intrinsic dependencies between arrival and departure operations at metroplex airports and the practices to counter those intrinsic interactions and dependencies.The former defines a metroplex, and the latter provides solutions to the metroplex problem.
+Characterization of Metroplex and Metroplex OperationsBased on the site-survey studies, through subject domain expert evaluation and qualitative analyses, the team identified and rank listed major metroplex issues.The rank-orderd list of 12 major metroplex issues identifies the intrinsic dependencies in metroplex operations.Among them, "multi-airport departure merge over common departure fix" was identified as the most critical issue across the four metroplex sites surveyed.Other issues of primary importance include:• Major volume-based traffic-flow-management (TFM) restrictions• Proximate-airport configuration conflicts• Slow inter-airport ground connectivity• Inefficient/high-workload airport configuration changes• Inefficient multi-airport departure sequencing• Major secondary-airport flow constraintsOther issues that are also critical but affect only certain metroplexes include:• Inefficient "flushing" of airport flows• Effects of special-use airspace (SUA) and terrain, which caused additional flow dependencies• Severe limitations on instrument procedures due to a proximate airport • Insufficient regional-airport capacityThe team focused on airspace-related issues and conducted detailed analysis of practices in handling these issues.The result was a categorization of airspace interactions into these six types:• Sharing of fixes through metering• Sharing of path segments through metering• Sharing of airspace volume through holding or stopping the flow• Vertical flow segregation• Lateral flow segregation• Downstream flow restrictions for multiple airports Through quantitative analyses, three sets of metrics were developed to categorize existing metroplexes in the NAS and to identify potential future metroplexes necessitated by regional traffic growth.By utilizing basic geographic information about metroplex airports, several metrics were developed to measure the intrinsic dependencies within each metroplex.Quantitative analyses using these metrics indicated that the four metroplex sites surveyed can be ranked in increasing order of intrinsic dependency as: Atlanta Large TRACON (A80) < Miami Tower/TRACON (MIA) < Southern California TRACON (SCT) < N90.Among the four metroplexes, N90 is the most complex-consistent with site-survey results and common understanding.The analyses also revealed that a metroplex could be normally identified with a core of a radius of 15 to 20 nautical miles (nm) within which the dependencies among airports are strongest.An arrival-flow airspace volume-based metric was used as the "distance" measure for clustering airports into metroplexes and identifying potential future metroplexes in the NAS.The clustering algorithm was calibrated to capture the 15 metropolitan areas identified in the Federal Aviation Administration's (FAA's) Operational Evolution Partnership (OEP) initiative.Applied to the projected terminal-area-forecast (TAF) data for 2025, the clustering algorithm identified 18 metroplexes, three of which were identified as new metroplexes in 2025: Minneapolis, Boston, and Cincinnati.
+Evaluation of Impact of NextGen and Team Proposed ConceptsTo implement the framework for evaluating the impact of NextGen and team-proposed future concepts and capabilities, temporal control was represented by:• Traffic-flow coordination or scheduling that provided target times (e.g., fix-crossing times and takeoff times)• Traffic-flow metering or surface management to achieve the target times Spatial control was represented by:• Lateral and vertical separation standards• Airspace design geometries and segregated three-dimensional (3-D) routes based on separation standards and aircraft (AC) performance limitsThe temporal-control concepts were modeled as several prototype arrival scheduling algorithms and models of metering accuracy.The spatial-control concepts were modeled as different airspace geometries.For the Generic Metroplex study, four geometries were developed:• Geometry 1 (baseline airspace) represented a standard four-corner post route structure.• Geometry 2 represented a shared-route airspace.• Geometry 3 (decoupled airspace) consisted of duplicate entry fixes at each corner to segregate traffic flows to the two airports.• Geometry 4 consisted of 32 entry and exit fixes, each associated with a fully segregated, most-direct route to each airport.For the N90 Metroplex, a NextGen fully decoupled route structure was developed.A method using "intersect-flow" metrics was developed to measure the complexity of trafficflow interactions within a metroplex terminal area.Applying this analysis to the Generic Metroplex revealed that geometry 3 (decoupled airspace) reduced the traffic-flow interaction over the baseline (geometry 1), while geometry 2 (representing extensive path sharing) and geometry 4 (a fully segregated most direct route) increased traffic-flow interaction over the baseline.A Generic Metroplex delay versus arrival-rate sensitivity analysis also revealed that when runways (as opposed to entry fixes) are the choke points, increasing the number of entry fixes to segregate traffic to different airports would not necessarily help in reducing delays.As such, the Generic Metroplex simulation focused on the baseline (geometry 1) and the dualcorner-fix (geometry 3) airspace only.This report summarizes the aggregated effects of scheduling and decoupled airspace from the Generic Metroplex linked-node queueing simulation study and the N90 Metroplex Airport and Airspace Delay Simulation Model (SIMMOD) simulation study.The Generic Metroplex simulation revealed that, when scheduling was applied to coordination of arrival traffic flows, the systemwide arrival delays incurred at the metroplex terminal-area boundary and within the terminal area were reduced by 73% in the case of the baseline airspace.Without scheduling, the use of dual-corner fixes did not achieve delay reductions.With scheduling, the dual-corner fixes provided a 23% delay reduction from the baseline, achieving a combined 79% delay reduction from the case of baseline airspace without scheduling.The N90 simulation revealed that, applied separately, the NextGen fully decoupled airspace and the arrival scheduling reduced systemwide arrival air delay incurred within the N90 terminal area by 28% and 60%, respectively, from current-day operations.Combined together, the decoupled airspace and the arrival scheduling reduced the systemwide arrival air delay from the level in current-day operations by 79%, the same result as was observed in the Generic Metroplex study.In both the Generic Metroplex study and the N90 simulation study, scheduling provided greater delay reductions than the segregated route airspace geometries.The Generic Metroplex simulation also revealed that, with lower metering accuracy, the effectiveness of scheduling was negatively impacted, but the majority of delay reductions from scheduling were retained even with the worst-case metering accuracy.This finding suggests that scheduling tools can be developed to achieve revolutionary delay reductions even with current-day metering accuracy.Future four-dimensional trajectory (4-DT) operations would then provide further enhancements to the traffic scheduling and coordination.As presented in this executive summary and documented later in this report, a significant range of metroplex issues and inefficiencies have been identified, a range of potential metroplex concepts have been analyzed, and significant potential benefits of metroplex concepts have been quantified, in both a set of representative Generic Metroplex configurations and for N90.The definition of these potential metroplex concepts, and quantification of the potential benefits, is the beginning of a broader set of metroplex research and development tasks and benefitsassessment tasks that the National Aeronautics and Space Administration (NASA) plans to perform to fully validate the concepts and requirements of improved metroplex concepts before transitioning such concepts to the FAA.In general these broader future metroplex research tasks can be categorized as:• The development of refined concept modeling and prototype metroplex decision support tools• Further investigation into the analysis of metroplex concept impactsThe recommendations are described in detail in section 9.2.The research results of the GaTech team and the NASA metroplex research recommendations are critical to improving current and future NAS metroplex operational efficiency.As traffic demand increases in the future, more regions in the NAS are expected to become metroplexes.Thus, as these metroplexes grow, so will the expected levels of metroplex-induced air traffic delays due to the multiple metroplex issues and inefficiencies that have been studied in the current research.It is therefore important for NASA to take additional metroplex research steps such as those suggested in the previous discussion to move metroplex concepts out from a low technology-readiness-level (TRL) concept exploration phase, which forms the basis of this work, and move these concepts further along the TRL scale towards future operational implementation and deployment.This process will help ensure that the NAS will be prepared to minimize the expected significant growth in future metroplex delays.
+INTRODUCTIONThe National Aeronautics and Space Administration (NASA) Contract NNX07AP63A, titled "Characterization of and Concepts for Metroplex Operations", was performed by a team led by the Georgia Institute of Technology, Atlanta, Georgia (GaTech).].It is expected that much of that demand growth will be in major metropolitan areas.Metropolitan areas with high demand are often served by a system of two or more airports whose arrival and departure operations are highly interdependent.Such an airport system is refferred to as a metroplex as defined by the JPDO [JPDO07].The projected traffic growth will increase the coupling of operations in the metroplexes that already exist, and will potentially create new metroplexes.The coupling of operations requires that the solution for the airspace structure around, and the traffic flows to and from airports within, a metroplex must be solved cooperatively as a system.Metroplex operations as of today are nominally loosely coordinated.The parameters that determine the coupling of operations across a broad range of conditions and airspaces, and underlying issues and factors that drive metroplex operational complexity and practices, are not well understood.Understanding these parameters is critical to enable the full runway infrastructure benefit of a metroplex in order to meet the demands anticipated by major metropolitan areas.An understanding of current metroplex operations and managementparticularly the underlying issues and factors that drive metroplex operational complexity and practices-is essential to the development of approaches to coordinate operations effectively among increasingly coupled airports in the NextGen.The objectives of this project were:• To identify the issues and constraints that dictate current practices (dependencies and interactions between metroplex airports) and are likely to remain issues and constraints to the development of NextGen concepts• To characterize the impact the introduction of NextGen concepts and capabilities will have on metroplex operations• To investigate alternative concepts for significantly increasing capacity in high-demand metropolitan areasThis document is the final report of this research effort.This section (section 2) describes the background, objectives, and basic research approach.The remainder of the report includes results from the literature review (section 3), followed by a description of the major facts and outcomes from the site surveys (section 4).The qualitative and quantitative metroplex characterization effort is described next (section 5), and then a temporal-spatial framework developed from these steps for analyzing metroplex operations is described in detail (section 6).Results from two experiments conducted utilized the framework are presented (sections 7 and 8), followed by conclusions and recommendations for the next steps and beyond (section 9).Appendix A presents some of the metroplex-related concepts proposed during the GaTech team brainstorm, and appendix B summarizes publications by the research team in support of this final report.A list of acronyms is provided at the beginning of the report.For the sake of simplicity, airtraffic-control (ATC) facilities are referred to by their identification codes in this report.The name of a facility may or may not be provided when it is first referred to, depending on the context.For this reason, a list of facility identifications is also provided so the reader can easily look up facility names found in the text.
+Definition of the Term Metroplex and the Metroplex ProblemAlthough the term metroplex was used as early as the 1950s [J65], the North Texas Commission (NTC) maintains that the term "metroplex" was coined and copyrighted by NTC in 1972 to establish the identity of the now 12-county U.S. Census Metropolitan Statistical Area, which contains the two primary cities of Dallas and Fort Worth [NTC08].It is said [R08] that the term "metroplex" was created by NTC from combining the words "metropolitan" with "complex".To differentiate itself from a chamber of commerce, NTC states that it represents the entire 12-county Dallas-Fort Worth Metroplex rather than just one community, and that NTC chooses to work on issues, challenges, and opportunities that can best be addressed cooperatively as a region.This concept represents some core concepts of the term, i.e., a large metropolitan area containing several cities, and the coordination among them to address common issues.Since the early 1970s, some scholars have used the term "metroplex" to describe a large urbanized area, including and surrounding several central cities, along with the adjacent hinterland [M84].Inevitably, within the aviation community the use of the term "metroplex" is most often associated with the Dallas-Fort Worth International Airport [DFW92, CSF01, and HW06].However, researchers in the aviation community used the term "metroplex" as early as 1964 to refer to a system of several airports [C64 and DKMS73].The term has also been used for other specific meanings such as a group of three or more radar sensors [PWS80], a mini-hub type operation [H02], and "super" terminal radar approach control facilities (TRACONs) that involve the consolidation of individual TRACONs and support multiple high-traffic airports such as the "Potomac Metroplex" and the "Atlanta Metroplex" [HR97].In developing the NextGen, the JPDO has officially defined the term "metroplex" as "a group of two or more airports whose arrival and departure operations are highly interdependent" [JPDO07].The task of understanding and developing a solution for the airspace structure around and the traffic flows to and from airports in a metroplex is referred to as the "metroplex problem."
+Research ObjectivesThe research objective of this project was to develop a deeper understanding of the constraints on metroplex operations that reduce capacity and to use this understanding to develop and evaluate new metroplex design and operational techniques to increase capacity in high-demand metropolitan areas.This goal is essential to enable the NAS to accommodate the air traffic demand projected in the NextGen time frame.Reaching the goal will require research in not only airport operations and procedures but also high-density terminal airspace operations and procedures.The specific task objectives were as follows:Identify the dependencies and interactions between metroplex airports that affect metroplex operations.The specific parameters, processes, and procedures that define and characterize metroplexes will be identified by investigating examples of metroplex constraints in the current instantiation of the NAS.This investigation will leverage prior research, site visits, and telephone interviews with subject-matter experts (SMEs), and documents such as airport master plans and FAA facility standard operating procedures (SOPs).To confirm and clarify the findings from these sources, data from recent operations will also be analyzed.
+Develop a classification scheme for metroplex dependencies.The scheme will be developed by conducting factor analyses to determine those factors or combinations of factors that have the greatest correlation with performance.The resulting correlations will be used to identify those metropolitan areas that currently meet the definition of a metroplex and to project new metroplex operations in the NextGen time frame.It will also enable NASA to model metroplex operations in super-density operations concept evaluations.Determine the impact that the introduction of NextGen concepts and capabilities will have on metroplex operations.The impact of NextGen concepts such as four-dimensional (4-D) trajectory-based operations, performance-based services, and increased environmental awareness will be analyzed using the advanced airport and terminal airspace demand projection, modeling, and simulation capabilities of the team.The results of these studies will be evaluated in terms of the classification scheme described in the previous objective so as to simplify the comparison of various possible NextGen scenarios.
+Investigate new and innovative methods for significantly increasing the capacity of metropolitan airspace and airports.Combining the team's expertise in optimization of airport and airspace capacity and environmental impact, new concepts and capabilities will be proposed, extending beyond those currently envisioned by the JPDO and analyzed in the previous objective.Again, analyses and simulations will be conducted to evaluate the potential benefits, and the results interpreted based on our metroplex classification scheme.
+Research ApproachTo achieve project objectives, a comprehensive research approach was developed and implemented.An overview of the research approach is presented as a block diagram in Figure 1.Four categories of information are presented.From left to right, the first group is the high-level project schedule depicted by block arrows pointing downwards from July 2007 to December 2009.Each year is denoted by a different color with increasing intensity.The schedule includes a three-month non-cost documentation period after the conclusion of the funded period ending September 30, 2009.For this reason, the period from October to December 2009 is denoted by the background color.The second group depicts the research objectives that were presented in more detail in section 2.2.The third group includes the task processes.This piece is the central piece of the research approach.Task processes are grouped by the same colors denoting different calendar years to show the progress of each task.These task processes are described in more detail in the following paragraphs.The fourth group lists major outcomes from each of the task processes.Three types of connectors are used in the block diagram.Thick arrows denote the direction of task process flow.Thin arrows denote the direction of information flow.Connectors starting with a dot denote an objective-task supporting relationship.Thin arrows denote an output.The research project started with the literature review, which focused on three areas:• the state of the art for metroplex operations today• the concepts and capabilities relevant to future metroplex operations• the identification of candidate metroplex sites for site-survey study that would followThe outcome of the literature review was a standalone report.The results from this task are also briefly summarized in section 3. The literature review supported the first objective: Identify dependencies and interactions.With the identified candidate sites, a series of comprehensive metroplex site surveys were conducted.The goals of the site surveys were:• To develop a thorough understanding of the metroplex issues and constraints by studying real-world examples• To catalog the traffic-flow dependencies and interactions at each site• To document the best practices at each facility to handle metroplex issues, constraints, and dependencies To collect information about planned future developments relevant to the metroplex problem at each siteThe performance data analysis and reporting system (PDARS) [dBS03] was used to analyze traffic flows at each site.The site surveys provided a solid foundation for the remainder of the metroplex research tasks.Four metroplexes were studied: Atlanta (A80), Los Angeles Basin (SCT), New York (N90), and Miami (MIA).Outcomes of site surveys were documented in four standalone site-survey reports, one for each site studied.The results of the site survey are summarized through a comparative study presented in section 4.This task supported the following objectives:• Identify dependencies and interactions.• Develop a classification scheme for metroplex operations.The next task process, characterization of metroplex operations, was initiated not long after the metroplex site survey.Information and data collected through the literature review and site surveys were further processed.Qualitative analyses and internal subject domain expert evaluations were employed to develop classifications of metroplex issues and airspace interdependencies.Quantitative metrics were developed to measure the intensity and types of interactions between metroplex airports and specific traffic flows.The Tool for Analysis of Separation and Throughput (TASAT) [RC08] was employed to generate ideal arrival trajectories.These analyses served to synthesize the knowledge about and deepen the understanding of metroplex dependencies and interactions.Outcomes for this task were classification schemes, both qualitative and quantitative, of metroplex dependencies and documentation of these dependencies.Summaries of these analyses are presented in section 5.This task supported the following objectives:• Identify dependencies and interactions.• Develop a classification scheme for Metroplex operations.The knowledge achieved through the previous three task processes led to metroplex concept analysis and the development of an experiment plan.Practices to handle traffic interdependencies and traffic coordination were abstracted into the temporal-spatial displacement concept on which an evaluation framework was based and developed.Traffic interdependencies and coordination can be addressed only through temporal displacement (delaying from ideal speed profile), or spatial displacement (moving away from the most direct route), or both, of one or more flows.The existing NextGen concepts were carefully reviewed and compared against the temporal-spatial concept to identify the most relevant concepts.Based on this principle, new concepts were also proposed to close any gaps in forming innovative solutions for significantly increasing the capacity of, and improving the efficiency for, metroplex operations.The experiment plan was then developed to form control parameters that reflect the end effects of various concepts studied in lieu of modeling any specific concepts.The automated future flight demand-generation tool, referred to as AvDemand [SHD07], was used to generate future traffic demand.The outcome of this task was documentation of metroplex concepts and experiment strategies; results are presented in section 6.This task is part of the effort to support the following objectives:• Determine the impact of NextGen.• Investigate new and innovative methods.Two separate metroplex experiments were formed to first test the basic concepts with a Generic Metroplex model and then develop solutions and verify them with a model of N90-the most complex metroplex in the NAS.The Generic Metroplex experiment was based mainly on a linked-node queueing-process model that can be reconfigured to test different metroplex airspace designs and traffic-coordination techniques.Additional analyses were performed to evaluate the complexity of flow-interaction dependencies and the impact of different traffic scheduling algorithms.The N90 experiment was conducted using SIMMOD (see section 8.1.1).The general strategy for the experiment was to first simulate current-day conditions, and then test increasingly higher levels of metroplex technologies defined by control parameters developed in the previous task process.The outcome of this task is the quantitative assessment of the impact of NextGen and newly proposed concepts.The results are presented in sections 7 and 8 for the Generic Metroplex and the N90 experiments, respectively.This task is part of the effort to support the following objectives:• Determine the impact of NextGen.• Investigate new and innovative methods.The last task process, analyses and documentation, is the process of analyzing experiment results and reviewing all research outcomes achieved.The outcome is this metroplex final report and recommendations of metroplex technologies and further metroplex research that is needed to achieve the ultimate goal of mitigating metroplex interactions.This task supported all four research objectives specified in section 2.2.
+LITERATURE REVIEWThe objective of the literature review [RS09] was to determine the state of the art for managing interdependent airport operations under current and anticipated future operational situations, and assess the commonalities and significant differences across the range of "metroplex definitions" within the air-traffic-management (ATM) and research communities.This section also identifies and provides justification for candidate metroplex sites that warrant further investigation.
+Selection of Literature for ReviewThe literature was selected and reviewed for its value to metroplex operations research.Sources for the literature included websites of related agencies, past research publications, simulation programs, and other items.Several team members collectively reviewed the literature and identified the objectives of each report, challenges, methods used to achieve the goals, and the results, or effects of implementation.Reviewers also provided critiques and stated the relevance of the literature to the metroplex research.
+State of the Art for Metroplex Operations TodayThis section is an integrated high-level summary of existing literature relevant to today's metroplex operations.Subjects covered include dependencies and interactions, the state of the art for managing interdependent airport operations under current operational situations, and the commonalities and significant differences across the range of "metroplex definitions" within the ATM and research communities.
+Location-Specific StudiesOne of the main goals of this research was to identify factors affecting dependencies among airports within a metroplex.Previous studies have explored the problem at numerous sites and documented the measures being employed to handle the problem, although in very limited scope.For example, Newark International Airport in New Jersey (EWR) is particularly prone to adverse weather and is also affected by delays at John F. Kennedy International Airport (JFK) and LaGuardia (LGA), both in New York City.Consequently, different measures are put into place-such as fix restrictions; reroutes; decision support tools, e.g., the Departure Spacing Program (DSP); and communication among facilities-to tackle the delay problem, and these measures are well examined in [EC00].In response to noise concerns regarding the New York/New Jersey/Philadelphia airspace redesign, MITRE Corporation presented numerous examples of traffic flow interactions between New York Metroplex airports and the existing measures to handle them [M07].These measures are mostly procedural measures to restrict certain areas in the airspace to arrivals or departures to and from certain runways at certain airports, or to force arrivals and departures to fly certain flightpaths or vertical profiles.However, under certain conditions, departure traffic flows have to merge over a departure fix where coordination between airports becomes necessary.In some of these cases, longitudinal spacing between traffic from different airports is enforced, even though traffic may have already been vertically separated.For the measures discussed, environmental concerns are often raised.Another airspace system design project focused on a case study of the Chicago area [V00].As an initial assessment, the study focused on traffic to and from the hub airport Chicago O'Hare International Airport (ORD) rather than interdependencies among metroplex airports.One of the major issues in this analysis was the inability to gain access to quality data, so the outcome of the model simulation lacked validation.The aircraft trajectory model and safety model employed in the Chicago case study, however, may be of interest to the metroplex research.The supplemental environmental assessment for the Las Vegas Four Corner Post Plan [LV07] provided some details of the existing and planned alternatives of operations at Las Vegas McCarran International Airport (LAS), along with the impacts of increased utilization of other airports in the vicinity of LAS.The criteria for screening alternative designs developed in this study may also be useful in judging alternative metroplex designs.The Los Angeles International Airport (LAX) Master Plan [LA07] is a public-access web site containing documents and facts about the LAX Master Plan.However, the Master Plan is focused on LAX infrastructure capacity and ground-movement operations.Therefore, it does not offer any substantive analysis of airspace interactions or relationships with/to other airports within the region of influence.It should thus act as a stepping-stone to a larger, more inclusive regional view that would be needed for a Los Angeles Metroplex study.Each of these previous studies had specific local focus; however, some had resource limitations, and were thus very limited in terms of completely defining and explaining the metroplex problem.None of the location-specific studies had thoroughly explored the interdependencies between metroplex airports, whether in terms of runway configuration or airspace interactions.The intent of the metroplex site surveys conducted as part of this research effort was to fill gaps in knowledge pertaining to interdependencies between metroplex airports in relationship to runway configurations and/or airspace interactions.
+Nation-Wide Analyses and General ReferenceTwo previous studies looking at the nation-wide airspace system are of interest to the metroplex research.A study on the emergence of secondary airports [BH05] examined 26 regional airport systems (see Figure 2(a)) within the United States.The regional airport systems were classified into several categories.The study analyzed the effects of utilizing secondary airports on National Airspace System (NAS) capacity and reliability, airline network efficiency, interdependencies between core and secondary airports, and environmental implications.However, the study lacked quantification of the features or characteristics of the airport systems.The regional airport system classification used in this study could, on the other hand, form a basis for identifying a classification scheme for metroplex interactions.Another study by the same authors explored the potential impacts of the entry of very light jets (VLJs) in the NAS [BH06].VLJs are three-to eight-passenger turbofan aircraft that have a maximum takeoff weight below 10,000 lb.These aircraft have lower costs than conventional light jets, offer better performance (e.g., higher cruise speeds) than comparably priced turboprops, and are predicted to enter the NAS in large numbers.This study identified the fact that, in existing operations, light jet traffic tended to concentrate over key metropolitan areas, implying a potential impact of VLJs on future metroplex operations.Another implication was a strong interaction between VLJs and larger jet aircraft because they fly similar altitude profiles, and consequently new air-traffic-control (ATC) procedures and tools will be required to handle this situation in the terminal areas.Of general interest to the improvement of metroplex operations is legislative and funding support.As early as 1997 [HR97], it was identified in a Federal Aviation Administration (FAA) benefit-cost analysis that a single consolidated metroplex control facility is the most costeffective option for the Washington, D.C. metropolitan area, with a benefit-to-cost ratio of 17 to 1.The 105th Congress thus authorized the consolidation of terminal radar approach control facilities (TRACONs) in the Washington, D.C. area.The same benefit-cost study showed a huge benefit from accelerating the construction of the Atlanta Large TRACON, so the capital plan level of funding that had been previously reduced was restored.This restoration implies the importance of benefit-cost analyses for future metroplex operational concepts.
+State-of-the-Art Technologies Relevant to Metroplex ResearchNumerous technologies have been developed and studied for application in metroplex operations, and some of them have been implemented, including technologies that enhance both ground operations and operations in terminal airspace.Strategic conflict probe tools such as the User Request Evaluation Tool (URET) have been successfully applied to en-route airspace, providing benefits to both the controller and the airspace user.The application of URET in the terminal airspace, particularly for the large "metroplex" TRACONs, was also evaluated [K03].The study indicated that the functional accuracy of the conflict probe and trajectory modeling functions could be improved with intent information, and this improvement would allow the use of URET in metroplex terminal areas.The transition from one airport configuration to another reduces capacity at the airport for a certain time period.While the transition is necessary when meteorological conditions around the airport change, runway usage can be optimized with respect to factors such as runway conditions and airport acceptance rate.The concept of an airport configuration planner and optimizer has been explored [S06 and S07].An inventory of information relevant to runway usage at the 20 U.S. airports with the largest number of runway operations in 2005 was developed.A modeling framework was also developed that would enable runway usage optimization within a metroplex, accounting for uncertainty in the data and identifying a solution technique that would be sufficiently fast.Some of these analyses could be used as a reference for developing more sophisticated airport configuration planning and optimization concepts in highly interdependent metroplexes.In quantifying delay-reduction benefits for aviation convective weather decision support systems such as the integrated terminal weather system (ITWS) and the corridor integrated weather system (CIWS), Evans et.al. [EA04] presented a comprehensive enumeration of different methods used.The discussion uncovered the complexity involved in such an analysis.Individual characteristics of metroplexes such as Atlanta and New York must be considered when assessing benefits of convective weather delay reducing systems rather than extrapolating the benefits from less busy airports or from other metroplexes.Benefit assessment by the direct comparison of delay statistics from time periods before and after a system is introduced is complicated by the need to equalize many factors affecting the delay.For such comparisons associated with metroplex assessments, specific "similar" situations before and after system introduction need to be examined, or the delay values need to be equalized by weather-impact indices.NASA's Virtual Airspace Modeling and Simulation (VAMS) project [FS06]
+Concepts and Capabilities for Future Metroplex OperationsThis section presents an integrated high-level summary of the concepts and capabilities currently under development or evaluation that could be applied to metroplex operations under future operational situations.
+Future Airspace ManagementThe Next-Generation Air Transportation System (NextGen) Concept of Operations (ConOps) [JPDO07] identified eight key enabling capabilities:• Network-enabled information access• Performance-based operations and services (PBS)• Weather assimilated into decision making• Layered, adaptive security• Positioning, navigation, and timing (PNT) services aircraft trajectory-based operations (TBO)• Equivalent visual operations (EVO)• Super-density arrival/departure operations (SDO)Most of these capabilities are relevant to future metroplex operations.Some of the capabilities have been explored in previous studies.One of the PBS concepts is the extended use of flight-management-system (FMS) and areanavigation-system (RNAV) arrival procedures.A preliminary study [M01] indicated that by flying a set of four FMS/RNAV procedures from each of the four corner posts designed for Seattle-Tacoma International Airport in Seattle, Washington (SEA) runway 16R, three minutes average delay reduction per aircraft and 14% increase in runway throughput could be achieved.The current split between en-route and terminal ATC services is a legacy of compromises among automation/surveillance, traffic demand, and the more tactical procedures needed to manage arrivals and departures to and from airport runways.While these compromises do not restrict the flow at smaller airports, in the nation's larger metropolitan areas-especially the eight largestthese restrictions limit the efficiency of flow and the full utilization of capacity at the associated airports.To address the issues, the Integrated Arrival/Departure Control Service (Big Airspace) Concept of Operations was developed [FAA05b].A complementary suite of validation activities has been performed to evaluate the feasibility of the concept and to assess benefits and assumptions [FAA07d].Big Airspace promises two main deliverables that, if achieved, will dramatically impact NAS operations:• Integrated arrival/departure airspace• Improved air traffic services to serve as the model for the futureThe concept validation research found that an integrated arrival and departure concept would be applicable and beneficial for any major metropolitan area with very large airports, particularly those with multiple airports whose arrival and departure flows interact.The validation report emphasizes that there must be improvement in technology for communications, surveillance equipment, situational analysis (traffic management), and information processing (i.e., controller information tools) for the concept to work effectively.Another future concept of operations that has been studied was to enhance the NAS payload capacity by moving away from hub-and-spoke operations to point-to-point (PTP) direct services for travelers and shippers [SKKJ04].The core idea of PTP was to:• Provide smaller towered and nontowered airports with enhanced low-visibility operations and ATM automation• Utilize terminal-area time-based ATM• Integrate strategic en-route ATM and flight management• Expand traffic-flow management• Expand fleet ground operations capability• Leverage advanced aircraft avionics and aircraft types to increase capacity A benefit-cost analysis [SH04] indicated that for a predicted year 2022 demand level (twice the level of year 2002), PTP provides an average of 9.8 minutes of delay time savings per flight, despite an increase of 30% more total number of flights than the predicted non-PTP case.For the Chicago area, PTP is estimated to provide a capacity increase on the order of 70% relative to the non-PTP case.Results from a terminal simulation [PSB05] indicated that, for the non-PTP case using the conventional routing, the percentage of aircraft in conflict remained roughly constant at 50% as traffic density increased to the 2022 demand level.With the introduction of the PTP concept, the percentage of aircraft in conflict dropped to a 25% level.Also, in the PTP case, conflicts were evenly distributed as opposed to the non-PTP case, where conflicts increased dramatically as aircraft approached major airports (within 15 nm).For metroplex operations, this increase appears to suggest that future demand growth may be best accommodated by secondary airports.In another study, knowledge of weather effects on noise impact was applied to a case study of developing noise-abatement procedures [CH04].The authors postulated a revised noiseabatement procedure for departures on runway 4R at Boston Logan that adapts to different weather conditions to minimize the number of residents within the sound exposure level (SEL) above 70 dBA noise contour.Similar strategies could be employed in metroplex design to either mitigate noise or improve traffic throughput.
+Future Traffic DemandThe development of operational concepts in the NextGen depends on the accurate prediction of future traffic demand.Sensis Corporation has developed an automated future flight demandgeneration tool, referred to as AvDemand [SHD07].This tool provides the user with extensive options in defining future demand datasets tailored to their evaluation needs, a capability that does not exist in other demand-generation methods.The tool generates future demand as a large number of flights with well-defined schedules that are accurately defined in time and space throughout the flight by a flight plan.A demand-loading analysis for a future 3X demand set (relative to the year 2002 level) was conducted in order to evaluate the impact of future NextGen system requirements [SHW07].Future unconstrained demand loading for both airports and the NAS were analyzed using airport and airspace demand-to-capacity metrics resulting from the output of the AvDemand tool.The potential impact of both capacity-increasing concepts and potential demand changes were evaluated.The results suggest that 3X demand sets represent very demanding scenarios for future ATM concepts, and that expected heterogeneous demand growth will result in required improvements to NextGen local airport and airspace capacities that are significantly greater than three times current levels.This tool and the methodology can be used in the metroplex project to design experiments for proposed NextGen metroplex operation concepts.
+Justification for Candidate Metroplex Sites That Warrant Further InvestigationWhile there exists much literature related to metroplex operations, the metroplex problem has not been systematically studied before.As discussed earlier, the predicted future traffic growth will increase the coupling of operations in exiting metroplex airspace, and will potentially create new metroplex areas.The natural first step in exploring the metroplex problem is to investigate existing metroplex sites in the NAS to obtain a deeper understanding of the metroplex problem in real-world operations.Given the limited resources and time available, only a small number of metroplex sites could be studied.Candidate metroplex sites were selected by reviewing the list of metroplexes identified in the literature and comparing their basic characteristics.The FAAs couldOperational Evolution Partnership (OEP) initiative [FAA07b] has identified that over the next 20 years, U.S. population and economic growth are expected to be concentrated in 15 metropolitan areas.These metropolitan areas are listed in Table 1 [FAA08b].To identify the issues and constraints that dictate current practices (dependencies and interactions between metroplex airports) and to determine the state of the art for managing interdependent airport operations, a list of candidate metroplex sites needed to be determined for further investigation.The FAA's list of OEP 15 metropolitan areas was used as the starting point.Figure 2 shows the location of candidate metroplex sites identified in previous studies.Figure 2(a), borrowed from Bonnefoy and Hansman [BH05], lists metroplexes identified in a study of the emergence of secondary airports.Figure 2(b) is quoted from Sensis' work for the NASA NextGen Airspace Project [FS06]).Note the existence of two 3-OEP-airport metroplexes (New York: EWR/JFK/LGA, and Washington, D.C.: BWI/IAD/DCA), and two 2-OEP-airport metroplexes (Chicago: ORD/MDW, and Miami: MIA/FLL), all of which were included as candidate metroplexes for further study.A list of major airports was also developed according to their projected demand/capacity ratio based on 3X demand and the 2015 OEP baseline capacity [SHW07] for identifying candidate metroplexes.This list is shown in Table 2 along with identified capacity needs from the FAA document "Capacity Needs in the National Airspace System (FACT-2)" [FAA07a].The number of candidate sites to be surveyed was limited to a subset of existing metroplexes, and sites were selected to represent the breadth of metroplex definitions and operational concepts across the ATC community today.The metroplexes described in the following sections are but a representative sample of the wide range of operations that can be observed in the NAS today.The descriptions of interactions and dependencies are not intended to be complete.Rather, the descriptions are intended to illustrate the breadth of issues that can be encountered.In-depth analyses of the surveyed sites are presented in site-survey reports [RC09b, SL09, TL09, and SR09] and the contrast and comparison report [RC09a].
+The New York MetroplexThe airspace around the New York metropolitan area is arguably the most complicated in the United States.The New York Metroplex contains three OEP airports-EWR, JFK, and LGAas well as another major general aviation airport-TEB-within a circle of radius 10 nm.These four airports averaged almost 4000 operations per day in 2006 [OP08].There are also 15 secondary airports in the vicinity, four of which are among the 100 busiest U.S. airports.Although the New York airspace has been carefully designed to minimize the need for coordination between airports under typical operating conditions, the configuration and operations of the airspace does in part depend on the runway configurations at the various airports within the metroplex.In severe weather, many ATC facilities in the New York area use the DSP developed by the FAA to schedule departure releases at adapted airports so that the resulting demand at departure flow fixes does not surpass prevailing flow rates at the fixes.Operations in the New York Metroplex are supported by the New York TRACON (N90) and the New York Air Route Traffic Control Center (New York ARTCC, New York Center, or ZNY).- √ - - √ √ (SCT) √ 1 - 2010 - ORD 2.27 √ √ √ - √ √ √ (C90) √ ? √ E - LAX 2.02 - - √ - √ √ √ (SCT) √ 8 √ 2008 - CLT 1.75 - √ √ - √ √ - √ 11 - E - ATL 1.71 - - √ - √ √ √ (A80) √ 8 √ E - BOS 1.58 - - √ - - - - √ 10 √ 2009 - SFO 1.57 - - √ - √ √ √ (NCT) √ 13 √ - - SNA 1.51 - √ √ - √ √ √ (SCT) - 1 - 2009 - SJC 1.51 - - - - √ √ √ (NCT) - 2 - - - DTW 1.37 - - - - - - - √ 6 √ √ √ (N90) √ 9 - 2011 - DFW 1.07 - - - - - - √ (D10) √ 16 √ 2010 - JAX 0.89 - - - - - - - - 14 - - - SDF 0.79 - - - - - - - - 12 - E -Demand/Capacity ratio based on 3X demand and the 2015 OEP baseline capacity [SHW07].Based on VAMS report Terminal Area Capacity-Enhancing Concept (TACEC) Operations Analysis [FS06].Year: Government Fiscal Year (GFY) in which ASDE-X will be commissioned at the airport; E: Existing ASDE-X Installation airport.E: Existing Aerobahn Installation airport.
+The Los Angeles Basin MetroplexLAX is the fourth busiest airport in the United States, averaging 1800 operations per day in 2006.Within 30 nm of LAX in the Los Angeles metropolitan area are seven other airports, all among the 150 busiest U.S. airports.Furthermore, three of these airports-VNY, LGB, and SNA-rank in the top 25, with an average total of 3100 operations per day, and are within 20 nm of LAX; but the vast majority of their flights are general aviation (GA).The close proximity of these airports causes their arrival and departure paths to cross over and under each other, and some of the airports also compete for arrival and departure fixes.Because LAX has the majority of the commercial traffic, it generally is given the priority, and the other airports alter their operations as required.To minimize the coordination required for runway configuration changes and to maximize the use of the preferred runway configurations and terminal-area paths, the threshold for calm-wind runways tends to be 10 knots rather than the usual 5 knots.Operations in the Los Angeles Basin Metroplex are supported by the Southern California TRACON (SCT) and the Los Angeles ARTCC (ZLA).
+The San Francisco Bay MetroplexThe San Francisco Bay metropolitan area includes only one OEP airport-SFO-but it also includes two other major airports-OAK and SJC.These three airports are within a circle of radius 15 nm.SFO and OAK are about 10 nm apart, but SJC is about 25 nm away from both of them.The average daily total number of operations for these three airports in 2006 was 2500.In comparing this figure to other metroplexes, however, one must keep in mind that much of the traffic at OAK is air cargo, which tends to occur in the late evening or early morning.There are also four other airports in the area that are among the 150 busiest U.S. airports.The runway configurations at the major airports in this metroplex are closely coordinated.Typically, SFO chooses its configuration, and the other two major airports use their configurations that are most aligned with SFO.If doing so would be unsafe, then they contact SFO, which changes its configuration if possible.Even when the runway configurations are properly aligned, east operations are complex because the arrival path to SFO runway 19 crosses over the arrival path to OAK runway 11 twice, a situation that generally causes a restriction on the OAK arrival flow rate.Operations in the San Francisco Bay Metroplex are supported by the North California TRACON (NCT) and the Oakland ARTCC (ZOA).
+The Washington, D.C. MetroplexThe Washington, D.C. metropolitan area contains three OEP airports-BWI, DCA, and IAD-within a circle of 30-mile radius.IAD and DCA are about 20 nm apart, and BWI is less than 30 nm from DCA. IAD averaged 1200 operations per day in 2006, but BWI and DCA each had only 800, giving a total of 2800 daily operations.The runway configurations of these three airports are independent.They do share departure fixes, however, and there are altitude restrictions on some arrival and departure paths to avoid conflicts.Operations in the Washington, D.C. Metroplex are supported by the Potomac TRACON (PCT) and the Washington ARTCC (ZDC).
+The Chicago MetroplexThe Simultaneous visual departures from DAL are not allowed in north flow because their departure paths head toward the DFW departure paths.When using instrument-landing-system (ILS) approaches in south flow, only a single stream of arrivals to DAL is allowed in order to avoid dependency with DFW arrivals because the extended final approach courses of the two airports converge.Operations in the Dallas-Fort Worth Metroplex are supported by the Dallas-Fort Worth TRACON (D10) and the Fort Worth ARTCC (ZFW).
+The Miami MetroplexThe Miami Metroplex is the only other metroplex with two OEP airports (i.e., MIA and FLL)within 20 nm of each other.Dependencies within this metroplex are expected because of the proximity of the airports.However, traffic volume at airports in this metroplex is relatively moderate as compared with many other metroplexes; the dependencies are likely less severe.A unique characteristic of the Miami Metroplex is that MIA, FLL, and major secondary airports in this metroplex have similar runway orientation and runway configurations.Thus, this metroplex seems to provide an example of unqiue practices for handling dependencies among airports with similar runway configurations.Operations in the Miami Metroplex are supported by the Miami TRACON (MIA), the Palm Beach TRACON (PBI), and the Miami ARTCC (ZMA).
+The Atlanta MetroplexThe
+METROPLEX SITE-SURVEY STUDYThe objective of the metroplex site-survey study is to develop a deeper understanding of these parameters and issues through examining the current operations at representative metroplexes in the National Airspace System (NAS).Within the resource and time-frame limitations of this project, the research team visited Atlanta, Los Angeles, New York, and Miami.Among the sites visited, Atlanta represents a metroplex with a single dominant large hub [FAA09a] airport and much smaller satellite airports [RC09b].The Los Angeles (LA) Basin represents a metroplex with multiple medium-to-large hub airports that are heavily affected by terrain and special-use airspace (SUA) [SL09].The New York Metroplex represents a metroplex with multiple, tightly spaced large hub airports.Thus, operations are confined in limited airspace [TL09].Miami represents a metroplex with two large hub airports and relatively small satellite airports such that interactions between two airports with similar configuration can be investigated [SR09].The locations of the sites visited are shown in Figure 3 along with other major metroplexes in the NAS.The results of this effort are summarized in the following sections.
+Site-Survey ProcedureThe steps employed to collect, review, analyze, and disseminate information on operations at the specific metroplex sites studied are discussed in the following sections.
+Site VisitPrior to each site visit a detailed questionnaire was prepared and sent to the air-traffic-control (ATC) facility, and later used as a guideline during the visit.The questionnaire, developed with the assistance of experienced controllers, covers both generic aspects of metroplex operations and unique operational and environmental conditions specific to the site.Questions were normally related to hub airport configurations, arrival/departure routes, traffic-flow management (TFM), terrain, SUA, weather, noise restrictions, and most importantly, interaction and coordination with adjacent facilities.These facilities may include an air route traffic control center (ARTCC), terminal radar approach control facilities (TRACON), air traffic control tower (ATCT, or Tower), airport ramp tower, and military ATC.The site visit typically consisted of a briefing on facility operations and traffic-management procedures, followed by a roundtable interview with a facility manager, a representative from the Traffic Management Unit (TMU), and sometimes controllers.Major discussion focus was given to specific traffic-flow interactions and coordination procedures, as well as to system automation and TFM tools that might have been used to assist the coordination procedures.Each facility provided an overview on how dependent or independent adjacent airport flows either conflicted or operated as single airports.Within the metroplex facilities, primary airports were identified and examined as to their interaction and control of adjacent facility configurations and/or traffic flows.Traffic flow and departure spacing were also discussed and determined if selective airports received priority flows or releases.Often, a tour of the control room or tower cab provided opportunities for reviewing procedures and tools working with live traffic.Training materials were also collected during these visits.Facilities visited included, in chronological order: Atlanta Large TRACON (A80), Southern California TRACON (SCT), New York TRACON (N90) and Center (ZNY), and Miami Tower/TRACON (MIA).The New York site visit also included visits to the Towers at John F. Kennedy (JFK), LaGuardia (LGA), Newark (EWR), and to the Continental Airlines ramp tower at EWR and Delta ramp tower at JFK.
+Data AnalysisAirport statistics, traffic flows, standard-terminal-arrival-route (STAR) and standard-instrumentdeparture (SID) procedures, facility standard operating procedures (SOP), letters of agreement (LOAs), navigation charts, and relevant literature were reviewed prior to the site visits.Also reviewed were SOPs of adjacent facilities not visited to determine interactive flows.After the visit, detailed analyses were conducted.These analyses fell into four categories, described in the following sections.
+Airport Data and Traffic StatisticsFor each metroplex, a list of airports was generated based on the distance from the "core" hub (the largest airport, or the airport that is given highest operational priority), runway length, traffic statistics, Federal Aviation Administration's (FAA's) airport categorization [FAA09a], and supporting architecture [FAA09b].The airport list provided a basis for data-analysis efforts.Detailed traffic demand versus capacity analysis was performed for large hub airports in the metroplex.Capacity and operational constraints, and issues that have implications on metroplex operations, were identified through analyzing data collected during the site visit, from the airport owner and operator, and from government databases.
+Traffic-Flow AnalysisTraffic-flow analysis was performed utilizing the performance data analysis and reporting system (PDARS), which processes both en-route and terminal flight data and radar data (including every radar hit).Sample data were filtered by aircraft category (jet, or tuboprop, and props), airport, and operation (arrival, departure, or over flight) to reveal traffic patterns and flow interactions.Shared arrival and departure fixes were identified and viewed using PDARS in order to identify possible choke points or congestive flows.Different meteorological conditions, such as visual meteorological conditions (VMC), instrument meteorological conditions (IMC), and storm events, as well as runway configuration changes, were analyzed.Results were represented both in static and replay format indicating proximity of airports, airspace boundaries, crossing points and altitude assignments, arrival and departure transition areas (arrival and departure area (ATA and DTA, respectively), special-use airspace (SUA) and terrain, etc.).Sample data were also provided to the team for quantitative analysis (see section 7.2.3).
+Air-Traffic-Control ProceduresATC procedures are defined by published STARs and SIDs, facility SOP, and LOAs with interacting ATC facilities or military regarding the use of SUA.These procedures also cover the use of special ATC automation tools and programs across facilities such as the Severe Weather Avoidance Plan (SWAP) [FAA09c].In-depth analysis focused on detailed traffic-flow interactions and coordination procedures.An interaction is defined as an extra spatial or temporal restriction imposed on one ATC facility because of the proximity of another.Interactions include airspace delegation, arrival and departure routes and altitudes, coordination of departure release, restrictions on runway use, interdependencies between runway configurations at different airports, and initiation and use of special programs.A scheme was developed to use a tree structure to present individual interactions as leaves.Analysis results are presented with details as an appendix to each of the site-survey reports, and as sections in the main body of those reports highlighting key points.
+Analysis of Environmental ConstraintsFor each metroplex site, available noise studies and Environmental Protection Agency (EPA) regional air-quality classification standards [EPA08a] were reviewed to determine noise and airquality impacts and constraints affecting future metroplex design.Water-quality impacts at airports originate primarily from the use of deicing and anti-icing chemicals and specific operational practices.Greenhouse gases were not addressed.It is important to note that increased aviation activity will contribute to greenhouse gases [FAA05a] and that inventory and control of these contributions [S09] is likely to be a factor in some aspects of metroplex design.
+Facility ComparisonThe metroplexes were contrasted and compared based on the data documented in metroplex sitesurvey reports [RC09b, SL09, TL09, and SR09].The TRACON, as the primary ATC facility managing terminal-area operations, is the primary focus in the following discussion.Because a TRACON may serve more than one metroplex (e.g., Southern California TRACON (SCT) serves the Los Angeles (LA) Basin and San Diego), when focus is given to specific metroplexes, metroplex names may be used.It should be noted that TRACON identifications (IDs) are sometimes used loosely to reference both the TRACONs and the relevant metroplexes in context (e.g., SCT may also be used when referencing the LA Basin).Because of its complexity and its importance in this research, the comparison of metroplex operations is discussed in a separate subsection.
+Facility OverviewThe geographic location, the airspace boundary, and major operational areas for each of the four TRACONs are shown in Figure 4.While the size of the airspace boundary reflects the geographic scope of responsibility, the number of operational areas in a TRACON may be an indication of operational complexity, without regard to traffic volume.Among the four, MIA is the smallest and it has only a single operating area, so it could be expected to be the least complex.SCT has six areas, but it should be noted that Palm Springs International Airport (PSP)and Miramar Marine Corps Air Station (NKX, which serves San Diego) are some distance away from the other four areas.N90 has five areas and they all have overlaps, so it could be expected to be the most complex.A80 has the largest coverage and operational areas.The complexity of A80 could be expected to be somewhere between the complexities of MIA and SCT.A comparison of other facility characteristics is shown in Table 4.In Table 4 the usable airspace is defined as the percentage of the volume of TRACON airspace above minimum vectoring altitude with respect to the total airspace above mean sea level (MSL), so it should be an indication of terrain constraints.Other items should be self-explanatory.From the table, one can conclude that A80 hosts a metroplex with a single dominant large hub airport.SCT hosts two metroplex operations with LA Basin representing a metroplex with multiple medium-to-large hub airports (six air carrier airports) that is significantly affected by terrain and SUA.N90 hosts a metroplex with multiple, tightly spaced, large hub airports (three major airports within a 10-nm radius), so operations near the airport are severely confined by airspace.MIA hosts a metroplex with two large hub airports and relatively small satellite airports such that interactions between two airports may be studied relatively easily.
+Traffic StatisticsThe numbers of annual instrument operations for 2007 for the four TRACONs are listed in Table 5.Also listed are the FAA rank of each TRACON and a loading derived by dividing the annual operations by the coverage area from Table 4.Of interest is MIA, with the smallest number of annual instrument operations yet the highest traffic loading per unit of surface area covered.Given the much lower percentage of usable airspace, SCT still qualifies as the busiest TRACON in the world.
+Core Hub AirportsA core hub airport is the airport with the highest traffic volume or highest overall operational priority within the metroplex; often these two aspects are aligned.A comparison of core hub airports would thus reveal the most critical issues related to hub airports that may be of significance at the metroplex level.The comparison of metroplex core hubs, namely ATL, LAX, JFK, and MIA, are summarized in Table 7.All sites have ground transportation congestion issues, with Los Angeles and New York facing the most serious problem.Atlanta currently has only one commercial airport, but that may change as demand grows.Ground connection between JFK and LGA is relatively short, but connections with other airports are almost unacceptable for connecting a flight.The situation is similar for Los Angeles Metroplex airports.The connection between MIA and FLL, however, is improving with a new multimodal transit center under construction.Airport demand and capacity are represented by a typical VMC weekday in 2007.The demand was divided into quarter-hour slots and then compared with VMC and IMC capacities from the FAA 2004 capacity benchmark [FAA04].A total daily demand/capacity ratio [WL01] was calculated by dividing the total daily operations with 16 hours worth of VMC capacity.It is seen that, with the exception of MIA, the core hub airports are very congested, with the worst situation at JFK.However, the capacity constraints at ATL and LAX are currently surface limitations (LAX has one-tenth of the acres of Dallas) while at JFK it is more an airspace problem, although limited arrival gates and construction causes gridlock during peak periods.Three of the core airports have east or west operations with one direction used more often.JFK has many different configurations because of the crossing runway layout.At N90 the JFK/LGA and EWR/TEB airports require close coordination procedures to maximize traffic flows, primarily because of airspace congestion and the little airspace available to vector aircraft for additional spacing.
+Environmental ConstraintsMetroplex design and operation is influenced by environmental sustainability.As more aircraft are squeezed into densely populated regions and as smaller airports are more frequently utilized, noise, air quality, and water are concerns for metroplexes.Thus, one must consider a range of environmental factors.Table 8 summarizes and compares the environmental constraints and issues of each metroplex.There are substantial regional differences related to weather patterns and population distributions, and there are similarities related to urban locations where air quality is an issue.Three of the four metroplexes are classified as nonattainment (not meeting air-quality standards) for 8-hour ozone [EPA08a,EPA08b] and 24-hour particulate matter 2.5 (PM2.5)[EPA08a,EPA06].Air quality is a very important issue in Southern California, as evidenced by its current nonattainment classification, which is expected to remain through 2020.Increases in metroplex traffic may further impact SCT air quality, making it more difficult to meet EPA standards.Noise constraints are at the forefront for each metroplex.Airports located in densely populated areas receive very little support to expand surface area or adjust traffic flows to improve operations.All N90 airports are noise sensitive.The New York/New Jersey/Philadelphia Final Environmental Impact Statement (FEIS) [FAA07c] addressed noise constraints for five major airports.The FEIS found that each of the alternatives would result in changes where noise exposure is increased to within one of the FAA criterion thresholds, indicating the challenges facing metroplex operations in meeting increased demand.Water quality appears to be of least concern to the impacted regions.All of the metroplexes have acceptable procedures in place that control the amount of runoff from airport surfaces.
+Operation Comparison
+Nominal Traffic FlowsVMC nominal traffic flows are presented in Figure 5.These traffic flows reflect the ATC response to the metroplex problem in today's environment.There are dramatic differences among the four metroplexes.ATL's four-corner post-arrival operation is clearly seen.Because of high traffic volume at the northeast corner, two independent entry flows may be used.Traffic flows from the other feeds may be adjusted based on the demand from the northeast corner.Where departure flows cross arrival flows, altitude restrictions are enforced.Satellite flows are normally routed around and below ATL traffic (not shown).Turboprop and jet departures of secondary airports can be stacked (11,000 and 13,000 ft) with the ATL traffic in the feed to ZTL.In Miami, although MIA and FLL do not have traditional standard four-corner post operations, the arrival corridors do serve the same purposes.Because of their distance (18 nm), traffic flows from these two airports-especially the high-volume traffic to and from the north-may cross with proper vertical separation and use different arrival and departure gates.Less-congested airspace also allows for mixing of air traffic from satellite airports (smaller airports surrounding MIA and FLL) with no problem.ZMA uses transition areas and often reroutes arrival and departure traffic during weather events.Since ZMA and MIA regularly operate with A four-corner post operation is not observed in the LA Basin because of airspace constraints, terrain, and adjacent airport flows (six air carrier airports).Sharing arrival and departure gates/fixes is common, although other airport flows (arrival and departures) from the east are pushed below the primary LAX flow.Traffic flows from different airports do merge and cross, but that normally occurs some distance away from the airport.Flows seem to be confined, but gaps do exist (see north of ONT and south of CNO).Those gaps are actually terrain to be avoided-ONT airport sits in a valley east of LAX.SCT and N90 both have high business jet and turboprop traffic to an adjacent airport (SNA, LGB, VNY, SMO).Traffic flows in the New York Metroplex are dense and very complex.If multiple colors were not used, the traffic pattern would not be discernable.Sharing arrival and departure gates is very common, although JFK traffic flows are less dependent because of the ocean arrivals.The crossing and merging of traffic flows occur much closer to the hub airports.Because the three large hub airports are so close to each other, there is not much airspace available for vectoring within the terminal area.Using an extended final approach to manage arrival traffic is not possible because airspace is shared with other arrival and departure areas.LGA and JFK have highly dependent operations; EWR and TEB operations are also highly dependant, especially when operations are set to EWR runway 4 and TEB runway 6. Business jet/turboprop airports HPN and TEB share arrival fixes and departure fixes.Holding is also a frequent problem at multiple entry fixes.
+Airspace Delegation and Operating ProceduresThe comparison of airspace delegation and operating procedures is summarized in Table 9. Airport configuration coupling is a problem for the LA Basin and New York, but the problem is most severe for the latter, mostly because of the proximity of airports and local winds.Configuration change is difficult for all metroplexes investigated, except for Miami; the two major airports (MIA and FLL) have similar east-west configurations and are laid along the northsouth coast line, resulting in fewer restrictions.Weather is a common issue, although situations are not all the same.The west coast airports deal with low stratus clouds and winds, while the east coast airports have more severe weather problems.Terrain and SUA are significant constraint factors for the LA Basin, but less a problem for others.The eastern seaboard SUA located off the east coast and extending up to the New York area can now be used by civilian traffic under a LOA with the military to relieve congestion during severe weather or during holidays (normally released under Presidential Directive).The term interaction represents either the direct results of airport dependencies or the ATC response to those dependencies.For the LA Basin, the impact of BUR's configuration on VNY during Santa Ana winds is an example of the former type.Routing satellite traffic around ATL traffic is an example of the latter, meaning that a different measure could be taken given proper technology.Atlanta, by sacrificing the performance of satellite traffic, has achieved high throughput at a single large commercial hub to serve a metropolitan area.Miami, by spatially separating traffic at two hub airports, achieves similar success.When demand increases dramatically, and when multiple airports are involved (see Table 6) complicated issues emerge, and simple solutions may no longer keep up with demand.This situation can be exasperated by flow constraints to other Operational Evolution Partnership (OEP) airports, especially for metroplexes with constrained airspace or when large hubs are closely located, as illustrated by the interactions in the LA Basin and New York Metroplex as shown in Table 9.
+Automation ToolsThis section identifies tools that assist in the coordination of traffic at different airports but are beyond those commonly used for normal tasks.Some of these tools are developed specifically for, and tailored to, each facility.The Airport Resource Management Tool (ARMT) assists with balancing runways and reducing ground delays (at ATL and MIA).The Daparture Spacing Program (DSP) allows departure traffic from multiple airports (eight in the N90 airspace) to share spacing over specific fixes and reduces delay by allowing the Towers to manage ground movement and stage aircraft according to a priority list.TFM tools include a suite of tools that allow the sharing of traffic flows with adjacent ATCTs and the Center.TFM tools also address Ground Delay Programs (GDPs), Airspace Flow Programs (AFPs), SWAP, mile-in-trail (MIT) compliance, and other local flow-management systems.Airport Surface Detection Equipment, Model X (ASDE-X) is a runway safety tool that enables controllers to detect potential conflicts.It is primarily a tower tool, but it can be shared with TRACONs to share ground movement and congestion information.Ramp towers at JFK and EWR use traffic flow-management tools that provide smoother ground staging of aircraft and collaborative coordination.The Continental Airline ramp tower at EWR has an excellent rapport with the EWR tower.Center TRACON Automation System/Traffic Management Advisor (CTAS/TMA) allows for the spacing and sequencing of arrivals into primary metroplex airports through automated flow assigned delays at higher altitudes.This tool provides individual flow and multicenter capabilities that are progressing into metroplex areas.Current application of these tools at the metroplex sites is shown in Table 10.Note that all four metroplexes have CTAS/TMA installed (LAX uses TMA most of the time, MIA part time; ATL is developing TMA; and N90 is testing TMA during selective periods to EWR) and they all have some TFM tools.DSP directly supports metroplex operations, but is installed only in New York.Currently the application of most of the tools is experimental in nature.Experience gained during the process should be valuable for supporting future development.
+Lessons Learned and Implications for Metroplex Performance and Design
+Summary of Metroplex Site VisitsThe team's review of four representative metroplex sites in the United States-Atlanta, Los Angeles, New York, and Miami-was conducted based on a detailed study of interdependencies among airports in proximity within the resident TRACON.These four metroplexes provided an interesting study since they present different metroplex characteristics based on traffic flows, airport geographic proximity, terrain, crossing routes, weather patterns, and airport demand.SCT and N90 have similar metroplex operational complexities due to traffic density and flows, although the N90 operation presents two closely coupled operations (EWR/TEB and JFK/LGA) within a major metroplex.MIA and ATL metroplex operations present independent operations with fewer constraints or flows to adjacent airports.SCT is constrained by terrain and airport location, placing a constraint on individual flows.Although LAX determines the Southern California airport flows, the other five major air carrier airports create complexity along with business jet and turboprop traffic into other adjacent airports.SCT airports and flows are primarily east-west.Much of the arrival/departure traffic flows from/to the east and north and airport flows are highly structured and constrained because of traffic density.Most of the LA Basin traffic is restricted based on the primary LAX flow, and is dependent on the operation of LAX.SAN works within a separate but smaller metroplex, although SAN traffic flows to and from the LA Basin are based on the configuration of the LAX traffic flow.SUA is another constraint factor for the LA Basin.Because of terrain and SUA constraints, departures are more constrained than arrivals in SCT.N90 presents the most complex metroplex operations and restrictive airport flows.A New York-New Jersey airspace redesign is underway; workgroups and facilities are studying 77 identified problems [ARC07].The dependencies between EWR and TEB operations and airport configurations often restrict arrival and departure flows.A proposed area-navigation-system (RNAV)/required-navigation-performance (RNP) approach for runway 6 at TEB should assist with operations when EWR is on a runway 4 operation.JFK and LGA are highly dependent on each other's operations as well as the demand and configuration of EWR.N90 primarily decides the optimal configuration of the airports, and firmly controls the arrival and departure demand into these airports.The Air Traffic Control System Command Center (ATCSCC) removed the three primary New York airports from OEP departure flow restrictions in order to alleviate departure delays at LGA/EWR/JFK.N90 is fed by three adjacent En-Route Centers and adjacent TRACONs that create traffic-flow restrictions to manage the number of routes.Similar to SCT, departures from major airports within N90 share certain departure fixes.DSP and TMA are currently being used and tested in N90.However, the potential capabilities of these tools have not been utilized to their full extent because of a lack of adequate information sharing between different systems and implementation-related issues.A80 operates independent flows to ATL and adjacent airports.The ATL metroplex does not have the number of air carrier airports or the complexity that the other metroplexes experience.ATL can operate east or west without an impact on the configuration of adjacent airports.Secondary airport flows are routed around and below ATL flows.ATL does experience airspace constraints while landing to the west, and it experiences difficulty with aircraft exiting Class B airspace to the east.ATL maximizes use of the runways and delivers traffic at minimal spacing.Compared to SCT and N90, ATL does not have a terrain problem, airspace constraints, or competing flows from other commercial airports.MIA Metroplex operations are not as constrained, and the primary airports (MIA/FLL) can continue independent operations in east or west configurations.All of the MIA area airports have east-west runways, and flows at secondary airports can operate independently in either the eastflow or west-flow configurations.Flows to and from MIA and FLL are routed via separate gates and fixes.ZMA/MIA does not have airspace, SUA, or terrain constraints.Traffic flow from the adjacent business aircraft airport does not create issues with the flow from the two primary airports.During the winter PBI flows are segregated from flows to MIA/FLL because of high business jet demand ("snowbird flights") during this period.Although MIA is the primary airport, FLL operates independently except during severe weather conditions, in which SWAP procedures are efficiently implemented to continue operations.
+Implications on Metroplex Performance and DesignThe different ways that interdependencies and traffic coordination are managed imply that current practices were evolved over years of operations and thus are most often location-specific.An initial review of these practices identifies some patterns.For example, in A80, flights to and from secondary airports are routed to fly longer routes so that flights to and from ATL can use routes that are more direct.In MIA, flights to and from MIA use different routes and gates from those for FLL flights.By employing this system, routes for flights at both airports are more or less displaced from routes that are more direct to their corresponding airports.In both cases, the effect appears to be spatially displacing traffic so that safe separation can be achieved without heavy temporal coordination between the airports.When lateral airspace is limited, traffic for different airports may be vertically stacked over a common fix, but often some traffic coordination or spacing is still needed.On the other hand, when certain departure gates are shared (frequently in SCT and N90), departures from certain airports may need to get approval before release, or in extreme cases, departures at certain airports may be temporarily stopped or held to make airspace available for operations at nearby bigger airports.In this case, temporal displacement is used as the sole means to separate traffic.These observations motivated additional rigorous analysis in order to characterize and classify metroplex operations from which a unified framework may be developed to systematically study the metroplex problem.The site survey also highlighted the need to study more sites, to more fully capture all the aspects of the metroplex problem, and to identify best practices across metroplexes in the NAS.Through some of the measures, such as segregated routing, traffic flows within a metroplex may operate independently.However, airspace dependencies would still exist.One has to pay a price to getthe flow segregated.It is thus important to maintain the distinction between the intrinsic dependencies between arrival and departure operations at metroplex airports and the practices to counter those intrinsic interactions and dependencies.The former defines a metroplex and the latter provides solutions to the metroplex problem.
+CHARACTERIZATION OF METROPLEX OPERATIONSThe ultimate goal of this task was to develop an abstract of metroplex operations to guide their evaluation and future study.This work was based on the literature review and site surveys described in sections 3 and 4. In the process, both qualitative and quantitative steps have been taken.This work focused on four areas.The first was a qualitative evaluation of the impact of major metroplex issues on metroplex operations.The second was a categorization of metroplex airspace dependencies, based on examples of measures being taken by the air traffic control (ATC) to handle traffic flows in response to the intrinsic dependencies among metroplex airports.The third area was a quantitative measurement of the intrinsic interactions and dependencies among airports within a metroplex, accounting for geographic locations, traffic volume, and infrastructure at metroplex airports.The measurement can be applied to characterize different metroplex sites.The last area was a metroplex clustering analysis that used an arrival flow airspace volume-based metric as the "distance" measure.This analysis was developed to clustering airports into metroplexes and identifying potential future metroplexes in the National Airspace System (NAS).The following sections describe these steps.
+Metroplex IssuesThis section presents a set of metroplex issues that were identified as a result of the metroplex literature review [RS09] and the metroplex site-survey studies at A80 [RC09b], SCT [SL09], N90 and ZNY [TL09], and MIA [SR09].A subjective evaluation process was employed to prioritize the issues identified in order to rank their (adverse) impact on the metroplex operations.Figure 6 provides an overview of this process.Note the GMN acronym in the figure is the Gorman VORTAC ATC facility.
+ScopeFrequency Severity For each identified issue, the team collected documentation and verbal information from operational experts for each of the four site-visit locations and from the metroplex literature survey.Subject-matter experts (SMEs) on the research team used this information and their judgment to give each metroplex issue a "total score" that was intended as a qualitative way to rank its importance.The score is based on ratings in the categories of "scope", "frequency", and "severity".The "scope" rating was determined based on the relative geographical extent of the issue in and across the different sites.Both a metroplex site-specific Scope rating as well as an overall scope rating was determined; the overall scope rating was applied.A rating of high (numerical value of 3), medium (numerical value of 2), or low (numerical value of 1) was provided, with high given to an issue found at most or all of the sites and low given to an issue found at only one of the sites."Frequency" rating was similarly scored, based on how frequently (i.e., in terms of times per day) the metroplex issue would be expected to be encountered."Severity" rating was also similarly scored, based on how severe the relative traffic disruption is expected to be when the metroplex issue is encountered.For each metroplex issue, the expected total impact was computed as:Expected Total Impact = Scope Rating × Frequency Rating × Severity RatingEach of the identified metroplex issues underwent the scope, frequency, and severity analyses and was assigned a total score with a maximum value of 9 and a minimum value of 1 based on the formula shown.The metroplex issues were then prioritized and ordered based on the total scores in a decreasing manner, as shown in Table 11.Major secondary airport flow constraints typically involve additional coordination of, and flight delays for, secondary airport traffic to safely merge with primary airport flows.Three issues have "medium" expected total impacts.Inefficient "flushing" of airport flows concerns dynamic tactical preference of the departure or arrival flow of one airport to "flush" congestion while delaying the opposing flow of traffic of the "flushing" airport (e.g., departures, if arrivals are being flushed, and vice versa) as well as proximate airport traffic.The final two issues of "medium" impact were tied to the impacts of special-use airspace (SUA) and terrain, reducing the usable airspace for traffic flows and causing additional airport flow dependencies.Two additional issues were identified that were rated "low" impact: severe limitations on instrument procedures due to proximate airport primarily involved the severe limitations on arrival traffic to EWR runway 29, when the issue of insufficient regional airport capacity concerns the region's available runway capacity relative to existing demand.Occurs when a significant level of TFM restrictions due to demand-to-capacity overloads exist at airspace fixes or at airports.
+Proximate-Airport Configuration ConflictsOccurs when an airport configuration change of one of at least two proximate airports puts restrictions on flights flying to/from other proximate airport(s).This change involves flows from one impacting another airport's flows, causing significant rerouting or delays.Slow Inter-Airport Ground Connectivity Occurs when inadequate surface transportation of passengers between airports causes significant delays and consequently limits the efficient use of airports by passengers.
+Inefficient/High Workload Airport Configuration ChangesOccurs when any major airport configuration change requires significant workload because of reasons such as: coordination of a large number of personnel, FAA facilities, and airports; and sector reconfigurations.Inefficient Multi-Airport Departure Sequencing Occurs when departure sequencing of flights from multiple airports requires conservative flight restrictions.
+Major Secondary Airport Flow ConstraintsOccurs when conflicts between a primary airport and a secondary airport lead to constraints on secondary airport flows.Typically, secondary airport traffic will be held below primary airport traffic flows or will be routed around the primary airport traffic patterns, resulting in longer flightpaths.Inefficient "Flushing" of Airport Flows Occurs when ATC uses a "flushing" technique that constrains other airport traffic flows in order to expedite one airport's arrival or departure flights as a way to solve a particular congestion problem (e.g., airport arrival gridlock).External SUA Causes Flow Dependencies Occurs when SUA external to the TRACON constricts TRACON flows into narrow corridors and forces inter-airport traffic-flow dependencies.
+Terrain Causes Flow DependenciesOccurs when terrain internal to the TRACON constricts TRACON flows into narrow corridors and forces inter-airport traffic-flow dependencies.
+11Severe Limitations on Instrument Procedures due to Proximate AirportOccurs when the use of instrument procedures is severely constrained because of the existence of a proximate airport.12 Insufficient Regional Airport CapacityOccurs when there is generally not enough TRACON runway capacity to efficiently serve the air traffic demand.
+Categorization of Metroplex Airspace InteractionsMetroplex airspace dependencies were categorized based on observations from metroplex site visits and performance-data-analysis-and-reporting-system (PDARS) traffic-flow data analysis.The ultimate goal of these dependency categorizations was to determine the most severe "types" of metroplex interdependencies and the best solution to handle them.The following methodology was followed: Metroplex site-visit notes from the Georgia Institute of Technology (GaTech) team (A80 [RC09b], SCT [SL09], N90 and ZNY [TL09], and MIA [SR09]) and Mosaic-ATM team (NCT [A07] and N90 [A08]) were studied and information was extracted on the observed interdependencies for each of the visited sites.This information was analyzed and the interdependencies were categorized according to the way the airspace is shared among traffic flows.Through this process, six metroplex airspace interdependency categories were identified.They are illustrated in Table 13.Next, analysis was made to assess the possible ATC techniques or airspace/procedure design to mitigate each category of airspace interdependency.During the analysis, it was recognized that the local solutions generated for each category of interdependency could be different at different facilities.Mitigation approaches for the identified interdependencies are summarized as follows along with one or two real-world examples for each category.For each category, the mitigation approaches that could be used are listed in a perceived order of efficiency.
+Category 1: Sharing Common Points in AirspaceExamples:• SCT: Departures from eight airports in the LA Basin towards northern California are merged over the GORMAN (GMN) departure fix.• N90: TEB south departures have to be merged with EWR south departures and then pass over shared departure fixes.
+Mitigation Approaches:• Keep departure flights from each airport on physically separated routes until just before they reach the terminal-radar-approach-control-facilities (TRACON) boundary and then merge them at the departure fix, without coordination of takeoff times.o Handle flights as/when they show up on the TRACON radar.o Ask for excess mile-in-trail (MIT) separation from each airport.• Keep departure flights from each airport on physically separated routes until they are past the TRACON boundary and let the center merge them.• Disallow the use of certain boundary fixes to a particular airport.• Arrival/departure from one airport shuts down arrivals/departures from the other airport.• One airport needs to call the TRACON for departure release.o Departure from this airport has to be fit into a gap in the arrival/departure stream going to the other airport.• Both airports need to call the TRACON for departure release.• Coordinate four-dimensional (4-D) trajectories of arrival and departure flights from both airports.
+Category 2: Sharing Common Path SegmentsExample:• NCT: During the night (10 p.m. to 7 a.m.), quiet/silent departure procedures are used at SFO and OAK-both airport departures have to follow the same route within the immediate departure sector and hence departure times have to be coordinated across the two airports.
+Mitigation Approaches:• Keep flights from each airport on physically separated routes until just before they reach the standard-terminal-arrival-route/standard-instrument-departure (STAR/SID) start point and then merge them at the start point, without coordination of takeoff/landing times.o Handle flights as and when they show up on the TRACON radar.o Ask for excess MIT separation from each airport and center.• Disallow the use of certain STARs/SIDs to a particular airport.• Arrival/departure from one airport shuts down arrivals/departures from the other airport.• One airport needs to call the TRACON for departure release.• Both airports need to call the TRACON for departure release.• Reduce complexity by coordinating airport runway configuration changes across both the airports.• Coordinate 4-D trajectories of arrival and departure flights from both airports.
+Category 3: Intending to Share Airspace Volume but Profile Altered for Vertical SeparationExamples:• NCT: Under VMC, HWD arrivals keep below OAK arrivals, and these two arrival streams are independent.
+Mitigation Approaches:• Departures/arrivals to one airport have altitude restrictions-e.g., departures/arrivals to one airport have to keep out of the way of traffic of the other airport.• Departures/arrivals to both airports have altitude restrictions to keep out of each other's way.• Increase in-trail spacing between an arrival/departure stream to one airport to avoid wakes generated by an arrival/departure stream to the other airport.• Reduce complexity by coordinating airport runway configuration changes across both airports in the metroplex.• Disallow usage of a certain route by one airport when the other airport experiences heavy traffic over a proximate route.• Deliver aircraft to the adjacent center/accept aircraft from the adjacent center in stacksdifferent altitudes for traffic going to/coming from different metroplex airports.• Coordinate 4-D trajectories of arrival and departure flights from both airports.
+Category 4: Intending to Share Airspace Volume but Path Altered for Lateral SeparationExamples:• A80: PDK arrivals from south of ATL are routed around ATL patterns.• N90: SWF airport traffic flows are routed around the New York metropolitan area, through ZBW, before going south.
+Mitigation Approaches:• Departures/arrivals to one airport are assigned indirect routes between the airport and the metroplex boundary to keep out of the way of traffic of the other airport.• Departures/arrivals to both airports are assigned indirect routes between the airport and the metroplex boundary to keep out of each other's way.• Reduce complexity by coordinating airport runway configuration changes across all airports in the metroplex.• Disallow usage of a certain route by one airport when the other airport experiences heavy traffic over a proximate route.• In bad weather, congestion effects are amplified because there is already a large volume of unusable TRACON airspace.o Respond by applying ground holds and en-route airborne holding.• Coordinate 4-D trajectories of arrival and departure flights from both airports.
+Category 5: Intending to Share Airspace Volume but Temporally SeparatedExamples:• N90: Morristown Municipal Airport (MMU) always calls for release-calls departure handoff position at N90 (in the EWR area).Cannot rely on MMU hitting departure release time, so vectoring has to be used to merge stream (with EWR) before handing off to EWR.
+Mitigation Approaches:• One airport calls the TRACON for departure release.o Departure from this airport has to be fit into a gap in the arrival/departure stream going to the other airport.• Both airports call the TRACON for departure release.• Merges/crossings in the TRACON airspace are handled tactically.o Handle flights as and when they show up on the TRACON radar.o Ask for excess MIT separation from each airport and the center.• Arrival/departure to one airport shuts down arrivals/departures to the other airport.• Reduce complexity by having a tight coordination of runway configurations across both airports.• Have one airport tower act as the "tower for own airport plus the other airport" using surface surveillance capabilities.• Use Traffic Management Advisor (TMA)/the Departure Spacing Program (DSP) or other similar temporal coordination tools.• Coordinate 4-D trajectories of arrival and departure flights from both airports.
+Category 6: Downstream RestrictionsExamples:• ZNY typically applies one Air Traffic Control System Command Center (ATCSCC)generated restriction per plane in departure queue (except in rare cases): approval request (APREQ), DSP, MIT, expected departure clearance time (EDCT), or other.
+Mitigation Approaches:• Use DSP or another similar temporal coordination tool to handle the imposition of multiple downstream constraints like MIT requirements APREQs, departure release times, ground-delay programs, etc.• Departure trajectories are tactically modified to provide enough spacing at the TRACON boundary.o Handle flights as and when they show up on the TRACON radar.o Ask for excess MIT separation from each airport.• Coordinate 4-D trajectories of arrival and departure flights from both airports.
+ConclusionsAll observed interdependencies across the visited metroplex sites can be divided into a few categories.Mitigation techniques to these interdependencies are site-specific and they differ widely.Local procedures tend to be evolved rather than freshly designed.The most prevalent kind of metroplex interdependency is flights to/from multiple airports that use a common volume of airspace.Separating flows in horizontal space or coordinating departure or landing time are the most common responses to these interdependencies.A single Next-Generation Air Transportation System (NextGen) concept with the potential to most efficiently solve the observed metroplex problems is: coordinated planning of 4-D trajectories for all arrivals and departures to and from all metroplex airports.
+Geographic Metroplex Dependency MetricsA close inspection of dependencies and the interactions between arrival and departure operations at metroplex airports suggests that they can be divided into two fundamental types.The first can be categorized as preexisting conditions, while the second can be categorized as the ATC response to those preexisting conditions.The difference between these two types is that different measures can be taken to counter the same set of preexisting conditions, or dependencies, as illustrated by the metroplex site-survey findings.Searching for the best solution to counter the intrinsic dependencies between arrival and departure operations at metroplex airports is the ultimate goal of this research.However, it is important to understand those dependencies first.A set of geographic metroplex dependency metrics has been developed to measure those dependencies.With these metrics, the metroplexes in the NAS can be characterized, and ideally, measures demonstrated to be effective in one metroplex can be evaluated to identify their potential effectiveness in another.This set of metroplex dependency metrics was developed to measure dependencies contributed by factors that are a subset of preexisting conditions such as airport geographic locations, runway length, and traffic volume.They are not developed to measure ATC response solutions such as airspace design, traffic patterns, and operational procedures.
+Pairwise Airport DependencyAs shown in Figure 7, the most important geographical factor for a pairwise airport dependency is the distance between the two airports.The dependency reduces as distance increases.The length of the runways is another factor.Dependency increases as runway length increases because of the ability of the airport to accommodate larger aircraft.Traffic volume is also an important factor.Other factors include the airport configuration and runway orientation, surrounding terrain, and nearby special-use airspace.Because of time and resource limits, these other factors are not considered in the current study.As an initial attempt to model the effect of the distance between a pair of airports, a Gaussian base function was selected (Figure 8), which has been commonly used in many fields to model spatial correlation.The Gaussian base function is used to represent the dependency between a pair of airports at any given distance apart.The selected function is normalized to 1 (fully dependent) at 0 nm, and 2% (somewhat arbitrary) at 70 nm.This distance was selected because Class B airspace is normally limited to 35 nm from the hub airport, and thus dependencies between airports more than 70 nm apart are normally no longer a terminal-area problem. Figure 9.The runway factor.The effects of runway length and traffic volume are modeled as a weight function for each airport, also normalized to have a maximum value of 1.To measure the contribution of runway length, the Federal Aviation Administration (FAA)-recommended runway length for a 10+ seat small airplane is used as a reference.This runway length is a function of airport elevation and the mean daily maximum temperature in the hottest month [AC150-5325-4B].A runway length less than 0.18 times the reference length is rated at 0 and the runway length greater than 1.8 times the reference length is rated at 1 because the former can accommodate only ultralights and the latter can accommodate large jets.A linear relationship is assumed for the runway factor, as shown in the following equation and graphically in Figure 9.The runway ratio in the equation and Figure 9 is defined as the ratio of the longest runway at the airport over the recommended runway length for a 10+ seat small airplane operating at the airport.     < ≤ ≤ - - > = 18 . 0 , 0 8 . 1 18 . 0 ), 18 . 0 8 . 1 /( ) 18 . 0 ( 8 . 1 , 1 o RunwayRati o RunwayRati o RunwayRati o RunwayRati or RunwayFactTo measure the contribution of traffic volume, an annual level of itinerant operations of 365,000 (equivalent to an average of 1,000 operations per day) or more was selected to represent an impact factor of 1.This level represents a typical level of operations for the busiest airports in the NAS; 21 such airports in 2007 [TAF08] had this level of annual operations.Unlike runway length, the minimum value for the traffic-volume factor is set to 0.1 even if the number of itinerant operations is zero.This value is set to avoid excluding under-utilized airports from the analysis.There may be potential traffic growth in the future if sufficient runway infrastructure is in place.A linear relationship is assumed for the traffic-volume factor, as shown in the following equation and graphically in Figure 10.The airport weight is given by the product of these two factors:     = ≤ ≤ × > = 0 , 1 . 0 000 ,tor TrafficFac or RunwayFact ght AirportWei × =The pairwise dependency is defined as the product of the weights of the two airports and the value of the Gaussian base function corresponding to the distance between the two airports.By this definition, the pairwise dependency could have a maximum value of 1 (fully dependent) and a minimum value of 0 (independent).Example pairwise dependencies between the major hub airports and other airports at the four metroplex sites studied during the site survey are listed in Table 14.These dependencies were the highest pairwise dependencies at each site.As can be seen, the pairwise dependencies were highest for JFK because the other two interacting airports are also Operational Evolution Partnership (OEP) large hub airports.In A80, although ATL was (still is) the busiest airport in the world, the second busiest airport in the metroplex, PDK, had a much lower traffic volume.As such, the pairwise dependency for ATL was lowest among all airport pairs listed in the table.The pairwise dependencies for LAX were less than that for JFK, but greater than that for MIA.
+Metroplex-Wide Airport DependenciesWith the pairwise airport dependency, new metrics can be defined to measure the metroplexwide airport dependencies.For this purpose, a metroplex was defined to have an outer range (radius) limit from the central hub airport.Airports outside this limit were ignored.A metroplex is also assumed to have an inner core with a given radius from the central hub airport.Airports inside the core are referred to as "core airports" and airports outside the core ring, but within the metroplex range limit they are referred to as "outlying airports," as shown in Figure 11.To calculate metroplex-wide dependencies, dependencies between core airports and all airports within the range limit are accounted for.For outlying airports, only dependencies with core airports are accounted for.Dependencies between outlying airports are assumed to be local issues at remote areas, and are ignored.Special cases of the core radius include:• 0 nm, the core consists of the central hub airport only• 35 nm, the core consists of all airports within the central hub Mode C area• Radius limit, all airports within the range limit An intermediate metric is the one-to-all dependency-the sum of dependencies between one airport and all other airports within the metroplex range limit.This metric is most suitable for characterizing dependencies among a major hub and other airports within a metroplex.A 75-nm metroplex range limit was selected for use in this analysis.One-to-all dependencies for major hub airports within the four metroplexes studied are listed in Table 15.Again, the table shows that dependencies are highest for N90 large hub airports and least for the A80 large hub airport.Three hub airports in SCT, i.e., SNA, BUR, and ONT, are medium hub [FAA09a] airports; thus they have lower one-to-all dependencies than those of any of the large hub airports.The metroplex-wide metric is defined by the core-to-all dependency-the sum of dependencies between each airport inside the core range ring and all other airports within the metroplex range limit.The metric is most suitable for measuring the total level of dependencies within a metroplex.The metric was calculated for the four sites studied, with a core radius varying from 0 to 75 nm.The results are shown in Figure 12.For any given metroplex, the higher the core-to-all dependency, the higher the overall dependency is within the metroplex.When the metroplex core radius is set to 0, the core-to-all dependency reduces to the one-to-all dependency for the central hub airport.When the metroplex core radius is set to the range limit (75 nm in this case), the core-to-all dependency becomes the sum of all pairwise dependencies.Thus, the radius of the core is an important parameter for the core-to-all metric to be comparable between different metroplexes.As seen from Figure 12, there appears to be some value for the core size in all four metroplexes below which the core-to-all dependency grows rapidly with increasing core size.Above this value, the core-to-all dependency may still grow, but at a much slower pace.This value appears to be a natural selection of the core size of 16-18 nm, as shown in Figure 12 by the shaded vertical band.Another important observation is the clustering of the core-to-all dependencies for N90 and SCT on the higher side, and the clustering of MIA and A80 on the lower side.This observation is consistent with the findings from the site-survey study that the coupling of metroplex traffic flows is strongest in N90 and SCT, and relatively moderate in MIA and A80.A derived metric can be defined as the ratio of metroplex core-to-all dependency and the central hub one-to-all dependency.This metric is most suitable for measuring the concentration of dependencies within a metroplex.The minimum value of this metric is 1, indicating that the metroplex is dominated by the central hub airports.The higher the value is, the less the dominance of the central hub.The high value would likely represent the existence of multiple major hub airport operations in the metroplex.The results of calculation of this metric for the four metroplex sites studied are shown in Figure 13.As seen from Figure 13, while there exist a clustering of N90 and SCT, and one of MIA and A80, there are differences within the clusters.For example, as shown in Table 15, MIA has a higher one-to-all dependency than ATL; if the one-to-all dependencies were the same, the difference between MIA and A80 could have been higher than indicated by the figure because MIA has two major hub airports, while ATL is the only hub airport in Atlanta.
+SummaryBy utilizing basic geographic information about metroplex airports, several metrics were developed to measure the intrinsic dependencies within each metroplex.The pairwise airport dependency metric is the basis on which other metrics were built.The one-to-all dependency is most suitable for measuring dependencies between a major hub and other airports within a metroplex.The core-to-all dependency is most suitable for measuring the total level of dependencies within a metroplex.The metroplex dependency ratio metric is most suitable for measuring the concentration of dependencies within a metroplex.An interesting observation is that, for these four metrics, the consistent order of increasing metrics indicates A80 < MIA < SCT < N90.Among the four metroplexes, N90 is the most complex.
+Metroplex Clustering AnalysisTwo valid questions for any study of metroplexes and their inherent dependencies is which airports, given their traffic levels, form a metroplex and where are the locations of the metroplexes in the NAS.To attempt to answer these two questions, a new numerical metric of metroplex dependency was defined based on studying unrestricted arrival trajectories.This metric was then used as the distance measurement for a clustering analysis to identify metroplexes in the NAS with current and future traffic levels at each airport.
+Dependency Metric for Clustering AnalysisA numerical metric is desirable for understanding the growth of each metroplex, determining when future traffic levels dictate that an airport rises to join a nearby metroplex, and studying the creation of new metroplexes as traffic increases.The notion of this metric is that each airport has an unrestricted arrival airspace (volume) surrounding it, and if the arrival airspace of two neighboring airports overlaps, aircraft flying through this shared space would cause interaction.This interaction is a measure of the added complexity required to properly separate traffic from the interacting neighboring airport.This pairwise complexity could typically be mitigated through procedure design, airspace design, scheduling and coordination, or any other method used to reduce airspace complexity.The metric presented here (a more detailed description can be found in [MC09]) attempts to capture such interaction.Before considering how much one airport will affect its neighbor, one must first understand the unrestricted operations of an airport.Ideally, arrivals would follow a most direct route to the runway and fly most economical vertical and speed profiles-on which the engine power would remain idle until the aircraft is established on the final approach.To provide a more precise approximation to the arrival space, the aircraft trajectory simulation functionality of the Tool for Analysis of Separation and Throughput (TASAT) [RC08] was used to generate 4-D trajectories for several aircraft types, arriving from each degree of direction at the top of decent to the runway threshold.The trajectories were generated using 360 unrestricted continuous-descent arrivals (CDAs), one for each degree of arrival direction.Each lateral track was defined by three waypoints:• Entry point at top of descent• Turn onto final (10 nm from runway threshold)• Runway threshold These flightpaths were used to define the required airspace of "optimal" arrival into an independent airport.An example of these flightpaths for several aircraft types, including a B737-800, B747-400, and B757-200, is given in Figure 14.This figure depicts an arrival airspace that approximates a cone.This cone was used to represent the area of arrivals for a truly independent airport with unrestricted operations.Finding the interaction between optimal arrivals at different airports is a slightly more complicated matter.Here the maximum altitude and minimum altitude CDA flightpaths were used to define a cone with thickness.Two of these cones were overlaid on two separate airports respectively.For the sake of discussion, these airports are referred to as airport i and airport j .The volume of cone i that lies in cone j is used as the measure of the interaction.To calculate the volume of intersection for airport i , the volume of the cone i shell that lies within the convex hull of the truncated cone j was integrated.This volume represents the space that, if an aircraft was descending through this space into airport i , would require some effort to keep it deconflicted from any aircraft arriving into airport j .This effort is not necessarily the effort required by an air traffic controller, but could also be the work required to develop spatially deconflicted STARs, or even the cost in implementing an advanced time-based metering system.To account for the amount of traffic that actually has to deconflicted, the annual traffic volume from the 2008 TAF [TAF08] database as provided by the FAA was used.The pairwise interdependency metric can then be defined as:2 fullvolume traffic traffic volume volume = metric j i j i j i,
+⋅ ⋅ ⋅Where:• volume i is the volume of integration of cone i in cone j .• volume j is the volume of integration of cone j in cone i .• traffic i is the annual number of operations at airport i from the TAF database.• traffic j is the annual number of operations at airport j from the TAF database.• Fullvolume is the full volume of the cone used for normalization.
+Identifying Metroplexes in the NASOnce the metric was defined, values of it were calculated for all airports included in the TAF database.These values were then used in a clustering analysis to determine which airports should be clustered into metroplexes.For this clustering analysis, a Quality Threshold clustering algorithm [HKY99] was used.To tune the threshold value, the number of metroplexes was selected to be 15, to match as best as possible the 15 metropolitan areas listed in Table 1.Example results for the calibration with 2008 data are shown in Figure 15(a).This figure depicts the 15 "metroplexes" as defined by our clustering algorithm.The relative sizes of the points relate to the relative strengths of the total interaction for each metroplex.Notably, the Los Angeles Metroplex and the San Diego Metroplex were identified as a single metroplex by this algorithm; and the New York City Metroplex, the Philadelphia Metroplex, and the Washington, D.C. Metroplex were also identified as a single metroplex.The Minneapolis Metroplex was not identified as a metroplex.Denver, Dallas-Fort Worth, Orlando, and Cleveland were identified as additional metroplexes.The discrepancies came from the fact that the FAA 15 OEP metroplexes were identified based on future capacity needs.
+FRAMEWORK FOR EVALUATING METROPLEX OPERATIONAL CONCEPTSWith the knowledge achieved so far, this section sets up a framework for evaluating metroplex operational concepts.The observed practices to handle traffic interdependencies and traffic coordination were abstracted into a temporal-spatial displacement concept.Existing Next-Generation Air Transportation System (NextGen) concepts were carefully reviewed and compared against the temporal-spatial concept to identify the most relevant concepts, along with new concepts proposed to close any gaps in metroplex operations.The experiment strategy was developed to test the end effects of various concepts studied in lieu of modeling any specific concepts.Spatial and control parameters were then discussed.It was determined that a Generic Metroplex experiment was to be employed to test various combinations of control parameters to identify the most promising concepts and capabilities for metroplex operations.Selected concepts were to be tested using an Airport and Airspace Delay Simulation Model (SIMMOD) N90 model to verify the effectiveness of those concepts in a specific metroplex environment.Traffic coordination techniques, used in both the Generic Metroplex and N90 Metroplex experiments, are also discussed in this section.
+Temporal-Spatial Displacement ConceptFrom studying four metroplexes that span the range of airspace design geometries and trafficflow interactions seen in the National Airspace System (NAS), it was observed that there is no one specific metroplex solution strategy that is employed exactly the same at all of the metroplexes.There are significant differences in the way the traffic flows are handled to deal with metroplex traffic dependencies.For example, when the proximity of airports causes interactions between traffic flows to and from different airports, traffic flows may be laterally segregated at one location, e.g., the segregation of FLL and MIA flows in the MIA Terminal Radar Approach Control Facilities (TRACON), while traffic flows at another location may involve traffic at one airport being stopped or requiring prior approval before being released for departure, e.g., departures from LGA and EWR merging over the ELIOT fix in N90.However, there are also some similarities in the way air traffic controllers handle traffic.For example, flights from major or dominant hub airports are frequently given priority, allowing them to operate unrestricted arrivals and departures while traffic at nearby airports is either routed around the traffic to/from the priority airports or is restricted at departure time such that it fits in the gaps within the flow to/from the priority airports.In section 5.2, the observed airspace interactions are abstracted into six distinct categories.The goal of air traffic control (ATC) in these categories, and in the case of restrictions at departure times, is to spatially separate aircraft at any given time so that limited resources can be shared by different traffic flows.Metroplex airspace interdependencies and control actions are based on traffic flows, so they are limited to four dimensions: three space dimensions and one time dimension.At the most abstract level, the strategies and tactics employed at the different metroplexes in response to dependencies involve either spatial or temporal displacement from ideal four-dimensional trajectories (4DTs): routing, vertical profiles, desired departure time, or desired speed profiles.Traffic is separated by one or both of the two methods.The traffic can be deconflicted by stretching paths of some or all flights so that different traffic flows will traverse different volumes of airspace separated spatially per the minimum separation requirement, as shown in Figure 16.Alternatively, traffic can be deconflicted by regulating the time of some or all flights so that different flights will traverse the same volume of airspace, or a given point, at different times separated by a certain minima.Three examples of temporal control methods are shown in Figure 17, including holding, acceleration, and deceleration.The spatial separation in the former option and the temporal separation in the latter can be employed to achieve similar effects because an equivalent separation exists in terms of either distance or time.The properties of the spatial displacement strategy and the temporal displacement strategy are summarized as: Control actions for a given flight or traffic flow may include one or both types of displacements to achieve a total displacement that can be expressed in terms of time:•) ( ) ( speed holding vertical lateral temporal spatial total T T T T T T T Δ + Δ + Δ + Δ = Δ + Δ = Δor in terms of a generic energy metric:) ( ) ( speed holding vertical lateral temporal spatial total E E E E E E E Δ + Δ + Δ + Δ = Δ + Δ = ΔThe two abstracted displacement strategies and the six interaction categories from section 5.2 provide bases for a framework for analyzing the impact of new NextGen technologies on metroplex operations.In current-day operations, significant spatial and temporal inefficiency exists because of a lack of coordination between facilities, a lack of well-defined flightpaths, and the significant amount of temporal uncertainties.The optimal solution to the metroplex problem would be one that satisfies the required total time displacement while minimizing the energy metric.The required time displacement itself is influenced by many factors, such as: runway geometry, airspace geometry, separation standards, traffic demand as a function of time, and operating condition and constraints (weather, airspace, environmental, and uncertainties).Given these factors, the energy metric is influenced by the trade-off between spatial displacement and temporal displacement, and the specific design and performance of each of the two control strategies.Searching for the optimal solution would require employing concepts that would, first, minimally satisfy the required displacements, and then minimize the energy metric for the required displacement.
+NextGen and Team-Proposed Concepts and Their ImplicationsAs described in the previous section, the nature of the metroplex problem results in significant spatial and temporal inefficiencies for flights into and out of proximate airports with coupled air traffic flows.What is the best way to alleviate these spatial and temporal inefficiencies, given a particular set of feasible technologies and procedures?The answer remains to be discovered, but a common way to express one or more solutions is in the form of new "metroplex concepts".These concepts can be specifically focused on alleviating metroplex inefficiencies, or can have an indirect impact on metroplex inefficiencies.The research investigated both types of concepts."True" metroplex concepts specifically focus on directly alleviating multi-airport inefficiencies, and are discussed in the following section.Specific quantitative metroplex assessments are discussed in sections 7 and 8. "Incidental" metroplex concepts are NextGen concepts that affect metroplex inefficiencies, but are not specifically targeted to alleviate the multi-airport trafficflow dependencies.A qualitative analysis of their impacts was performed.
+"True" Metroplex ConceptsOver the course of this project, the evolution of the Joint Planning and Development Office's (JPDO's) NextGen Concept of Operations (ConOps) [JPDO07] was followed, in pursuit of any new NextGen "true" metroplex concepts proposed by the JPDO community.The JPDO NextGen ConOps defines the term metroplex, but it does not identify future concepts to mitigate metroplex dependencies.The only explicit reference to a metroplex is for flow contingency management (FCM) to address "multiple types of constraints, including airspace, airport, and metroplex constraints".Investigation into the JPDO's Integrated Work Plan [JPDO08a,JPDO08b] revealed that the JPDO has added metroplex-related NextGen concepts as two major Operational Improvements: "Efficient Metroplex Merging and Spacing", to be operational in 2018, and "Integrated Arrival/Departure and Surface Traffic Management for Metroplex," to be operational in 2022.The Efficient Metroplex Merging and Spacing concept focuses on using airborne merging and spacing and improved Air Navigation Service Provider (ANSP) capability and procedures to allow greater traffic throughput and reduced ANSP workload in terminal areas by reducing spacing buffers between traffic streams approaching and departing multiple metroplex runways.These capabilities are similar to the airborne merging and spacing capability described in [BAK04].The Integrated Arrival/Departure and Surface Traffic Management for Metroplex (IADSTMM) concept supports efficient metroplex traffic-flow planning and execution through new procedures, metroplex airspace planning, and traffic-flow-management (TFM) automation, as well as trajectory management automation for real-time management of all aircraft 4-D trajectories for the ANSP.This concept also supports "better-equipped, better-served" air-trafficmanagement (ATM) preferences and is a step towards gate-to-gate 4-D trajectory management.These capabilities are similar to the Integrated Metroplex Planning and Control concept described in [V06], and the Integrated Metroplex Departure Planner described in [SS08] and [SS09a].As a result of successful operational introduction, these two JPDO metroplex concepts could serve to mitigate metroplex issues identified in Table 11, including:• Multi-airport departure merge over common departure fix The Georgia Institute of Technology (GaTech) team took some time to further flesh out the details of the IADSTMM concept.A similar concept was independently formulated in team discussions and it was concluded that the automated, multipoint scheduling and 4-D trajectorybased traffic-management approach had significant merit in terms of potential reduction in current-day metroplex inefficiencies.The core scheduling element of such a concept is shown in Figure 18.This concept provides automated generation of optimal schedules for arrivals, departures, and surface movements throughout the metroplex airspace and multiple airport surfaces.The next section describes the IADSTMM concept, followed by brief mention of other concepts that the GaTech team brainstormed.•
+Integrated Arrival/Departure and Surface Traffic Management for Metroplex (IADSTMM) ConceptIn a metroplex environment, multiple proximate airports compete for the concurrent usage of shared airspace resources like common points (e.g., arrival fixes, departure fixes, other merge points), common routes (e.g., standard terminal arrival routes (STARs), standard instrument departures (SIDs)), or common volumes (e.g., arrival corridors).ANSP responses to such crossairport interactions encompass the entire spectrum from pure temporal separation to pure spatial separation.With pure temporal separation, the ANSP controls the times at which aircraft enter the terminal-radar-approach-control (TRACON) airspace or times at which aircraft cross certain points in the airspace; with pure spatial separation, the ANSP keeps traffic flows to multiple interacting airports from conflicting by separating them in altitude or in the lateral dimension.The IADSTMM concept is on the pure temporal separation end of this spectrum of responses.A decision support tool that will enable the ANSP to temporally separate interacting traffic flows originating from/going to multiple metroplex airports is needed to enable safe and efficient traffic flows in the NextGen metroplex environment.With such a tool available to the ANSP, flights from individual airports will be able to fly their most efficient arrival/departure routes, with the ANSP providing temporal controls to keep flights safely separated.The GaTech team proposes a similar temporal separation tool for allocation of shared airspace resources, and airport resources like runways and gates.Each user (a metroplex airport in this case) would be allowed to share the resource by allocating a time slot to it.Each resource will have a dynamically computed schedule of usage, which shall be computed by optimizing over traffic coming from all metroplex airports.For example, in the case of New York Metroplex, the busiest departure fix-ELIOT-is commonly shared among LGA, EWR, and TEB departures.The proposed temporal scheduler would compute an optimized departure fix-crossing schedule for all flights expected to take off from all three airports within some look-ahead time (LAT) window.TRACON controllers use the IADSTMM decision support tool to meter traffic crossing the boundary fixes to balance the arrival/departure demand across multiple boundary fixes, multiple TRACON sectors, and multiple metroplex airport runways, and to handle merging and crossing traffic by utilizing the tool-provided target fix-crossing times.Airport ground controllers also use the tool as guidance for building the sequence of departures so that the departure traffic load is balanced across all TRACON departure sectors and departure fixes.The tool can simplify the job of airport local controllers by delivering a sufficiently spaced and order-optimized sequence of aircraft on final approach.The IADSTMM tool is a decision support tool for TRACON and air-traffic-control-tower (ATCT) controllers.The tool re-plans periodically with a certain LAT window.For example, it can be configured to re-plan every 5 minutes with a LAT window of 30 minutes.At the beginning of each re-plan cycle the tool takes in the following data as input:• Estimated pushback, spot-out, takeoff times, and departure fix-crossing times for all departure flights expected to take off from airports in the metroplex within the LAT window• Estimated arrival-fix-crossing times, landing times, and spot-in and gate-in times for all arrival flights expected to cross any arrival fix on the metroplex boundary within the LAT window• Estimated merge-point/crossing-point crossing times for all merging or crossing traffic expected to be in the metroplex airspace within the LAT windowThe IADSTMM tool then computes the time-access schedule for each shared metroplex resource-arrival/departure fix, merge point/crossing point, runway, shared airspace corridorby using an optimization-based scheduling algorithm.The optimized deconflicted crossing times/landing times/takeoff times are sent back to the controllers.The controllers use these times as guidance for metering and routing the traffic within their spheres of control.The IADSTMM temporal separation tool maximizes metroplex throughput and decreases controller workload by assisting:• Ramp controllers at individual metroplex airports in figuring out the correct departure sequence by providing target pushback times for flights• Ground controllers at individual metroplex airports in figuring out the correct departure sequence by providing target takeoff times for flights and spot release times• Local controllers at individual metroplex airports by delivering aircraft with just enough separation (i.e., separation as close as possible to the minimum required spacing) on the final approach• TRACON arrival and departure controllers by providing target meter-fix-crossing times for arrival and departure flights• TRACON arrival and departure controllers in properly handling merging and crossing traffic within the metroplex by providing target intermediate-fix-crossing times, target landing/takeoff times, etc., and by de-conflicting traffic at the merge/cross points• The air-route-traffic-control-center (ARTCC) controllers in providing arrival-fixcrossing time sequences and crossing times that support maximum metroplex throughput but are sensitive to dynamic metroplex constraints A nominal IADSTMM architecture is shown in Figure 19.As a result of the successful implementation of this concept:• Improved flow management in the metroplex can reduce delays for aircraft that arrive into major hub airports during heavy rush periods as well as reduce the standard deviation of TRACON transit times, thereby increasing the predictability of aircraft operations.Improved fuel efficiency also results from the reduced delays.• The IADSTMM tool can reduce controller workload by providing the controllers with deconflicted target fix-crossing times and target landing or takeoff times.Also, improved flow management will enable more efficient utilization of metroplex resources (boundary fixes, runways, terminal routes) during rush periods, resulting in increased throughput, as well as increased Traffic Management Initiative compliance.The implementation of an IADSTMM in its full four-dimensional trajectory (4-DT) multipoint scheduling capability provides an opportunity for future NextGen metroplex automation to alleviate both spatial and temporal metroplex inefficiencies.The beneficial impact of the IADSTMM concept depends the temporal and spatial accuracy and uncertainties inherent in the underlying traffic flows, as well as the fundamental metroplex geographical and air traffic demand constraints.Therefore, a parametric analysis of the potential benefits of the IADSTMM concept for a "generic airspace" was conducted, and is detailed in section 7 of this report.In addition, some other potential metroplex decision support tool features could be added to IADSTMM, as suggested in Figure 20.These features include Metroplex Airspace Capacity Management, Metroplex Airport Capacity Management, and Metroplex Trajectory Management in addition to the nominal, multipoint scheduling inherent in the Metroplex Flow Contingency Management.
+Expected Impact of JPDO "True" Metroplex ConceptsIt is believed that both of the JPDO-derived "true" metroplex concepts would reduce metroplex temporal and spatial inefficiencies.Temporal inefficiency involves queueing delays due to metroplex interdependencies and associated uncertainties.Spatial inefficiency involves aircraft separation via routing and altitude restrictions.The expected impacts of the two JPDO metroplex concepts are shown in Table 16.Efficient metroplex merging and spacing are enabled by using more flexible aircraft trajectories along with tighter inter-aircraft spacing, and thus are expected to have both temporal and spatial impacts.The Integrated Arrival/Departure and Surface Traffic Management for Metroplex concept builds on metroplex-wide scheduling and 4-DT trajectory-based airborne and surface operations, so it also has both temporal and spatial impact.
+Other Metroplex ConceptsDuring the project, based on specific knowledge gained during the site visits (especially the New York Metroplex visits), the GaTech team brainstormed other concepts to alleviate the metroplex inefficiences.These concepts are listed in appendix A.
+Expected Impact of Team-Proposed "True" Metroplex ConceptsIn addition to the "true" metroplex concepts defined in the JPDO NextGen ConOps, the research team examined other new concepts leveraging future capabilities to mitigate identified metroplex issues.An analysis was conducted on the impact of each of the new concepts on metroplex temporal and spatial inefficiencies.These results are listed in Table 17.These capabilities would support increased, more efficient, and more environmentally sensitive utilization of existing metroplex runways and airspace for current and future aircraft.
+"Incidental" Metroplex Concepts"Incidental" metroplex concepts are NextGen concepts (or basic metroplex infrastructure improvements) that affect metroplex inefficiencies, but are not specifically targeted to alleviate multi-airport traffic-flow dependencies.A set of these candidate "incidental" metroplex concepts was identified, and a qualitative analysis of their impacts was conducted.First, the JPDO Integrated Work Plan (IWP) [JPDO08a,JPDO08b] was analyzed to identify a broad set of representative NextGen concepts, and to postulate their reductions on metroplex temporal and spatial inefficiencies.The identified candidate concepts are shown in Table 18.All of the concepts are expected to mitigate temporal and/or spatial metroplex trajectory inefficiencies.
+Experiment StrategiesAs seen in section 6.2, there are many new concepts that could contribute to improving metroplex operations.From a temporal-spatial displacement point of view, a given type of displacement (as defined in section 6.1) can be achieved by one or more metroplex concepts, although potentially through different mechanisms.Assessments to evaluate each metroplex concept would be very time-consuming, and would generate redundant results, yet key driving factors influencing system performance could be buried in repeated information.As such, the experiments were developed around the abstracted impact of concepts, not around the details of implementations of individual concepts.The impact of baseline operations and future metroplex concepts was represented as a set of variables, each spanning a range of values reflecting the advancement of technology from the current state to that in a future NextGen time frame.As discussed in section 6.1, system performance is influenced by many exogenous factors such as: runway geometry, airspace geometry, separation standards, traffic demand as a function of time, and operating conditions; constraints including weather, airspace, environmental, and uncertainties; and design parameters directly targeted to improve metroplex operations.Thus a two-pronged set of metroplex concept impact analyses was chosen.The first method was to conduct a quantitative parametric analysis of a Generic Metroplex that can be configured to span the range of geometries within the NAS to provide broadly applicable results.The second method was to conduct a quantitative analysis of a specific metroplex.As a result of previous studies and the site-survey and quantitative metrics analyses, the N90 Metroplex was selected as the site for the specific analysis.It is the most complex metroplex, and therefore, the one with the greatest expectation of potential metroplex concept benefits.In the Generic Metroplex parametric analysis, the intention was to vary each parameter to span all the NextGen technologies as well as technologies that have been conceptualized by the metroplex team.In the specific N90 study, only those technologies that are indicated through the Generic Metroplex parametric study to be beneficial to N90 were studied.As shown in the experiment strategy in Figure 21, all concepts are mapped to parameters that represent control types and control precision.As discussed earlier, there are two types of controls: spatial and temporal.Spatial control includes airspace geometry design and operational procedures employing lateral navigation (LNAV) and verical navigation (VNAV).Temporal control includes scheduling and coordination of arrivals and departures, metering of traffic into a given airspace volume, and the location and number of metering points.The spatial control accuracy can be presented by required-navigation-performance (RNP) values, both lateral and vertical (in the NextGen time frame), and their associated separation standards.The temporal control accuracy can be presented by metering accuracy in terms of the bias of the mean and variance of the arrival time relative to the ideal arrival time.Control parameters were prioritized and cross-checked for compatibility and consistency to reduce the number of test cases that had to be executed.A full test matrix was developed for the Generic Metroplex assessment to span a wide range of parameter space to identify the most promising design points.The N90 assessment then focused on the reduced test matrix that analyzed only the identified design points that were identified as potentially suitable for improving N90 operations.The experiments focused on operations under visual meterological conditions (VMC) because in the NextGen time frame, improved capabilities will enable VMC-type operations under today's instrument meterological conditions (IMC).Because of time and resource limitations, special weather conditions such as convection were deferred to future studies.Based on similar considerations, terrain and special-use airspace (SUA) were not considered in the current study.
+Spatial Design and Control ParametersSpatial design and control parameters fall into two basic categories:• The spatial uncertainty/containment and minimum safe separation between aircraft and between aircraft and obstructions• The geometric layout of the airspace, nominal procedure routes, and vertical profilesThe definition of the former category is driven by safety requirements; the definition of the latter is driven by capacity and efficiency needs, while satisfying all safety requirements.In a traditional radio navigation environment, the spatial containment is defined using the alongtrack tolerance (ATT) and cross-track tolerance (XTT) [FAA-H-8261-1A].For obstacle protection, the primary area width is 2 × XTT.RNP is a statement of the navigation performance necessary for operation within a defined airspace.With this concept, the lateral performance requirement associated with a given procedure is specified in terms of RNP values given in nautical miles.The required performance is obtained through a combination of aircraft capability and the level of service provided by the corresponding navigation infrastructure.A key feature of RNP is the concept of onboard monitoring and alerting, meaning the navigation equipment is accurate enough to keep the aircraft in a specific volume of airspace, which moves along with the aircraft.RNP levels are actual distances from the centerline of the flightpath, and they must be maintained for aircraft and obstacle separation.Longitudinally, this block is centered at the true longitudinal position known to the aircraft.The aircraft is expected to remain within this block of airspace for at least 95% of the flight time.Additional airspace outside the 95% area is provided for continuity and integrity, so that the combined areas ensure aircraft containment 99.9% of the  The minimum vertical clearance that must exist between an aircraft and the highest ground obstruction within the obstacle evaluation area of instrument procedure segments is the required obstruction clearance (ROC).For RNP arrivals, the minimum ROC value for the feeder segment is 1,000 ft (2,000 ft over designated mountainous terrain); for the initial approach segment, the ROC is 1,000 ft; and for the intermediate approach segment, it is 500 ft [FAA JO8260.52,FAA JO8260.3B].For RNAV departures, the minimum ROC value over areas not designated as mountainous is 1,000 ft, and over mountainous areas it is 2,000 ft [FAA JO8260.3B].Special requirements are in place for final approach and initial climb when aircraft are close to the ground.Currently there are no standards for lateral separation between simultaneous use of RNP and RNAV routes in the terminal area.A 3-nm lateral separation (5 nm beyond 40-nm distancemeasuring equipment (DME) from the radar site) and 1,000-ft vertical separation are the minimum requirements.In a multiple-airport environment, for the purpose of airspace design, the minimum lateral separation between parallel tracks to the same airport is required to be 3 nm.The lateral separation requirement between tracks to adjacent airports is 4 nm, or 3 nm for highvolume traffic with dual tracks for each airport, with the 3-nm area between adjacent tracks designated as a no transgression area [FAA JO7400.2G].When the traffic pattern associated with an airport overlaps the airspace encompassed by a standard instrument approach procedure (IAP) for an adjacent airport, the minimum vertical separation between the traffic pattern and the affected portion of the adjacent IAP is 500 ft.If heavy jets are involved, the minimum vertical separation is 1,000 ft [FAA JO7400.2G].Given the range of options already defined in the current RNP, standard values are shown in Table 19 and Table 20; no significant change to these standards is expected in the NextGen environment.The standard 1,000-ft vertical separation and the 500-ft vertical separation between traffic pattern and IAP are also likely to remain unchanged, partially because of the size of the "heavy" category of jet aircraft, and partially because of the wake vortex separation requirements.However, the application of these standards, especially the smaller RNP values, is expected to increase.Improved vertical navigation would increase the use of better-defined vertical profiles, and thus would improve airspace efficiency and capacity.Rigorous system analysis, multidisciplinary airspace optimization, and improved fleet-wide navigation performance would allow increased use of four-dimensional (4-D) trajectories decoupled from each other in a high-volume environment.The control parameters would be simplified to different route and vertical profile structures reflecting levels of increased decoupling of 4-D trajectories.The route and vertical profile structures are defined by airspace design geometry parameters such as the number of entry and exit fixes, their lateral (distance from metroplex center and distance from each other) and vertical spacing (single altitude or stacked), the extent of shared common path segments (complexity vs. airspace efficiency), and turn radii at various segments.Details of these route and vertical profile structures are discussed more specifically in sections 7 and 8, where the Generic Metroplex experiment and N90 experiments are presented.
+Temporal Design and Control ParametersFor the Generic Airspace Metroplex Sensitivity Analysis, the desired arrival and departure temporal uncertainty values are a set of temporal uncertainties for the generic airspace terminalarea boundary crossing (external to the terminal area) and a set of temporal uncertainties for the generic airspace terminal area (internal to the terminal area).Section 6.2 discussed qualitatively the impact of the NextGen and team-proposed new concepts and technologies on the reduction of the temporal uncertainties.Recommendations for the values of these uncertainties were deduced from numerous sources and are discussed in the following paragraph.Table 21 summarizes these recommended metroplex temporal uncertainty assumptions.These uncertainties are measured at points shown in Figure 22.In Table 21, the bias is defined as the difference between the mean and the nominal value; the grouping is defined as the spread of the uncertainty around the mean, given as 2 times the standard deviation.The current system-arrival-fix-crossing time temporal uncertainty value is based on data for aircraft controlled by en-route air traffic controllers with the Multi-Center Traffic Management Advisor (MC-TMA) operating [LF03].The future 4-DT system-arrival-fix-crossing time, future 4-DT system-arrival landing time, and future 4-DT system-departure fix-crossing time temporal errors were assessed based on recent controlled-time-of-arrival (CTA) research sponsored by Eurocontrol's Partnership Project: CTA-ATM System Integration Studies (CASSIS) [KAM09].The current system-departure takeoff time temporal error was estimated by traffic flow management system (TFMS) expected-departure-clearance-time (EDCT) compliance accuracy data analysis [L09].The future surface management system (SMS) system-departure takeoff time temporal uncertainty is based on SMS system prediction accuracy as measured in a series of operational trials at Memphis International Airport (MEM) [AJ04].A future airport guidance and control system where aircraft would receive and use detailed 4-D trajectories for guidance, as well as a datalink for communication and full airport surface surveillance (both ramp and movement area), would provide higher levels of aircraft takeoff prediction accuracy.The values for future 4-DT system-departure takeoff time temporal error were chosen through team subject domain expert evaluation.The current system-arrival landing time temporal uncertainty is based on measurements from previous NASA Collaborative Arrival Planning research [QZ98].Current system-departure fix-crossing time temporal uncertainty should be larger than the current system-arrival-fix-crossing time temporal uncertainty because of the lack of a current system such as Traffic Management Advisor (TMA) that provides scheduled times of arrival to which controllers try to control flights, the naturally increased variation in aircraft weight during the departure phase, and other similar factors.For this research, it is assumed that the current system-departure fix-crossing time temporal uncertainty is double the value for current arrivalfix-crossing time temporal error.
+Discussion of Metroplex Scheduling AlgorithmsMetroplex scheduling is a means to determine nominal entry fix-crossing times for flights destined to the metroplex airports or nominal departure takeoff times for flights originated from metroplex airports.Together with metering techniques and spatial control methods, the primary goal of metroplex scheduling is to maximize metroplex throughput.A secondary goal is to minimize the delays that occur within the terminal area; delays within the terminal area are more costly from the fuel-burn and emissions perspectives than delays that occur in the en-route environment.Reduced delays in the terminal area can also reduce the noise impact of aircraft operations.This section discusses a metroplex arrival scheduling algorithm.This scheduling algorithm was used in the Generic Metroplex simulation study described in section 7.5 and the N90 Airport and Airspace Delay Simulation Model (SIMMOD) simulation study in section 8.The Georgia Institute of Technology (GaTech) team also studied alternative scheduling algorithms.Details of these alternative scheduling algorithms and the analysis results are presented in section 7.4.
+Architecture of the Metroplex Arrival Scheduling AlgorithmThe arrival scheduling algorithms assume that the estimated time of arrival (ETA) at entry fixes and the runway assignments are known for all flights destined to the metroplex airports on an entire day.This assumption is not a requirement, but rather one for simplifying the implementation of the algorithm so that it can be easily applied to the simulation studies.In the real-world environment, the algorithm could be implemented to work on a rolling window of a given time horizon, within which the ETA and runway assignments may be reliably predicted.A two-stage scheduling algorithm architecture is employed to generate a required time of arrival (RTA) for each aircraft.In the first stage, the time between successive aircraft destined for a given runway is minimized, thereby maximizing the throughput of that runway (thus achieving the primary goal).In the second stage, the impact on runway throughput of conflict resolution actions required at all the metering fixes is minimized (thus achieving the secondary goal without compromising the primary goal).These two stages are discussed in the following paragraph.
+Maximizing Runway ThroughputThe runway throughput is maximized by minimizing the time between successive aircraft that cross a given runway threshold subject to runway constraints only.In other words, the constraints at all the entry metering fixes are relaxed in this stage.Any potential conflicts that could occur between different traffic streams via a common entry fix are ignored, thereby treating the aircraft destined to a given runway as independent of aircraft destined to other runways.Using the ETA at the entry fix as the starting point, the earliest possible runway arrival time for each aircraft destined to a given runway is determined by using its future unimpeded trajectory, assuming it is able to conduct a continuous-descent arrival (CDA) at the optimal speeds.The result is a sequence of arrivals ordered by their earliest possible runway arrival time.In this sequence, time intervals between runway arrival times might actually be less than those corresponding to the minimum required separations between successive aircraft landing on the same runway.Because this sequence is determined from the earliest possible runway arrival time for each aircraft, the only way to resolve the conflict at the runway threshold is to push back the runway arrival times to satisfy runway separation constraints.The result is an ideal runway schedule that maximizes runway throughput subject to runway separation constraints.From the ideal runway schedule, runway-ideal entry fix-crossing times for aircraft destined to the same runway can be determined by back propagating each unimpeded CDA trajectory to its corresponding entry fix.
+Minimizing Impact of Conflict Resolution at Metering FixesThe runway-ideal schedule at an entry fix can be obtained by combining runway-ideal entry fixcrossing times for all aircraft destined to different runways at metroplex airports.Because the runway-ideal entry fix-crossing times are determined independently for different runways, the time intervals between consecutive aircraft in the runway-ideal schedule, given an entry fix, might actually be less than that corresponding to the minimum separation required at the fix (normally 5 nm).The impact on runway throughput of conflict resolution actions at entry metering fixes is minimized by minimizing the net increase in fix-crossing times that are required between different traffic flows.The runway-ideal fix-crossing times could be simply pushed back to satisfy minimum separation requirements at the fix, just like what is done currently at the runway.This push-back would result in additional gaps in the runway arrival stream beyond whatever gaps might exist because demand is less than capacity.It is thus determined to selectively advance the fix-crossing times from the runway-ideal fix-crossing times to achieve the minimum required separation at the fix.Any advance is limited not to be earlier than the corresponding original ETA.Because of the limit imposed, the minimum required separation might not be achieved for all consecutive aircraft pairs.If for an aircraft pair the minimum required separation is not satisfied, the entry fix-crossing time of the trailing aircraft is pushed back as little as possible to achieve the minimum required separation.
+Application of Metroplex Arrival Scheduling AlgorithmThe adjustment of the fix-crossing times is determined using a linear program.The net result is the schedule of desired runway fix-crossing times.The same algorithm can be applied to both the Generic Metroplex simulation and the N90 Metroplex simulation proposed in section 6.3.The algorithm takes sequences of ETA at entry fixes as input and outputs the schedule (sequences of RTA) at entry fixes, which will in turn be used in the corresponding simulation as the new input.
+GENERIC METROPLEX ANALYSIS AND SIMULATIONThe Generic Metroplex analysis and simulation were developed to systematically study the impacts of the spatial and temporal control parameters on metroplex operations.Two types of control parameters and metroplex performance metrics were analyzed to form the basis for the experimental methodology.An experiment process flow was developed to implement this methodology.Details of the experiment methodology are presented in section7.1.to provide background information for the discussions that follow.The Generic Metroplex Model was developed as the platform for all analyses and simulation studies in this section.This model includes four different airspace geometries with the area-navigation-system (RNAV) and required-navigation-performance (RNP) procedure to represent the broad range of spatial control parameters.To support the analysis and simulation, a demand model and an aircraft traffic spacing model were also developed, as described in section 7.2.Four separate analysis and simulation tasks were conducted to test the Generic Metroplex system performance.The first task was an airspace interdependency and complexity analysis referred to as the Intersect Flow analysis, as described in section 7.3.Using the developed intersect flow metrics, the analysis compared the four different airspace geometries under the demand set described in section 7.2.2.The second task was an analysis of a set of alternative scheduling algorithms (different from the one described in section 6.6) and their impact on metroplex delays.This study analyzed the performance of different geometries and different airports in the Generic Metroplex when the proposed scheduling algorithms were applied.It identified some issues that warrant further examination in the future.The proposed scheduling algorithms and the analysis results are presented in section 7.4.The third task, also the most important of part of the Generic Metroplex study, was a linked node queueing process simulation.Different demand levels, the scheduling algorithm described in section 6.6, and different temporal control accuracy values were tested.Details of the linked-node queueing-process model, the test case design, and simulation results are presented in section 7.5.The last one of the four tasks was an environmental analysis of different Generic Metroplex geometries.The analysis methods, fuel burn, and emissions results of this task are presented in section 7.6.
+Experiment Methodology
+Experiment Hypotheses and Experiment MetricsAccording to the metroplex evaluation framework discussed in section 6, there are two basic categories of strategies for managing air traffic flows in a metroplex.The first category is temporal strategies, which involves de-conflicting traffic flows to and from multiple airports sharing points, routes, or volumes of airspace by controlling flight-by-flight arrival times at the shared resource to maintain separation.The separation allows flights to take direct routes between their origin/destination airports and within the metroplex terminal area, but might lead to excess delays.The second category is spatial strategies, which involve de-conflicting traffic flows to and from multiple airports by using different routes that are separated either vertically or laterally.In this second category, controllers do not have to worry about temporal separation between any two traffic flows going to/coming from two different airports.However, this scenario might lead to extra distance flown, which in turn can be translated into additional flying time.In most cases, the control of interweaving traffic flows might involve a combination of these two de-confliction strategies.In addition, the benefits of advanced metroplex spatial or temporal control strategies might diminish with increased levels of uncertainty or decreased levels of control accuracy.To systematically test these two types of strategies, the metroplex operational environment and the control strategies were mapped into a set of variables.These variables form a multidimensional design space.Each point in the design space represents a specific metroplex and a set of specific control strategies and their associated performance.Exogenous variables are those defining a specific metroplex environment that the metroplex operations depend on.Theoretically, some of these exogenous variables can be adjusted to influence metroplex operations, but these changes may take decades to implement.Typical exogenous variables include: • Terminal-area arrival and departure-route structure (shared common path segments or fully segregated)•• Turn radii at various route segments• Terminal-area arrival and departure vertical profiles (step down or optimized profile)• Lateral containment and minimum safe separation (see section 6.4)• Vertical containment and minimum safe separation (see section 6.4)Temporal design and control variables include:• Arrival and departure scheduling (with or without metroplex scheduling)• Metering strategy (truncation to ensure minimum separation or target seeking to achieve required time of arrival (RTA))• Longitudinal containment and safe separation (radar, visual, wake vortex)• Temporal control accuracy (see section 6.5)
+Selected Experiment VariablesBecause of time and resource limitations, only a subspace of the whole design was tested.Parameters were prioritized and cross-checked for compatibility and consistency to reduce the number of test cases that had to be executed.The subspace was defined by the following grouped variables:
+Generic Metroplex airspace design:The Generic Metroplex airspace design is detailed in section 7.2.1.This design was defined by two airports in the metroplex, each with two parallel runways; four alternative route structures representing different numbers of entry and exit fixes, and the use of fixes and routes by traffic flows at the two airports; continuous-descent arrival (CDA)-type arrival vertical profiles and unrestricted departure profiles; and the use of maximum RNAV/RNP design standards that reflect minimum spatial containment achievable by best-equipped aircraft.
+Metroplex demand model:Two separate demand models were tested.A Poisson arrival model with varying arrival rates was used to test system performance under different traffic volumes.A Generic Metroplex demand model to represent arrivals and departures on a typical day was used for all other analysis.The future demand model is described in section 7.2.2.
+Scheduling algorithms:Several different prototype scheduling algorithms were developed, including the ones described in sections 6.6 and 7.4.The baseline nonscheduling case was the arrival and departure times derived directly from the Generic Metroplex demand model.
+Metering strategies and temporal control accuracy:As an attempt to realistically model metering strategies and temporal control accuracy, an interaircraft spacing model was developed (see section 7.2.3).This model was used to adjust the nominal fix crossing times and takeoff times for the metroplex demand model.A range of temporal control accuracy values, defined by the standard deviation of entry fix-crossing times relative to the nominal fix-crossing times, were tested.Details of these temporal control accuracy values and the simulation results are presented in section 7.5.5 following.Delay was selected as the primary metric for the Generic Metroplex study.A set of metroplex intersect flow metrics was also developed to measure the metroplex flow complexity.For energy metrics, fuel burn was used.Emissions were used as the environmental metrics.Noise was omitted because it is an issue directly related to populations distribution, which was not considered in the Generic Metroplex model for the sake of simplicity.
+Generic Metroplex Experiment Process FlowIt is assumed that arrivals and departures are operationally independent in the initial Generic Metroplex studies, so they are assessed separately.Figure 23   Demand generation creates an initial schedule of arrivals to and departures from each Generic Metroplex airport according to the specified arrival and departure capacities and demand-tocapacity ratio of the airport.This process includes assigning each flight to an arrival or departure fix and estimating its time of arrival to that fix.Merging the arrivals schedules of the metroplex airport yields the raw scheduled demand for each arrival fix.Traffic spacing adjusts spacing at arrival fixes or departure interval at runways in accordance with an empirically derived interflight probability distribution and the implied traffic volume.This adjustment yields realistic fixcrossing times or runway departure times.If metroplex sequencing and scheduling is applied, the fix-crossing times or runway departure times will be provided as input to that module.Airspace design creates a set of arrival and departure procedures coupling each metroplex airport with its associated arrival and departure fixes.Merge points or other points of interaction between different procedures are also defined.Additional input to airspace design is the spatial precision and the anticipated lateral and vertical navigation performance of the aircraft executing the procedures.The outputs of airspace design are the Generic Metroplex terminal airspace procedures and their merge or interaction points.Provided with nominal arrival transit times and initial crossing times at arrival fixes, the arrival sequencing and scheduling determine the new sequence and new arrival-fix-crossing times that satisfy minimum required spacing and minimize delay.Provided with nominal departure transit times and initial runway departure times, the departure sequencing and scheduling algorithm determines the new sequence and new runway departure times that satisfy minimum required spacing and minimize delay.In addition, the sequencing and scheduling may account for the temporal precision in estimating aircraft transit times and in meeting scheduled times of crossing at control points.The scheduled arrival-fix-crossing times or runway departure times are then in turn perturbed as per the temporal precision to yield actual times at these points.These inputs are fed into the queueing network (referred to as linked-node queueing-process model) to simulate queueing delays that may incur during the execution of arrival or departure procedures.The computed flight delays at fixes, merge points, and runways are then used to compute total delay, fuel burn, and cost metrics.Key components of this process are described in the following sections.
+The Generic Metroplex ModelThis section presents the Generic Metroplex model.Key components of the model include Generic Metroplex airspace design, the demand generation, and the inter-aircraft spacing model.These components are described in the following sections.Some other factors, which could be considered in a thorough Generic Metroplex experiment study, are skipped in the current research because of time and resource limitations.Those other factors are discussed briefly for the completeness of the subject.
+Generic Metroplex Airspace DesignThe Generic Metroplex airspace design started with a set of four basic airspace design geometries.The team first conducted discussions on the notional procedure design and identified major issues that needed to be considered in the procedure design: vertical profiles, lateral path, potential trajectory conflicts, separation standards, and optimization of trajectory spatial displacement.The purpose of spatial displacement of trajectories is to reduce temporal coupling between trajectories to improve overall throughput, efficiency, and safety.The initial Generic Metroplex model explored in this study consists of two airports, A and B, located 20 nm apart.Airport elevation is assumed to be at mean sea level (MSL) for the sake of simplicity.Each airport has two parallel runways that are separated by 5,000 ft, permitting independent simultaneous parallel operations.Runways are oriented perpendicular to the northsouth straight line connecting the two airports.Each runway is 9,000 ft, assumed sufficient for today's heavy commercial jets.The layout of this initial metroplex model is shown in Figure 24, where the most direct routes are presented.The four basic airspace design geometries are designed to represent the different levels of route efficiency outside the terminal area and the spatial segregation that can be achieved within the terminal area.These four geometries are briefly discussed as follows:• Airspace geometry 1: There are four equally spaced arrival fixes and four departure fixes located at the 40-nm ring from terminal radar approach control (TRACON) center.Each fix is shared by both airports.• Airspace geometry 2: There are four equally spaced arrival fixes and four departure fixes located at the 40-nm ring from TRACON center.Each fix is shared by both airports.In addition, arrival and departure paths are maximally shared by aircraft sharing the same entry or exit fix.This geometry represents the use of common standard terminal arrival routes (STARs) and standard instrument departures (SIDs).• Airspace geometry 3: There are four pairs of arrival fixes and four pairs of departure fixes at the 40-nm ring.Each fix in the pair is used by only one airport.• Airspace geometry 4: There are 16 arrival fixes and 16 departure fixes.Arrival fixes and departure fixes are alternately distributed on the 50-nm ring.Each fix is used by only one airport.Unrestricted arrival and departure profiles are important reference design solutions as they present the most fuel-efficient design.The unrestricted arrival profile represents a continuousdescent-arrival (CDA)-type vertical profile.In this profile, the aircraft descend from high altitudes along a performance-based vertical profile such that the thrust setting remains in idle for as long as operational conditions permit.The unrestricted departure profile represents a profile in that the overall efficiency is optimized, although the optimization objectives may be operatorspecific.Reduced-rate departures may also be employed to save fuel, to reduce aircraft noise impact to the community below the flightpath, or to save engine maintenance cost.An example of arrival and departure profiles is shown in Figure 25.Per the current design standard, an RNP arrival procedure would provide the possibility for the most direct routes for different airspace setups.Lower RNP values may reduce lateral separation minima and help de-conflict traffic flows in the metroplex, but would limit the availability of procedures to the minority of current-day fleet mix equipped with that capability.Within the current RNP procedure design standards, a wide range of arrival procedures can be developed.The availability of new capabilities and technologies in the future would mostly tend to enable expanded use of RNP procedures by more aircraft and in a wider range of external conditions.The availability of new capabilities in the future, however, probably would not tend to dramatically reduce the RNP values from today's standards.The radius of turn is determined by indicated airspeed, tailwind component, maximum segment altitude, and the design of the turn arc.With a reduced radius of turn, design flexibility can be improved, and it would help separate different paths.However, if the selected radius of turn is too small, it may reduce efficiency because the aircraft have to decelerate to a slow speed early and the availability of such routes may be limited to certain aircraft types.Based on these assumptions, a set of procedure design parameters were selected to develop a set of most-direct-route structures based on the four basic geometries.These route structures are shown in Figure 24.In Figure 24, the red routes are arrivals and the green routes are departures.The four route structures are the bases for the Generic Metroplex simulation analysis.
+Generic Metroplex Demand GenerationMetroplex demand generation is the process for creating a traffic demand set (set of scheduled arrivals and departures) for Generic Metroplex airports to support simulation-based evaluation of hypothetical terminal airspace configurations.Demand-generation process inputs comprise a current-day traffic demand set; a user-specified National Airspace System (NAS) airport after which to model traffic demand to a particular metroplex airport, and an hourly capacity value; and target 24-hour demand-to-16-hour capacity ratio for the airport.The demand-generation process comprises the following computational steps:1.The traffic demand set is processed using the AvDemand tool to grow the traffic to a specified volume and to estimate gate arrival times for each flight [HS07].2. Those flights to/from the specified NAS airport are captured.3. A portion of the flights of interest are removed to achieve the specified demand-tocapacity ratio as per the specified generic airport hourly capacity [WL01].4. The remaining flights-i.e., the arrival flights to and departure flights from the generic airport-are assigned to a peripheral source/sink airport at a specified radius beyond the terminal airspace.5. Each metroplex airport arrival and departure flight is assigned to an arrival or departure fix on the hypothetical terminal airspace boundary with the en-route airspace.6.The terminal and en-route transit times of each flight are updated to reflect the airspace geometry.7. After transit times are computed, distinct, randomly generated gate departure times are assigned to all the generic airport flights in order to eliminate coincident scheduled takeoffs.Finally, the generated schedule of generic airport arrivals and departures is written to a simulation input file of the appropriate format.The following input parameters are used to generate traffic demand sets for airports A and B in the Generic Metroplex assessments.The seed traffic dataset is an enhanced traffic management system (ETMS)-derived record of instrument-flight-rules (IFR) flights for September 26, 2006 [ETMS].The seed traffic dataset was "grown" using AvDemand to three times the total traffic volume in accordance with 2008 terminal-area-forecast (TAF) forecasts [TAF08].From the grown traffic demand set, ATL traffic is used to create traffic demand sets for both Generic Metroplex airports A and B. Arrival and departure traffic volumes for Generic Metroplex airports A and B are in accordance with capacity of each airport of 60 arrivals/hour and 60 departures/hour (each airport has two operationally independent parallel runways) and their respective demand/capacity ratios, 0.7 for airport A and 0.35 for airport B [N04]. Figure 26 depicts the generated traffic-demand profile with total capacity for Generic Metroplex airports A and B.The metroplex demand-generation process is effective in preserving the directional distribution of scheduled traffic to the specified reference NAS airport.The directional traffic distribution determines the relative loading of the metroplex arrival and departure fixes, in turn impacting controller workload and possibly requiring airspace configurations and traffic-management strategies to accommodate it.Figure 27 depicts the directional distributions of of Generic Metroplex airports A and B from the metroplex demand-generation process.The heavy ATL scheduled demands in the 45-to 60-degree and 15-to 165-degree ranges are preserved in the Generic Metroplex demand set.
+Inter-Aircraft Spacing ModelOne of the research questions in modeling temporal control variables is how the metering strategies are practiced in the existing environment and how this practice would change in the future environment when traffic levels are significantly increased.Uncertainties in spacing are one of the major sources of queueing delays.Accurate modeling of spacing is thus important to simulate current system performance and to develop and test future concepts and automation.This research explores the problem of modeling the probability distribution of spacing under different traffic levels and the separation minima in effect.Spacing is usually measured in terms of time or distance, depending on the purpose of the study.One of the efforts is to characterize the probabilistic distribution of the time interval between two successive flights at arrival fixes and departure runways under various traffic levels through analyzing data collected from real-world operations.This work is motivated by the need to provide realistic inputs to the metroplex experiments for current and future traffic scenarios.Most inter-arrival time probability distribution models fit in the exponential family.Mathematical manipulation of the exponential family of models can be facilitated through parameterization of canonical and mean values.A specific model can be characterized by a location parameter γ , a dispersion parameter β , and a shape parameter α .The location parameter has a one-to-one transformation of the mean; normally it is a shift from the mean.The dispersion parameter measures the spread of the distribution and may correlate with the shape parameter that normally controls the skewness of the distribution.It is intuitive to expect that under high traffic conditions the inter-arrival time would concentrate towards the minimum separation in effect as controllers would keep spacing as small as possible while maintaining separation requirements.Spacing under low traffic conditions is expected to have a larger dispersion.The corresponding probability density function would shift to the right (longer time between arrivals) with a larger mean as the observed spacing intervals would be more likely driven by random events.The following sections discuss the modeling approach, the calibration of the model, and modeling results.
+Modeling ApproachIn this work, models of inter-arrival time probability distribution at TRACON entry fixes and models of inter-departure time probability distribution at the runways were developed.Interarrival time is selected to represent spacing because it can be directly used in metroplex queueing simulations.One day's worth of archived performance-data-analysis-and-reporting-system (PDARS) data from October 2, 2008, representing typical west-flow operations at ATL were used for model development.The arrival fixes for A80 are along an approximately 40-nm radius from ATL.For each arrival flight, the intercept point at the 40-nm radius was calculated from the PDARS track, along with the crossing time.PDARS data were also used to determine the runway usage and runway times for both arrivals and departures.To examine the distribution of inter-arrival time under different traffic conditions while maintaining reasonable sample sizes, the arrival rates at the entry fixes were divided into four discrete levels (see Table 22).The full day's worth of data were divided into 24 one-hour bins.The arrival rate for each one-hour bin was calculated for each entry fix.Assuming sufficient data points exist within each time bin, one model can be developed for each such bin.With the resulting 24 models from one day's worth of data, a good estimate of the variability of probability distribution parameters can be developed as a function of traffic levels.However, for each one-hour bin, the available data points are limited, especially for the one-hour bins with low arrival rates or departure rates that contain fewer observations.This issue is resolved by grouping the one-hour bins into the defined four traffic levels.Because of the limited data available for the analysis, the modeling process was simplified based on the following assumptions:1.The probability distribution function of spacing remains in the same form under different traffic levels, with only the values of (some of) its parameters varying with the traffic level.2. Observations within a time bin during a typical day are representative subgroups of the population.3. The arrival process at primary arrival fixes or the departure process at runways is homogeneous for all such arrival fixes or runways.The homogeneity applies to entry fixes and runways as if the operations are conducted independently, but at the same fix or runway.4. Instead of performing statistical tests to identify subpopulations within each discrete traffic level, it is assumed that the sample mean arrival rate corresponding to the discrete traffic level is representative of all the data points grouped into the same traffic level.This assumption helps to maintain a reasonable sample size for each traffic level.5. The parameters of the statistical model are determined only by the mean spacing, or arrival rate and departure rate.From these assumptions, making an inference to the distribution under any given traffic condition depends on the model parameters.The maximum-likelihood estimator (MLE) [LK07] was selected to estimate the parameters from the independent and identically distributed (IID) data for each selected traffic level.The relationship between model parameters was established through the fundamental parameter for the exponential family known as the mean of time intervals.In other words, the parameters of the statistical model can be written as a function of average arrival or departure rate for the given traffic level.With model parameters determined for the four defined traffic levels, the trending of model parameters with arrival or departure rate can be determined using regression.
+Model Development ResultsFor arrivals, the 40-nm radius crossing times were first grouped according to the existing arrival fixes, i.e., DIRTY, CANUK, HONIE, and ERLIN.This grouping was done by comparing the bearing of the crossing point and the bearing of each arrival fix, both relative to ATL.Data points with time separation less than 1.07 minute (assuming a ground speed of 280 kt, this separation is equivalent to a 5-nm longitudinal separation) were simply excluded, as these flights might have been laterally or vertically separated.Close examination of flights with similar characteristics would be conducted in future analysis when more data are processed to assure this assumtion is valid.For departures, only operations from runways 26L and 27R were analyzed.The number of departures from runway 28 was too small to be used.The effect of wake vortex separation minima was considered for the departure process.Categorizing data into four traffic levels for both arrivals and departures was done in a way to allow relatively equal sample sizes among the levels.Lower-frequency data were placed in levels with a wider range of arrival/departure rates, increasing the variation in the model for those levels.It is an iterative process to obtain a balance between sample size and population segregation, as both are important to the accuracy of the model.The hypothesized distributions for both arrival and departure were selected to be Weibull distributions [KS08], which have an overall better fit.The Weibull distribution has the probability density function     >       - - - = - - otherwise , 0 if , ) ( exp ) ( ) ( 1 γ β γ γ αβ α α α x x x x f where 0 , > β α .The Weibull distribution reduces to the exponential distribution when 1 = α .It appears to be a bell-shape and a reversed J-shape distribution for 1 > α and, 1 < α respectively.Table 22 shows estimated Weibull distribution parameters using the MLE for different traffic levels, along with the corresponding traffic levels given in terms of mean time intervals between flights, and average arrival or departure rate.The probability density functions (PDFs) generated from the model parameters listed in Table 22 are shown in Figure 28, with arrivals on the left and departures on the right.Note that for departures, the wake vortex separation is applied; thus the minimum time interval varies with the aircraft pair.Because of the low proportion of heavy jet operations at ATL, sufficient data were not available for aircraft pairs involving a heavy jet.The departure data listed in Table 22 and the departure PDFs shown in Figure 28 are for large-large aircraft pairs only.From the PDF curves, it can be seen that the dispersion of time interval between departure flights at the runway threshold was less than that for arrivals, implying that controllers had more precise control over departures times.The dispersion of time intervals between flights for arrivals at the TRACON entry fixes was greater than for departures, implying the strong influence of random events.For both arrival and departure operations, the dispersion becomes greater at lower traffic levels, reflecting less restriction to operations and more random-event-type behavior.The dispersion or uncertainty, especially for arrivals at high traffic volume, is a measure of traffic-flow efficiency.With the model parameters determined from the data, the distribution of arrival or departure rates other than those listed in Table 22 can be obtained through a trending analysis.The trend of parameters describes how a family of statistical models varies as traffic load varies.The mean of the Weibull distribution can be determined by parameters γ β α , , using the following equation:) 1 ( ) ( α α β γ Γ + = X EFor consistency, the model parameters for a given arrival or departure rate must satisfy this equation.If all three Weibull distribution parameters were to be determined through trending for a given arrival or departure rate, the resulting mean time interval may be different from that corresponding to the given arrival or departure rate.It was thus determined that only estimated Although the model parameters were determined from the ATL data for the four selected traffic levels, the same principle can be applied to a different set of traffic-level definitions.If additional data can be obtained, model accuracy can be improved.With the parameter trending, traffic levels above the current level can be simulated, especially in a future increased traffic-demand scenario.From the investigation, and from observations of metroplex operations, the system does not always grow in the same form.However, the proposed spacing models could still be used as reference indicators to make inferences and perform comparisons across different types of metroplex operations.For arrival operations, the model is based on the flow crossing times at a cylindrical boundary around the metroplex terminal area.The spacing information can be interpreted as a result of traffic coordination before flights arrived at the boundary.Data from different facilities could be used to identify additional independent variables such as traffic-flow coordination usage.For departure operations, spacing characteristics could also be correlated with the runway configuration used.Separation models written as a function of runway configuration would be more intuitive in this case.
+Other Factors Airspace RestrictionsSpecial-use airspaces (SUAs) or terrain features impose airspace restrictions on metroplex operations.These restrictions can be modeled as blocked (i.e., unusable) volumes of cylindrical airspace defined laterally by a polygon and vertically by an altitude range given the bottom and the top of each cylindrical volume.Following this definition, the difference between a SUA and a terrain feature is not distinguishable except that a SUA becomes hot (the restrictions are enforced) only during a certain period, while the terrain restrictions are always enforced.For the Generic Metroplex model, SUAs and terrain features can be modeled parametrically, following samples from metroplexes in the NAS.Existing SUAs can be found in the Federal Aviation Administration (FAA) National Flight Data Center (NFDC) publications.Terrain features can be also found in the NFDC publications.The minimum vectoring altitude (MVA) used by air traffic control (ATC) can be used as definitions of large terrain features such as mountains.Because of limited resources, in the initial Generic Metroplex model described in section 7.2.1, the effect of SUAs is not considered and the MSL is used as the Earth's surface.
+Weather ConditionsThe two basic elements of the weather model are wind profiles and temperature profiles.Typical wind and temperature profiles can be used to explore the range of trajectory variations and their impact on the separation between trajectories, and consequently minimum displacements and travel times.For the sake of simplicity, standard atmosphere and zero wind were assumed in this study.Stochastic wind short-term wind variations [RC08], however, were used in the simulation study.
+Metroplex Intersect Flow Analysis
+Metroplex Intersect Flow MetricsThis section introduces two metrics for quantifying the complexity of metroplex airspaces.Complexity of the airspace surrounding two or more closely spaced airports will increase with the amount of overlap between their aircraft flows, defined as aggregations of flights following a perceptible pattern.Flights are grouped into flows by the proximity of their tracks in space and time.In order to quantify the interaction of flows, the notion of an aircraft flow envelope is developed and used to define two metrics for flow interactions: flow envelope intersections and flight pairs.
+Aircraft Flow EnvelopesFor analysis of existing metroplexes, historical track data can be used to define aircraft flows.All of the tracks occurring during a specified window of time can be displayed in three dimensions using Metron Aviation's Airspace Design Tool (ADT).The grouping of tracks into flows can be determined visually or in an automated way using clustering algorithms within ADT [WC04].For future metroplex design studies, the planned three-dimensional (3-D) paths from the arrival and departure fixes to the runways are employed to define the metroplex flows.The aircraft flow envelope is a "minimal" volume of airspace encompassing most or all of the traffic in a flow.For existing metroplexes, ADT is used to define these envelopes by creating low-and high-altitude "backbones" for each flow.These backbones follow the lateral center of the tracks in each flow, and either the lowest or the highest altitude track, using a series of nodes along the tracks with lateral "dispersions" that encompass all of the tracks.For future metroplex design, flow envelopes are created by first dividing each arrival and departure path into equallength sections, defining "nodes" along the path, and assuming the paths are linear between these nodes.Vertical and horizontal dimensions are then added to each node in accordance with a specified RNP standard.In order to find the intersections of these flow envelopes using the Intersect Flows algorithm, they must first be divided into convex polyhedra.The method for dividing them is already suggested by the division of the centerline paths into linear segments divided by nodes, as described previously.In the case of an existing metroplex, the low-and high-altitude backbones for each flow use identical latitudes and longitudes so they can be used to define the set of convex polyhedra.Similarly, in the future metroplex design case the RNP vertical and horizontal designations at each node along the path naturally define the division of the flow into convex polyhedra.Figure 30 shows the overhead view of a set of flow envelopes that have been divided into convex polyhedra using this method.
+Flow Envelope Intersections MetricThe Flow Envelope Intersections Metric is simply the sum of all pairwise intersection volumes of distinct flow envelopes in the metroplex.The formula for the intersection of one such pair of flows is given by the following:• Let I(j,k) be the volume of the intersection of the jth and kth convex polyhedra from Flow1 and Flow2, respectively.• The envelope intersection is defined as Σ j Σ k I(j,k).The sums are taken over the polyhedra of Flow1 and Flow2, respectively.The total Flow Envelope Intersections Metric for a metroplex is the sum of all volume intersections of distinct pairs of flow envelopes in the metroplex, shown as green volumes in Figure 30.
+Static and Temporal Flight-Pairs MetricsThe Flight-Pairs Metric utilizes the idea of flow envelopes described previously, but creates a conceptually more realistic metric describing interactions of flights rather than volumes of airspace.The difference is that instead of computing the volume of airspace in the intersection of two convex polyhedra, the "expected" number of "flight pairs" contained in the intersection is calculated.The idea of flight pairs is to count the expected number of flights from Flow1 and Flow2 that are in proximity.
+Static and Temporal Flight-Pairs MetricsThe Flight-Pairs Metric utilizes the idea of flow envelopes described previously, but creates a conceptually more realistic metric describing interactions of flights rather than volumes of airspace.The difference is that instead of computing the volume of airspace in the intersection of two convex polyhedra, the "expected" number of "flight pairs" contained in the intersection is calculated.The idea of flight pairs is to count the expected number of flights from Flow1 and Flow2 that are in proximity.The calculation is done by intersecting each pair of convex polyhedra coming from Flow1 and Flow2 as previously, but instead of taking the volume intersection, the expected number of flights from Flow1 contained in that volume intersection is computed, as well as the expected number of flights from Flow2 in the volume intersection.Multiplying these numbers together yields the expected number of flight pairs for this intersection.To be explicit, this metric finds the number of expected flights in the intersection coming from polyhedron j as the number of flights in j multiplied by the proportion of the volume of j included in the intersection.It then finds the product of the number of flights coming from each pair of polyhedra, and sums them over all possible pairs coming from Flow1 and Flow2.Formally, define:V(i,j), the volume of the jth convex polyhedron for Flow i, i = 1,2.T(i,j), the number of tracks in the jth convex polyhedron for Flow i, i = 1,2.I(j,k), the volume intersection of the jth and kth convex polyhedra from Flow1 and Flow2, respectively.Then the Flight-Pairs Metric is defined as:Σ j Σ k [T(1,j) ⋅ I(j,k)/V(1,j)] ⋅ [T(2,k) ⋅ I(j,k)/V(2,k)]The sums are taken over all polyhedra in Flow1 and Flow2, respectively.The total Flight-Pairs Metric for a metroplex is the sum of all the flight pairs for distinct pairs of flows in the metroplex.The flight-pairs definition given previously is "static" in the sense that it uses a single set of track data.This concept can naturally be extended to define a Temporal Flight-Pairs Metric by dividing the scheduled flight traffic demand into time bins, computing the static Flight-Pairs Metric for each time bin, and summing them.
+Metroplex Intersect Flows Analysis ResultsThis section presents the airspace complexity comparison between the four Generic Metroplex geometries, as measured by the Flow Envelope Intersections Metric and the Flight-Pairs Metric described earlier.Previous preliminary analyses of airspace complexity have been conducted on existing airports in A80 [CTWDL09] using these metrics with the flows defined by bundling historical flight tracks.
+Generic Metroplex Flow ShapesFor the Generic Metroplex study, aircraft flow envelopes are defined starting with the 3-D paths for each geometry as given in section 7.2.1 and adding width and height dimensions to each path in accordance with the horizontal and vertical parameters in section 6.4.In particular, take the parameters shown in Table 23 as the maximum width and height, defining four flow shapes: Then the width and height at each point along the path is given as a function of distance from the runway by linear interpolation between the values shown in Table 24.Each flow envelope is then divided into convex polyhedra having length 1 nm along the path centerline.Figure 31 shows a plan view of the result for geometry 3 using flow shape 2. Intersections of the flow envelopes are colored in green.The number of polyhedra in each flow envelope are determined by the length of the path from arrival or departure fix to runway.
+Application of Generic Metroplex Demand SetThe traffic demand set for the Generic Metroplex described in section7.2.2 is used in calculating the Flight-Pairs Metric.For this study the demand traffic was divided into 15-minute time bins by the scheduled "time in flow": the arrival or departure fix crossing time, adjusted by adding 5 minutes for arrivals and subtracting 5 minutes for departures.The Static Flight-Pairs Metric for each time bin (see section 7.3.1) is then computed and added together to obtain the Temporal Flight-Pairs Metric.
+Intersect Flows Results for the Generic Metroplex ModelsAirspace complexity for the four Generic Metroplex Geometries was compared using each of the four flow shapes defined previously.This analysis uses only the original demand as described in section7.2.2.Analyses on the demand set with scheduling (for geometries 1, 2, and 3; see section 7.2.1) were conducted, but no significant difference from the original schedule results was found, undoubtedly because the time-bin size of 15 minutes is too large to be sensitive to the small adjustments in arrival and departure fix-crossing times given by the optimized schedule.
+ConclusionsTable 25 shows that the four flow shapes have dramatically different Flow Envelope Intersection and Flight-Pairs Metrics for the same Generic Metroplex geometry.Table 26 through Table 29 show that no matter which flow shape is used, geometry 3 shows an improvement in both Flow Envelope Intersection and Flight-Pairs Metrics, while geometries 2 and 4 show increases in both metrics over the baseline geometry 1.
+Delay Comparison Based on Analysis of SchedulingThe Generic Metroplex airspace design (section7.2.1) presented in detail the different spatial de-confliction strategies (metroplex airspace geometries 1 through 4) that were tested.Section 6.6 presented a metroplex arrival scheduling algorithm that was used in the Generic Metroplex simulation study described in section 7.5 and the N90 Airport and Airspace Delay Simulation Model (SIMMOD) simulation study in section 8.This section presents in detail a set of alternative temporal de-confliction strategies (scheduling algorithms) and a delay comparison analysis based on these strategies.Because of the time available for this project, only arrival algorithms that assumed departure flows would be spatially separated from arrival flows were considered.Departure scheduling algorithms should be studied in future research activities in this area.
+Candidate Generic Metroplex Arrival Scheduling AlgorithmsIn this study, two options for arrival scheduling algorithms were studied.One option was a "no intelligent scheduling" option, which served as the baseline for comparing against all other scheduling options.Also studied was an "optimal scheduling" algorithm that minimizes overall delay cost by giving higher priority to flights belonging to the busier of the queues (among the queues at the arrival fix and queues at the runway).Following is a brief description of these two scheduling options.
+No Intelligent Scheduling OptionUnder this option, it was assumed that the TRACON arrival controller has no prior information about the estimated arrival-fix-crossing time or estimated runway landing time for incoming flights until they reach the TRACON boundary.Because of a lack of information, whenever a flight enters the TRACON (i.e., crosses the arrival fix), the TRACON arrival controller basically assigns it to land right after the latest flight (going to the same airport) that crossed the TRACON boundary just before the current flight.This strategy was a first in, first out (FIFO) strategy.
+Optimal Scheduling OptionUnder this option, it was assumed that the TRACON arrival controller and the upstream en-route controller would have information about the estimated arrival-fix crossing time and the estimated runway landing time for incoming flights at some look-ahead time before the flights reach the TRACON boundary.Because of the availability of this information, the en-route controller would have some flexibility in changing the sequence of arrival-fix crossings and the TRACON controller would have some flexibility in changing the sequence of runway landings.The optimal scheduling algorithm utilizes this flexibility to compute the optimum sequence of arrival-fix crossings and runway landings among leading flights going from each arrival fix to each runway such that an overall delay cost is minimized.In this algorithm, a set of leaders to each airport is picked at each arrival fix.Sequence changes between leader flights are allowed at the arrival fix and also at the runway.If any leader is not within a user-specified time window (default: 2 min), starting at the earliest estimated arrival-fix crossing time among the leaders, then it is dropped from the leaders' set.Each possible combination of arrival-fix-crossing orders and runway landing orders is evaluated for the flights in the leaders' set.The minimum cost combination is picked and the leading flights to each runway among the picked combination are scheduled (i.e., their arrival-fix and runway times are fixed).Here, delay cost is equal to the sum over the leader flights of Flight Delay × Queue-delay factorwhere Queue-delay factor for flight i = Number of flights following flight i within a userspecified time window (e.g., 2 min) of its estimated arrival-fix-crossing time.The scheduled flights are removed from the leaders' set, a new leaders' set is formed, and the process is repeated until all flights are released.
+Implementation of Scheduling AlgorithmsThe following paragraphs describe different pieces of the implementation that were used to test the scheduling algorithms.
+AssumptionsThe following assumptions were implicit in the modeling/assessment process:• Estimated runway-landing time is computed as the estimated arrival-fix-crossing time (taken from the input demand set) plus the nominal TRACON transit time, assuming a CDA profile (taken from a table of transit times as a function of the distance from the runway per aircraft weight class, generated by the Tool for Analysis of Separation and Throughput (TASAT) [RC08]).• Nominal arrival-fix crossing speeds and runway landing speeds are taken from aircraft weight class-dependent tables (source: Airspace Concept Evaluation System (ACES) data).• There are only two de-confliction points-the arrival fix and the runway.It is assumed that between these two points controllers will keep flights separated and try to meet the scheduled times at the runway.• The arrival-fix minimum spacing requirement between consecutive arrival-fix crossings is 5 nm.• The runway spacing requirement between consecutive runway landings is taken from an aircraft weight class-dependent matrix of distance spacing requirements (source: ACES data).
+Realization of the AlgorithmsMATLAB [MW09] was used as the platform for developing the implementation of the FIFO and optimal scheduling algorithms.Input demand sets processed by the MATLAB script consisted of initial estimated arrival-fix-crossing time, initial estimated runway landing time, aircraft weight class, landing runway/airport identifier, and arrival-fix identifier for each flight.The MATLAB script processes the initial estimated arrival times of the flight by applying processing in line with FIFO or optimal scheduling to compute the scheduled arrival-fix and runway times.Enroute, TRACON, and total delay per flight are computed as the difference between the initial and scheduled times.Delay metrics for each generic airspace geometry under both scheduling options were collected and compared.The results of delay comparison are presented in the next subsection.
+Scheduling-Analysis ResultsThis section presents results of the Generic Metroplex analysis based on the alternative scheduling options.The metrics collected during initial simulation runs were:• En-route delay per flight (= Delay absorbed before reaching the arrival fix)• TRACON delay per flight (= Delay absorbed between the arrival fix and the runway)• Total delay per flight (= Sum of en-route and TRACON delays)• Distribution of the delay across flights going to airport A vs. airport B vs. overall delay
+Generic Metroplex Geometries 1 and 2Figure 32 shows the total delay distribution for airport A, airport B, and overall for geometry 1 or 2 (shared arrival fixes/shared arrival fixes and terminal arrival routes).It is seen that the total delay at an airport/runway was roughly correlated to the number of operations at the airport/ runway, but the relationship was not linear.Airport A had twice as much traffic as airport B, hence it had a much larger amount of delay.Also, it is seen that optimal scheduling (green bar) saves approximately 12% delay overall as compared to the no scheduling (FIFO) option (red bar).Figure 33 shows the distribution of delay between TRACON and en-route for geometry 1 or 2. It is seen that most of the delay was absorbed within the TRACON, because excess arrival-fix spacing was not provided between successive arrival flights and that lack of spacing led to significant congestion inside the TRACON and at the runways.Also, it is seen that optimal scheduling tended to transfer some delays from TRACON to en-route delays while minimizing total delays.
+Generic Metroplex Geometry 3Figure 34 shows total delay distribution for airport A, airport B, and overall for geometry 3. Again delay was roughly correlated with the number of operations at each runway.The optimal scheduling saved approximately 10% delay over no scheduling here.Figure 35 shows the distribution of delay between TRACON and en-route for geometry 3. Again, it is seen that most of the delay was absorbed within the TRACON.The imbalance between en-route and TRACON delays was more severe here because in geometry 3 each arrival fix served only one airport and hence flights going through the fix did not have to be spaced with respect to flights going to the other runway.This situation created an excess influx of flights into the TRACON that the busier runway could not handle without excessive TRACON delay.
+Comparison and ConclusionFinally, Figure 36 shows a comparison of total airport/runway delay with the optimal scheduling and without scheduling for both geometries 1 and 3.It is seen that both with and without scheduling, total arrival delays were 57% lower in geometry 1 airspace than in geometry 3 airspace.However, with scheduling, arrival delay at airport B for geometry 3 was 45% lower as compared to geometry 1, while with scheduling, arrival delay at airport A for geometry 3 was 62% higher as compared to geometry 1.From this analysis, it was shown that geometry 3 (exclusive arrival fixes per metroplex airport) was better as compared to geometry 1 (shared arrival fixes) only for airports with a low demand/capacity ratio.For airports with high demand/capacity ratios the runway was the main constraint.The analysis based on the scheduling algorithms described in sections 7.4.1 and 7.4.2shows that it might be better to have shared arrival fixes across multiple metroplex airports.This observation is worth further examination in the future; factors that could be considered include traffic volume at each arrival fix and possible enhancements to the scheduling algorithms presented in this section to improve the handling of shared arrival fixes.In any case, the optimal scheduling saved around 12% delay over no scheduling for geometry 1 generic airspace and around 10% for geometry 3 generic airspace.Figure 36.Comparison of total delay between geometry 1 (or 2) and geometry 3.
+Generic Metroplex Queueing SimulationTo thoroughly evaluate the impact of future metroplex concepts and identify the most promising concepts, a linked-node queueing-process-based simulation was created to determine the delay of arrival operations.In this simulation study, the intention was to vary each parameter to span the range of all the Next-Generation Air Transportation System (NextGen) capabilities as well as technologies that have been conceptualized by the Georgia Institute of Technology (GaTech) team.Details of the linked-node queueing-process model and the associated assumptions are presented in the next subsection.The parameters tested and their ranges of variation, the test conditions, and specific test cases are described in section 7.5.2.Results from each test case are presented as a separation subsequent subsection.
+Linked-Node Queueing-Process ModelBecause of limited time available for this project, only arrival operations were studied.As illustrated in Figure 37, two types of shared resources are modeled in the linked-node queueing process: entry fixes and runways at metroplex airports.Theoretically, points where traffic flows merge or cross (at the same altitude) could also be modeled, but they are omitted for the sake of simplicity.The model is reconfigurable to have any number of entry fixes and any number of runways.Each entry fix is modeled as a single-server FIFO queue with infinite capacity.The service time is a random variable corresponding to minimum required separation at the arrival fix (i.e., 5 nm) because of the random fix-crossing speed.If an aircraft arrives at the entry fix when the queue is empty and no aircraft is being served (meaning the spacing from the previous aircraft is greater than the minimum required separation), it is released to enter the metroplex terminal area immediately, thus no queueing delay is incurred.When another aircraft is being served, regardless of queue length, the aircraft has to wait until the server is free.The waiting time in the entry fix queueing is referred to as the entry delay.Each runway at a metroplex airport is also modeled as a single-server FIFO queue with infinite capacity.Note that the runway queue capacity is physically limited because of the limited volume of airspace within the terminal area.When runway queue is full, holding may be implemented at the entry fixes.Assuming an infinite runway queue capacity simplifies the coding of the simulation, it also allows schematic trend analysis as the arrival-rate approaches very large values, as discussed in section 7.5.3.The service time is a random variable corresponding to minimum required separations at the runway threshold (i.e., wake vortex separation as a function of aircraft weight class) and the random final approach speed.Similar to entry fixes, queueing delays may incur at the runway threshold.This delay is referred to as the runway delay.In the real world, this delay may be incurred anywhere between the entry fix and the runway through path stretching or speed adjustment.Based on the temporal-spatial displacement concept, the delay is assumed to incur at the runway threshold without losing generality.Potential ground infrastructure limitations are ignored in the model, assuming that no other runway delays will incur except the queueing delays due to the required wake vortex separation.Inputs to the linked-node queueing-process model are aircraft arriving at entry fixes and destined to predefined runways.For each aircraft the aircraft type is specified.The arrival aircraft sequence at an entry fix can be specified either by an arrival rate with a specified inter-arrival time distribution or by a sequence of arrivals (normally one day's worth of traffic) with the fix arrival time for each aircraft specified.The links between the entry fix nodes and the runway thresholds are reconfigurable, ranging from each entry fix linked to a specific runway (fully segregated traffic flows, e.g., Generic Metroplex geometry 3) to every entry fix linked to every runway (fully shared entry fixes, e.g., Generic Metroplex geometry 1).The link between an entry fix and a runway threshold is a terminal-area arrival transition assuming a CDA-type vertical profile and speed profile overlaid on the lateral path given in the Generic Metroplex airspace design.A large pool of CDA trajectories were simulated for different aircraft types using TASAT [RC08] with uncertainty factors such as random aircraft weight, short-term wind variations, and random pilot-action delays.For a specific aircraft, a trajectory is randomly sampled from the pool.As such, the transition time from an entry fix to a runway threshold is a random variable.The arrival time at the runway queue is thus a random variable determined by the release time at the entry fix and the random terminal-area arrival transition time.The linked-node queueing-process model is implemented as a discrete-event simulation in SimPy-an object-oriented, process-based discrete-event simulation language based on standard Python [MV03].The output of the simulation is a log of events associated with each aircraft, including aircraft identification, entry fix, entry delay, entry fix-crossing time, runway, runway delay, and runway threshold-crossing time.The system performance can then be measured by entry delay, runway delay, and total delay on a per-aircraft basis or as a cumulative system-wide total.
+Generic Metroplex Test-Case DesignIn this simulation study, the intention was to vary each parameter to span the range of all the NextGen capabilities as well as technologies that have been conceptualized by the GaTech team.Parameters considered include traffic demand, airspace geometry, arrival scheduling, and metering accuracy.The variations of these parameters were grouped into three test cases.Delays were the output metrics being measured.The parameter setup of the three test cases is described as follows.
+Delay vs. Arrival Rate
+HypothesisThe hypothesis of this test case was that as the arrival rate (i.e., traffic volume) increases the chokepoint of the Generic Metroplex system might shift from runways to entry fixes and, in addition, different Generic Metroplex geometries might behave differently under different arrival rates.
+Control VariablesIn this test case arrival rate was the major control variable.The total metroplex arrivals were equally divided as independent Poisson processes among the available entry fixes for the Generic Metroplex geometry being tested.Arrivals at each entry fix were modeled as a Poisson process that was fully defined by the arrival rate at the fix.The total metroplex arrival rate varied from 0 aircraft per hour (AC/hour) to a theoretical value of 2,000 AC/hour.The same arrival-rate range applied to all geometries tested.The airspace geometry was the second control variable.Generic Metroplex airspace design geometries 1, 3, and 4 were tested.Geometries 2 and 1 have the same number of entry fixes.Because merge points within the Generic Metroplex terminal area were not modeled, geometries 2 and 1 were thus viewed as the same for all three test cases.For this analysis, arrival scheduling was not implemented and the metering accuracy was assumed perfect to eliminate any possible nuisance effect on the output.This is to say, no additional uncertainty was applied to the entry fix arrival times generated by the Poisson process.
+Execution of Test RunsThe number of test runs for this test case was given by
+Number of Geometries (3) × Number of Arrival Rate ValuesFor each test run, i.e., a given airspace geometry at a given arrival rate, the simulation lasted for 24 hours.The 24-hour simulation time assured that the queueing system reached the steady state for the major part of the entire simulation time.The results of this test case are presented in section 7.5.3.
+Impact of Arrival Scheduling
+HypothesisThe hypothesis of this test case was that with the scheduling algorithm runway delays might be significantly reduced, while at the same time the increase in entry delays might be limited, resulting in reductions in overall delay; additionally, the effectiveness of the scheduling algorithm might vary with the specific Generic Metroplex airspace geometry being used.
+Control VariablesIn this test case, the Generic Metroplex demand sets described in section 7.2.2 were used.A demand set was generated for each of the Generic Metroplex airspace geometries.In each demand set, the inter-aircraft time interval at each entry fix was adjusted per the spacing model described in section 7.2.3 to emulate the spacing effort by Center controllers.Each demand set covers a 24-hour period.The arrival scheduling was the major control variable.When no scheduling algorithm was used, the sequences of estimated time of arrival (ETA) at the entry fixes given by the demand set were used as the aircraft arrival times at the entry fixes.When the scheduling algorithm (as described in section 6.6) was used, the sequences of ETA were adjusted by the scheduling algorithm to generate sequences of RTA that were then fed into the simulation as arrival times at the entry fixes.The airspace geometry was the second control variable.In this test case, only Generic Metroplex airspace design geometries 1 and 3 were tested.Geometry 4 was not tested because: the intersect flow analysis (see section 7.3) indicated a significant increase of traffic flow complexity for geometry 4 over geometry 1and the sensitivity analysis of delay vs. arrival rate indicated that without a sophisticated scheduling algorithm, all delays would be incurred within the terminal area, resulting in significant inefficiencies.The metering accuracy was assumed perfect, i.e., no additional uncertainty was applied to the original entry fix arrival times generated by the demand set or the new entry fix arrival times adjusted by the scheduling algorithm.
+Execution of Test RunsThe number of test runs for this test case was given byNumber of Geometries (2) × Number of Scheduling Options (2)For each test run, i.e., a given airspace geometry with or without scheduling, the simulation started when the first flight in the demand set arrived at the entry fix and ended when the last flight in the demand set crossed the runway threshold.The results of this test case are presented in section 7.5.4.
+Impact of Temporal Control Accuracy
+HypothesisThe hypothesis of this test case was that en-route temporal control (metering) accuracy might have a significant impact on delays.The lower the metering accuracy the higher the delay might be.The metering accuracy might impact the delay more when scheduling was used because the metering accuracy would negate the performance of scheduling and the impact might be dependent on Generic Metroplex airspace geometry.
+Control VariablesAs in the previous test case, the Generic Metroplex demand sets described in section 7.2.2 were used.Again, each demand set covers a 24-hour period.The metering accuracy was the major control variable.The metering error was defined by the difference between the actual time of arrival and the nominal time of arrival.The nominal times of arrival were the original sequences of ETA for arrivals without scheduling and the adjusted sequences of RTA for arrivals with scheduling.The metering error was assumed to follow normal distributions with a bias of zero sec and a standard deviation ranging from zero (perfect metering) to 30 sec with a 6-sec step size.The airspace geometry was the second control variable.As in the previous test case, the Generic Metroplex airspace design geometries 1 and 3 were tested.The arrival scheduling was the third control variable.For each geometry tested the metering accuracy was tested with arrival scheduling and then without arrival scheduling.Again, the scheduling algorithm described in section 6.6 was used for this purpose.
+Execution of Test RunsThe number of test runs for this test case was given by For each test run, i.e., a given airspace geometry with or without scheduling and a given metering accuracy, the simulation started when the first flight in the demand set arrived at the entry fix and ended when the last flight in the demand set crossed the runway threshold.The results of this test case are presented in section 7.5.5.
+Sensitivity Analysis of Delay versus Arrival RateAs specified in the test-case design, this analysis was conducted to study the effects of the arrival rate on Generic Metroplex system-wide delays.The results of this analysis are shown in Figure 38, Figure 39, and Figure 40 for the Generic Metroplex airspace design geometries 1, 3, and 4, respectively.As mentioned before, geometries 2 and 1 have the same number of entry and exit fixes.They thus were viewed as the same for this analysis.In each of the figures average queueing delay per aircraft at the entry fix, average queueing delay per aircraft at the runway, and the total delay per aircraft (sum of the previously mentioned two) are plotted versus metroplex total arrival rate on the left and versus metroplex mean time between arrivals (MBA) on the right.Because there are only two airports within the Generic Metroplex model, high arrival rates such as those greater than 500 AC/hour would be way beyond the runway capacity.Data at those values are presented to highlight the general trend.40 show that for all three airspace geometries, delays at runways started to diverge much earlier than delays at entry fixes.There were several reasons behind this observation.The ground speed during approach is normally much lower than that at entry fixes.The compression effect requires spacings larger than separation minima in effect at entry fixes.If only the separation minima were enforced at entry fixes, delays would have to be absorbed within the TRACON as traffic volume increased.In the Generic Metroplex model there were only two arrival runways, one at each of the two airports, while there were 4 entry fixes for geometry 1, 8 for geometry 3, and 16 for geometry 4. Aircraft from different fixes would have to be merged to the runway at each airport.Even if proper spacings were enforced at entry fixes, issues would likely rise at the runways.Another connected observation is that as the number of entry fixes increased from 4 for geometry 1 to 16 for geometry 4, and the divergence of delays at entry fixes occurred at a later time and at a slower rate as the total metroplex arrival rate increased.Because the same volume of metroplex total traffic was divided into more fixes, delays incurred because spacings in the traffic stream at each entry fix were lower.In addition, because more entry fixes existed, the ripple effect at entry fixes due to runway congestion was weaker.As the arrival rate continued to increase, the delays at runways reached a saturation value.From that point on, even if the injected arrival rate continued to increase, the runway delays remained the same.The transition from the increasing runway delay to constant runway delay occurred when the entry fixes reached their capacities; thus the rate of aircraft entering the TRACON airspace remained constant from that point on.Any additional aircraft injected at the entry fixes were held at the entry fix.Delays at entry fixes started to rapidly diverge at the same time.As the number of entry fixes increased, the metroplex total arrival rate corresponding to entry fix saturation point became larger.As a result, the maximum delay per aircraft at runways increased.The transition point was about 400 AC/hour for geometry 1, 800 AC/hour for geometry 3, and 1,600 AC/hour for geometry 4, corresponding to 100 AC/hour/fix.Figure 38, Figure 39, and Figure 40 also show that the average total delay per aircraft remained roughly the same for the three Generic Metroplex geometries studied.This result indicates that, without arrival coordination (as was the case with this analysis), making more entry fixes available for the metroplex would allow some delays that previously were absorbed in en-route airspace during very busy periods to be transferred to terminal-area airspace.Delays within the terminal-area airspace normally cost more than the same amount of delays within the en-route airspace because aircraft are more efficient in the en-route airspace when aircraft are at higher altitudes and cleaner configurations.Thus, extra entry fixes may not always be beneficial under high traffic conditions, unless existing arrival fixes are identified as the bottleneck (not the case with this analysis), or when arrival coordination capabilities are in place.As seen in Figure 25, with 16 arrival fixes and 16 departure fixes, the airspace structure is very complex.Managing and coordinating traffic flows under high traffic conditions would be a very challenging task.Additionally, any airspace geometry design change would likely face different environmental issues.In interpreting the results, one must note that with multiple entry fixes, such as in geometry 4, arrivals can fly routes that are more direct as compared to the four-corner-post design such as geometry 1.Under relatively light traffic conditions, the benefits of flight time and fuel savings from flying short routes will out-weigh possible delays in the terminal area.In this case, however, a large number of predefined entry fixes may not be essential.In fact, at light traffic conditions, arrivals are often cleared "direct to" the airport without going through the predefined arrival gates that have to be used during busy periods.Based on these discussions,only geometries 1 and 3 are discussed in the analyses to be presented in the next few sections.
+Impact of Arrival SchedulingAs specified in the test-case design, this analysis was conducted to study the effect of scheduling.For the given demand generated for the Generic Metroplex model, simulation was first done without applying the scheduling algorithm to the arrival traffic and then repeated with the scheduling applied.To compare system performance of each airspace geometry design, the cumulative delay is plotted against cumulative aircraft count for the entire day of traffic, as shown in Figure 41.In these plots, the instantaneous slope at each point indicates the throughput per unit delay; the shallower the slope, the better the system performance.The overall position of the curve indicates system performance over time; the lower the curve, the better the performance.As shown in the figure, both entry delays and runway delays were significantly reduced by arrival scheduling.In terms of cumulative total delay, a 75% reduction was achieved.Similar delay reductions results were observed for both geometries 1 and 3.Another interesting observation from Figure 41 is that, without scheduling, the cumulative entry delay was slightly lower for geometry 3 than for geometry 1, apparently because of the increased number of entry fixes available.However, the cumulative runway delay was slightly higher for geometry 3 than for geometry 1.Because traffic flows at entry fixes were less constrained in geometry 3, the runway thus had to absorb more delays than the runway in geometry 1.The cumulative total delay, however, remained roughly the same.With scheduling, the cumulative total delay was much lower for geometry 3 than for geometry 1, indicating improvements resulted from the combination of temporal control and spatial control.Figure 41 also shows that, regardless of Generic Metroplex geometry and scheduling, the cumulative runway delay was always much higher than the cumulative entry delay.In the initial Generic Metroplex design, there were only two airports, each having only one arrival runway.The demand capacity ratio of 0.7 at airport A was actually relatively high, close to the demand capacity ratio of ATL [RC09b].This setup determined that runways were choke points in the system and consequently the majority of delays were incurred at runways.The high delay reductions from arrival scheduling reflect the necessity of scheduling for managing critical shared resources.In addition to segregating traffic flows from and to different airports, the increased number of entry fixes increases the total entry fix capacity.As the number of airports increases, the capacity at entry fixes may become more critical, and consequently entry delay will increase.The benefits of airspace geometries with more entry fixes, such as geometry 3, would be higher.As shown in Figure 43, with scheduling, the average total delay per aircraft was reduced to 0.54 min for flights destined to airport A in geometry 1 and 0.44 min for flights destined to airport A in geometry 3, corresponding to reductions of 75% and 80%, respectively over the unscheduled case.For airport B, the average total delay per aircraft was reduced to 0.18 min in geometry 1 and 0.09 min in geometry 3, corresponding to reductions of 46% and 73%, respectively over the unscheduled case.Again, while without scheduling the average total delay per aircraft was roughly the same for flights destined to airports in both geometries, with scheduling, the average delay per aircraft was 18% lower for airport A in geometry 3 than for airport A in geometry 1, and almost 50% lower for airport B in geometry 3 than for airport B in geometry 1.With scheduling, geometry 3 contributed to further reductions in average per aircraft delays.
+Impact of Temporal Control AccuracyThis analysis was conducted to study the effect of arrival-fix-crossing metering accuracy on delays.As shown in Table 21, the bias, i.e., the difference between the mean fix-crossing time To further the understanding of the relationship between delays and metering accuracy, the overall total delays of the entire day are plotted against σ values in Figure 45.Results for arrivals without scheduling are on the left, and those with scheduling are on the right.In this figure, the shaded bands are linear regressions of data points.The vertical axes have different lower and upper limits but the same scale.Thus, the slope of the curves can be compared with each other.As seen in Figure 45, in all cases the trend of increasing delays with increasing σ values was observed.Again, it is seen that the trend was more consistent throughout different σ values for arrivals with scheduling.The slopes for arrivals with scheduling were also slightly higher, meaning that the metering accuracy had a stronger impact on scheduling.It was observed that for the largest σ value tested (54 sec), nearly one-third of the 75% overall total delay reductions could be lost.Still, even in this worse case, the delays were reduced by 50% from the case without scheduling.It is also observed that, for any given metering accuracy, the delays were lower in geometry 3 (segregated traffic flows to different airports) than in geometry 1 (shared entry fixes).
+Conclusions of the Generic Metroplex Queueing SimulationWith the developed linked-node queueing-process model, three simulation studies were conducted: sensitivity analysis of delay vs. arrival rate, the impact of arrival scheduling, and the impact of temporal control accuracy.For arrivals, the entry fixes at the boundary of the metroplex terminal area and the runways at metroplex airports are two sets of flow check points.The arrival-rate sensitivity analysis revealed that when runways are the choke points (capacity limits), increasing the number of entry fixes to segregate traffic to different airports would not necessarily help reduce delays.In this case, the entry fixes serve as regulators to limit the number of flights to runway queues and thus limit terminal area delays.Without arrival scheduling, at high traffic volumes, the average delay per aircraft remained roughly the same for the Generic Metroplex geometry 1 (4 shared entry fixes), geometry 3 (8 fixes, segregated routes), and geometry 4 (16 fixes, segregated routes).Actually, delays incurred within the terminal area tended to be higher as the number of entry fixes increased and therefore higher fuel-burn costs would be incurred.It is expected that to realize the benefits of more direct routing and decoupled traffic flows from an increased number of entry fixes, some mechanism to regulate arrival traffic should be in place.It is also expected that for metroplexes with multiple large hub airports, entry fixes may become major choke points; consequently an increased number of entry fixes would improve system-wide throughput.The simulation revealed that arrival scheduling greatly reduced both entry delays and runway delays.Under the given simulation conditions, total delays for the entire day were reduced by roughly 75%.Delays were similar for geometries 1 and 3 when no scheduling was applied; with scheduling the decoupling of traffic flows in geometry 3 provided additional delay reductions.The simulation also revealed that the delay reductions realized by scheduling were most significant at busy airports.On average delay per aircraft was reduced by roughly 1.5 min from over 2 min to the order of 0.5 min for airport A, the busy airport in the Generic Metroplex.The temporal control accuracy, or metering accuracy, affected delays whether or not scheduling was applied.The impact is more evident when scheduling was applied.Because the lower metering accuracy reduced the compliance to target fix-crossing times, some delay reduction benefits would be negated.However, even with the worst possible metering accuracy, two-thirds of the delay reductions from the perfect metering still could be retained, suggesting that even without the temporal control accuracy that is expected for future 4-DT operations, scheduling would still result in revolutionary delay reductions.
+Generic Metroplex Environmental-Impact AnalysisIn this section, the environmental-impact analysis conducted for the four Airspace Geometries of the Generic Metroplex is described.This analysis was conducted as an attempt to evaluate the fuel-burn and emissions impact of the four Generic Metroplex geometries.The effects of scheduling and temporal control strategies were not considered for the sake of simplicity.The analysis utilized the NAS-wide Environmental Impact Model (NASEIM) [M09] to model fuel burn and emissions.The results for each geometry were compared to the baseline (geometry 1).
+NASEIM Fuel-Burn CalculationsFuel-burn values in NASEIM are derived from Eurocontrol's Base of Aircraft Data (BADA) [BADA09], which contains fuel-consumption rates for specific airframe and engine combinations at various altitudes and modes of flight (thrust settings).For portions of the flight below 3,000 ft above ground level (AGL), fuel burn is given by the Emissions and Dispersion Modeling System (EDMS) [EDMS].The basic formula for calculating fuel burn is given by: F a,i = ne a × ff a,i × tm a,i where:F a,i = the fuel burned by aircraft a, while in mode i ne a = the number of engines on aircraft a ff a,i = the fuel flow rate of aircraft a, while in mode i tm a,i = the time aircraft a spends in mode i Flight modes are defined as climb, cruise, and descent, and are related to the engine thrust settings.The BADA and EDMS data tables specify fuel-flow rates by altitude and flight mode.The tables specify fuel-burn rates for low, nominal, and high aircraft weights; NASEIM assumes nominal aircraft weight.Fuel-flow rates for intermediate altitudes are interpolated from the table values.Fuel burn is then calculated by multiplying the specified fuel flow rate by the time spent between each node in the flight trajectory.Summing the fuel burn for each trajectory segment gives the fuel burn for the entire flight.
+NASEIM Emissions CalculationsEmissions calculations in NASEIM utilize the value of fuel burned in each of several operational phases to estimate the mass of pollutants generated.For each of several pollutants (CO, HC, NO x , and SO x ), the mass is given by:M i,total = Σ m (F m * EI i,m )where F m is the fuel burned in mode m (kg) and EI i,m is the emission index for pollutant i in mode m (g/kg fuel).Engine-specific International Civil Aviation Organization (ICAO)/EDMS taxi/idle fuel-flow values are used to derive the fuel burn during the taxi phase, and are combined with ICAO/EDMS taxi/idle emission factors to compute the pollutant totals emitted during surface movement.The airborne aircraft trajectory is broken into several phases.Below 3000 feet AGL, enginespecific ICAO/EDMS fuel-flow rates and emissions indices are applied, with takeoff values used from takeoff to 1000 feet AGL, climb values between 1000 and 3000 feet on departure, and approach values between 3000 feet and touchdown.The mapping from aircraft type to engine type is made based on a review of the domestic commercial fleet and default engine assignments specified in the EDMS.Above 3000 feet AGL, aircraft-specific BADA fuel-flow factors are used.Each distinct segment is classified as a climb, cruise, or descent segment.The mean altitude of the segment is used to determine the corresponding BADA fuel-flow rate for that segment type.
+Conclusions for the Generic Metroplex Environmental Impact StudyFrom Table 33, Table 34, and Table 35 it can be seen that geometry 3 had slightly better environmental impact for arrivals using this demand set than geometry 1, whereas for departures the numbers were slightly worse.The total values for geometries 1 and 3 look very similar.Emissions and fuel consumption for geometries 2 and 4 were higher than for geometry 1 for both arrivals and departures, with the values for geometry 4 being significantly higher.The initial Generic Metroplex model consisted of only two airports, each with a pair of parallel runways.This model represents a relatively simple metroplex, thus greatly simplifying the analysis and simulation.This simplification allowed the effects of NextGen concepts on system performance to be evaluated and the most promising concepts to be identified within the short study time frame, as demonstrated by the major findings presented in section 7.7.1.On the other hand, because of the simplifications, runway capacity was more of a constraint than entry fixes and exit fixes, whereas in the most complex metroplexes of the NAS, such as N90 and SCT, entry fixes and exit fixes frequently become major constraints.It is thus recommended that additional airports and runways be added to the Generic Metroplex analysis, with varying airport locations and runway orientations for future simulations studies to refine the concepts and identify concept technical challenges.As the number of airports increases and the traffic flows become more complex, the size of the metroplex terminal area may also need to be adjusted from the current 40-to 50-nm radii to allow for better airspace geometry design concepts.Additionally, more flexible entry fix and exit fix setups need to be introduced.In the initial Generic Metroplex design, interactions between crossing routes were simplified; the route design method and the linked queueing model need to be extended to reflect those interactions.One of the important factors that affect metroplex operations is airspace restrictions.As identified from the metroplex site-survey study, these restrictions include the effects of SUAs and terrain features.Because of limited resources available for the current project, SUA and terrain constraints were not considered in the initial Generic Metroplex model.These factors can be included in future Generic Metroplex analysis.SUAs and terrain features can be modeled parametrically to explore the impact of airspace restrictions on metroplex system performance.Various airspace designs and traffic coordination algorithms can be explored to identify best approaches to the problem.For similar reasons, standard atmosphere and zero wind were assumed in the Generic Metroplex study.Severe weather was not considered either.These factors can be considered in future Generic Metroplex studies to test the robustness of the airspace design strategy and the scheduling algorithms.Problems associated with system response to severe weather can also be identified and explored.
+N90 SIMMOD SIMULATION STUDY
+Introduction and BackgroundThe goal of the N90 simulation study was to verify in a real-world metroplex the Next-Generation Air Transportation System (NextGen) technologies that were down-selected as a result of analysis and the Generic Metroplex simulation study.As discussed in section 6.1, the two most important abstractions of NextGen technologies relevant to metroplex operations are spatially segregating terminal-area routes to and from multiple airports, and temporally coordinating arrivals and departures.N90, the most complex metroplex studied by the team, provides a suitable experiment platform.First, the effectiveness of the selected NextGen technologies can be tested in the most challenging environment.In addition, experiment results can provide recommendations to operational improvements in N90, to which 60% of the NASwide delays can be traced.Members of the Georgia Institute of Technology (GaTech) team have been performing extensive modeling and simulation of the N90 airspace as part of the NASA Project NNH07ZEA001N-IAC1, entitled "Integration of Advanced Concepts and Vehicles into the Next Generation Air Transportation System (NextGen)".The focus of this research effort was to conduct a systems study that addresses the issues associated with deploying new/advanced vehicles by exploring the trade among procedures, vehicle characteristics, and overall NextGen performance.This work involved the analysis of advanced vehicles expected to be available for commercial use in the 2025 to 2040 time frame.The detailed modeling of current and NextGen procedures and technologies for the N90 Metroplex was directly applicable to achieving the objectives of the Metroplex Project, and the modeling used for the Advanced Vehicles project was leveraged to successfully meet these goals.
+Simulation ToolThe simulation tool used for this effort was ATAC Corporation's Airport and Airspace Delay Simulation Model, SIMMOD.SIMMOD is a discrete-event simulation model that traces the movement of individual aircraft and simulated air-traffic-control (ATC) actions required to ensure aircraft operate within procedural rules.This tool computes capacity and aircraft delayrelated metrics caused by a variety of inputs, including traffic demand and fleet mix, route structures (both in the airspace and on the airport surface), runway use configurations, separation rules and control procedures, aircraft performance characteristics, airspace sectorization, interactions among multiple airports, and weather conditions.SIMMOD uses a node-link structure to represent the airspace route structure and the surface system, including runways, taxiways, and gates.Based upon a user-input scenario, SIMMOD tracks the movement of individual aircraft through an airport/airspace system, detects potential violations of separations and operating procedures, and simulates ATC actions required to resolve potential conflicts.The model properly captures the interactions within and between airspace and airport operations, including interactions among multiple neighboring airports.
+Model DevelopmentThe current-day airspace route structure was developed using radar flight track and flight plan data extracted from the performance-data-analysis-and-reporting-system (PDARS).The selected day (19 March 2007) represents typical visual-meterological-conditions (VMC) flight operations in the N90 Metroplex.Four runway plan changes made during the day were included in the overall simulation model.However, because of the complexity of accounting for the dynamics of the plan changes in the temporal scheduling, only a single runway plan was utilized for the entire day.The airports modeled in SIMMOD include the four primary N90 Metroplex airports: JFK, EWR, LGA, and TEB; and four secondary airports, including FRG, HPN, ISP, and SWF.When more than one arrival or departure runway was available, the distribution of runway operations was based upon the PDARS data.Aircraft arriving to one of the eight airports were injected into the simulation based on the time they first appear in the radar data.For departures, radar flightpaths typically began when an aircraft was approximately 200 ft above ground level (AGL).To account for this reality, departure injection times were adjusted to account for the time it takes to taxi from the departure queue, the runway ground roll, and the flight time to 200 ft AGL.The impact of the surface movement was not required for this effort, so departures were injected into the simulation at the departure queue rather than at a gate.Next the current-day airspace model was developed and its performance was calibrated against the PDARS data.The primary metrics used for calibration were runway throughput and arrival and departure transit times.Runway throughput was compared between the simulated results and what was observed in the radar data, as shown in Figure 51.The transit times for each runway/departure or arrival route combination were also compared between the simulated results and what was observed in the radar data.The comparison of arrival transit times is shown in Figure 52.As a secondary comparison, transit times by aircraft weight class were made to ensure that these differences were accurately accounted for in the model.To study the potential impacts of NextGen technologies and procedures, a second simulation model was developed.The SIMMOD NextGen airspace structure was constructed as a combination of the current-day airspace structure and the NextGen airspace designed by the Georgia Institute of Technology (GaTech).Figure 53 presents a schematic of the GaTech airspace design.The NextGen airspace provided the "inner" airspace of the model while the current-day routes were connected to the various entry and exit points of the inner airspace.Speed and altitude profiles were then adjusted to reflect a continuous-descent arrival (CDA) profile for arrivals and continuous ascent and acceleration for departures.The most important characteristic of the NextGen airspace is the fact that all routes are decoupled from each other.Procedures associated with each arrival or departure fix-runway combination do not interact with each other, significantly simplifying the airspace operations since operations at one airport do not affect the operations at another airport.One potential concern with the decoupled airspace is its conformance to existing noise constraints restrictions in N90.In the decoupled airspace design, the arrivals and departures assumed near optimal profiles, thus the noise footprint should be smaller.The purpose of the design was to test delay and throughput impact of the decoupled airspace concept.Although some of the arrival or departure routes might fly over noise-sensitive areas because of the simplified design, design improvements could be incorporated in the future to address the noise concern should an implementation be desired.
+Simulation Setup
+Selection of Major Control VariablesFrom the analysis in the previous sections and what is already available in the SIMMOD N90 models, the control variables were selected as follows:
+Spatial Control VariablesTwo spatial route structures were selected to present spatial control variables.The first one reflects current-day operations, and the second one was the proposed NextGen fully decoupled route structure.Both the strategic-level airspace structure and tactical-level separation standards were embedded into these two route structures.
+Temporal Control VariablesTwo levels of temporal control were selected.The first one reflects current-day N90 operations, and the second one was a proposed future proactive scheduling and temporal control.The scheduling and metering strategies and temporal control uncertainties were carefully integrated into two separate scheduling modules that were applied to the SIMMOD model inputs before those inputs were fed into the SIMMOD model for simulation.
+Current-Day Scheduling and MeteringAs described in section 8.1.2,the current-day scheduling and metering were developed from 24 hours of operations on a representative day, i.e., 19 March 2007.PDARS data were analyzed to obtain arrival and departure traffic properties such as inter-arrival times and traffic loading on each fix or runways.
+Future Scheduling and MeteringThe scheduling algorithm described in section 6.6 was used to emulate the best strategy given potential traffic, weather, and other information that may be available in the NextGen time frame.Metering accuracy was also improved to assume NextGen capability.Nominal schedule and sequencing was generated first, and then the algorithm was applied to adjust the schedule and sequencing of the aircraft at the arrival fixes.A tolerance of +/-10 seconds was applied to the arrival times, and the simulation was run for five iterations to account for the expected variability of actual operations.
+Selection of Other Experiment VariablesIn addition to these two major control variables, the following variables had to be provided for the simulation:
+Demand LevelsBased on previous discussions with NASA, current-day demand levels were selected for the current phase of the project because of time constraints.However, a wider range of demand level would provide an opportunity to explore the response of candidate solutions to system demand.The nominal demand selected for simulation is the 100% current-day demand derived from a representative day, i.e., from 19 March 2007, as described in section 8.1.2.
+Weather ScenariosBased on previous discussions with NASA, and given the complex nature of the weather, only a VMC scenario was modeled for the current phase of the project.
+Runway ConfigurationsBecause of the complexity of the runway configuration changes and based on discussions with NASA, it was decided to use constant runway configurations for an entire day.Comprehensive analysis of runway configuration changes requires an understanding of the behavior of the controller and of the specific NextGen technology dynamics.Given the time available, using a constant configuration was a reasonable approach and produced valid experimental results.
+Test-Case DesignFigure 54 presents the test-case matrix used for the N90 experiment.In each test case, identical demand, weather, and runway configurations were used.Test cases were designed to explore the impacts of temporal control variables and spatial control variables separately and jointly.Four test cases were conducted, starting from current-day N90 operations to conceptual future operations.
+Current-Day N90 OperationsThis test was a straightforward experiment of the current-day N90 operations.Conditions included current-day demand, current terminal-area route structure, and current flow control and metering performance.
+Simulation with Current Route Structure and Improved Temporal ControlThis test was an experiment of the current-day N90 operations with improved metering.Conditions included current-day demand, current terminal-area route structure, improved flow control and metering performance, and traffic coordination between different airports.
+Simulation with Decoupled Route StructureThis test was an experiment of the future decoupled route structure.Conditions included currentday demand and current flow control and metering performance.
+Simulation with Future Route Structure and Improved Temporal ControlThis test was an experiment of the future decoupled route structure.The current-day demand was imposed to the system, but the same improved flow control and metering performance and traffic coordination were applied.
+Delay and ThroughputA primary metric to compare the impact of the spatial route decoupling and the temporal shift of arrival times at the arrival fix is the amount of air delay incurred by aircraft within the N90 terminal-radar-approach-control-facilities (TRACON) boundaries.Figure 55 presents the average arrival air delay per aircraft, in minutes, for the four test cases.Two significant findings are contained in these data.First, in almost all cases the decoupled airspace had significantly lower arrival air delay than the current airspace configuration.The second result is that the temporal shift at the arrival fix significantly reduced arrival air delay after aircraft were released at the arrival fix for both airspace configurations.
+Causes of Arrival DelayIn the current airspace, arrival air delay may be incurred for several reasons.The first is due to the arrival timing at the arrival fix.In the simulation, the aircraft were injected at the arrival fix, and any pair of aircraft injected so that insufficient spacing would cause the trailing aircraft to incur delay.This same effect can be observed at any merge point along their trajectory, since the trailing aircraft would have to absorb delay so that sufficient separation from the leading aircraft exists at all times.For heavily utilized routes, each merge point caused a ripple effect to all trailing aircraft.Within the simulation, this effect was somewhat mitigated by increasing the in-trail spacing upstream from the merge points so that the two streams of aircraft have greater spacing prior to entering the merge point and can merge with less delay.However, making this spacing too large would potentially penalize arrivals during periods of low demand or even when there were no merging conflicts.Sophisticated modeling capabilities required to make these types of air-traffic-control (ATC) decisions is available only in the advanced version of SIMMOD and was not available for this research.Another potential source of delay results from faster aircraft trailing slower aircraft.The amount of delay incurred by the trailing aircraft is a direct function of the common path length between the two aircraft.Any type of procedure that segregates slow aircraft from fast aircraft will reduce delay.In-trail wake vortex separation requirements between different aircraft weight categories could also result in increased delay.Optimal sequencing of heavy, large, and small aircraft categories would reduce arrival delay by minimizing the overall separation distances required between successive pairs of aircraft.
+The Impact of Decoupled AirspaceIn the decoupled airspace, these same factors are still present, with one exception.The decoupled airspace removes the dependency of arrival operations at one airport from the operations at another airport.In the decoupled airspace used for this research, each arrival runway was also decoupled from other arrival runways, decreasing the number of merge points and the number of aircraft that were required to merge.The benefits of decoupling the airspace can be observed by the results presented in Figure 55 for JFK runways 13L and 22L.The aircraft arriving to these runways incurred 64% and 75% less delay, respectively, on average in the decoupled airspace compared to the current airspace.In addition, this figure shows that the decoupling of the airspace provided significantly more improvement than the temporal shifting of arrival fix times for JFK runways 13L and 22L.These effects were also present at all other modeled airports except LGA runway 22 and EWR runway 22L.The potential noise impact of the decoupled route structure was not analyzed in this study to allow for evalution of the design concept in a short time frame.As mentioned earlier, the decoupled airspace would utilize near optimal arrival and departure profiles, and the noise footprint would be smaller than current-day operations.It is expected that some of the routes in decoupled airspace may fly over noise-sensitive areas, and thus may raise concerns about community noise impact.The issue, however, could be addressed by considering the noise constraints in refining the airspace design without losing much of the benefit of the overall concept.
+The Impact of Temporal ImprovementAgain, referring to Figure 55, the temporal shift of arrival times at the arrival fix provided additional and significant decreases in delay incurred within the N90 airspace.One thing to note about this reduction of delay is that it was actually a shifting of delay.Delay that was originally incurred within the N90 airspace boundary was moved into the en-route airspace by specifying arrival time at the fix.This specification allowed the arriving aircraft to travel through the terminal airspace closer to their nominal travel times, but required some procedure for the aircraft to arrive at their designated arrival-fix time.It is important to note that for the decoupled airspace, any delay reduction observed was a true reduction in overall flight time.The impact of temporal shifting on delay reduction can be significant; however, shifting demand to reduce terminal airspace congestion could result in reduced arrival throughput.Two interesting cases within the N90 Metroplex illustrate how a reduction in arrival delay may or may not affect throughput.For a runway operating near or below capacity, the temporal shift reduces arrival delay by allowing two aircraft arriving at the arrival fix at about the same time to be deconflicted with minimal impact on aircraft further up the arrival stream.However, when arrival demand exceeds the runway capacity, the temporal shift will affect all aircraft upstream, requiring a similar temporal shift to be applied to all of them.The scheduling of the aircraft effectively serves as a way to meter the arrivals so that they do not exceed the runway capacity, resulting in a reduction in runway throughput.Figure 56 presents examples of this throughput reduction for the decoupled airspace when scheduling was applied.The charts present the cumulative difference in runway throughput (15-min time bins) between the scheduled and unscheduled cases for EWR runway 22L and LGA runway 22.For EWR runway 22L, the chart on the left shows that the cumulative runway throughput for the scheduled case never deviated more than two aircraft (in any given 15-min time bin) from the unscheduled case.However, forLGA runway 22, the scheduled arrival stream fell significantly behind during several periods in the day, and up to 17 aircraft at one point.In both of these cases, the scheduling of the arrival demand achieved a significant reduction in arrival delay.Part of the reason of the momentary throughput reduction was that the demand of LGA runway 22 was about 15% higher than that of EWR runway 22L.Referring to Figure 55, EWR runway 22L had a 65% decrease in arrival air delay within the N90 airspace and LGA runway 22 had a 75% decrease, a reduction of flight time with the N90 airspace.A close inspection revealed that that during the periods in whichLGA runway 22 throughput was observed, the throughput (demand) was high and fluctuated significantly when the scheduling was not applied.When the scheduling was applied, the throughput was at the roughly the same level but with smaller fluctuations.
+Comparison of Decoupled Airspace and Temporal ControlFigures 57 and 58 present the cumulative arrival air delay versus cumulative arrival throughput for the entire N90 Metroplex.The results in Figure 57 are comparisons between the current airspace and decoupled airspace.In this figure, the comparison for the unscheduled arrivals is shown on the left and the comparison for the scheduled arrivals is shown on the right.For both unscheduled and scheduled arrivals, the decoupled airspace provided significant benefit relative to the current airspace configuration.It can be seen that, in terms of absolute values, the decoupled airspace contributed greater reduction in arrival air delay when no scheduling was applied to arrivals than the case when scheduling was applied (1,128 minutes vs. 788 minutes).Percentagewise, the decoupled airspace reduced total arrival air delay by 50% from that in the current airspace when scheduling was applied versus 28% when no scheduling was applied.The total delay reduction from current airspace without scheduling to the decoupled airspace with scheduling was 79%-which happened to be the same number as in the Generic Metroplex study.Figure 58 presents a similar comparison, showing the impact of scheduling for each type of airspace (current and decoupled).Incorporating arrival scheduling into the current airspace reduced total arrival air delay in the metroplex from 3,992 minutes to 1,608 minutes for a reduction of 60%.The benefit of scheduling the decoupled airspace was an overall reduction of 2,044 minutes, or 71%.
+Fuel Burn and EmissionsFuel burn and emissions were calculated to evaluate the energy and environmental impact of the spatial route decoupling and the temporal shift of arrival times at the arrival fix.
+Fuel BurnFigure 59 presents the arrival fuel burn for the four test cases, with total fuel burn per runway on the left and average fuel burn per flight on the right.As seen, in both current airspace and decoupled airspace, the scheduling saved fuel for arrival flights at most runways.However, mixed results were observed for the decoupling of the airspace.For JFK runway 22L and LGA runway 22, the decoupled airspace saved total fuel burn.For EWR runway 22L, FRG runway 19, and JFK runway 13L, the decoupled airspace actually slightly increased the total fuel burn.No significant total fuel-burn changes were observed for other runways.On a per-flight basis, similar results were observed for the NextGen airspace, mostly because in the decoupled airspace arrival routes were longer for almost all runways.A system-wide fuel burn reduction of 11% was still observed; it was due to reduced delays and improved arrival profiles (see Table 36).The differences in average fuel burn between runways were mixed effects of different aircraft types that landed at the runways and the runway-dependent operating efficiency.Figure 60 presents the departure fuel burn for the four test cases, with total fuel burn per runway on the left and average fuel burn per flight on the right.As seen, arrival scheduling did not significantly affect departure fuel burn.The decoupled airspace, on the other hand, actually increased fuel burn for aircraft departed at most runways.In the decoupled airspace, departure routes were longer for almost all runways.Compared with arrivals, the room for profile improvement was also limited.Thus, the use of improved departure profiles was not able to compensate for the effect of extended route length.The end result is increased fuel burn with the decoupled airspace.E W R 1 1 E W R 2 2 L F R G 1 9 H P N 1 6 I S P 2 4 J F K 1 3 L J F K 2 2 L L G A 2 2 S W F 2 7 T E BE W R 1 1 E W R 2 2 L F R G 1 9 H P N 1 6 IS P 2 4 J F K 1 3 L J F K 2 2 L L G A 2 2 S W F 2 7 T E BE W R 2 2 R F R G 1 9 H P N 1 6 I S P 2 4 J F K 1 3 R J F K 2 2 R L G A 1 3 S W F 2 7 T E B 1 9 T E B.2 E W R 2 2 R F R G 1 9 H P N 1 6 IS P 2 4 J F K 1 3 R J F K 2 2 R L G A 1 3 S W F 2 7 T E B 1 9 T E B
+EmissionsThe amount of CO 2 emitted from aircraft engines is directly proportional to fuel burn.Each kilogram of jet fuel produces 3.155 kilogram of CO 2 .The same observations in fuel burn directly apply to the CO 2 emission.The NO x and particulate matter (PM) emitted from aircraft engines are correlated only to fuel burn, but also influenced by engine operating conditions.Figure 61 presents the average NO x and PM per flight for arrivals to different runways.As can be seen, for arrivals to most runways, both the NextGen decoupled airspace and scheduling contributed to NO x emission reductions because of the CDA profiles employed in the NextGen airspace and the reduced delays within the terminal area.Less reduction was observed in the PM emission.For some runways, such as EWR runway 22L and JFK runway 13L, an increase in the PM emission was observed for the NextGen airspace.However, consistent reductions in PM were observed with scheduling.The overall total emissions for arrivals are summarized in Table 37.Figure 61 presents the average NO x and PM per flight for departures from different runways.Similar to fuel-burn results, arrival scheduling significantly affected departure emissions.The NextGen decoupled airspace increased emissions for the major runways for similar reasons that caused fuel-burn increases.E W R 1 1 E W R 2 2 L F R G 1 9 H P N 1 6 IS P 2 4 J F K 1 3 L J F K 2 2 L L G A 2 2 S W F 2 7 T E B 1 9 Runway Average NOx, kg Current Airspace Unscheduled Current Airspace Scheduled NextGen Airspace Unscheduled NextGen Airspace Scheduled 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 E W R 1 1 E W R 2 2 L F R G 1 9 H P N 1 6 IS P 2 4 J F K 1 3 L J F K 2 2 L L G A 2 2 S WE W R 2 2 R F R G 1 9 H P N 1 6 IS P 2 4 J F K 1 3 R J F K 2 2 R L G A 1 3 S W F 2 7 T E B 1 9 T E B 2 4 Runway Average NOx, kg Current Airspace Unscheduled Current Airspace Scheduled NextGen Airspace Unscheduled NextGen Airspace Scheduled 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 E W R 2 2 R F R G 1 9 H P N 1 6 I S P 2 4 J F K 1 3 R J F K 2 2 R L G A 1 3 S W F 2 7 T E B
+Summary of Major FindingsBased on the Generic Metroplex analysis and simulation study, four test cases were developed to test the performance of two concepts in N90.The first concept was a fully decoupled airspace route structure that enabled flights to and from different runways to operate independently.The second concept was an arrival scheduling algorithm that recommended entry-fix-crossing times adjustments so that operations within the N90 Metroplex terminal area can operate with much fewer conflicts.The SIMMOD simulation revealed that, applied separately, both the NextGen fully decoupled airspace and the arrival scheduling significantly reduced arrival air delay incurred within the N90 terminal area; 28% and 60% system-wide reductions from current-day operations were realized, respectively.Combined, the decoupled airspace and scheduling reduced the system-wide arrival air delay from current-day operations by 79%.Consequently, fuel burn and emissions were also significantly reduced.The reductions from the NextGen decoupled airspace verified the hypothesis drawn from the Generic Metroplex linked queueing simulations.Results indicated that when entry fixes become major choke points, increasing the number of entry fixes and decoupled routes would improve system-wide performance.That said, scheduling showed a higher impact on system-wide delay reductions, similar to the results from the Generic Metroplex simulation.In the SIMMOD simulation some issues were also identified.In the NextGen decoupled airspace, with the application of scheduling, the cumulative throughput for LGA runway 22 was below the throughput without scheduling during some busy periods, mainly because the scheduling algorithm intended to smooth out demand fluctuations at the entry fixes.In any case, this phenomenon warrants further study in the future.Another issue in the N90 NextGen decoupled airspace departure routes had longer ground tracks than that in the current airspace.Effort was taken to utilize improved departure profiles, but these improvements were limited and did not compensate for the impacts of longer ground tracks.Thus, the departure fuel burn was higher in the NextGen decoupled airspace than in the current airspace.Optimization could be employed to improve the NextGen decoupled airspace design to mitigate this effect.
+DISCUSSION AND CONCLUSIONSThis section synthesizes the findings and issues identified through the current phase of the metroplex research.Observations are explained, results interpreted, and inferences conducted; implications of this research are discussed.
+Major ConclusionsIn this research the Georgia Institute of Technology (GaTech) Metroplex team systematically studied the parameters that determine the coupling and inefficiencies in metroplex operations; developed a framework to evaluate concepts and capabilities that manage the coupling of metroplex operations; and conducted the initial simulations to evaluate the impact of downselected technological capabilities to identify the most promising concepts.Specifically:1.The team conducted survey studies at four representative metroplexes in the National Airspace System (NAS): Atlanta (A80), Los Angeles (SCT), New York (N90), and Miami (MIA).The team developed comprehensive site-survey reports documenting major facilities, constraints, traffic demand, operational procedures, air-traffic-control (ATC) automation tools, and potential future developments at each site.2. Based on the site-survey studies, through subject domain expert evaluation and qualitative analyses, the team identified and rank-ordered major metroplex issues, including: airspace entry fix and exit fix sharing; major volume-based traffic-flow management (TFM) restrictions; proximate-airport configuration conflicts; slow interairport surface connectivity; inefficient and high-workload airport configuration changes; inefficient inter-airport departure sequencing; flow constraints; inefficient "flushing" of airport flows; external special-use airspace (SUA)-caused flow dependencies; terraincaused flow dependencies; severe limitations on instrument procedures due to proximate airport; and insufficient regional airport capacity.3. The team conducted detailed analysis of airspace-related issues and categorized airspace dependencies into six types, including: sharing of fixes through metering; sharing of path segments through metering; sharing of volume of airspace through holding or stop of one flow; vertical flow segregation; lateral flow segregation; and downstream flow restrictions for multiple airports.4. Through quantitative analyses, the team developed three sets of metrics to categorize existing metroplexes in the NAS and identify the potential need for future metroplexes because of regional traffic growth.A set of geographic-based metrics was developed to measure intrinsic dependencies within each metroplex.An arrival flow airspace volumebased metric was used as the "distance" measure for clustering airports into metroplexes and identifying potential future metroplexes in the NAS.A set of intersect flow metrics was developed to measure the complexity of traffic flow interactions within a metroplex terminal area.Among the four sites surveyed, the metrics identified N90 as the most complex metroplex, followed by SCT.It was also identified that N90 and SCT have many similarities.A80 and MIA could be categorized as moderately coupled metroplexes.5. The team then developed a framework for evaluating the impact of NextGen and teamproposed future concepts and capabilities for managing metroplex operations.In this framework, measures handling interdependencies among metroplex traffic flows were abstracted as two distinct control strategies: temporal displacement, or spatial displacement from unimpeded ideal four-dimensional (4-D) arrival and departure trajectories.6. Metroplex concepts were then presented by their temporal impact and spatial impact on metroplex operations.In the evaluation framework, the temporal control was represented by traffic-flow coordination or scheduling that provided target times, e.g., fix-crossing times and takeoff times; and traffic-flow metering or surface management to achieve the target times.The spatial control was represented by lateral and vertical separation standards; and airspace design geometries and segregated 3-D routes based on separation standards and aircraft performance limits.To evaluate the impact of temporal control concepts on metroplex performance, the team developed several prototype scheduling algorithms and models of metering accuracy.To evaluate the impact of spatial control concepts on metroplex performance, the team developed schematic prototype airspace geometries and 3-D aircraft routes that aimed to decouple metroplex traffic flows.The team conducted two studies to evaluate the impact of metroplex concepts: a parametric Generic Metroplex simulation study and a N90 simulation study.7. The Generic Metroplex linked-node queueing simulation revealed that arrival delays incurred at the metroplex terminal-area boundary and within the terminal area were reduced by 73% from the standard four-corner post geometry 1 through employing scheduling algorithms to coordinate arrival traffic flows alone.Without scheduling, geometry 3 (with duplicate entry fixes to segregate traffic flows to different airports) did not achieve delay reductions.With scheduling, geometry 3 provided a 23% delay reduction from geometry 1.Additionally, geometry 3 achieved a combined 79% delay reduction from the case of geometry 1 without scheduling.Scheduling provided more significant delay reductions than the segregated route airspace geometry.The simulation also revealed that, with lower metering accuracy, the effectiveness of scheduling was impacted but most delay reductions from scheduling were retained even with the worstcase metering accuracy.This finding suggests that scheduling tools can be developed to achieve revolutionary delay reductions, even with current-day metering accuracy.Future four-dimensional trajectory (4-DT) operations would then provide further enhancements to the traffic scheduling and coordination.8. The New York Airport and Airspace Delay Simulation Model (SIMMOD) simulation revealed that, applied separately, the NextGen fully decoupled airspace and the arrival scheduling reduced system-wide arrival air delay incurred within the N90 terminal area by 28% and 60%, respectively, from current-day operations.Similar to the Generic Metroplex simulation study, scheduling provided more significant delay reductions than the fully decoupled airspace design.Combined together, the decoupled airspace and the scheduling reduced the system-wide arrival air delay from the level of current-day operations by 79%.Consequently, fuel burn and emissions were also significantly reduced.Based on the extensive study of metroplex operations, their inefficiencies, and potential metroplex solutions, the GaTech team developed numerous research recommendations.These research recommendations include suggested next steps beyond the work that has been performed to date in this project as well as a summary of implications for NextGen and beyond that are summarized in the next two sections.
+Next Steps and Beyond for Metroplex ResearchAs documented in this report and summarized in the previous section, a significant range of metroplex issues and inefficiencies have been identified, a range of potential metroplex concepts have been analyzed, and significant potential benefits of metroplex concepts have been quantified, both in a set of representative Generic Metroplex configurations and for N90.The definition of these potential metroplex concepts and quantification of the potential benefits constitute the beginning of a broader set of metroplex research and development tasks and benefits-assessment tasks.NASA plans to fully validate these tasks and improved metroplex concepts and requirements before transitioning the research to the Federal Aviation Administration (FAA).In general these broader future metroplex research tasks can be categorized as the development of refined concept modeling and prototype metroplex decision support tools, and further investigation into the analysis of metroplex concept impacts.
+Refine Concept Modeling and Develop Metroplex Decision Support ToolsAs described in section 6.2, many potential metroplex concepts have been identified as metroplex-specific Joint Planning and Development Office (JPDO) Operational Improvements as well as other metroplex concepts that the GaTech team has identified as having potential to reduce metroplex inefficiencies.Moving forward, it is suggested that NASA take numerous additional steps to refine, develop, and test new metroplex concepts.Ultimately, the successful development and testing of such metroplex concepts should lead to additional new metroplex concepts, technologies, and procedures that could be transitioned to the FAA through existing, e.g., integrated arrival-departure surface research transition teams (RTTs) or future, e.g., metroplex-specific RTTs.The recommend steps include:1. Select one or more desired metroplex concepts from the concepts discussed in section 6.2.Ideally, these concepts should be ones that address the major metroplex issues identified from site surveys and either showed up as a high-priority issue in section 5.1; or addressed the major inefficiencies quantified in Generic Metroplex (section 7) or N90 (section 8) analyses; or were less dependent on technologies that require extensive longterm research so that they can benefit metroplex operations in the near term.2. Expand and refine the concept description from section 6.2 and appendix A into a fullblown metroplex concept-specific concept of operations.Details would include the concept goals; expected concept benefits; stakeholders; system requirements; roles and responsibilities of the relevant operational personnel; functional, technical, and operational system architecture; and user-interface requirements.Additionally, it should describe how the future operation would work (and ideally the baseline "no-metroplexconcept" system as well), through a series of "cognitive walk-through" nominal and offnominal operational scenarios including nominal and off-nominal scenarios.3. Adapt and enhance the metroplex scheduling algorithms from sections 6.6 and 7.4 and other related metroplex concept assessment assumptions from section 6 to build fast-time, concept-specific algorithms to significant levels of fidelity for conducting performancebased impact assessments.These assessments should emulate the full scope of the expected planning horizon (e.g., including en-route, terminal, and surface planning elements) and account for expected system uncertainties (e.g., gate pushback uncertainties, runway takeoff time-compliance emulations, and arrival fix-crossing time uncertainties) such as those described in sections 6.4 and 6.5.4. Develop proof-of-concept mockups of the user interfaces for the different metroplex automation and communication input and output devices, elicit operational subject-matter expert review, and perform human-in-the-loop studies.This development should be done for all of the major operational positions that will be significantly impacted by the new metroplex concept.5. Leverage the proof-of-concept mockups and feedback and human-in-the-loop experiment results for the development of real-time operational software that provides the metroplex advisories with appropriate user interfaces and integration with emulations of real-world input data, e.g., Airport Surface Detection Equipment, Model X (ASDE-X) data, traffic flow management system (TFMS), integrated-terminal-weather-system (ITWS) weather data, and en-route-automation-modernization (ERAM) flight plans.Then, test this operational software in a real-time simulated environment such as NASA's Future Flight Central (FFC).6. Adapt the software to work in real-world operational environments such as provided at NASA's North Texas Research Station, testing the efficacy of this system with qualified operational personnel in either live-use or shadow mode.In general, the choice of the right operational environment depends on the magnitude and frequency of the inefficiencies within the proposed metroplex that are mitigated by the concept.Therefore, if analysis of the concept does not suggest significant benefits can be achieved within a metroplex such as Dallas/Ft.Worth, it may make more sense to choose a location such as New York or Los for a focused operational test.
+Extend Metroplex Concept Impact AnalysesIn addition to developing the metroplex decision support tools, it is suggested that NASA extend the metroplex concept impact analyses presented in sections5, 7, and 8 in a direction that supports the goals of understanding more broadly and/or deeper the impact of metroplex concepts described in section 6.2.These suggested future impact analyses include analyzing the sensitivity to a range of metroplex problem exogenous variables; extending the analysis to other metroplexes; studying different metroplex airspace designs, algorithms, and concepts for planning and control operations; analyzing different airport demand allocation schemes; quantifying the metroplex inefficiency impact on flights from secondary airports; and extending the range of impact metrics quantified.Numerous broader-scope and higher-fidelity impact analyses that are suggested follow: 1. Extend previous analyses to conduct a broader scope of high-fidelity, simulation-based evaluations.The team proposes to extend the Generic Metroplex analysis described in section 7 by performing a sensitivity study of metroplex performance to key metroplex exogenous variables, refined airspace geometries, and scheduling approaches.The team proposes to extend the N90 airspace analysis described in section 8 by considering additional scheduling approaches, considering airspace geometries developed with sitespecific constraints, and extending analysis to other metroplexes.In performing the latter, the team will leverage the metroplex extended dependency analysis in section 5.3 and clustering analysis described in section 5.4 as the source for identifying the future metroplexes.Lastly, the team proposes extending both the Generic Metroplex and N90 analyses to assess different metroplex designs, algorithms, and concepts for planning and control operations for the related metroplex tools discussed in section 6.2.2. The GaTech team has identified a multitude of metroplex system exogenous variables and inputs, including traffic demand, metroplex geometry, weather, and Traffic Management Initiatives.Traffic variables that could be explored include the metroplex airport daily traffic volumes and the relative ratios of traffic across the metroplex airports, traffic time-over-the-day profiles, traffic directional distributions, and airport demand-tocapacity ratios mentioned in section 7.2.2.Metroplex geometric variables include the number of metroplex airports; their relative proximities and runway orientations and their capacities; the number of arrival and departure fixes and their availability schedules; the shapes, volumes, and availabilities of metroplex airspace regions; and the design of arrival and departure procedures to and from the metroplex airports.Metroplex weather variables include impacted portions of metroplex airspace, weather probabilities, and resulting metroplex resources availabilities.Traffic Management Initiative impacts include miles-in-trail or minutes-in-trail restrictions, expected departure clearance times, coded departure routes (CDRs), and other major restrictions [SS09a].The values and ranges of each exogenous parameter may be obtained by analyzing current-day metroplexes and their operations and anticipated future metroplexes identified in section 5.4 through analysis of the extensive amount of ASDE-X, Automated Radar Terminal Systems (ARTS), host, and Enhanced Traffic Management System (ETMS) operational data along with other airport, airspace, and weather data available to the team.3. Conduct a metroplex performance sensitivity analysis to characterize the impacts of the full range of exogenous variables on metroplex operations and to identify the variables with the greatest impact on metroplex performance.This analysis would help pinpoint the most effective approaches for improving the metroplex operations individually and collectively.The study would likely leverage the Generic Metroplex assessment process, which abstracts the metroplex to these fundamental variables.Findings from the Generic Metroplex studies could isolate variables for closer analysis in high-fidelity, simulationbased studies of N90 and other metroplexes.4. Formulate and refine various metroplex-scheduling algorithms and conduct further performance sensitivity analyses via fast-time simulations.As described in section 5.2, many metroplex operational complexities and constraints hindering performance result from the sharing of common fix, route, and/or airspace resources among metroplex airports.Referring to section 6.1, scheduling is the only way to temporally coordinate the traffic among airports vying for use of shared metroplex resources where spatial control cannot be exercised.With respect to traffic-flow scheduling algorithms, the GaTech team has developed and evaluated multi-airport traffic-flow scheduling algorithms for this study, as discussed in sections 6.6 and 7.4.The team has investigated alternative optimization approaches and members have developed other metroplex traffic scheduling algorithms [SS09a].The study would extend the analysis to consider algorithms pertaining to metroplex-wide airport configuration scheduling, airspace resource scheduling, and trajectory management.Thus, the sensitivity of metroplex performance to both spatial and temporal spacing techniques would be studied as well as combinations thereof.For instance, an example is the metroplex concept that would include the dynamic process of providing procedural spatial separation only when the delays inherent in the temporal separation reach a critical threshold.The study would leverage the Generic Metroplex assessment process to evaluate different scheduling and spacing approaches and their sensitivities to exogenous variables (especially traffic and weather) and operational uncertainties (such as those identified in sections 6.4 and 6.5), and the most promising methods could be evaluated in high-fidelity, simulation-based studies of N90 and other metroplexes.Special cases such as metroplex terminal with en-route Traffic Management Advisor (TMA) scheduling interactions, inter-metroplex interactions (e.g., N90-PHL), and secondary airport traffic-flow interactions (e.g., LVK interactions with major San Francisco Bay Area flows) could be studied.5. Formulate and evaluate demand allocation approaches and algorithms for metroplex-wide strategic and tactical flight allocation to individual airports as per metroplex concepts proposed in section 6.2.1 and appendix A. The study would explore the benefits gained from such approaches and the sensitivities of various approaches on airport interconnectivity times, different demand scenarios (traffic volumes, fleet mixes, tail connectivity, other), metroplex resources availabilities, and Airline Operation Center business models.The study would leverage the Generic Metroplex assessment process, and the most promising methods could be evaluated in high-fidelity, simulation-based studies of N90 and other metroplexes.6. Of particular interest to evaluating both metroplex-wide inefficiencies and metroplexwide scheduling tools, the team proposes to quantify the delay incurred by departure flights from smaller secondary airports in the current call-for-release paradigm in order to establish a baseline delay performance at smaller, secondary metroplex airports lacking major infrastructure or staffing.Even though overall traffic levels at these secondary airports may be significantly lower, the lower priority given to this traffic, the greater number of airports that fit into this category, and the lack of readily available operational data on such operations make this topic an interesting one for additional research.An open research question that remains is whether the total metroplex system delay is greater at the higher-traffic but favored primary airports, or at the lower-traffic and not-favored secondary airports.For such flights, airfield measurements of estimated desired takeoff time and actual takeoff time obtained through site visits, observations, and radio communication monitoring would be used to estimate delay incurred by each flight.Site visits to several secondary airports within one or more metroplexes would likely provide sufficient data for this analysis.For each analysis simulation output data will be analyzed to assess performance metrics, including throughput, delay, fuel burn, noise and emissions, and controller taskload.For these studies, the effort will leverage the vast warehouses of ASDE-X, ARTS, host, and ETMS data available, as well as the existing tools and experience necessary to analyze these data.Data analyses will be applied towards metroplex model formulation and calibration, verification and validation of models and simulation analysis results, and establishing a baseline performance analysis for performance improvement comparison.Candidate tools for the aforementioned analyses are the Generic Metroplex assessment process and associated tools developed by the GaTech team for the current study as discussed in section 7, the SIMMOD-based N90 assessment process employed in this study and discussed in section 8, or other simulation tools capable of representing metroplex terminal areas.These tools include high-fidelity simulations of airport surface operations, such as NASA's Airspace Concept Evalution System (ACES), Airspace Traffic Generator (MACS), Test of Reaction and Adaptation Capabilities (TRAC), Surface Management System-Airspace Traffic Generator (SMS-ATG), or Sensis' AvTerminal simulation.The chosen tools should provide high-fidelity modeling of metroplex terminal airspace, explicit instantiation of scheduling algorithms, and real-time adjustment of metroplex exogenous geometric and traffic variables.
+Implications to NextGen and BeyondThe research results of the GaTech team summarized in section 9.1 and the NASA metroplex research work suggested in section 9.2 are critical to improving current and future NAS metroplex operational efficiency.As traffic demand increases in the future, more regions in the NAS are expected to become metroplexes.Thus, as these metroplexes grow, so will the expected levels of metroplex-induced air traffic delays due to the multiple metroplex issues and inefficiencies that have been studied in the current research.Thus, it is important for NASA to take additional metroplex research steps such as those suggested in the previous section to move metroplex concepts out from a low technology readiness level (TRL), concept exploration phase that has been the basis of this work, to further along the TRL scale towards future operational implementation and deployment.This process will help ensure that the NAS will be prepared to minimize the expected significant growth in future metroplex delays.6-nm separation minima or 5-nm increments for MIT.At very busy airports, a minimum 2.5 nm (noninteger) has been used between aircraft established on final approach to improve throughput and significantly reduce delay when the leading aircraft has a large or lighter weight class and the trailing aircraft has a large or heavier weight class.Greater throughput and delay reductions could be achieved when spacing required by metroplex traffic-flow scheduling is given in fraction numbers, e.g., 8.7 nm instead of 10 nm, but may be very difficult for the controller to quickly "eyeball" whether or not enough spacing exists in the traffic stream, particularly during heavy traffic periods.The Integer Concept is to implement the necessary metering and delivery control to create gaps just big enough in the arrival stream (e.g., a gap of 2.05 times minimum spacing rather than 2.5 times minimum spacing).Aircraft RTA capabilities make this implementation easier.
+Mega-Airport NetworkThe Mega-Airport Network concept concerns allocation of flights among metroplex airports by aircraft type or other criteria determined to maximize metroplex throughput and efficiency.In turn, passengers are ferried between metroplex airports to meet connecting flights using vertical/short takeoff and landing (V/STOL) aircraft or other high-speed ground transportation options.This concept or some version of it may represent the George Mason University (GMU) Metroplex concept.
+Metroplex V/STOL TransCon/International Connection NetworkThe Metroplex V/STOL TransCon/International Connection Network is similar to the Mega-Airport Network.In this concept, international flights are segregated from metroplex traffic and isolated to an off-line airport (e.g., 100 miles away).The concept uses four-dimensional trajectory (4-DT) V/STOL aircraft or a high-speed train to take international passengers to the off-line airport.A concourse for passengers to check in would be provided at piers; e.g., for the N90 Metroplex, piers in downtown New York (similar to Hong Kong).The concept could be applied to multiple terminal radar approach control facilities (TRACONs) and would relieve traffic on the East Coast since international flights can disrupt otherwise stable operations at metroplex airports (e.g., JFK).
+"Perfect" AirportThe "Perfect" Airport concept concerns paving the entire surface surrounding each metroplex airport, i.e., eliminating individual runways; dynamically determining runways according to prevailing wind, weather, and traffic conditions; and distributing runway assignment and procedures information to individual aircraft in real time.
+Airport Arrival-Departure-Surface PlanningThe Airport Arrival-Departure-Surface Planning concept involves affecting metroplex arrivals and departures in coordination with surface operations.It involves using precise runway assignments for arrivals and departures (whereas currently the pilot requests a runway), and could involve crossing aircraft in the air as part of the dynamic runway assignment (whereas currently aircraft are not crossed in the airspace).
+Dynamic Weather Reconfiguration PlanningDynamic Weather Reconfiguration Planning concerns the dynamic allocation and assignment of fixes, routes, and runways to accommodate local metroplex weather patterns.Currently, if a pilot does not accept a route, the route is closed, and it is very difficult to open it again.The concept involves flexible routing and having a large number of configurations with transition plans between each.The concept uses datalink to support dynamic aircraft changes and calls for common situational awareness between pilot and controller.
+Improved Airport Configuration Management and CoordinationThe concept of Improved Airport Configuration Management and Coordination involves the closer coordination and planning across metroplex airports (e.g., PHL flights are coordinated with N90).Currently the tower operationally requests an airport configuration change and the TRACON approves/disapproves the request.The concept calls for surface surveillance and information sharing across all airports and arrival, departure, and configuration management planning tools.Currently only SFO has a formal runway use program in the Bay Area (flights can take off in a 15-kt tailwind instead of a 5-kt tailwind).
+Improved Airport Use Strategic PlanningImproved Airport Use Strategic Planning calls for better strategic origin-destination planning (e.g., international flights out of PHL, airport revenue sharing) as metroplex airports currently compete with one another for traffic and are not incentivized to allocate traffic or aircraft types among themselves to maximize their performance.For instance, closing LGA would allow the same amount of traffic through JFK and EWR as is currently operating at JFK, EWR, and LGA concurrently.Doing so would allow JFK to have greater capacity without interference from LGA operations in part due to human and current airport limitations.
+Dynamic SUA Configuration ManagementThe Dynamic SUA Configuration Management concept calls for the FAA to electronically reserve airspace by request (e.g., based on airspace and time that they need) as significant volumes of special-use airspace (SUA) remaining idle for extended periods of time could be used to offload flights from congested routes and airspaces.However, complications do arise when SUA is being used for commercial traffic and suddenly comes under military use.The concept calls for dynamic sharing of airspace as a function of usage, weather, and opening smaller portions of airspace (altitudes or airspace) if the airspace is not being used (e.g.,China Lake).For example, in Los Angeles, the Hector-Daggett corridor has one route currently, but with RNP level summary of SCT statistics and information.Then the major SCT airports are discussed in detail, followed by a detailed discussion of the SCT airspace.A sixth section covers major ongoing SCT airspace design changes.A seventh section covers findings associated with the potential for future decision support tools to improve SCT operations, followed by adocumentation of additional outstanding issues that were left unanswered from our analysis and that merit additional investigation.A following section covers important references.The major body of the document is followed by multiple appendices.A first appendix covers the site-visit questionnaire that was created in preparation for the site visit.Then, a second appendix provides the summary notes from the site visit.A third appendix provides the detailed person-by-person site-survey notes that were taken during the SCT site visit.A fourth appendix summarizes findings based on an analysis of the SCT standard operating procedures (SOPs) and letters of sgreement (LOAs).
+B.2.3 New York Site-Survey ReportCitation:
+Abstract:The New York site-survey report summarizes the key findings from the Georgia Tech team's visit to and associated analysis of the New York Metroplex.The site visit was conducted during the week of May 26, 2008, and included visits to the following facilities: the New York Center (ZNY), New York TRACON (N90), JFK International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA), and Teterboro Airport (TEB).The report summarizes the key New York Metroplex findings and provides an overview of the New York area airspace and facilities.It discusses the New York Metroplex airports in detail, first summarizing their current and forecasted operations volumes, and then providing detailed descriptions of the New York Metroplex main and satellite airports configurations, individual operational characteristics, interactions, and a quantification of the interdependencies among the airports.The report provides an extensive characterization of the N90 airspace necessary for understanding the complexities and limitations of current-day New York Metroplex operations and identifying opportunities for-and challenges in-improving operational efficiency.The report then discusses the main New York Metroplex airports traffic flows and their interactions, the N90 coordination of shared resource use (e.g., departure fixes, airspace) among the main airports, decision support tools N90 uses to manage traffic, a characterization of the typical traffic-flow management (TFM) restrictions N90 encounters, and key complications to efficient current-day operations.It continues with an overview of the N90 SOPs and LOAs governing current-day operations, and then discusses the constraints to current and future N90 operations, including terrain, environmental considerations (water quality, air quality, and noise), special-use airspace (SUA), and weather.It is followed by discussions of the Center airspaces N90 interfaces with in managing traffic flows to and from the New York Metroplex airports, and the requirements each places on N90 operations.Other topics discussed include the current and future arrival and departure procedures and airspace design changes to N90 and ZNY that will impact New York Metroplex operations; future plans for expanded use of current decision support tools used and use of new decision support tools by N90, ZNY, and/or airport tower for managing metroplex operations; and outstanding issues for future New York Metroplex intelligence gathering and research.The report also includes three appendices: the questions formulated for the New York site visit, the site-visit notes captured by the Georgia Tech team members, and a detailed summary and analysis of the standard operating procedures governing New York Metroplex air-traffic-control (ATC) facilities interactions.
+Abstract:The Miami Metroplex has two Operational Evolution Partnership (OEP) airports, i.e., Miami International (MIA) and Fort Lauderdale-Hollywood International (FLL), within 18 nm of each other, along with numerous satellite airports.Operations in the Miami Metroplex are supported by the Miami Terminal Radar Approach Control Facilities (MIA TRACON), and the Air Route Traffic Control Center (Miami ARTCC, also known as the "Miami Center", or ZMA).The MIA TRACON is a combined TRACON and Air Traffic Control Tower (ATCT or Tower) facility; hence the facility performs both functions.The Miami Metroplex represents the case in which two busy major airports of comparable configuration and traffic volume are located close to each other.It is thus a very good site to examine real-world interdependencies and interactions among multiple airports in close proximity with each other.The site survey started with collecting and assimilating documents relevant to the current operations in the Miami Metroplex, including operational statistics at the airports, published and standard operational procedures (SOP), and letters of sgreement (LOAs) between facilities.Also, information about the environmental aspects of the Miami Metroplex airports were collected and environmental analyses were performed for both the current status and future scenarios.The next step was to examine traffic-flow patterns of current operations in Miami TRACON to identify flow interactions among airports in the area.This assessment was done mainly by examining archived radar tracks utilizing the performance data analysis and reporting system (PDARS) developed by ATAC Corporation.This work also helped to prepare the team for an efficient site visit at MIA.For additional preparation, a questionnaire was developed by the team and submitted to NASA project management and MIA TRACON prior to the site visit.This questionnaire also guided the team during the site visit.On November 14, 2008, the Metroplex Project Team visited the MIA Next-Generation Air Transportation System (NextGen) concepts were analyzed against metroplex issues to identify their potential impact on metroplex operations.This study provides bases for developing novel NextGen metroplex design and operating concepts.The process and the results from the contrast and comparison of metroplex at the aforementioned four metroplex sites are documented in this paper.
+Abstract:This document describes the identified metroplex issues and the associated prioritization approach.An overview and summary of the findings is presented, followed by a description of the identified metroplex issues, with detailed examples.Then the prioritization approach is described, followed by a presentation of the result of the prioritization of the metroplex issues.Then the metroplex issues are grouped into a set of metroplex interdependency categories and these categories are prioritized according to the severity of the adverse impact on metroplex operations.Finally, the relationship between the prioritized metroplex issues and the planned parametric metroplex quantitative system assessments is discussed.Essential reference materials are provided at the end of the document.
+Abstract:A metric was developed to determine the strength of the pairwise interaction between two airports.This metric is based on both the air traffic and the displacement of that traffic off of a notional "optimal" approach.A clustering algorithm was implemented using this metric as a "distance" to determine the groups of airports with strong interactions.Such a group is viewed as a metroplex.
+B.3.3 Metroplex Intersect Flow MetricsCitation:Figure 1 .1Figure 1.Research approach, supporting tools, output, objectives, and schedule..................... Figure 2. Location of candidate metroplex sites and metroplexes in the NAS.......................... Figure 3. FAA's 15 OEP metropolitan areas with visited sites highlighted..............................Figure 4. A80, N90, SCT, and MIA TRACON boundary and operational areas with same scale................................................................................................................... Figure 5.Comparison of metroplex nominal traffic flows......................................................... Figure 6.Metroplex issues prioritization process......................................................................Figure 7. Considered contributors to pairwise airport dependencies......................................... Figure 8.The Gaussian base function used to model the distance effect on pairwise dependency................................................................................................................. Figure 9.The runway factor....................................................................................................... Figure 10.The traffic-volume factor............................................................................................ Figure 11.The metroplex geographic dependency model........................................................... Figure 12.Metroplex core-to-all dependency versus core size.................................................... Figure 13.Metroplex dependency ratio versus core size............................................................. Figure 14.Flightpaths defining arrival cone................................................................................ Figure 15.Location of metroplex clusters-current and future................................................... Figure 16.Total displacement due to spatial de-confliction........................................................ Figure 17.Total displacement due to temporal de-confliction.................................................... Figure 18.The Integrated Arrival/Departure and Surface Traffic Management for Metroplex (IADSTMM)............................................................................................. Figure 19.Nominal IADSTMM architecture............................................................................... Figure 20.Additional integrated metroplex features for the IADSTMM.................................... Figure 21.Metroplex experiment strategy................................................................................... Figure 22.Temporal uncertainty measuring point....................................................................... Figure 23.Generic metroplex assessments process flowchart..................................................... Figure 24.Most-direct-route structures for Generic Metroplex................................................... Figure 25.Example of unrestricted arrival and departure profiles............................................... Figure 26.Generic Metroplex airport A (left) and airport B (right) traffic-demand profiles and capacities.............................................................................................................. Figure 27.Directional traffic distribution for ATL and Generic Metroplex airport A. ............... Figure 28.Probability density function for arrival and departure operations at ATL. ................. Figure 29.Parameter trend for arrival and departure operations at ATL. .................................... Figure 30.Plan view of 3-D flow envelope intersection shown in green.................................... Figure 31.Plan view of 3-D aircraft flow envelopes for Generic Metroplex geometry 3. ........ Figure 32.Geometry 1 or 2 -Distribution of total delay across airports in the Generic Metroplex..................................................................................................................
+Figure 4 .4Figure 1.Research approach, supporting tools, output, objectives, and schedule..................... Figure 2. Location of candidate metroplex sites and metroplexes in the NAS.......................... Figure 3. FAA's 15 OEP metropolitan areas with visited sites highlighted..............................Figure 4. A80, N90, SCT, and MIA TRACON boundary and operational areas with same scale................................................................................................................... Figure 5.Comparison of metroplex nominal traffic flows......................................................... Figure 6.Metroplex issues prioritization process......................................................................Figure 7. Considered contributors to pairwise airport dependencies......................................... Figure 8.The Gaussian base function used to model the distance effect on pairwise dependency................................................................................................................. Figure 9.The runway factor....................................................................................................... Figure 10.The traffic-volume factor............................................................................................ Figure 11.The metroplex geographic dependency model........................................................... Figure 12.Metroplex core-to-all dependency versus core size.................................................... Figure 13.Metroplex dependency ratio versus core size............................................................. Figure 14.Flightpaths defining arrival cone................................................................................ Figure 15.Location of metroplex clusters-current and future................................................... Figure 16.Total displacement due to spatial de-confliction........................................................ Figure 17.Total displacement due to temporal de-confliction.................................................... Figure 18.The Integrated Arrival/Departure and Surface Traffic Management for Metroplex (IADSTMM)............................................................................................. Figure 19.Nominal IADSTMM architecture............................................................................... Figure 20.Additional integrated metroplex features for the IADSTMM.................................... Figure 21.Metroplex experiment strategy................................................................................... Figure 22.Temporal uncertainty measuring point....................................................................... Figure 23.Generic metroplex assessments process flowchart..................................................... Figure 24.Most-direct-route structures for Generic Metroplex................................................... Figure 25.Example of unrestricted arrival and departure profiles............................................... Figure 26.Generic Metroplex airport A (left) and airport B (right) traffic-demand profiles and capacities.............................................................................................................. Figure 27.Directional traffic distribution for ATL and Generic Metroplex airport A. ............... Figure 28.Probability density function for arrival and departure operations at ATL. ................. Figure 29.Parameter trend for arrival and departure operations at ATL. .................................... Figure 30.Plan view of 3-D flow envelope intersection shown in green.................................... Figure 31.Plan view of 3-D aircraft flow envelopes for Generic Metroplex geometry 3. ........ Figure 32.Geometry 1 or 2 -Distribution of total delay across airports in the Generic Metroplex..................................................................................................................
+Figure 7 .Figure 33 .733Figure 1.Research approach, supporting tools, output, objectives, and schedule..................... Figure 2. Location of candidate metroplex sites and metroplexes in the NAS.......................... Figure 3. FAA's 15 OEP metropolitan areas with visited sites highlighted..............................Figure 4. A80, N90, SCT, and MIA TRACON boundary and operational areas with same scale................................................................................................................... Figure 5.Comparison of metroplex nominal traffic flows......................................................... Figure 6.Metroplex issues prioritization process......................................................................Figure 7. Considered contributors to pairwise airport dependencies......................................... Figure 8.The Gaussian base function used to model the distance effect on pairwise dependency................................................................................................................. Figure 9.The runway factor....................................................................................................... Figure 10.The traffic-volume factor............................................................................................ Figure 11.The metroplex geographic dependency model........................................................... Figure 12.Metroplex core-to-all dependency versus core size.................................................... Figure 13.Metroplex dependency ratio versus core size............................................................. Figure 14.Flightpaths defining arrival cone................................................................................ Figure 15.Location of metroplex clusters-current and future................................................... Figure 16.Total displacement due to spatial de-confliction........................................................ Figure 17.Total displacement due to temporal de-confliction.................................................... Figure 18.The Integrated Arrival/Departure and Surface Traffic Management for Metroplex (IADSTMM)............................................................................................. Figure 19.Nominal IADSTMM architecture............................................................................... Figure 20.Additional integrated metroplex features for the IADSTMM.................................... Figure 21.Metroplex experiment strategy................................................................................... Figure 22.Temporal uncertainty measuring point....................................................................... Figure 23.Generic metroplex assessments process flowchart..................................................... Figure 24.Most-direct-route structures for Generic Metroplex................................................... Figure 25.Example of unrestricted arrival and departure profiles............................................... Figure 26.Generic Metroplex airport A (left) and airport B (right) traffic-demand profiles and capacities.............................................................................................................. Figure 27.Directional traffic distribution for ATL and Generic Metroplex airport A. ............... Figure 28.Probability density function for arrival and departure operations at ATL. ................. Figure 29.Parameter trend for arrival and departure operations at ATL. .................................... Figure 30.Plan view of 3-D flow envelope intersection shown in green.................................... Figure 31.Plan view of 3-D aircraft flow envelopes for Generic Metroplex geometry 3. ........ Figure 32.Geometry 1 or 2 -Distribution of total delay across airports in the Generic Metroplex..................................................................................................................
+Figure 2 .2Figure 2. Location of candidate metroplex sites and metroplexes in the NAS.
+Figure 3 .3Figure 3. FAA's 15 OEP metropolitan areas with visited sites highlighted.
+Figure 4 .4Figure 4. A80, N90, SCT, and MIA TRACON boundary and operational areas with same scale.
+Figure 5 .5Figure 5.Comparison of metroplex nominal traffic flows.
+Figure 6 .6Figure 6.Metroplex issues prioritization process.
+Figure 7 .Figure 8 .78Figure 7. Considered contributors to pairwise airport dependencies.
+Figure 10 .10Figure 10.The traffic-volume factor.
+Figure 13 .13Figure 12.Metroplex core-to-all dependency versus core size.
+Figure 14 .14Figure 14.Flightpaths defining arrival cone.
+Figure 1515Figure 15(b) shows the metroplexes identified using the projected TAF data for 2025.Using the same threshold, as was determined from tuning with the 15 metroplexes for 2008, resulted in a total of 18 metroplexes with this dataset.The differences between Figure 15 (a) and (b) include growth in most metroplexes identified for current traffic level, and three newly identified metroplexes: Minneapolis, Boston, and Cincinnati.For further results and analysis, please see the separate report [MC09] (abstract cited in B.3.2).
+Figure 15 .15Figure 15.Location of metroplex clusters-current and future.
+Spatial displacementsoFigure 16 .Figure 17 .1617Figure 16.Total displacement due to spatial de-confliction.
+Major volume-based TFM restrictions • Inefficient/high workload airport configuration changes • Inefficient inter-airport departure sequencing • Major secondary airport flow constraints • Inefficient "flushing" of airport flows • Insufficient regional capacity
+Figure 18 .18Figure 18.The Integrated Arrival/Departure and Surface Traffic Management for Metroplex (IADSTMM).
+Figure 19 .19Figure 19.Nominal IADSTMM architecture.
+Figure 20 .20Figure 20.Additional integrated metroplex features for the IADSTMM.
+Figure 22 .22Figure 22.Temporal uncertainty measuring point.
+Number of airports in the metroplex • Relative location and distance between metroplex airports • Orientation of runways and distance between them at the metroplex airports • Traffic-demand levels and priorities of different airports • Internal and external airspace constraints (terrain, special-use airspace (SUA), etc.) • Weather phenomena • Environmental constraints • Facility evolution (airspace jurisdiction) Following the temporal-spatial framework, design variables and control variables are divided into spatial control variables and temporal variables.Spatial design and control variables include: • Size and shape of the metroplex terminal-area airspace • Number of entry and exit fixes at the metroplex terminal-area airspace boundary • Use of the entry and exit fixes (shared or segregated)
+Figure 23 .23Figure 23.Generic metroplex assessments process flowchart.
+Figure 24 .Figure 25 .2425Figure 24.Most-direct-route structures for Generic Metroplex.
+Figure 27 .27Figure 26.Generic Metroplex airport A (left) and airport B (right) traffic-demand profiles and capacities.
+parameters β  and γ  are determined from trending, and the estimated α  parameter is determined from solving the nonlinear equation.It should be noted that, per the definition of Weibull distribution, both β α , ˆ must be positive.Such determined parameter trending results are shown in Figure 29, with arrivals on the left and departures on the right.
+Figure 29 .29Figure 29.Parameter trend for arrival and departure operations at ATL.
+Figure 30 .30Figure 30.Plan view of 3-D flow envelope intersection shown in green.
+Figure 31 .31Figure 31.Plan view of 3-D aircraft flow envelopes for Generic Metroplex geometry 3.
+Figure 32 .32Figure 32.Geometry 1 or 2 -Distribution of total delay across airports in the Generic Metroplex.
+Figure 33 .33Figure 33.Geometry 1 or 2 -Distribution of delay between en-route and TRACON.
+Figure 34 .34Figure 34.Geometry 3 -Distribution of total delay across airports in the Generic Metroplex.
+Figure 35 .35Figure 35.Geometry 3 -Distribution of delay between en-route and TRACON.
+Figure 37 .37Figure37.The linked-node queueing-process model.
+Figure 38 .38Figure 38.Total delay per aircraft versus arrival rate and MBA for geometry 1.
+Figure 39 .39Figure 39.Total delay per aircraft versus arrival rate and MBA for geometry 3.
+Figure 40 .40Figure 40.Total delay per aircraft versus arrival rate and MBA for geometry 4.
+Figure 38 ,38Figure38, Figure39, and Figure40show that for all three airspace geometries, delays at runways started to diverge much earlier than delays at entry fixes.There were several reasons behind this observation.The ground speed during approach is normally much lower than that at entry fixes.The compression effect requires spacings larger than separation minima in effect at entry fixes.If only the separation minima were enforced at entry fixes, delays would have to be absorbed within the TRACON as traffic volume increased.In the Generic Metroplex model there were only two arrival runways, one at each of the two airports, while there were 4 entry fixes for geometry 1, 8 for geometry 3, and 16 for geometry 4. Aircraft from different fixes would have to be merged to the runway at each airport.Even if proper spacings were enforced at entry fixes, issues would likely rise at the runways.
+Figure 42 .Figure 43 .4243Figure 42.Comparison of total delay per aircraft between geometries, with and without scheduling.
+Figure 45 .45Figure 45.Overall total delays versus metering accuracy.
+Figure 47 .47Figure 47.Runway plans of simulated airports.
+Figure 48 .48Figure 48.N90 current airspace route structure.Modeled flight demand was based on the demand that occurred on the representative day.Only arriving and departing flights for the eight airports modeled were considered in the demand schedule.Figures 49 and 50 present the number of arriving and departing aircraft for each of the modeled airports.
+Figure 51 .51Figure 51.N90 SIMMOD model calibration -throughput.
+Figure 52 .52Figure 52.N90 SIMMOD model calibration -transit times.
+Figure 53 .53Figure 53.N90 NextGen decoupled route structure.
+Figure 5454Figure 54.N90 SIMMOD simulation test-case design.
+Figure 55 .55Figure 55.N90 average arrival air delay per flight.
+Figure 56 .56Figure 56.Cumulative throughput difference due to scheduling in NextGen airspace.
+Figure 60 .60Figure 60.N90 departure fuel burn for each runway.
+Figure 61 .61Figure 61.Average arrival NO x and PM emissions for each runway.
+B.3. 22Metroplex Clustering Analysis Citation: McClain, Evan; Clarke, John-Paul; Huang, Alex; and Schleicher, David: Traffic Volume Intersection Metric for Metroplex Clustering Analysis.AIAA-2009-6069, AIAA Guidance, Navigation, and Control Conference, Chicago, Ill., Aug. 10-13, 2009.
+Table 1 .1OEP 15 Metropolitan Areas with Projected Fast Growth ........................................... Table 2. Airports Sorted by Demand/Capacity Ratio at 3X Demand ....................................... Table 3.Some Characteristics of Metroplex Examples ............................................................ Table 4. Metroplex Facility Comparison .................................................................................. Table 5. Annual TRACON Instrument Operations (2007 Data) .............................................. Table 6.Annual Itinerant Operations at Metroplex Airports with Annual Itinerant Operations of 100,000 or More ................................................................................... Table 7. Metroplex Core Hub Airports ..................................................................................... Table 8.Environmental Comparison ........................................................................................ Table 9. Airspace and Operation Comparison .......................................................................... Table 10.Comparison of the Use of Automation Tools ............................................................. Table 11.Metroplex Issues Prioritization Summary .................................................................. Table 12.Identified Major Metroplex Issues .............................................................................. Table 13.Major Metroplex Airspace Interdependencies ............................................................ Table 14.Major Pairwise Airport Dependencies for Four Metroplexes ..................................... Table 15.Dependencies between Hub Airports and All Others within 75 NM of theCentral Hubs ...............................................................................................................Table 16.Impact of JPDO NextGen "True" Metroplex Concepts on Metroplex Inefficiencies ............................................................................................................... Table 17.Impact of Team-Proposed "True" Metroplex Concepts on Metroplex Inefficiencies ............................................................................................................... Table 18.Impact of JPDO NextGen "Incidental" Metroplex Concepts on Metroplex Inefficiencies ...............................................................................................................
+Table 19 .19Standard RNP Values for RNP Approach-Procedure Segments ................................Table 20.Standard RNP Values for RNAV Departure-Procedure Segments ............................ Table 21.Recommended Metroplex Temporal Uncertainty Assumptions Summary ............... Table 22.Maximum-Likelihood Estimation for Different Traffic Levels .................................. Table 23.Flow-Shape Parameters ............................................................................................ Table 24.Flow Width and Height as Functions of Distance from the Runway ....................... Table 25.Intersect Flows Results for Generic Metroplex Geometry 1 ................................... Table 26.Intersect Flows Changes from Baseline (Geometry 1) for Flow Shape 1 ................ Table 27.Intersect Flows Changes from Baseline (Geometry 1) for Flow Shape 2 ................ Table 28.Intersect Flows Changes from Baseline (Geometry 1) for Flow Shape 3 ................ Table 29.Intersect Flows Changes from Baseline (Geometry 1) for Flow Shape 4 ................ Table 30.Arrival Fuel Burn and Emissions for Generic Metroplex, by Airspace Geometry................................................................................................................... x LIST OF TABLES (cont.)
+Table 31 .31Departure Fuel Burn and Emissions for Generic Metroplex, by Airspace Geometry................................................................................................................... Table 32.Total Fuel Burn and Emissions for Generic Metroplex, by Airspace Geometry ...... Table 33.Fuel Burn and Emissions Percent Change from Baseline, Arrivals ......................... Table 34.Fuel Burn and Emissions Percent Change from Baseline, Departures ..................... Table 35.Fuel Burn and Emissions Percent Change from Baseline, Totals ............................ Table 36.N90 Arrival Fuel Burn .............................................................................................. Table 37. N90 Arrival Emissions ..............................................................................................xi
+TABLE 1 .1OEP 15 METROPOLITAN AREAS WITH PROJECTED FAST GROWTH
+Metro Area (TRACON) Associated Airports OEP Airport ID, Name ID Airport Name State CityAtlanta (A80)PDK Dekalb-PeachtreeGA AtlantaATL, Atlanta Hartsfield Intl.RYY Cobb County-McCollumGA AtlantaFieldFTY Fulton County Airport-GA AtlantaBrown FieldCharlotte (CLT)JQF Concord RegionalNC ConcordCLT, Charlotte/Douglas Intl.UZA Rock Hill/YorkSC Rock HillCounty/Bryant FieldChicago (C90)ARR Aurora MunicipalILChicagoMDW, Chicago MidwayUGN Waukegan RegionalILChicago/WaukeganORD, Chicago O'Hare Intl.AirportLOT Lewis University AirportILChicago/RomeovilleIGQ Lansing Municipal Airport ILChicago/LansingDPA DupageILChicago/WestChicagoPWK Chicago ExecutiveILChicago/WheelingRFD Chicago/Rockford Intl.ILRockfordMKE General Mitchell Intl.WIMilwaukeeENW Kenosha RegionalWIKenoshaGYY Gary/Chicago Intl.INGaryHouston (I90)HOU Houston HobbyTXHoustonIAH, George Bush Intl.EFD Ellington FieldTXHoustonCXO Lone Star ExecutiveTXHoustonDWH David Wayne HooksTXHoustonIWS West HoustonTXHoustonSGR Sugar LandTXHoustonLVJPearland RegionalTXHoustonAXH Houston SouthwestTXHoustonLas Vegas (L30)VGT North Las VegasNV Las VegasLAS, Las Vegas McCarran Intl. HND Henderson ExecutiveNV Las VegasLos Angeles (SCT)VNY Van NuysCA Van NuysLAX, Los Angeles Intl.WHP WhitemanCA Los AngelesPOC Brackett FieldCA La VerneCNO ChinoCA ChinoBUR Bob HopeCA BurbankSNA John Wayne Airport-CA Santa AnaOrange CountyONT Ontario Intl.CA OntarioLGB Long Beach /DaughertyCA Long BeachFieldMinneapolis (M98)ANE Anoka CountyMN Minneapolis
+TABLE 1 . OEP 15 Metropolitan Areas with Projected Fast Growth (CONT.) Metro Area (TRACON) Associated Airports OEP Airport ID, Name ID Airport Name1StateCity
+TABLE 1 . OEP 15 Metropolitan Areas with Projected Fast Growth (CONT.) Metro Area (TRACON) Associated Airports OEP Airport ID, Name ID Airport Name State City1South Florida (MIA, PBI)FXE Fort Lauderdale Executive FLFort LauderdaleMIA, Miami Intl.TMB Kendall-Tamiami Executive FLMiamiFLL, Fort Lauderdale-LNA Palm Beach County ParkFLWest Palm BeachHollywood Intl.OPF Opa LockaFLMiamiPBIPalm Beach Intl.FLWest Palm BeachWashinton Baltimore (PCT)JYO Leesburg ExecutiveVALeesburgIAD, Washington Dulles Intl.HEF Manassas Regional/Harry P.VAManassasDCA, Ronald Reagan NationalDavis FieldBWI, Baltimore-WashingtonDMW Carroll County RegionalVAWestminsterIntl.W66 Warrenton-Fauquier County VAWarrentonMTN Martin StateMD BaltimoreFDK Frederick MunicipalMD Frederick
+TABLE 2 .2AIRPORTS SORTED BY DEMAND/CAPACITY RATIO AT 3X DEMAND
+Airport 3X Demand Analysis Problem Airport? (Demand/Capacity Ratio 5 ) FACT-2 Report Problem Airport? (Needs Additional Capacity by Year) FACT-2 Report Problem Metropolitan Area? (Needs Additional Capacity by Year) Airport Falls in a Metroplex Studied by the Metroplex Project? OEP Airport FAA AC 150/5060-5 Airport Class TACEC Airport 6 Existing or Planned ASDE-X Airport 7 ? Existing or Planned Aerobahn Airport 8?200720152025200720152025LAS3.94-√√-√√-2010 √ (N90) √ 18 √ √ 10 √ 2009-SAN2.36-
+Chicago metropolitan area includestwo OEP airports-ORD and MDW-less than 15 nm from each other.There are no other airports in the TRACON that are among the 150 busiest in the United States.For the most part, ORD, which is the second busiest airport in the UnitedStates with 2600 daily operations in 2006, operates independently; and MDW, with 800 dailyoperations, changes its arrival and departure procedures to avoid conflicts. Typically, thisadjustment requires changing only the flightpaths; but, when ORD is departing off runway 22L,MDW departures off runway 31C must be cleared by the departure controller to avoid conflicts.The most extreme interdependence in this metroplex is the interference of MDW arrivals onrunway 13C with both departures from runway 22L and arrivals to runway 14L at ORD. In fact,departures off runway 22L must be stopped because aircraft turning onto the 13C final approachare only 7 nm south of ORD. Operations in the Chicago Metroplex are supported by the ChicagoTRACON (C90) and the Chicago ARTCC (ZAU).Dallas-Fort Worth MetroplexDFW, the third busiest airport in the United States with 1900 daily operations in 2006, is about10 nm west-northwest of DAL, which averaged 700 daily operations.
+The Dallas-Fort Worth metropolitan area issimilar to the Chicago metroplex in terms of the number of major airports and the distance between them, but DFW and DAL have significantly fewer operations than ORD and MDW.Additionally, the DFW metroplex has approximately twice as many secondary airports in the top 500, with over twice as many operations as the secondary airports in the Chicago Metroplex.The runway configurations at DFW and DAL are typically aligned.
+Atlanta Metroplex contains the busiest airport in the United States with 2700 dailyoperations in 2006. Operations in this metroplex are dominated by the traffic to and from AtlantaHartsfield International Airport (ATL). Traffic to and from other, smaller airports is normallyrouted around the ATL traffic pattern. A corridor over ATL exists to allow departure traffic fromsmaller airports to fly direct to their destinations. Atlanta thus represents another type ofmetroplex operation. Operations in the Atlanta Metroplex are supported by the Atlanta LargeTRACON (A80) and the Atlanta ARTCC (ZTL).Some characteristics of these metroplexes are summarized in Table 3. This table, in conjunctionwith the descriptions of dependencies in this section, also indicates that these examples provide agood breadth of metroplex operations.
+TABLE 3 .3SOME CHARACTERISTICS OF METROPLEX EXAMPLESSFNumber of AirportsNYLABayDCChicago DFW Miami AtlantaOEP Airports31132121Top 50 Airports34232221Top 100 Airports85632242Top 200 Airports13101243553Ops/Day at Top 50 Length Scale (nm) a3400 4900 1900 10 20 15-253000 20-303400 152600 101900 202700 N/Aa The length scale indicates distance between primary airports.
+TABLE 44. METROPLEX FACILITY COMPARISONItemA80SCTN90MIAOverviewServes worlds busiest airport -ATLWorlds busiest TRACONFour busy airports (3 OEP + TEB) within 10-nm radiusAll major airports aligned north-south along coastCoverage (nm 2 /ft)25,110/up to 14,00014,920/up to 17,00017,246/up to 17,0005,817/16,000Usable Airspace76%45%82%99%Airports2549~5010aOEP Large Hub Airports1: ATL2: LAX, SAN3: JFK, LGA, EWR2: MIA, FLLFAA Towers317114FederalContract4752TowersMilitary Towers3611Class B Airspace1: ATL2: LAX, SAN1: JFK, LGA, EWR1: MIAClass C Airspace1: CSG4: BUR, ONT, SNA, RIV (SAN)1: ISP1: FLLTerminal Radar Service Area1: MCN1: PSPNoneNoneMilitary Restricted Area1 cluster inside; 2 clusters surrounding1 cluster inside; 5 clusters surrounding1 cluster inside; 2 clusters surroundingNone inside; 1 cluster surroundingAir Defense Identification Zone (ADIZ) & Warning Areas1 cluster inside 4 clusters surroundingNone inside; 6 clusters surroundingNone inside; 2 clusters surroundingNone inside; 4 clusters surroundingInteracting ARTCCZTLZLAZNY, ZBW, ZDCZMAGeographic LocationSoutheast InlandSouthwest CoastNortheast CoastFlorida PeninsulaInternational BorderNoneMexicoNoneNoneNote: Potomac TRACON (PCT), an airport at the MIA/Palm Beach International Airport (PBI) TRACON boundary, is officially supported by PBI, so it is not counted in the number of airports.
+Table 66lists the annual 2007 itinerant (traveling from one airport to another) air carrier operations, and total operations at metroplex airports whose annual total itinerant operations are 100,000 or more.Total itinerant operations include air taxi, general aviation, and military operations that are not listed in the table.The Metroplex Total is the sum total for listed airports in the metroplex.Weight is the percentage of metroplex traffic to/from a given airport indicating traffic distributions among metroplex airports.The data show that the Atlanta metroplex has the busiest hub airport and fewest heavily trafficked airports.The New York Metroplex has the highest number of heavily trafficked airports.
+TABLE 5 .5ANNUAL TRACON INSTRUMENT OPERATIONS (2007 DATA)ItemA80SCTN90MIAFAA Rank a Operations a (1,000) Loading (1,000/nm 2 )5 1,433,000 57.071 2,243,000 2,066,000 2 150.34 119.809 943,000 162.11[FAA08a]source: "Administrator's Factor Book," November 2008[FAA08a]
+TABLE 6 .6ANNUAL ITINERANT OPERATIONS AT METROPLEX AIRPORTS WITH ANNUAL ITINERANT OPERATIONS OF 100,000 OR MOREMetroplexIDAirport Annual Statistics a Air Carrier Total Growth WeightMetroplex TotalAtlantaATL PDK713,815 24989,295 163,1722.45% 0.40%86% 14%1,152,467LAX467,071672,0951.58%39%Los Angeles BasinSNA LGB ONT BUR92,450 26,668 89,970 58,970252,624 195,303 142,666 183,9300.46% 0.73% -1.72% -1.85%15% 11% 8% 11%1,714,664VNY0268,0460.68%16%JFK350,421453,2580.41%23%EWR273,752444,8810.38%22%LGA201,374401,410-0.15%20%New York MetroISP HPN27,558 11,116111,934 184,9750.41% 0.82%6% 9%2,011,295FRG201106,9610.26%5%TEB6202,1280.41%10%MMU0105,748-0.18%5%MIA294,068386,6451.52%39%MiamiFLL TMB189,310 32304,595 122,1651.99% 2.72%31% 12%979,445FXE0166,0400.54%17%[TAF08] source: "2008 Terminal Area Forecast (TAF)," January 2009[TAF08]
+TABLE 7 .7METROPLEX CORE HUB AIRPORTSItemA80: ATLSCT: LAXN90: JFK (+ LGA, EWR)MIA: MIA (+ FLL)AirportLayoutLocation• 11 statute miles south of Atlanta• 15 statute miles southwest of Los Angeles• 12 statute miles east of Lower Manhattan• 5 statute miles west of Miami downtowndowntowndowntownInter-• No secondary commercialAirportairportGroundConnection• > IMC capacity for 21 slots• > IMC capacity for 7 slots• > IMC capacity for 33 slots• < VMC/IMC capacityDemand and Capacity• > VMC capacity for 8 slots ratio: 0.77; very • Total daily• > VMC capacity for 1 slot congested • Total daily ratio: 0.72, very• > VMC capacity for 21 slots congested 0.88, very • Total daily ratio:• Total daily ratio: 0.44, not congestedcongested• Limited gates for the volume• Limited airport real estate:• Limited airport real estate at hub• At both MIA and FLL, surfaceSurface Limitation (Arrival throughput must be limited to avoid gridlock)• Lack of a "penalty box" or overflow areas • Surface limitation may become a factor for arrival rates during busy periods when tri-runway landings in effectlimited taxi areas and gates • Limited holding space between closely spaced runway pairs • Endangered species limit feasibility of western end-around taxiways • Runway incursionairports: limited taxi areas layout • Surface limitations less an issue • Runway capacity mostly driven by airspacetraffic congestion is generally not considered a major problem • Dade County Aviation Department controls certain loading ramps; coordination with Tower necessaryproblemsAirport Configura-tion• East, west • West used more often• East, west • West is dominant• Many, Runways 31L/R used more often• East, west • East used most of time• Flyaway bus to VNY 60 min • Congestion a problem • No rail connection • Van/express bus to LGA 30 min, to EWR ~90 min • No direct rail connection • Car/shuttle to FLL ~45 min • Tri-Rail connects MIA and FLL (<1 hour), and PBI
+TABLE 8 .8ENVIRONMENTAL COMPARISONItemA80SCTN90MIA• 8-hour ozone:• 8-hour ozone:• 8-hour ozone:• 8-hour ozone:Air Quality EPA Standards: 8-hour ozone: 0.075 ppm 24-hour; PM2.5: 35 µg/m 3-Non-attainment around A80 ATL area -Attainment by 2020 a • 24-hour PM2.5: around ATL by 2020 a -Non-attainment around A80 ATL area -Reduced non-attainment-SCT non-attainment -To remain non-attainment in 2020 a • 24-hour PM2.5: visibility issue • Air quality high -SCT non-attainment -To remain non-attainment in 2020 a-Non-attainment -Some distant areas to remain non-attainment in 2020 a • 24-hour PM2.5: -Non-attainment -Metro area to remain non-attainment in 2020 a-Attainment -To remain attainment in 2020 a • 24-hour PM2.5: -Attainment -To remain attainment in 2020 a• ATL mitigation programs: noise• Very sensitive issue• Noise sensitive 24 hours a day for• Very sensitive issueabatement, land use• Major growth constraintNew York Metroplex airports• Major growth constraintNoise• 65 DNL contours slowly increase • Major issue is night operations• Most strict noise program at SNA, other airports as well • Curfews, noise abatement procedures in place• Land use, runway use, over flights, increased major measure • Procedures a operations, and nighttime operations (due to delay) major contributorseast, north, and south of MIA • Curfews, noise abatement procedures in place• Impact increase due to 5th• Process in place to ensure the• HPN, ~750 ft from source of 90% of• Additional runwayrunwayproper disposalNew York City'sdevelopmentWater Quality• Sewer system is sufficient • Mitigation plan in placeof non-storm water discharge at some airports (e.g., LAX, LGB) • Protect the quality of storm water, e.g., at VNYdrinking water, thus protected • HPN surrounding water continuously monitored • No adverse effect from New York Metroplex airportsat FLL will negligibly increase annual surface water pollution • Sewer system is sufficient • Mitigation plan in placea. EPA projection. Data source: 8-hour ozone [EPA08b], 24-hour PM2.5 [EPA06]
+TABLE 9 .9AIRSPACE AND OPERATION COMPARISONItemA80SCTN90/ZNYMIA• Independent of each other• Coupled with each other to• Strongly coupled• No coupling, unconstrainedAirport Configura-• ATL change during busy hours avoided ifcertain degree • Change must also be coordinated• Determined by TRACON; JFK given higherairspace • Change frequently astionpossible forwith ZLApriorityneededChangethroughput; may even change in advance to avoid delay• Only when absolutely needed• Difficult to change; flushing and stop may be needed• TRACON positions remain the same, altitude flipsAirspace Structure Issues• Class B lack northeast corner extension; plan in place• Uneven TRACON top ranging from 6,000 to 17,000 ft• Lack airspace in N90/ZNY; little room for maneuver• Need to expand Class B to include FLL (Class C)Weather• Convective weather (CW)• Santa Ana winds/May-August coastal• Summer CW/winter snow storm (de-icing)• Extensive summer thunderstormsfog/CW• Not a major problem• Complex, confines traffic• Eastern seaboard SUA• Not a major problemSUAflowcan now beused duringweather• World's busiest airport• VNY may be shut down if BUR• Little room for EWR 29• MIA and FLL arrivals fromInteraction among Traffic at Different Airports• Satellite traffic routed around and below ATL traffic • Satellite departures handled by "release and hope"unable to change to certain configuration • Share arrival and departure fix; northbound departure extremely congestedlanding/11 missed approach because of proximity of LGA • Competing airspace with traffic commonsouthwest and northeast tend to share the same STAR • Other hub traffic is often spatially separated • Satellite arrivals mixed in and• PDK jet often released with altitude-restricted climbs• Sharing departure queue information desired by SCT• Sharing arrival/departure routes requires temporal vertical ormay call TRACON for departure releaseseparationInteraction with Center Airspace• CSG, MCN, and AHN areas may be released back to ZTL• Configuration changes require ZLA sector changes• Arrival flows pushed back into en-route; lacks airspace• Configuration changes require altitude changes onlyTerrain• No major terrain • Large mountains confine traffic• No major terrain • No major terrain
+Airport Departure Merge over Common Departure FixProcess Process Metroplex Issue Impact Analysis8 Airports Merge SCT 8 Airports Merge SCT Multi-Severity Secondary A80 Secondary A80 Example Over GMN Airports Merge with ATL Flow M H Severity Example Over GMN Airports Merge with ATL Flow M HYes, at many N90 Yes, at many N90 common departure fixes H common departure fixes HNone MIA None MIA L LRate of the IssueRate RateScope H HFrequency H HSeverity H HScore Score333
+TABLE 11 .11[SS09b]EX ISSUES PRIORITIZATION SUMMARYLowThe definitions of the issues shown in Table11are listed in Table12for quick reference.Detailed examples of these metroplex issues have been cataloged in a separate document[SS09b].The abstract of that document is also given in appendix B.3.1.Readers are referred to the document for a complete description.Issues in Table11are arranged in the decreasing order of their expected total impact.Per the aforementioned rating process, 2 of the 12 issues have "very high" expected total impacts.One of these, Multiple Airport Departure Merges over a Common Departure Fix, in the case of SCT included up to 8 airports feeding traffic over a common fix.The other issue that was rated "very high" was Major Volume-based TFM Restrictions, which relates to either standing departure MIT restrictions or flow-rate restrictions on peak arrival flows.Five issues have "high" expected total impacts.Proximate-airport configuration conflicts in the table concern dependencies where the otherwise unconstrained departure or arrival flows were significantly constrained by other proximate airport flows.Slow inter-airport ground connectivity limits the flexibility of passengers and air carriers serving them to maximize use of proximate airports.Inefficient/high workload airport configuration changes concern the high airScopetraffic disruption due to significant multifacility coordination; for SCT, this coordination requires multi-airport, multi-TRACON sector, and ARTCC sector coordination.Inefficient multi-airport departure sequencing concerns the difficulty of coordinating departure sequencing and timing to maximize flow across departure fixes, and the simultaneous sequencing of departures to support existing Traffic Management Initiatives, noise restrictions, and individual airport throughput.
+TABLE 12 .12IDENTIFIED MAJOR METROPLEX ISSUES#Metroplex IssueDefinitionMulti-AirportDeparture Merge overOccurs when flights from at least two separate airports areCommon Departureprocedurally merged over at least one common departure fix.FixMajor Volume-basedTFM Restrictions
+TABLE 1313. MAJOR METROPLEX AIRSPACE INTERDEPENDENCIES # Diagram Definition 1 Arrivals/departures to/from two or more proximate airports use the same points in the airspace -Arrival/Departure fixes 2 Arrivals/departures to/from two or more proximate airports use common path segments -STARs and SIDs 3 Arrivals/departures to/from two or more proximate airports intend to use the same volume of airspace but they are vertically separated 4 Arrivals/departures to/from two or more proximate airports intend to use the same volume of airspace but they are laterally separated 5 Arrivals/departures to/from two or more proximate airports intend to use the same volume of airspace but they are temporally separated 6 Downstream restrictions, applied across multiple airports in the metroplex
+TABLE 14 .14MAJOR PAIRWISE AIRPORT DEPENDENCIES FOR FOUR METROPLEXESLocationCentral Hub Code Weight Code Weight Other Airports Distance, nmDependencyQualitative RatingA80ATL1.00PDK0.3115.60.25MediumSCTLAX1.00VNY LGB0.91 0.5416.5 14.80.73 0.46High MediumN90JFK1.00EWR LGA1.00 0.9018.1 9.30.77 0.84High HighMIAMIA0.99FLL OPF0.83 0.2718.3 6.80.63 0.26High Medium
+TABLE 15 .15DEPENDENCIES BETWEEN HUB AIRPORTS AND ALL OTHERS WITHIN 75 NM OF THE CENTRAL HUBSCore Range Core RangeRange Limit Range LimitRing RingCore CoreRemote RemoteLocal Dependency Local DependencyOutlying OutlyingFigure 11. The metroplex geographic dependency model.
+TABLE 1616. IMPACT OF JPDO NEXTGEN "TRUE" METROPLEX CONCEPTS ONMETROPLEX INEFFICIENCIES#NextGen ConceptSpatial ImpactTemporal ImpactM1Efficient metroplex merging and spacingImproved routing and airspace footprintReduced variation in inter-arrival timeIntegration of trajectory, separation, and capacityIntegrated arrival/departurefunctions enables full situation awareness and efficientM2surface traffic managementcollaboration to balance demand and maximize runwayfor metroplexand airspace use, allowing for both spatial and temporalimpacts.
+TABLE 1717. IMPACT OF TEAM-PROPOSED "TRUE" METROPLEX CONCEPTS ONMETROPLEX INEFFICIENCIES#New ConceptSpatial ImpactTemporal ImpactN1arrival Optimized profile 4-D RNPoccupied profile reduces airspace RNP lateral path and verticalMore predictable flight timeN2departure Optimized profile 4-D RNPoccupied profile reduces airspace RNP lateral path and verticalMore predictable flight timeEnables lateral spacing ofLateral path may beN3departure gates Closely spaced arrival andairspace previously permitting traffic when traversing throughshortenedonly one streamN4Vertically stacked arrival departure gatesEnables vertical spacing of the same geographic location traffic when passing throughLateral path may be shortenedOptimized multi-airportNo impactReduced time uncertaintyN5departure sequencing andand delayschedulingDynamic transition routingDifferent routes may beDifferent routes may beN6and dynamic anchor points (multiple fixes per anchorselected to spatially separate traffic based on real-timeselected for different flights to achieve metering withinpoint)traffic conditionsthe terminal areaN7Integrated TRACON/Center airspace redesignImproves transition of traffic between TRACON and Center airspace. Aircraft trajectory least constrained by airspace delegation (impacts both)Integrated metroplexTraffic allocation at different metroplex airports significantlynetwork (air/groundaffects metroplex operations. It influences the actualN8connection between airports and trafficseparation minima used at each airport, and narrows the fleet mix for flights to and from a given airport or runwayallocation among metroplex(impacts both).airports)Environmental Management System (EMS) impacts theN9Environmental management for operationstactical and strategic operation of the metroplex. It will interface with other decision-support and planning tools supporting metroplex operations. It significantly affectsroutings and arrival/departure profiles (impacts both).Optimized configurationEnables runwayN10Metroplex runway configuration plannerselection among airports; traffic flows during runway enables effective change ofconfiguration change at optimal time to reduce delayconfiguration change
+TABLE 1919The criteria of levels 1 and 2 in Table20are for public RNAV departure procedures.Level 2 is the standard criteria.Level 1 is applied when narrower obstacle clearance areas than level 2 are required.Level 3 criteria are for special RNAV departure procedures only.. STANDARD RNP VALUES FOR RNPAPPROACH-PROCEDURE SEGMENTSSegmentMaximumRNP Values (nm) StandardMinimumFeeder221.0Initial110.1Intermediate110.1Final0.50.30.1Missed approach110.1
+TABLE 2121. RECOMMENDED METROPLEX TEMPORAL UNCERTAINTYASSUMPTIONS SUMMARYTemporal Uncertainty CategoryTemporal UncertaintyBiasGrouping (2-sigma)Arrival-boundaryCurrent system-arrival-fix-crossing time0 sec60 seccrossingFuture 4-DT system-arrival-fix-crossing time0 sec12 secDeparture-boundary crossingCurrent system-departure takeoff time Future SMS system-departure takeoff time Future 4-DT system-departure takeoff time-4.5 min -2 min 0 min22 min 15 min 5 minArrival-terminal areaCurrent system-arrival landing time Future 4-DT system-arrival landing time0.2 min 0 sec4.4 min 35 secDeparture-terminal areaCurrent system-departure fix-crossing time Future 4-DT system-departure fix-crossing time0 min 0 sec2 min 12 sec
+depicts the Generic Metroplex simulation process for assessing arrivals to or departures from the Generic Metroplex airports.The general data inputs to the process include airspace geometry, the spatial precision of aircraft navigation and guidance, probability density functions of spacing at the arrival fixes or departure runways, minimum required spacing values at the arrival fix, procedure merge points, airport runways, and temporal precision in delivering aircraft at scheduled times.Data outputs are delay, fuel burn, emissions, and other possible cost metrics.Computational elements are metroplex traffic demand generation, metroplex airspace design, traffic spacing, sequencing/scheduling, and queueing model.The simulation process is as follows.Airspace geometry specifies the number of metroplex arrival and departure fixes and their locations, as well as exogenous variables, including the number of metroplex airports and their locations, runway orientations, and capacities.Data from this step are inputs to both the demand-generation and airspace-design process.Function FunctionATAC ATACMin Req'd Min Req'dMin Req'd Min Req'dMin Req'd Min Req'dData DataGaTech GaTechSpacing at Spacing atSpacing at Spacing atSpacing at Spacing atArrival Fix Arrival FixMerge Pts Merge PtsRunway RunwayMetron MetronSpacing PDF Spacing PDFTemporal Precision Temporal PrecisionSensis Team Sensis TeamDemand Generation Demand Generation Demand Generation Demand GenerationEstimated Traffic Loading at Entry Fixes or Runways Estimated Traffic Loading at Entry Fixes or RunwaysSpacing at Entry Fix or Runway Spacing at Entry Fix or Runway Spacing at Entry Fix or Runway Spacing at Entry Fix or RunwayEstimated Fix Crossing or Runway Times (ETA's) Estimated Fix Crossing or Runway Times (ETA's)Sequencing/ Scheduling Sequencing/ Scheduling Sequencing/ Scheduling Sequencing/ Scheduling Sequencing/ Scheduling Sequencing/ SchedulingSTA's to Entry/Exit Fixes, Merge Points & Runway STA's to Entry/Exit Fixes, Merge Points & RunwayATA's to Entry Fixes & Departure Runway ATA's to Entry Fixes & Departure RunwayCompute Queue Delays Compute Queue Delays Compute Delays Delays Queue Compute QueueDelays at Entry/Exit Fixes, Merge Points & Runway Delays at Entry/Exit Fixes, Merge Points & RunwayCalculate Delay Metrics Metrics Calculate DelayAirspace Geometry Fixes locations, airspace dimensions, etc. Fixes locations, airspace Airspace Geometry dimensions, etc.Airspace Design Airspace Design Airspace Design Airspace Design Routes & Merge Points Airspace Design Airspace Design Airspace Design Airspace Design Airspace Design Airspace Design Airspace Design Airspace Design Routes & Merge PointsIntersect Flow Analysis Intersect Flow Analysis Intersect Intersect Flow Flow Analysis AnalysisCalculate Fuel Burn Metrics Calculate Cost Metrics Metrics Calculate Fuel Burn Metrics Calculate CostSpatial Precision Spatial PrecisionIntersect Flow Metrics Intersect Flow Metrics
+TABLE 22 .22MAXIMUM-LIKELIHOOD ESTIMATION FOR DIFFERENT TRAFFIC LEVELSTraffic LevelAverage Arrival/Departure RateLocation, γScale, β Shape, αMean, minStandard Deviation, minArrivalLEVEL1 LEVEL2 LEVEL3 LEVEL410.526 19.032 26.510 33.4421.1999 1.0833 1.0831 1.14854.1519 2.0582 1.1335 0.65000.855 0.9875 0.9178 1.01635.7004 3.1526 2.2633 1.79416.0846 2.2851 1.4399 0.6744DepartureLEVEL1 LEVEL2 LEVEL3 LEVEL411.925 24.621 33.065 44.8950.88299 2.1571 0.51094 0.84999 0.9700 0.56475 0.84996 0.5223 0.52299 0.84992 0.2913 0.556945.0315 2.4369 1.8146 1.33657.6210 2.9548 1.2896 0.59661Time Separation PDF at Arrival Fixes Level 1Time Separation PDF at Departure Runways 1 Level 1Probability density0.2 0.4 0.6 0.8Level 2 Level 3 Level 4Probability densityL-L combination case0.2 0.4 0.6 0.8Level 2 Level 3 Level 40 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 0 Time Interval, mins0 0 1 2 3 4 5 6 7 8 9 10 Time Interval, minsFigure 28. Probability density function for arrival and departure operations at ATL.
+TABLE 2323. FLOW-SHAPE PARAMETERSFlow Shape1234Maximum width, nm3220.6Maximum eeight, ft10001000200200
+TABLE 2424. FLOW WIDTH AND HEIGHT AS FUNCTIONS OF DISTANCEFROM THE RUNWAYDistance from RunwayFlow WidthFlow Height0 nm100 ft100 ft5 nm0.3 nmMaximum height> 10 nmMaximum widthMaximum height
+TABLE 2525. INTERSECT FLOWS RESULTS FOR GENERIC METROPLEXGEOMETRY 1Flow ShapeTotal Flow Envelope Volume, nm 3Flow Envelope Intersection Volume, nm 3Flight Pairs Using 15-Minute Time Bin1320.525.023349.22214.613.522852.7343.32.321496.9413.40.820759.2TABLE 26. INTERSECT FLOWS CHANGES FROM BASELINE (GEOMETRY 1) FORFLOW SHAPE 1GeometriesTotal Flow Envelope Volume, %Flow Envelope Intersection Volume, %Flight Pairs Using 15-Minute Time Bin, %2 vs. 14.619.56.53 vs. 1-1.2-42.9-6.44 vs. 1149.4240.726.0TABLE 27. INTERSECT FLOWS CHANGES FROM BASELINE (GEOMETRY 1) FORFLOW SHAPE 2GeometriesTotal Flow Envelope Volume, %Flow Envelope Intersection Volume, %Flight Pairs Using 15-Minute Time Bin, %2 vs. 14.534.27.73 vs. 1-1.2-34.1-4.64 vs. 1149.2275.828.1TABLE 28. INTERSECT FLOWS CHANGES FROM BASELINE (GEOMETRY 1) FORFLOW SHAPE 3
+Geometries Total Flow Envelope Volume, % Flow Envelope Intersection Volume, % Flight Pairs Using 15-Minute Time Bin, % 2 vs. 15.23.17.03 vs. 1-1.2-38.9-3.74 vs. 1148.8278.626.6TABLE 29. INTERSECT FLOWS CHANGES FROM BASELINE (GEOMETRY 1) FORFLOW SHAPE 4GeometriesTotal Flow Envelope Volume, %Flow Envelope Intersection Volume, %Flight Pairs Using 15-Minute Time Bin, %2 vs. 14.422.68.53 vs. 1-1.2-12.3-1.24 vs. 1147.1391.329.7
+TABLE 32 .32TOTAL FUEL BURN AND EMISSIONS FOR GENERIC METROPLEX,BY AIRSPACE GEOMETRYTotal COTotal HCTotal NOxTotal SOxTotal FuelTotal Distance(kg)(kg)(kg)(kg)(kg)(km)Geometry 13583.9204.266370.93506.83506830.4178336.7Geometry 23718.0211.867621.13595.43595416.2184806.4Geometry 33582.8204.166454.23509.43509397.3176968.5Geometry 44479.9254.974140.24084.84084767.2213024.8TABLE 33. FUEL BURN AND EMISSIONS PERCENT CHANGE FROM BASELINE, ARRIVALSGeometriesArrivalArrivalArrivalArrivalArrivalArrivalCO, %HC, %NOx, %SOx, %Fuel, %Distance, %2 vs. 12.612.612.612.612.614.413 vs. 1-0.94-0.94-0.94-0.94-0.94-1.894 vs. 110.910.910.910.910.918.23TABLE 34. FUEL BURN AND EMISSIONS PERCENT CHANGE FROMBASELINE, DEPARTURESGeometries DepartureDepartureDepartureDepartureDepartureDepartureCO, %HC, %NOx, %SOx, %Fuel, %Distance, %2 vs. 14.444.391.802.502.502.813 vs. 10.530.520.250.330.330.404 vs. 133.733.311.817.917.920.7TABLE 35. FUEL BURN AND EMISSIONS PERCENT CHANGE FROM BASELINE, TOTALSGeometriesTotal CO,Total HC,TotalTotalTotalTotal%%NOx, %SOx, %Fuel, %Distance, %2 vs. 13.743.721.882.532.533.633 vs. 1-0.032-0.0300.130.0730.073-0.774 vs. 125.024.811.716.516.519.5
+Timar, S.; Lewis, T.; Gutterud, R.; Ren, L.; Crisp, D.L.; Saraf, A.; Sliney, B.; Levy, B.; Rappaport, D.; Stefanidis, K.; Clarke J.-P.; Thompson, T.; and Schleicher, D.: Characterization of and Concepts for Metroplex Operations: NY Site Report.NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished, Mar. 23, 2009.
+Schleicher, D.R.; Ren, L.; Gutterud, R.; Timar, S.; Crisp, D.L.; Lewis, T.; Clarke J.-P.; and Saraf, A.: Characterization of and Concepts for Metroplex Operations: Miami Site Survey Report.NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished, May 5, 2009.B.2.4 MIA Site-Survey ReportCitation:
+Metroplex Operational Issues and Examples Citation:Schleicher, David; Saraf, Aditya; Lewis, Taryn Butler; Gutterud, Richard; and Georgia Tech team: NASA Project NNX07AP63A, Characterization of and Concepts for Metroplex Operations: Metroplex Issues Map.Aug. 28, 2009.B.3 METROPLEX CHARACTERIZATION STUDYB.3.1
+Cross, Carolyn M.; Thompson, Terence R.; White, Tyler H.; DiFelici, John; and Lewis, Taryn: Metrics for Aircraft Flow Interaction Complexity.AIAA 2009-7217, 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Hilton Head, S.C., Sept. 21-23, 2009.perturbations of distance and speed or time quantities.The output of the generic airspace demand-generation process is a schedule of generic airport arrivals and departures.Each scheduled flight has an assigned arrival or departure fix and source/sink airport, a scheduled gate departure time, and estimated takeoff, fix crossing, landing, and gate arrival times.
+			Georgia Institute of Technology, Atlanta, Georgia 30332
+			Sensis Corporation, Campbell, California 945008
+			ATAC Corporation, Sunnyvale, California 94085
+			Metron Aviation, Dulles, Virginia 20166
+		
+		
+			
+The comparison of total delay per aircraft between geometry 1 and geometry 3 with and without scheduling is shown in Figure 42.As can be seen, on average without scheduling, a total delay of 1.55 min per aircraft was incurred in both geometries 1 and 3.With scheduling, the average total delay per aircraft was reduced to 0.42 min in geometry 1 and 0.32 min in geometry 3, corresponding to reductions of 73% and 79%, respectively.While without scheduling the average total delay per aircraft was roughly the same in both geometries, with scheduling the delay was 23% lower in geometry 3 than in geometry 1.The comparison of total delay per aircraft between airport A and airport B with and without scheduling is shown in Figure 43.As can be seen, on average without scheduling, a total delay of 2.16 min per aircraft was incurred for flights destined to airport A, in both geometries 1 and 3.The average total delay per aircraft was 0.34 min for flights destined to airport B, in both geometries 1 and 3.The difference between airport A and airport B was mostly due to the difference in traffic demand at these two airports.While the traffic volume at airport B was about 50% of that at airport A, the average total delay per aircraft was 84% lower at airport B. This nonlinear relationship is typical of queueing systems.This observation suggests that, when airport runways are chock points, moving some operations from busy airports to a less-busy secondary airport may reduce metroplex system-wide delays because when demand is approaching capacity at busy airports, queueing delays tend to diverge.and the nominal boundary crossing time is zero in both current systems and future fourdimensional trajectory (4-DT) operations.Thus, in this analysis, only different grouping values (defined as the variation in terms of two times standard deviation) were tested.A Generic Metroplex queueing simulation was conducted with the standard deviation (denoted as σ) ranging from 0 to 54 sec, with a 6-sec step.Note that this range is wider than the uncertainty assumption presented in Table 21.It was selected to better illustrate the trend of metroplex performance.Figure 44 shows cumulative delay versus cumulative aircraft count for the entire day of traffic under three metering accuracy values: σ = 0, 24, and 54 sec.Arrivals without scheduling are shown for geometries 1 and 3 on the left and with scheduling on the right.It is seen in Figure 44 that in all cases, there was a trend of increase in delays as the metering accuracy decreases (larger σ values).However, in the cases of arrivals with scheduling, the trend was more consistent throughout the day.With scheduling, flights were planned to cross entry fixes at target times to reduce potential conflicts.Lower metering accuracy means less target time compliance, thus negating some of the scheduling benefits.By comparing results without scheduling on the left and results with scheduling on the right, it is seen that even for σ values comparable or larger than current operational performance (see Table 21), most of the scheduling benefits could still be retained.
+Generic Metroplex Inputs to NASEIMThe flightpaths and traffic demand set for each geometry used as inputs to NASEIM were described in section7.2.1.Fuel-burn and emissions calculations also require specification of aircraft types and times at each point along each track.Speeds along each path were taken as linearly interpolated between 350 knots at the arrival or departure fix and 150 knots at the runway threshold.The arrival or departure fix-crossing times for each track were defined in the traffic demand set, and the times at each point along each track were computed from these points using the interpolated speeds described previously.A single aircraft type was used for this study (similar to a Boeing 757-200).
+Results for the Generic Metroplex Environmental Impact StudyResults are presented for each of the four Generic Metroplex geometries.Fuel-burn and emissions totals, as well as total distance traveled, are given for arrivals, departures, and all flights.The percentage change for each of these relative to the baseline (geometry 1) is also shown.Table 30, 31, and 32 show that geometry 3 had slightly better environmental impact for arrivals using this demand set than geometry 1, whereas for departures the numbers are slightly worse.The total values for geometries 1 and 3 look very similar.Emissions and fuel consumption for geometries 2 and 4 were higher than for geometry 1 for both arrivals and departures, with the values for geometry 4 being significantly higher.
+Major Findings and Future Work
+Summary of Major Findings of Generic Metroplex Simulation StudyIn the Generic Metroplex model, four metroplex airspace design geometries were proposed.These geometries range from geometry 1 with a standard four-corner-post configuration and direct routing from entry fixes to runways, to geometry 4 with 16 entry fixes, 16 exit fixes, and fully segregated routes for traffic flows for the two airports.The intersect flow analysis indicated that geometry 3, with dual fixes as compared with the standard four-corner-post and segregated traffic flows, had lowest traffic flow interactions.The fuel-burn and emissions analysis indicated that geometry 3 had slightly better environmental impact.Geometry 4 would allow most direct routing for arrival and departures but it would also require numerous flow intersections, thereby increasing traffic-flow complexity.The linked-node queueing simulation with different arrival rates also indicated that, for the initial Generic Metroplex model with only two airports, runways were choke points.The increased number of entry and exit fixes in geometry 4 did not provide additional benefits.However, it is expected that as the number of airports and traffic demand increase, airspace geometries with more entry and exit fixes may become necessary.As such, additional simulation and analysis were focused on geometries 1 and 4.Both the scheduling analysis (see section 7.4) and the linked-node queueing simulation (see section 7.5) indicated that arrival traffic scheduling could greatly reduce the amount of delays incurred at the Generic Metroplex terminal-area boundary and the amount of delays incurred within the Generic Metroplex terminal area.The linked-node queueing simulation indicated a 73% and a 79% overall total delay reduction for the geometries 1 and 3, respectively.Regardless of scheduling, geometry 3 provided additional delay reductions over geometry 1.The linked-node queueing simulation indicated that the temporal control accuracy affected delay reductions provided by scheduling.Because the lower metering accuracy would affect the compliance to target fix-crossing times recommended by the scheduling algorithm, some delayreduction benefits would be lost.However, even with the worst possible metering accuracy, two-thirds of the delay reductions from the perfect metering still could be retained.This result suggests that even without the high temporal control accuracy that is expected for future 4-DT operations, scheduling would still bring in revolutionary delay reductions (see Figure 46).Advance in trajectory predictions and 4-DT operations would then provide incremental improvements.
+Future Generic Metroplex AnalysisBecause of the limited time and resources, the Generic Metroplex analysis and simulation focused on arrival operations.Departure operations were studied but in much less depth.It is thus recommended that a detailed analysis and simulation be extended to departure operations.One challenge in studying the departure operations is realistic modeling of the over-flight traffic and downstream en-route traffic because these are the constraints for departure scheduling.For best performance, the departure scheduling algorithm should be coupled with en-route sequencing and spacing; or at a minimum it should have access to real-time en-route traffic information.
+APPENDIX A SUMMARY OF OPERATIONAL CONCEPTS BRAINSTORMDuring the project, based on specific knowledge gained during the site visits (especially the New York Metroplex visits), the Georgia Institute of Technology (GaTech) team brainstormed other concepts to alleviate the metroplex inefficiences.This appendix lists these concepts in brief.
+Dynamic "TDMA" ConceptTime-division multiple access, or TDMA, involves the time-domain multiplexing of multiple signals for transmission along a single communication channel.Each signal is granted access to the channel for a sequence of time slots, and the time slots are dynamically assigned to each signal based on the traffic demand of each signal.Special mechanisms are in place to coordinate the timing of transmission to compensate for travel delays so that the packets arrive just in time to fully utilize the time slot.Dynamic allocation of time slots (rather than distributed based on prescribed scheme) enables all time slots to be fully used to accommodate varying demand among different users.In its application to the metroplex problem, the concept concerns the time allocation of shared resources among the multiple airports comprising the metroplex.It could be used to control airspace access.In turn, direct-to routing utilizing required-navigationperformance (RNP) routes could yield short routes cutting through currently segregated airspace, thereby making routes shorter.Automatic Dependent Surveillance -Broadcast (ADS-B) could be used to communicate route and time-allocation information to individual flights.The concept is similar to the Route Availability Planning Tool (RAPT) or expedite departure path (EDP), i.e., generating a schedule of resource availability to metroplex airports.A key concept of TDMA is the advance of transmission times to fit the packet in the allocated time slot.The same concept can be applied to the scheduling of flights to shared resources using both delay and advance to achieve the desired schedule, whereas in current operations delay is often used as the sole means.
+Departure Flow Management ConceptThe Departure Flow Manager (DFM) is a strategic decision support tool currently under development by the Federal Aviation Administration (FAA) and partner companies that automates the coordination of departures from multiple airports over shared and congested National Airspace System (NAS) resources.The metroplex concept is to extend the DFM to perform scheduling not just for time-period traffic-rate specification, but to ensure the tool incorporates all applicable restrictions (i.e., not just TFM restrictions, but local mile-in-trail (MIT) restrictions for merging, etc.), to schedule flights to reach allocated time slots at fixes and/or other shared resources, to incorporate continuous-climb departures into its planning, and to perform concurrent management of arrival and departures.
+Integer ConceptThe Integer Concept concerns efficient airspace or runway use by ensuring inter-flight spacing, particularly when merging, is no more than the minimum absolutely necessary; i.e., the spacing between two successive flights should be no more than one minimum spacing plus a minimum spacing buffer.This concept also requires separations minima or spacing targets to be presented in finer granularity than what is used in the current system.In current operations, separation minima and MIT restrictions are rounded up to at least integer nautical miles, i.e., 3-, 4-, 5-, or could double the number of corridors.Also, SUA east of ZDC airspace is not being used.Virginia Capes Operating Area (VACAPES) sometimes opens up airspace to offload traffic; otherwise all east-coast northbound traffic is routed over JFK.Currently the Military Airspace Management System (MAMS) is used as a tool for SUA management.
+Multi-Airport Arrival and Departure Bank CoordinationThe Multi-Airport Arrival and Departure Bank Coordination concept calls for the dynamic coordination across multiple metroplex airports in order to flush arrival and departure queues/banks.In N90 this coordination is done by the TRACON Traffic Management Unit (TMU) in a very ad-hoc manner.The concept is similar to what is currently done to coordinate traffic flows around road work on a highway system: two people with traffic signs at either end of the flow constraint coordinate to flush cars through the constraint.
+Terminal Advanced Airspace ConceptThe Terminal Advanced Airspace Concept calls for automating all the airport planning and air traffic control (ATC) to perform strategic and tactical 4-DT management.The concept uses datalink to have terminal ATC communicate 4-DT clearances to participating aircraft.Aircraft are outfitted with appropriate automation to implement the 4-DT clearances.
+Environmentally Driven Airport AssignmentThe Environmentally Driven Airport Assignment concept calls for allocating aircraft to different metroplex airports based on their environmental characteristics.This allocation would permit isolating noisier or more emissions-prone aircraft to airports where they have less impact.Aircraft types that should aggregate at different airports for noise and emission considerations would need to be determined, but in general the concept calls for heavier, noise-emitting aircraft to operate out of airports further away from populations and for airport assignment to be driven by environmental performance.
+Secondary Airport Traffic Flow Access ControlSecondary airport traffic-flow access control calls for automation to schedule and execute the release of departures from smaller metroplex airports having less staff and infrastructure in order to fit departures in to metroplex traffic streams.Currently many smaller metroplex airports must call the TRACON or the appropriate metroplex airport tower to release their departures, and during busy traffic periods such flights may be unduly delayed because of the difficulty in identifying available slots in overhead streams coupled with the time required to perform such coordination.
+APPENDIX B SUMMARY OF FURTHER READING MATERIALSThis appendix provides abstracts and summaries of additional detailed specific reports that support this final report.It gives the reader a chance to browse through the subjects before examining those detailed reports.
+Abstract:This report documents a thorough literature review of previous studies relevant to metroplex operations.The goal of the literature review was to first develop an overview of the metroplex phenomenon, and then identify typical metroplexes in the National Airspace System (NAS) that warrant further study.The state of the art in managing today's metroplex operations and previously studied concepts and methods that may be applied to improve the performance of metroplex operations are also reviewed.The intent was to identify areas that need more rigorous study and to identify candidate capabilities to be evaluated for future metroplex operations.The literature was selected and reviewed for its value to the current research on metroplex operations.The nature of the literature includes websites of related agencies, past research publications, simulation programs, and other items.The traditional use of the term "metroplex" is discussed along with the Joint Planning and Development Office (JPDO) definition of metroplex as a group of highly interdependent airports.The previous studies on interdependencies and interactions between metroplex airports are examined.The state of the art in managing these interdependencies is reviewed subsequently, followed by concepts and capabilities proposed in the literature that can be applied to improving the performance of metroplex operations.The report contains appendices detailing individual literature review notes.In each literature review note, the reviewer identifies the objectives of the literature, challenges and methods to achieve the goals, and the results or effects of implementation.The reviewer also provides a critique and states the relevance of the literature to the metroplex research.
+B.2 METROPLEX SITE-SURVEY STUDYThis section briefly introduces materials presented.
+B.2.1 A80 Site-Survey ReportCitation:
+Abstract:The Atlanta Metroplex includes the busiest airport in the world-Hartsfield-Jackson Atlanta International Airport (ATL) with an average of over 2700 daily operations in 2007.Operations in this metroplex are dominated by the traffic to and from ATL.Although corridors exist above ATL airport to allow departure traffic from smaller airports to direct to their destination [[to direct to their destination?Does that make sense?Do you mean: to go directly to their destination?]],traffic to and from other smaller satellite airports is normally routed around the ATL traffic pattern.Atlanta thus represents a unique type of metroplex operations.It was therefore selected by the Georgia Institute of Technology (GaTech) team as a candidate site for detailed survey study.Also, Atlanta Metroplex was selected as the first site to be surveyed because of the existing close collaboration between the GaTech team, the Atlanta Large Terminal Radar Approach Control facility (Atlanta TRACON, or A80) and the Atlanta Air Route Traffic Control Center (Atlanta ARTCC, or ZTL).A80 and ZTL support the operations of the entire Atlanta Metroplex.The A80 report documents in great detail key findings from the three major sources: analysis of documented materials relevant to A80 operations, analysis of trafficflow patterns, and A80 briefing materials and notes taken by the team members during the site visit.An overview of A80 is presented to provide background information such as the geographic location, organizational support structure, layout and dimensions of the TRACON airspace, major players in A80 operations, and system-wide operational statistics.Major airports supported by A80, such as ATL and numerous relatively busy secondary airports, are described with details such as their geographic location, runway layout, traffic demand, major operators based at each airport, and major constraints.A80 airspace and operational procedures are discussed with examples of detailed current-day ATL and secondary traffic-flow analysis.The environmental constraints, which constitute an important aspect of operations, are also presented.A80 future developments, such as the Atlanta Class B airspace redesign, the implementation of continuous-descent arrivals, a second commercial airport, and connection between ATL and suburban or exurban areas are also discussed.For the sake of simplicity and enhancement of information flow, certain artifacts of this site survey are listed as appendices, including the questionnaire developed by the GaTech team for the A80 site visit, the site-visit notes, a detailed summary of constraints and coordination as defined in the A80-ZTL letters of agreement, and a summary of observations of traffic flow into and out of ATL.Illustrations are used extensively throughout the report to help readers understand the subjects.
+Abstract:The SCT site-survey report discusses the key SCT findings organized as follows.The second section summarizes the major SCT findings, followed by a third section consisting of a higher-TRACON/Tower located at the airport.The site visit was mainly an interview session of the MIA TRACON/Tower staff.After the interview session, the team toured the MIA Tower Cab and the MIA TRACON control room located below the Tower.The goal of this site-survey report is to summarize key findings from the three efforts, i.e., the analysis of documented materials and data relevant to Miami operations, analysis of traffic-flow patterns, and the actual site visit.The MIA Site Visit Report discusses the MIA TRACON findings as follows.Following the introduction, the major MIA TRACON findings are summarized, followed by a higher-level summary of MIA TRACON statistics and characteristics.Then the major MIA TRACON airports are discussed in detail, followed by a detailed discussion of the MIA TRACON airspace.A sixth section covers major ongoing MIA TRACON airspace design changes, and a seventh section covers findings associated with the potential for future decision support tools to improve MIA TRACON operations.Then additional outstanding issues that were left unanswered from our analysis and that merit additional investigation are documented.Important references are then listed.The major body of the document is followed by appendices that cover the site-visit questionnaire prepared before the site visit and summarize findings based on an analysis of the MIA TRACON/Tower SOP and LOAs with interacting facilities.
+B.2.5 Contrast and Comparison of Metroplex OperationsCitation:Ren, Liling;
+Abstract:A metroplex is a group of two or more airports within a metropolitan area whose arrival and departure operations are highly interdependent; thus the solution for the airspace structure around and the traffic flows to and from constituent airports must be solved cooperatively as a system.Existing metroplexes in the National Airspace System have gone through different development paths and possess different characteristics, in large part because of differences in natural, social, environmental, and political considerations.Consequently, strategic and tactic air-traffic-control measures tend to be specific to a given metroplex and therefore not easily abstracted.However, to develop concepts for metroplex operations to meet future traffic demand in the 2025 time frame, it is necessary to develop a deeper understanding of the constraints on metroplex operations that limit system capacity, and to develop a model of metroplex operations.To this end, a study of the state of the art in air traffic management of metroplex operations was conducted at four metroplexes, Atlanta, Los Angeles, New York, and Miami, each representing unique characteristics.The results from the survey of each site, covering airport configuration dependencies, airspace delegation, traffic-flow interaction, weather, and environmental constraints, were compared to identify the most critical issues in today's metroplex operations.
+Abstract:In this paper, two metrics for quantifying the complexity of metroplex airspaces are introduced.Complexity of the airspace surrounding two or more closely spaced airports will increase with the amount of overlap between their aircraft flows.An aircraft flow is defined to be an aggregation of flights following a perceptible pattern.Flights are grouped into flows by the proximity of their tracks in space and time.In order to quantify the interaction of flows, the notion of an aircraft flow envelope was developed, and it was used to define two metrics for flow interactions: flow envelope intersections and flight pairs.The Flow Envelope Intersections Metric is simply the sum of all pairwise intersection volumes of distinct flow envelopes in the metroplex.The Flight-Pairs Metric utilizes the idea of flow envelopes, but creates a conceptually more realistic metric describing interactions of flights rather than volumes of airspace.The difference is that instead of computing the volume of airspace in the intersection of two convex polyhedra, the expected number of "flight pairs" contained in that intersection is calculated.The idea of flight pairs is to count the expected number of flights from Flow1 and Flow2 that come into close proximity.The IntersectFlows Metrics for airspace complexity comparisons were implemented both in the context of existing metroplexes (using historical track data), and in the Generic Metroplex Study.
+Abstract:Generic Airspace Demand Generation is a process for creating a traffic demand set (set of scheduled arrivals and departures) to a generic airport to support simulation-based evaluation of a hypothetical terminal airspace configuration.The hypothetical airspace comprises "m" generic airports within a specified terminal airspace boundary.Each generic airport has hourly arrival and departure capacities.Specified quantities of arrival and departure fixes lie along the terminal airspace boundary with the en-route airspace at particular locations.Peripheral to the terminal airspace are "n" source/sink airports distributed at a uniform angular interval and a specified radius.The traffic demand set generated for each generic airport derives from a historical Enhanced Traffic Management System (ETMS) schedule of instrument-flight-rules (IFR) traffic for a single day in the NAS, and reflects the current-day or forecasted traffic demand to a particular NAS airport.The traffic volume in the generated generic airport demand set reflects a specified airport demand-to-capacity ratio and its capacity.Each generic airport flight in the demand set is assigned to an arrival fix or a departure fix on the terminal airspace boundary and to a source/sink airport peripheral to the hypothetical terminal airspace.Arrival flights have estimated times of arrival to their assigned fix and the generic airport, and departure flights have estimated takeoff times and times of arrival to their assigned fix and sink airport.Times of arrival reflect the transit distances inherent in the hypothetical airspace geometry and per the fix assignment rules, estimated nominal transit speeds or times by domain, and stochastic			
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+			JérômeHuber
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+		Air Traffic Control Quarterly
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+		A study of aircraft taxi performance for enhancing airport surface traffic control
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+			American Institute of Aeronautics and Astronautics (AIAA)
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+		(2021) Volume 2, Issue 4 Cultural Implications of China Pakistan Economic Corridor (CPEC Authors: Dr. Unsa Jamshed Amar Jahangir Anbrin Khawaja Abstract: This study is an attempt to highlight the cultural implication of CPEC on Pak-China relations, how it will align two nations culturally, and what steps were taken by the governments of two states to bring the people closer. After the establishment of diplomatic relations between Pakistan and China, the cultural aspect of relations between the two states also moved forward. The flow of cultural delegations intensified after the 2010, because this year was celebrated as the ‘Pak-China Friendship Year’. This dimension of relations further cemented between the two states with the signing of CPEC in April 2015. CPEC will not only bring economic prosperity in Pakistan but it will also bring two states culturally closer. The roads and other communication link under this project will become source of cultural flow between the two states. Keyswords: China, CPEC, Culture, Exhibitions Pages: 01-11 Article: 1 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)01 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)01 Download Pdf: download pdf view article Creative Commons License Political Persona on Twittersphere: Comparing the Stardom of Prime Minister(s) of Pakistan, UK and India Authors: Maryam Waqas Mudassar Hussain Shah Saima Kausar Abstract: Political setup demands to use Twittersphere for preserving its reputation because of significant twitter audience, which follows celebrities and political figures. In this perspective, political figures frequently use twitter to highlight their political as well as personal lives worldwide. However, political figures take the stardom status among the twitter audience that follow, retweet and comment by their fans. The purpose of this study is, to analyze what kind of language, level of interest is made by political figures while communicating via twitter, text, phrases and languages used by political figures, and do their tweets contribute in their reputation. The qualitative content analysis is used for evaluation of the interests shared by PM Imran Khan, PM Boris John Son and PM Narendra Modi with the key words of tweets. A well-established coding sheet is developed for the analysis of text, phrases and words in the frames of negative, positive and neutral from March 2020 to May 2020. The results are demonstrating on the basis of content shared by Prime Ministers of three countries i.e., From Pakistan, Imran Khan, United Kingdom, Johnson Boris and India, Narendra Modi on twitter. The findings also reveal that varied issues discussed in tweets, significantly positive and neutral words are selected by these political figures. PM Imran tweeted more negative tweets than PM Boris Johnson and PM Narendra Modi. However, PM Boris Johnson and PM Narendra Modi make significant positive and neutral tweets. It is observed that political figures are conscious about their personal reputation while tweeting. It also revealed that the issues and tweets shared by these leaders contribute to their personal reputation. Keyswords: Imran Khan, Johnson Boris, Narendra Modi, Political Persona, Stardom, Twittersphere Pages: 12-23 Article: 2 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)02 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)02 Download Pdf: download pdf view article Creative Commons License An Empirical Relationship between Government Size and Economic Growth of Pakistan in the Presence of Different Budget Uncertainty Measures Authors: Sunila Jabeen Dr. Wasim Shahid Malik Abstract: Relationship between government size and economic growth has always been a debated issue all over the world since the formative work of Barro (1990). However, this relationship becomes more questionable when policy uncertainty is added in it. Hence, this paper presents evidence on the effect of government size on economic growth in the presence of budget uncertainty measured through three different approaches. Rather than relying on the traditional and complicated measures of uncertainty, a new method of measuring uncertainty based on government budget revisions of total spending is introduced and compared with the other competing approaches. Using time series annual data from 1973-2018, the short run and long run coefficients from Autoregressive Distributed Lag (ARDL) framework validate the negative effect of budget uncertainty and government size on economic growth of Pakistan regardless of the uncertainty measure used. Therefore, to attain the long run economic growth, along with the control on the share of government spending in total GDP, government should keep the revisions in the budget as close to the initial announcements as it can so that uncertainty can be reduced. Further, the uncertainty in fiscal spending calculated through the deviation method raises a big question on the credibility of fiscal policy in Pakistan. Higher will be the deviation higher will be the uncertainty and lower the fiscal policy credibility hence making fiscal policy less effective in the long run. Keyswords: Budget Uncertainty, Economic Growth, Government Size, Policy Credibility Pages: 24-38 Article: 3 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)03 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)03 Download Pdf: download pdf view article Creative Commons License Despair in The Alchemist by Ben Jonson Authors: Dr. Fatima Syeda Dr. Faiza Zaheer Numrah Mehmood Abstract: This research aims to challenge the assumption that The Alchemist by Ben Jonson is one of the greatest examples of the “explicit mirth and laughter” (Veneables 86). The paper argues that The Alchemist is a cynical and despairing play created in an atmosphere not suitable for a comedy. This is a qualitative study of the text and aims at an analysis of the theme, situations, characters, language, and the mood of the play to determine that Jonson is unable to retain the comic spirit in The Alchemist and in an attempt to “better men” (Prologue. 12) he becomes more satirical and less humorous or comic. This research is important for it contends that the play, termed as a comedy, may be read as a bitter satire on the cynical, stinky, and despairing world of the Elizabethan times. Keyswords: Comedy, Despair, Reformation Pages: 39-47 Article: 4 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)04 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)04 Download Pdf: download pdf view article Creative Commons License Analysis of Principles of Coordinated Border Management (CBM) in articulation of War-Control Strategies: An Account of Implementation Range on Pakistan and Afghanistan Authors: Dr. Sehrish Qayyum Dr. Umbreen Javaid Abstract: Currently, Border Management is crucial issue not only for Pakistan but for the entire world due to increased technological developments and security circumstances. Pakistan and Afghanistan being immediate states have inter-connected future with socio-economic and security prospects. Principles of Coordinated Border Management (CBM) approach have been extracted on the basis of in-depth interviews with security agencies and policymakers to understand the real time needs. The current research employs mixed method approach. Process Tracing is employed in this research to comprehend the causal mechanism behind the contemporary issue of border management system. A detailed statistical analysis of prospect outcomes has been given to validate the implication of CBM. Implication range of CBM has been discussed with positive and probably negative impacts due to its wide range of significance. This research gives an analysis of feasibility support to exercise CBM in best interest of the state and secure future of the region. Keyswords: Afghanistan, Coordinated Border Management, Fencing, Pakistan, Security Pages: 48-62 Article: 5 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)05 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)05 Download Pdf: download pdf view article Creative Commons License The Belt and Road Initiative (BRI) vs. Quadrilateral Security Dialogue (the Quad): A Perspective of a Game Theory Authors: Muhammad Atif Prof. Dr. Muqarrab Akbar Abstract: Containment is the central part of the U.S.'s foreign policy during the cold war. With the application of containment Policy, the U.S. achieved much success in international politics. Over time China has become more powerful and sees great power in international politics. China wants to expand and launched the Belt and Road Initiative (BRI). The primary purpose of The Belt and Road Initiative (BRI) is to achieve support from regional countries and save their interests from the U.S. In 2017, the American administration launched its Containment policy through Quadrilateral Security Dialogue (the Quad) to keep their interest from China. The Quadrilateral Security Dialogue (Quad) is comprising of Australia, the United States, Japan, and India. This Study is based on Qualitative research with theoretical application of Game theory. This research investigates both plans of China (BRI) and the U.S. (the Quad) through a Game Theory. In this study, China and the U.S. both like to act as gamers in international politics. This study recommends that Game theory can predict all developments in the long term. Keyswords: Containment, Expansionism, Quadrilateral Security Dialogue, The Belt and Road Initiative (BRI) Pages: 63-75 Article: 6 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)06 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)06 Download Pdf: download pdf view article Creative Commons License Narendra Modi a Machiavellian Prince: An Appraisal Authors: Dr. Imran Khan Dr. Karim Haider Syed Muhammad Yousaf Abstract: The comparison of Narendra Modi and Machiavellian Prince is very important as policies of Modi are creating problems within India and beyond the borders. The Prince is the book of Niccolo Machiavelli a great philosopher of his time. If Indian Prime Minister Narendra Modi qualifies as a Prince of Machiavelli is a very important question. This is answered in the light of his policies and strategies to become the undisputed political leader of India. Much of the Machiavellian Prince deals with the problem of how a layman can raise himself from abject and obscure origins to such a position that Narendra Modi has been holding in India since 2014. The basic theme of this article is revolving around the question that is following: Can Modi’s success be attributed to techniques of The Prince in important respects? This article analyzed Narendra Modi's policies and strategies to develop an analogy between Machiavellian Prince and Modi in terms of characteristics and political strategies. This research work examines, how Narendra Modi became the strongest person in India. Keyswords: Comparison, India, Machiavelli, Modus Operandi, Narendra Modi Pages: 76-84 Article: 7 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)07 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)07 Download Pdf: download pdf view article Creative Commons License Analyzing Beckett's Waiting for Godot as a Political Comedy Authors: Muhammad Umer Azim Dr. Muhammad Saleem Nargis Saleem Abstract: This study was devised to analyze Samuel Beckett’s play Waiting for Godot in the light of Jean-Francois Lyotard’s theory of postmodernism given in his book The Postmodern Condition (1984). This Lyotardian paradigm extends a subversive challenge to all the grand narratives that have been enjoying the status of an enviable complete code of life in the world for a long time. Even a cursory scan over the play under analysis creates a strong feel that Beckett very smartly, comprehensively and successfully questioned the relevance of the totalizing metanarratives to the present times. Being an imaginative writer, he was well aware of the fact that ridicule is a much more useful weapon than satire in the context of political literature. There are so many foundationalist ideologies that he ridicules in his dramatic writing. Christianity as a religion is well exposed; the gravity of philosophy is devalued; the traditional luxury that the humans get from the art of poetry is ruptured and the great ideals of struggle are punctured. He achieves his artistic and ideologically evolved authorial intentions with a ringing success. It is interesting to note that he maintains a healthy balance between art and message. Keyswords: Beckett, Lyotard, The Postmodern Condition, Waiting for Godot Pages: 85-94 Article: 8 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)08 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)08 Download Pdf: download pdf view article Creative Commons License Effect of Parenting Styles on Students’ Academic Achievement at Elementary Level Authors: Hafsa Noreen Mushtaq Ahmad Uzma Shahzadi Abstract: The study intended to find out the effect of parenting styles on students’ academic achievement. Current study was quantitative in nature. All elementary level enrolled students at government schools in the province of the Punjab made the population of the study. Multistage sampling was used to select the sample from four districts of one division (Sargodha) of the Punjab province i.e., Sargodha. A sample size i.e., n=960; students and their parents were participated in this study. Research scales i.e. Parenting Styles Dimension Questionnaire (PSDQ) was adapted to analyze and measure parents’ parenting styles and an achievement test was developed to measure the academic achievement of the elementary students. After pilot testing, reliability coefficient Cronbach Alpha values for PSDQ and achievement test were 0.67 and 0.71 Data was collected and analyzed using frequencies count, percentages, mean scores and one way ANOVA. Major findings of the study were; Majority of the parents had authoritative parental style, a handsome number of parents keep connection of warmth and support with their children, show intimacy, focus on discipline, do not grant autonomy to their children, do not indulge with their children and as well as a handsome number of students were confident during their studies and study, further, found that parental style had positive relationship with academic achievement. Recommendations were made on the basis of findings and conclusion such as arrangement of Parents Teachers Meetings (PTM‘s), parents’ training, provision of incentives and facilities to motivate families might be an inclusive component of elementary education program. Keyswords: Academic Achievement, Elementary Education, Parenting Styles Pages: 95-110 Article: 9 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)09 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)09 Download Pdf: download pdf view article Creative Commons License Kashmir Conflict and the Question of Self-Determination Authors: Izzat Raazia Saqib Ur Rehman Abstract: The objective of this paper is to explore relations between Pakistan and India since their inception in the perspective of Kashmir conundrum and its impact on the regional security. Kashmir is the unfinished agenda of partition and a stumbling block in the bilateral relations between Pakistan and India. After the partition of sub-continent in 1947, Pakistan and India got their sovereign status. Kashmir conflict, a disputed status state, is the byproduct of partition. Pakistan and India are traditional arch-foes. Any clash between Pakistan and India can bring the two nuclear states toe-to-toe and accelerate into nuclear warfare. Due to the revulsion, hostility and lack of trust between the two, the peaceful resolution of the Kashmir issue has been long overdue. Ever-increasing border spats, arms race and threat of terrorism between the two have augmented anxiety in the subcontinent along with the halt of talks between India and Pakistan at several times. Additionally, it hampers the economic and trade ties between the two. India, time and again, backtracked on Kashmir issue despite UN efforts to resolve the issue. Recently, Indian government has responded heavy-handedly to the Kashmiri agitators’ demand for sovereignty and revocation of ‘Special Status’ of Kashmir impacting the stability of the region in future. Keyswords: India, Kashmir Conundrum, Pakistan, Regional Security, Sovereignty Pages: 111-119 Article: 10 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)10 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)10 Download Pdf: download pdf view article Creative Commons License Exploring Image of China in the Diplomatic Discourse: A Critical Discourse Analysis Authors: Muhammad Afzaal Muhammad Ilyas Chishti Abstract: The present study hinges on the major objective of analyzing Pakistani and Indian diplomatic discourses employed in portrayal of image of China. Data comprises the official discourse which is used in diplomatic affairs of both the states. The extensive investigation seeks insights from the fundamentals of Critical Discourse Analysis propounded by van Dijk, Fairclough and Wodak with a special focus on Bhatia’s (2006) work. The study reveals that the image of China has always been accorded priority within Indian and Pakistani diplomatic discourse even though nature of bilateral relations among China, India and Pakistan is based on entirely different dynamics; Indian and Pakistani diplomatic discourses are reflective of sensitivities involved within the bilateral relations. Through employment of linguistic techniques of ‘positivity’, ‘evasion’ and ‘influence and power’, Indian diplomats have managed not to compromise over the fundamentals in bilateral relations with China despite Pakistan’s already strengthened and deep-rooted relations with China. While Pakistani diplomatic fronts have been equally successful in further deepening their already strengthened relations in the midst of surging controversies on CPEC, BRI and OBOR. Hence, diplomatic fronts of both the counties, through employment of ideologically loaded linguistic choices, leave no stone unturned in consolidation of the diplomatic relations with China. Keyswords: CDA, China Image, Corpus, Language of Diplomacy, Political Discourse Analysis Pages: 120-133 Article: 11 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)11 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)11 Download Pdf: download pdf view article Creative Commons License Students’ Perception about Academic Advising Satisfaction at Higher Education Level Authors: Rukhsana Sardar Zarina Akhtar Shamsa Aziz Abstract: The purpose of the study was to examine the students’ perception about academic advising satisfaction at higher education level. All the students from two years master (M.A) degree programme and four years (BS) degree programme of eight departments from International Islamic University Islamabad (IIUI), Faculty of Social Sciences were taken as a population of the study. 475 students were randomly selected as a sample of the study. The Academic Advising Inventory (AAI) was used to assess Academic Advising Style. For measuring level of the satisfaction, descriptive statistics was used. To compare the mean difference department-wise and gender-wise about academic advising satisfaction t.test was applied. It was concluded that from the major findings of the study those students who received departmental academic advising style are more satisfied as compared to those students who provided prescriptive academic advising style. Female students seemed more satisfied as compared to male students regarding the academic advising style provided to them. Students who satisfied from developmental academic advising style and they were also highly satisfied from the advising provided to them at Personalizing Education (PE) and this is the subscale of developmental academic advising whereas students who received prescriptive academic advising they were also satisfied from the advising provided to them regarding personalizing education and academic decision making but their percentage is less. It is recommended to Universities Administration to focus on Developmental Academic Advising Style and establish centers at universities/department level and nominate staff who may be responsible to provide developmental academic advising. Keyswords: Academic Advising, Higher Level, Students’ Perception Pages: 134-144 Article: 12 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)12 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)12 Download Pdf: download pdf view article Creative Commons License Perceptions of Sexual Harassment in Higher Education Institutions: A Gender Analysis Authors: Ruhina Ghassan Dr. Subha Malik Nayab Javed Abstract: Sexual harassment is a social issue which is present in every society, globally, which interferes in an individual’s social and professional life. It happens almost everywhere i.e. at workplaces, public places or institutes as well. The focus of the present study was to explore the differences of male and female students’ perception of sexual harassment. This study was a quantitative research. Sample of the study included of 400 students (200 males and 200 females) from two government and two private universities. In the present study, Sexual Harassment Perception Questionnaire (SHPQ) was used to find out these differences in perceptions as every person has his own view for different situations. The study revealed the significant differences in perception of students. Study showed that both genders perceived that female students get more harassed than male students. The factors that affect the perception frequently were gender and age. The findings recommended that regulations for sexual harassment should be implemented in universities; laws should be made for sexual harassment in higher education institutes. Students should be aware of sexual harassment through seminars, self-defense classes and awareness campaigns. And every institute should have a counseling center for the better mental health of students. Keyswords: Gender Differences, Higher Educational Institutions, Sexual Harassment Pages: 145-158 Article: 13 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)13 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)13 Download Pdf: download pdf view article Creative Commons License Role of IMF Over the Governance Structure and Economic Development of Pakistan Authors: Ali Qamar Sheikh Dr. Muhammad Imran Pasha Muhammad Shakeel Ahmad Siddiqui Abstract: Developing countries like Pakistan seeks for financial assistance in order to fulfil their deficits. IMF is one of the largest financial institution who give loans to countries who need it. This research has studied the IMF role and the effects of IMF conditions on the economy of Pakistan. To carry out this research, both quantitative data from primary sources has been gathered and qualitative analysis has been made to signify whither this borrowing creating and maintaining dependency of Pakistan on West and financial and governance structure constructed to curtail Countries like Pakistan. The results concluded that there is negative and insignificant relationship between GDP and IMF loans in the long run. The short-term dynamic shows that weak economic and Political Institutions in Pakistan. The Development dilemma constitutes dependency even today. The Current Budget Deficit Pakistan's fiscal deficit climbs to Rs 3.403 trillion in 2020-21 needs to be readdressed in such a manner that Pakistan can counter Balance of Payments and import/export imbalance. Keyswords: Dependency, Development, IMF, Loans, Debt, Pakistan, Governance structure Pages: 159-172 Article: 14 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)14 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)14 Download Pdf: download pdf view article Creative Commons License Climate Change and the Indus Basin: Prospects of Cooperation between India and Pakistan Authors: Sarah Saeed Prof. Dr. Rana Eijaz Ahmad Abstract: Climate change is transforming the global societies. The shift in average temperature is putting negative impacts on human health, food production and the natural resources. In the wake of the altered climate, water flow in the river systems is experiencing variability and uncertainty. This paper aims at studying the negative impacts of climate change on the water resources of the Indus Basin and investigate the prospects of cooperation between India and Pakistan; two major riparian nations sharing the basin. Adopting the case study approach, a theoretical framework has been built on the ‘Theory of the International Regimes’. It has been argued that institutional capacity and the dispute resolution mechanism provided in any water sharing agreement determine the extent of cooperation among the member states. Since India and Pakistan are bound by the provisions of the Indus Waters Treaty, this study tries to assess the effectiveness of this agreement in managing the negative consequences of the climate change. Keyswords: Climate Change, Cooperation, Dispute Resolution Mechanism, Institutional Capacity Pages: 173-185 Article: 15 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)15 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)15 Download Pdf: download pdf view article Creative Commons License Translation, Cultural Adaptation and Validation of Behavioral-Emotional Reactivity Index for Adolescents Authors: Saima Saeed Farah Malik Suzanne Bartle Haring Abstract: Measuring differentiation of self in terms of behavioral/emotional reactivity towards parents is important because of the complex parent-child connection. This needs a valid and reliable measure to assess the differentiation of self particularly in a relationship with parents. Behavior\Emotional Reactivity Index is such a tool that fulfills this purpose. The present study was carried out to culturaly adapt and translate BERI into the Urdu language and establish the psychometric properties of Urdu version. A sample of 303 adolescents of age (M = 16.07, SD = 1.77) was taken from different schools and colleges. Scale was split into Mother and father forms for the convenience of respondents. Findings supported the original factor structure of the BERI-original version. Higher-order factor analysis showed good fit indices with excellent alpha ranges (α= .91 to α=.80). BERI scores were compared for the adolescents who were securely attached with parents and insecurely attached with parents which showed a significant difference between the groups. BERI-Urdu version was found to be a valid and reliable measure in the Pakistani cultural context which gives researchers new directions to work with adolescents. Keyswords: Adolescence, Differentiation of Self, Behavioral, Emotional Reactivit, Index, Parental Attachment Pages: 186-200 Article: 16 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)16 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)16 Download Pdf: download pdf view article Creative Commons License Notion of Repression in Modern Society: A Comparative Analysis of Sigmund Freud and Herbert Marcuse Authors: Khadija Naz Abstract: One of the fundamental issues for modern civilized man is how to adapt a modern society without losing his individual status. Is it possible for an individual to adjust in a society where he/she loses his/her individuality and becomes part of collectivity? One point of view is that for society to flourish, man needs to be repressed. But to what extent is repression necessary for societies to rise and survive? This paper shall examine the above given questions from the standpoint of two thinkers who greatly influenced twentieth-century thought: Sigmund Freud and Herbert Marcuse. To undertake this task, first the term Repression shall be examined and then the notions of Freud and Marcuse will be discussed to determine the degree of repression required for the development of modern society. Keyswords: Modern Society, Performance Principle, Repression, Surplus-Repression, The Pleasure Principle, The Reality Principle Pages: 201-214 Article: 17 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)17 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)17 Download Pdf: download pdf view article Creative Commons License Perceptions of Teacher Educators about Integration of (ESD) in Elementary Teachers Education Program Authors: Dr. Rukhsana Durrani Dr. Fazal ur Rahman Dr. Shaista Anjum Abstract: Education and sustainable development have a close relationship as education provides sustainability to society. This study explored the perceptions of teacher educators for integration of Education for Sustainable Development (ESD) in B.Ed. 4 years’ elementary program. Four major components of ESD i.e., Education, Social & Culture, Economic and Environment were included in study. 127 teacher educators from departments of education were randomly selected from public universities of Pakistan who were offering B.Ed. 4 years’ elementary program. Data was collected through questionnaires from teacher educators. The findings recommended the inclusion of the components of Education for Sustainable Development (ESD) in curriculum of B.Ed. 4 years’ elementary program. Keyswords: B.Ed. 4 Years Elementary Curriculum, Sustainable Development, Integration, Teacher Education Pages: 215-225 Article: 18 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)18 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)18 Download Pdf: download pdf view article Creative Commons License Exploring TPACK skills of prospective teachers and challenges faced in digital technology integration in Pakistan Authors: Tariq Saleem Ghayyur Dr. Nargis Abbas Mirza Abstract: The current study was aimed to explore TPACK skills of prospective teachers and challenges faced in digital technology integration in Pakistan. The study was qualitative in nature and semi structured interview schedule was developed to collect data from prospective teachers. Purposive sampling technique was employed to collect data from 20 prospective teachers of 7 public sector universities. It was concluded that majority of the prospective teachers used general technological and pedagogical practices (GTPP), technological knowledge practices (TKP), Technological Pedagogical Knowledge practices (TPKP), Technological Content Knowledge practices (TCKP). Majority of prospective teachers reported multiple challenges in integration of digital technology in teacher education programs including lack of teacher training as one of the largest hurdle in digital technology integration, lack of digital technology resources or outdated digital technology resources, inadequate computer lab, lack of learning apps (courseware), financial constraints, lack of teachers’ motivation to use digital technology, slow computers available at computer labs, and unavailability of technical support. It was recommended that digital technology infrastructure should be improved across all teacher education institution and it was further recommended that TPACK model of digital technology integration should serve digital technology integration in teacher education programs in Pakistan. Keyswords: Challenges, Digital Technology Integration, Digital Technology Resources, Digital Technology, TPACK Pages: 226-241 Article: 19 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)19 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)19 Download Pdf: download pdf view article Creative Commons License Revisiting the Linkage between Money Supply and Income: A Simultaneous Equation Model for Pakistan Authors: Zenab Faizullah Dr. Shahid Ali Muhammad Imad Khan Abstract: A reliable estimate of the money supply is an important sign of the Gross Domestic Product (GDP) and many other macroeconomic indicators. It is widely discussed that over a long period of time, there is a strong link between GDP and money supply. This link is significantly important for formation of monetary policy. The main aim of this study is to estimate the income-money supply model for Pakistan. This study estimates the income-money supply model for Pakistan over the period of 2009 to 2019. The study uses Two Stage Least Square (2SLS) econometric technique due to the presence of endogeneity problem in the model under consideration. The existence of simultaneity between money supply (M2) and income (GDP) is also clear from the results of Hausman Specification test for simultaneity between M2 and GDP. The results further show that there exists a strong money-income relationship in case of Pakistan. Keyswords: Money Supply, Income, Simultaneous Equations Pages: 242-247 Article: 20 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)20 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)20 Download Pdf: download pdf view article Creative Commons License Analyzing the Mechanism of Language Learning Process by the Use of Language Learning Strategies Authors: Shafiq Ahmad Farooqi Dr. Muhammad Shakir Sher Muhammad Awan Abstract: This analytical research study involves the use of learning strategies to know the mechanism of learning a second language. People acquire their native language (L1) without any conscious effort and they have a complete knowledge of L1 and are competent in their native language even without going to school. It is believed that language learning is a process as well as an outcome and the focus of current study is to understand the process of learning a second language. The population in this study comprised of 182 boys and Girls Govt. Higher Secondary Schools studying at intermediate level in the 11 Districts of the Southern Punjab. The sample was selected through random probability sampling and consisted of 40 subject specialists teaching the subject of English in Govt. higher secondary schools with 400 students studying English at Intermediate level. A questionnaire comprising some common and easily accessible learning strategies was designed to determine the frequency of these strategies used in the classrooms by the language learners through the specialists of the subject. The data was collected from the selected sample through the subject specialists teaching in these schools. The data was collected quantitatively and was analyzed in the statistical package for social sciences (SPSS) version 20. The most common 27 language learning strategies (LLS) were applied to analyze the process of language learning. In the light of the results of the study, it was concluded that application of the learning strategies according to the nature of the text is helpful in understanding the language functions and its application. Keyswords: Language Acquisition, Learning Strategies, Mechanism of Language Learning Pages: 249-258 Article: 21 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)21 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)21 Download Pdf: download pdf view article Creative Commons License Secondary School Science Teachers’ Practices for the Development of Critical Thinking Skills: An Observational Study Authors: Dr. Muhammad Jamil Dr. Yaar Muhammad Dr. Naima Qureshi Abstract: In the National curriculum policy documents, to produce rationale and independent critical thinkers, different pedagogical practices have been recommended like cooperative learning, questioning, discussion, etc. This qualitative case study aimed at analyzing secondary school science teachers’ practices for the development of critical thinking skills in secondary school students. There were twelve classrooms (four from each subject of Physics, Chemistry and Biology) selected as cases. Video recording was used for the observations for six lessons in each classroom. In this way, a total of 72 observations were conducted lasting for approximately 35 minutes. Qualitative content analysis was used for data analysis through Nvivo 12. The findings of the observations revealed that all the teachers used the lecture method. They used this to cover the content at a given specific time. There was not much focus on the development of critical thinking. In a few of the classrooms, the students were engaged and active during learning different specific topics. Whiteboard was used as a visual aid by most of the teachers. Furthermore, to some extent, discussion, questioning, and daily life examples were used in different classrooms. It is recommended that teachers’ professional development should be conducted to focus on the development of critical thinking skills through pedagogical practices which have been recommended by the national education policy documents. Keyswords: Analysis, Critical Thinking, Curriculum Policy, Pedagogy, Secondary Level Pages: 259-265 Article: 22 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)22 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)22 Download Pdf: download pdf view article Creative Commons License Historical Development of Clinical Psychology in Pakistan: A Critical Review-based Study Authors: Muhammad Nawaz Shahzad Dr. Mushtaq Ahmad Dr. Muhammad Waseem Tufail Abstract: Clinical Psychology is clinical and curing psychological practices in Pakistan. The present research study endeavors to examine the contemporary status of Clinical Psychology in the country and descriptively analyzes the significant contribution of various psychologists in its development. The study also elaborates the emergence of Clinical Psychology and its treatment aspects in the country. The experimental approach of the treatment psychology has also been defined. The role of different scholars to set and promote the Clinical Psychology as discipline and dealing about treatment of Human mind has also been discussed here. The study also presented the scenario of the issues of legislative acknowledgment, qualifications mandatory for practice, communal awareness of cerebral treatment, the tradition of ethnic and native practices about the clinical psychological treatments has also been discussed. Keyswords: Approaches, Clinical Psychology, Psychologist, Therapist Pages: 266-272 Article: 23 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)23 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)23 Download Pdf: download pdf view article Creative Commons License Impact of Devolution of Power on School Education Performance in Sindh after 18th Constitutional Amendment Authors: Abdul Hafeez Dr. Saima Iqbal Muhammad Imran Abstract: Devolution of the authority from central units of empowering authorities to the local level to develop and exercise policies at local or organizational level is under debate in various countries of the world. The legation in with the name of 18th constitutional amendment in constitution of 1973 of Pakistan ensures more autonomy to federal units. The difference between province and federation mostly creates misunderstanding in the belief of cooperation and universalism of education standards, expenditures and service delivery. Very currently the ministry of education and local government encoring principles and headmasters to adopt self-management skills to be updated to accept the spin of power from higher authorities to lower authorities’ pedagogical and local schools. In this qualitative research semi structured questioner were incorporated as data collection tool equally, the data was analyzed by usage of NVivo software. In this regard Government of Sindh has introduced various reforms and new trends like objectives and policy pillars, better government schools, improved learning outcomes and increased and improved funding in the education sector Sindh government has so far been unable to effectively use its resources to implement effective governance system which provides quality and sustained education in the province. To achieve this basic universal education, equally fourth objective of Sustainable Development Goal (SDG) the educational leaders must develop a comparative education setup that help to educate planers to plan and design standards for school leaders, instruction, appropriate professional development of teachers, ways to support school leaders to change in mission. Parallel, develop new program for early childhood, school and class size and ensure school enrollment. Keyswords: 18th Constitutional Amendment, Devolution of Power, Sindh Education Performance Pages: 273-285 Article: 24 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)24 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)24 Download Pdf: download pdf view article Creative Commons License Legal Aspects of Evidence Collected by Modern Devices: A Case Study Authors: Muhammad Hassan Zia Alvina Ali Abstract: This paper is a qualitative research of different case laws dealing with modern technological evidence. Courts were required to adopt new methods, techniques and devices obtained through advancement of science without affecting the original intention of law. Because of modern technology, a benefit could be taken from said technology to preserve evidences and to assist proceedings of the Court in the dispensation of justice in modern times. Owing to the scientific and technological advancements the admissibility of audio and visual proofs has grown doubtful. No doubt modern evidence assist the court in reaching out to the just decision but at the same time certain criteria need to be laid down which must be satisfied to consider such evidence admissible. Different Case laws are discussed here to show how the cases were resolved on the basis of technological evidence and when and why such evidence have been rejected by the court, if it did. Moreover, legal practices developed in various countries allow our Courts to record evidence through video conferencing. The Honorable Supreme Court of Pakistan directed that in appropriate cases statement of juvenile rape victims and other cases of sensitive nature must be recorded through video conferencing to avoid inconvenience for them to come to the Court. Nevertheless, it has some problems. The most important among them is the identification of the witness and an assurance that he is not being prompted when his statement is recorded. In this paper protocols that are necessary to follow while examining witness through video link are discussed Keyswords: DNA Profiling, Finger Prints, , Telephone Calls, Video Tape Pages: 286-297 Article: 25 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)25 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)25 Download Pdf: download pdf view article Creative Commons License The Political Economy of Terrorisms: Economic Cost of War on Terror for Pakistan Authors: Muhammad Shakeel Ahmad Siddiqui Dr. Muhammad Imran Pasha Saira Akram Abstract: Terrorism and its effect on contemporary society is one of the core and vital subjects of International Political Economy (IPE) during the last years. Despite the fact that this is not a new phenomenon, special attention has been given to this issue, specifically after the terrorist attacks of 9/11, 2001. The objective of this paper analyzes to what dimensions terrorism affects the global economy mainly the two predominant actors of the conflict i.e. Pakistan and the United States. For this purpose, this article will take a look at the financial cost of War for Pakistan and how Pakistan’s decision to become frontline State has affected its Economy, its effect on agriculture, manufacturing, tourism, FDI, increased defense costs The normative and qualitative methodology shows a significant disadvantage between terrorist activities and economic growth, social progress, and political development. The results shows that Pakistan has bear slow economic growth while facing terrorist activities more than US. In this last section, the paper suggests ways and means to satisfy people around the world not to go in the hands of fundamentals and terrorists. Keyswords: Cost of War, Economic Growth, Frontline States, Pak Us Relations, Terrorism Pages: 297-309 Article: 26 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)26 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)26 Download Pdf: download pdf view article Creative Commons License A Comparative Study of Grade 10 English Textbooks of Sindh Textbook Board and Cambridge “O Level” in the perspective of Revised Bloom’s Taxonomy Authors: Mahnoor Shaikh Dr. Shumaila Memon Abstract: The present study evaluated the cognitive levels of reading comprehension questions present in grade 10 English Textbooks namely English Textbook for grade 10 by Sindh Textbook Board and compared it to Oxford Progressive English book 10 used in Cambridge “O Level” in the perspective of Revised Bloom’s Taxonomy. Qualitative content analysis was used as a methodology to carry out the study. To collect the data, a checklist based on Revised Bloom’s taxonomy was used as an instrument. A total of 260 reading comprehension questions from both the textbooks were evaluated. The findings of the study revealed that reading comprehension questions in English textbook for grade 10 were solely based on remembering level (100%) whereas the questions in Oxford Progressive English 10 were mainly based on understanding level (75.5%) with a small percentage of remembering (12.5%), analyzing (11.1%) and evaluating level (0.74%). This suggests that the reading comprehension questions in both the textbooks are dominantly based on lower-order thinking skills. Keyswords: Bloom’s Taxonomy, Content Analysis, Reading Comprehension, Textbook Evaluation Pages: 310-320 Article: 27 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)27 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)27 Download Pdf: download pdf view article Creative Commons License Assessing the Preparedness of Government Hospitals: A Case of Quetta City, Balochiatan Authors: Sahar Arshad Syed Ainuddin Jamal ud din Abstract: Earthquake with high magnitude is often resulting in massive destruction with more causalities and high mortality rate. Timely providence of critical healthcare facilities to affected people during an emergency response is the core principle of disaster resilient communities. The main objective of this paper is assessing the hospital preparedness of government hospitals in Quetta. Primary data was collected through questionnaire survey. Total of 165 sample size chosen via simple random sampling. Relative important index (RII) is used to analyze the overall situation of hospitals preparedness in term of earthquake disaster. Findings of the study showed that the preparedness level of government hospitals in Quetta is weak to moderate level. Based on the findings this study recommends the necessary measures to minimize the risk of earthquake disaster including training and exercise programs for the staff of hospital, proper resource management to efficiently use the existing machinery and equipment in the meeting of disaster to enhance employee’s performance and preparedness of government hospitals in Quetta to deal with earthquake disaster. Keyswords: Earthquake, Preparedness, Relative Important Index Pages: 321-329 Article: 28 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)28 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)28 Download Pdf: download pdf view article Creative Commons License Development of Reasoning Skills among Prospective Teachers through Cognitive Acceleration Approach Authors: Memoona Bibi Dr. Shamsa Aziz Abstract: The main objectives of this study were to; investigate the effects of the Cognitive Acceleration approach on the reasoning skills of the prospective teachers at the university level and compare the effects of the Cognitive Acceleration approach and traditional approach concerning reasoning skills of prospective teachers’ at the university level. The study was experimental and followed a pre-test post-test control group experimental design. The sample of the study included the experimental group and control group from the BS Education program in the Department of Education at International Islamic University Islamabad. A simple random sampling technique was used to select the sample after pre-test and pairing of prospective teachers. CTSR (classroom test for scientific reasoning) developed by A.E. Lawson (2000) was used to collect the data through pre-tests and post-tests. The experimental group’s perception about different activities of the experiment was taken through a self-made rating scale. Collected data were analyzed by calculating mean scores and t-test for hypothesis testing by using SPSS. The main findings of the study revealed that the Cognitive Acceleration teaching approach has a significant positive effect on the reasoning skills development of prospective teachers at the university level. Findings also showed that participants found this teaching approach effective and learned many new concepts and skills with the help of thinking activities. Based on findings it has been concluded that the Cognitive Acceleration teaching approach might be encouraged for training prospective teachers at the university level and training sessions about the use of the Cognitive Acceleration approach must be arranged by teacher education programs and institutions. Keyswords: Cognitive Acceleration Approach, Prospective Teachers, Reasoning Skills, Traditional Approach Pages: 330-342 Article: 29 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)29 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)29 Download Pdf: download pdf view article Creative Commons License Spatial Injustice in Shamsie’s Kartography Authors: Syeda Hibba Zainab Zaidi Dr. Ali Usman Saleem Sadia Waheed Abstract: Social space under postmodernism and wave of globalization have suffered in and its idealistic representations are lost and deteriorated which ultimately led to discursiveness in the lives of postmodern man, especially Karachiites. The boundaries of geographies play a significant role in shaping fates, biographies, social superstructures and shared collective histories of its residents. Considering this, Henri Lefebvre and Edward William Soja, argue that space is something which determines the living circumstances within the particular social framework and instigates and controls various societal happenings. City space of Karachi suffers from appalling distortions as a part of postmodern, globalized and capitalist world. By employing Lefebvre’s idea of spatial triad and Soja’s views of the trialectrics of spaciality, this paper foregrounds how social space enforces spatial injustice and serves for the inculcation of spatial cleansing in the lives of inhabitants of urban space. Using Shamsie’s Kartography as an interpretive tool for contemporary urban environment, this paper inquires the engrafting of spatial cleansing in the lives of Karachiites resulting in multiple standardization and segregation on the basis of living standards among different social strata. This research substantiates how in Kartography, Materialism nibbles the roots of social values and norms while sequentially administering Spatial Injustice in the lives of Karachiites. This paper proclaims the scarcity of execution of Spatial Justice in the lives of common people in this postmodern globalized capitalist era. This paper urges the possibility of a utopian urban space with enforced spatial justice where people can be saved from dilemmas of injustice and segregation, especially Karachiites. Keyswords: Capitalistic Hegemony, City Space, Globalization, Spatial Cleansing, Spatial Injustice Pages: 343-352 Article: 30 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)30 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)30 Download Pdf: download pdf view article Creative Commons License A Quasi-Experimental Study on the Performance and Attitudes of Pakistani Undergraduate Students towards Hello English Language Learning Application Authors: Wafa Pirzada Dr. Shumaila Memon Dr. Habibullah Pathan Abstract: With the advancement of technology, more and more avenues of bringing creativity and innovation in language learning have opened up. These exciting advances have given rise to a new field of study within linguistics, termed Mobile Assisted Language Learning (MALL). This paper aims to fill the gap of MALL research in the area of grammar teaching in the Pakistan. Two BS Part 1 classes from University of Sindh, Jamshoro, were chosen for this quasi-experimental study. In total, 62 out of 101 students volunteered to use the Hello English application for 2 months, making up the experiment group, and the remaining 39 students were put in a control group. Paired Samples T-Test was run on pretest and posttest results which revealed no significant difference in both groups’ performances, proving that Hello English application could not significantly improve students’ grammar performance. However, in spite of the lack of a significant difference between the test results, the data gathered through the attitudinal survey showed that students still found mobile application very easy to use and effective in language learning. Keyswords: Attitudes, Grammar Learning, Hello English, Mobile Language Learning, Technology In Language Learning Pages: 353-367 Article: 31 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)31 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)31 Download Pdf: download pdf view article Creative Commons License Impact of Determinants on the Profile Elevation of Secondary School Teachers in Pakistan Authors: Zahida Aziz Sial Dr. Farah Latif Naz Humaira Saadia Abstract: The foremost purpose of this research paper was to interrogate the effects of determinants on the educational and social profile of secondary school teachers in Pakistan. The key question taken was related to determinants that affect teachers’ profile. The Population of the study was secondary school teachers of Punjab province. A questionnaire was used as research instrument. The researcher personally visited the schools to administer the questionnaire. E-Views software was used for data analysis. Moreover, OLS regression model and LOGIT regression model were carried out. It was found that the variable years of teaching experience (EXPYR) (*** 0.03) can have a vital concrete effect upon the societal figuration of teachers as the experience of teachers grows, so does their social interactions with officials, colleagues, students and friends increases. The said variable is significant at 10 percent level. The variable, Residence (RESIDE) (** 0.53) have a significant impact upon civic links. This obviously associated with less community connection of country side teachers than the teachers residing in urban areas. Keyswords: Determinants, Elevation, Educational Profile, Social Profile, Secondary School Teacher Pages: 368-372 Article: 32 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)32 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)32 Download Pdf: download pdf view article Creative Commons License Impact of War on Terror on the Tourism Industry in Swat, Pakistan Authors: Sabir Ihsan Prof. Dr. Anwar Alam Aman Ullah Abstract: The present study was designed to ascertain the status of tourism before insurgency, during insurgency and after insurgency in District Swat-KP Pakistan. The study is quantitative and descriptive in nature. A diverse sample size of 370 out of 9014 was selected through convenient sampling strategy. Notwithstanding, the objectives of the study was achieved through structured questionnaire. Data was analysed through chi-square at Bi Variate level. Findings of the study revealed that earning livelihood in swat was significantly associated (P=0.016), (P=0.003) with tourism industry prior 2009 and present time respective, but the same statement was observed non-significant (P=0.075) at the time of insurgency. Arranging different festivals in the study area and establishment of different showrooms for local handcrafts, artificial jewellery and woollen shawl are some of the recommendations of the study. Keyswords: Business, Insurgency, Swat, Tourism Pages: 373-385 Article: 33 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)33 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)33 Download Pdf: download pdf view article Creative Commons License Challenges and Prospects of Pak-China Economic Corridor Authors: Muhammad Mudabbir Malik Prof. Dr. Muqarrab Akbar Abstract: Pak-China has historic relationships from the emergence of both states, and were proved long-lasting in every thick and thin times. In initial times they supported each other in foreign policies and regional issues. Pakistan and China have border disputes with India, which forced them to come close to counter India, letter on the economic interests strengthened these relations. In order to maximize the economic benefits, China announced economic corridor with the name China Pakistan Economic Corridor (CEPC). It was thought it will boost the economic growth of China, and as a prime partner Pakistan will also get economic benefits. In order to completely understand how Pakistan and China came on the same page and decided to put CPEC into reality we have to understand the Geo-political Importance of Pakistan, Strategic and economic importance of CPEC for China and Pakistan, Influence and concerns of West and neighboring countries including India. Domestic limitations and all the possible benefits and risks involved in this project for both Pakistan and China, this research acknowledges all these questions. Keyswords: Challenges, China, CPEC, Domestic Limitations Economic Growth, Pakistan, Western and Regional Concerns Pages: 386-404 Article: 34 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)34 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)34 Download Pdf: download pdf view article Creative Commons License An Analysis of Learning Practices and Habits of Children at Early Childhood Education: Students’ Perspective Authors: Masood Ahmad Sabiha Iqbal Shaista Noreen Abstract: The study was designed to analysis learning practices and habits of children at early childhood education. The major objective of the study was to find out the learning practices and habits of children. Problem was related to current situation, so survey method was exercised, 220 students were selected with the help of convenient sampling technique. Self-constructed questionnaire were exercised. The collected data was analyzed and calculate frequency, percentage, mean score, standard deviation and t-test of independent variable. The major findings of the study were; students learn from the pictures, cartoons and funny face; student’s eyes get tired of reading. When student read context continuously then they feel that their eyes get tired. There was a significance difference between male and female student about learning practices and habits of children. Keyswords: Early Childhood Education, Learning Practices and Habits, Pre-School Students Pages: 405-416 Article: 35 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)35 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)35 Download Pdf: download pdf view article Creative Commons License Gender Identity Construction in Akhtar’s Melody of a Tear Authors: Dr. Amna Saeed Hina Quddus Abstract: This study aims to discuss the notion of gender in terms of performativity and social construction. It also draws upon the idea of gender identity construction and how it relates to the society, performativity and biology. As its theoretical framework, the study relies upon the Performative Theory of Gender and Sex (1990) presented by Judith Butler and studies the gender identity construction in the female protagonist of Akhtar’s Melody of a Tear. Zara is a girl who is raised as a boy from his father and there is a kind of dilemma in Zara’s personality related to being masculine and feminine. The cultural norms of a particular gender are also a cause of this dilemma. Throughout the novel, she is in a conflicting state whether she should behave feminine or masculine. She is being depicted as an incomplete person until she finds and resolves this issue of gender identity. The paper discusses the gender performativity, social construction, cultural norms and identity as these are all contributing to the confusion and construction of the protagonist’s identity. Character analysis is used as the methodology of analysis. Keyswords: Cultural Norms, Femininity And Identity Confusion, Gender, Performativity, Masculinity, Social Construction Pages: 417-427 Article: 36 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)36 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)36 Download Pdf: download pdf view article Creative Commons License The Level of Impulsivity and Aggression among Crystal Meth and Cannabis Users Authors: Dr. Umbreen Khizar Muhammad Shafique Sana Nawab Abstract: Cannabis and crystal meth use is pervading in our society. Present study was conducted to explore the relationship between level of impulsivity and aggression among crystal meth and cannabis users. The sample of the present study was comprised of 100 participants. There were 50 cannabis and 50 crystal meth users who were diagnosed on the basis of DSM-V without any comorbidity. The sample were taken from all age range of population. The minimum education level was primary and maximum education level was graduation and above. The sample was selected from different drug rehabilitation centers of Rawalpindi and Islamabad, Pakistan. Demographic Performa was used to collect the initial important information, The “Barratt Impulsiveness Scale was used to measure the impulsivity and “Aggression Questionnaire” were used to measure the level of aggression. Finding of the study showed that there are significant differences among crystal meth and cannabis users on level of aggression. The calculated mean value for crystal meth user and for cannabis users indicates that crystal meth users have higher level of aggression as compared to the cannabis user. Over all analysis indicates a significant positive correlation of impulsivity with the variable aggression. The alpha coefficient value for all scale is acceptable. Keyswords: Aggression, Cannabis Users, Crystal Meth, Impulsivity Pages: 428-439 Article: 37 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)37 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)37 Download Pdf: download pdf view article Creative Commons License Impact of Social Factors on the Status of Tribal Women: A Case Study of the (Erstwhile) Mohmand Agency Authors: Sadia Jabeen Prof. Dr. Anwar Alam Muhammad Jawad Abstract: This study investigates the impact of socio-economic and cultural factors on the status of tribal women in the erstwhile Mohmand agency of the Ex-Federally Administered Tribal Area (FATA), Pakistan. Cultural practices and illiteracy impede the role of women in socio-economic development. The respondents were randomly selected from tehsil Ekka Ghund and Pindialai with a sample size of 370, through stratified random sampling. Data collected through structured interview schedule, FGD and observation technique. The study reveals that tribal practices early marriages, joint family system, tradition of forced marriages, compensation/Swara, exchange, purchase marriages, hampers women’s socioeconomic status. The illiteracy rate is high among the tribal women and it further undermines their role and negatively affects their socio-economic status. However, improvement in women status needs peace and stability, reforms in the constitution for women empowerment and active participation, improvement in the quality and quantity of education, women employability, skills development and women entrepreneurship Keyswords: Empowerment and Education, Marriage Types, Tribal Women Role, Tribal Women Status, Violence against Women Pages: 440-455 Article: 38 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)38 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)38 Download Pdf: download pdf view article Creative Commons License Effects of Heavy School Bags on Students’ Health at Primary Level in District Haveli (Kahutta) Azad Jammu and Kashmir Authors: Dr. Muhammad Mushtaq Shamsa Rathore Mishbah Saba Abstract: Heavy school bags is a very serious issue for the health of the primary level students throughout the world particularly in Azad Jammu and Kashmir. This study intends to explore the effect of heavy school bags on students’ health at primary level in district Kahuta. Naturally the study was descriptive and survey method was used, the population consists of one hundred ninety teachers and a sample of one hundred twenty seven teachers was selected using non probability sampling technique. A likert scale questionnaire was developed validated and distributed among the sampled respondents. The researcher personally visited the schools and collected the filled questionnaire. The data was coded and fed to the SPSS to analyze and interpret. The Chi Square test was applied to see the effect of heavy school bags on student’s health and academic achievement. The study found that heavy bags have negative effect on their health as well as their academic achievement. Students were found complaining their sickness, body and back pain. They were also found improper in their gait and their body postures. The researcher recommended the policy makers to take and develop strategies to decrease the heavy school bags. The school administration needs to make alternate days’ time tables of the subjects. Keyswords: Health, Primary Level, School, Bags, Students Heavy Pages: 456-466 Article: 39 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)39 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)39 Download Pdf: download pdf view article Creative Commons License Exploring the ‘Civil Repair’ Function of Media: A Case Study of The Christchurch Mosques Shootings Authors: Ayaz Khan Dr. Muhammad Junaid Ghauri Riffat Alam Abstract: This research endeavor is an attempt to explore and analyze the discourse produced by The New Zealand Herald; a newspaper from New Zealand and by The News International; a Pakistani newspaper. The researchers intend to determine whether and to what extent both the newspapers have the role of ‘civil repair’ played after the Christchurch mosques shootings. The researchers have incorporated the ‘lexicalization’ and the ‘ideological square’ techniques proposed by Tuen A. van Dijk within the scope of Critical Discourse Analysis. The findings of this study show that both the selected newspapers assuming the social status of ‘vital center’ performed the role of ‘civil repair’ in the aftermath of the shootings by producing the ‘solidarity discourse’. The ‘solidarity discourse’ has been produced in terms of the ‘we-ness’, harmony, understanding, and by mitigating the conflicting opinions. Keyswords: Christchurch Mosque Shootings, Civil Repair, Civil Sphere Theory, Lexicalization, Solidarity Discourse Pages: 467-484 Article: 40 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)40 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)40 Download Pdf: download pdf view article Creative Commons License China Pakistan Economic Corridor: Regional Dominance into Peace and Economic Development Authors: Tayba Anwar Asia Saif Alvi Abstract: The purpose of this qualitative study was to investigate the true motivations behind CPEC idea and the advantages it delivers to Pakistan and China. It also recognizes the Corridor's potential for mixing regional economies while dissolving geographical borders. The study is deductive in character, since it examines financial, political, and military elements of Pakistan and China's positions and situations. Enhancing geographical linkages through improved road, train, and air transport systems with regular and free exchanges of development and individual’s interaction, boosting through educational, social, and regional civilization and wisdom, activity of larger quantity of investment and commerce flow, generating and moving energy to provide more optimal businesses for the region. Keyswords: Geographical Linkages, Globalized World, Landlocked, Regional Connectivity, Regionalization Pages: 485-497 Article: 41 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)41 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)41 Download Pdf: download pdf view article Creative Commons License China’s New Great Game in Central Asia: Its Interest and Development Authors: Bushra Fatima Rana Eijaz Ahmad Abstract: Central Asia is rich in hydrocarbon resources. It’s geostrategic, geopolitical, and geo-economic significance has grasped the attention of multiple actors such as China, the USA, Russia, Turkey, the European Union, Pakistan, Afghanistan, and India. Due to its location, the Central Asian region appeared as a strategic hub. In the present scenario, China’s strategy is massive economic development, energy interest, peace, and stability. This article highlights China’s interest, political and economic development, and its role as a major player in the New Great Game in Central Asia. Shanghai Cooperation Organization (SCO) which presents as a platform where China is playing an active role in political, economic, and security concerns for achieving its objectives in Central Asia. The new step of the Belt and Road Initiative (BRI) sheds light on China’s progressive move in this region via land and sea routes, which creates opportunities for globalization. Keyswords: Belt and Road Initiative, Central Asia, China, New Great Game Pages: 498-509 Article: 42 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)42 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)42 Download Pdf: download pdf view article Creative Commons License Personality Traits as Predictors of Self-Esteem and Death Anxiety among Drug Addicts Authors: Umbreen Khizar Saira Irfan Iram Ramzan Abstract: This study seeks to investigate whether personality traits predict self-esteem and death anxiety among drug addicts. The sample consisted of 100 drug addicts taken from the two hospitals in Multan city. Only men between the ages of 20 and 65 were included in the study. Data was collected through reliable and valid questionnaires. Results revealed positive relationship between conscientiousness, openness to experience and self-esteem. Moreover, findings showed positive relationship between extraversion and death anxiety, and negative correlation between neuroticism and death anxiety. Findings also showed that self-esteem and death anxiety are significantly and negatively correlated. Additionally, findings revealed that conscientiousness positively predicted self-esteem and neuroticism negatively predicted death anxiety. Furthermore, significant differences were observed in self-esteem, and death anxiety based on age. Significant differences were also found in extraversion, agreeableness, openness to experience, and death anxiety based on location. Understanding how personality traits affect behavior can help drug addicts get the support they need to live a better life and reduce their risk of death anxiety and premature death. Keyswords: Death Anxiety, Drug Users, Personality Traits, Self- Esteem Pages: 510-524 Article: 43 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)43 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)43 Download Pdf: download pdf view article Creative Commons License Middle East: A Regional Instability Prototype Provoking Third Party Interventions Authors: Waseem Din Prof. Dr. Iram Khalid Abstract: Third party interventions always prolong the interstate or civil wars with unending sufferings and devastations. The entire Middle East region is fraught with tensions, conflicts, civil wars and rivalries. From strategic interests to power grabbing, sectarian divisions, flaws in the civil and social structure of the state and society, ethnic insurrections, and many other shapes of instability syndromes can be diagnosed in this region. In the post-Arab Spring, 2011, the emerging new regional hierarchical order for power/dominance, in addition to the weakening/declining dominant US power in the region, changed the entire shape of already conflict-ridden region. New weak or collapsing states and bifurcation of the ‘status quo’ and ‘counter-hegemonic’ states along with their respective allies, made this region a prototype of instability in the regional security complex of the Middle East, as a direct result of these developments. The perpetuation of these abnormalities would not recede this instability conundrum from the region, provoking third party intervention, if not contained. Keyswords: Conflicts/Civil Wars, Dominant Power, Instability, Intervention, Middle East, Middle Powers, Regional Hierarchy, Regional Powers, Security Complex, Weak State Pages: 525-542 Article: 44 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)44 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)44 Download Pdf: download pdf view article Creative Commons License Impact of Classroom Environment on Second Language Learning Anxiety Authors: Zohaib Zahid Abstract: Second language learning anxiety has attained the attention of the researchers in almost every part of the world. Pakistan is a country where English is taught as a second language from the very beginning of school education. Second Language learning anxiety is a phenomenon which has been prominently found among the learners because of their less proficiency in learning English language. This study has been conducted to investigate the effect of anxiety in learning and using English language in classroom, university and outside the classroom. There are variables that affect language learning performance of the learners but this paper has solely investigated the effect of anxiety. The paper has concluded that anxiety is a variable which has a striking affect in second language learning and its use inside classrooms. Keyswords: Effect of Anxiety, Proficiency, Second Language Learning Anxiety, Striking Affect Pages: 485-497 Article: 45 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)45 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)45 Download Pdf: download pdf view article Creative Commons License Struggling for Democracy: A Case of Democratization in Pakistan Authors: Ammara Tariq Cheema Dr. Rehana Saeed Hashmi Abstract: The objective of this research paper is to review the challenges for democratization in Pakistan. The problem of democratization and consolidation refers to the structure of democracy following the collapse of non-democratic regime. Ten factors as given by Michael J. Sodaro are considered effective in helping a democratically unstable state to stabilize its system in other words helps in the democratic consolidation. It is argued in this research that the ten factors of democratization as given by Michael J. Sodaro have been absent in the political system of Pakistan and working on these factors can lead Pakistan to the road of democratization. This study uses qualitative method of research and proposes a novel framework for the deed of parliament, because the effectiveness of parliament can contribute positively to democratization/consolidated democracy. Keyswords: Electoral Politics, General Elections, Political Participation, Women Empowerment Pages: 554-562 Article: 46 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)46 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)46 Download Pdf: download pdf view article Creative Commons License Impact of Dependency Ratio on Economic Growth among Most Populated Asian Countries Authors: Dilshad Ahmad Salyha Zulfiqar Ali Shah Abstract: Demographic transition through different channels significantly influences economic growth. Malthusian view postulated as dependency ratio adversely affects economic growth while Julian Simon's view is quite different, highlighted the long-run benefits of the population in the range of 5 to15 years on economic growth. This study can be a valuable addition in research to analyzing the association of dependency ratio and economic growth of the five most populated Asian countries (Bangladesh, China, Indonesia, India, and Pakistan). Empirical findings of the study indicated that a total dependency and younger dependency ratio has a positive and significant influence on economic growth in both short-run and long-run scenarios while the old dependency ratio shows a negative influence on economic growth in the long run while short-run results are unpredictable. There is a need for state-based proper policy measures in focusing the higher financing in human capital development specifically in education and health. Keyswords: Economic Growth, Gross Saving, Old Dependency Ratio, Young Dependency Ratio Pages: 563-579 Article: 47 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)47 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)47 Download Pdf: download pdf view article Creative Commons License Chinese Geo-Strategic Objectives and Economic Interests in Afghanistan under President Xi Jinping Authors: Farooq Ahmed Prof. Dr. Iram Khalid Abstract: China has its own distinctive interests, concerns and strategies with respect to the changing security dynamics in Afghanistan. China has taken an active interest, though retaining a low profile and avoiding direct military interaction. China has exclusively relished on economic engagement actively and provided numerous financial aid and financial support in the rebuilding of Afghanistan's economy. The aim of this research study is to analyze the geo-strategic objectives and economic interests of China under the leadership of President Xi Jinping. This study looks at the actual diplomatic, economic and protection commitments of both countries as well as the basis of the geopolitical complexities – core variables that form China's current foreign policy to Afghanistan. Keyswords: Afghanistan, BRI, China, NATO Withdrawal Pages: 580-592 Article: 48 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)48 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)48 Download Pdf: download pdf view article Creative Commons License The Argument Structure of Intransitive Verbs in Pashto Authors: Abdul Hamid Nadeem Haider Bukhari Ghani Rehman Abstract: This study focuses on the description and categorization of intransitive verbs in terms of its argument structure. The study concludes that the unaccusative verbs only project an internal argument. It does not require the event argument. However, the said verb can be causativised by adding external argument and at the same time the event argument gets included in the valency of the derived causative of the unaccusative root. The unergative, on the other hand, requires an external argument as an obligatory argument while the internal argument is not the obligatory argument of the verb. The event argument is also a part of the valency of the verb. The APFs require one argument which is the internal argument of the verb. However, since the external argument is not available, the internal argument of the verb gets realized as the subject of the verb. The verb does not project event argument. The ergative predicates are derived by the suppression of the external argument and by the externalization of the internal argument. Keyswords: Argument Structure, Ergative Case, Event Argument, External Argument, Internal Argument, Valency Pages: 593-610 Article: 49 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)49 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)49 Download Pdf: download pdf view article Creative Commons License Positive, Negative and Criminal Orientation of Beggars in Okara: Perspective of Students Authors: Shahzad Farid Saif-Ur-Rehman Saif Abbasi Hassan Raza Abstract: This study aimed to measure the perspective of students about the criminal orientation of beggars. The sample size of the study (i.e., 100 students) was explored using Taro Yamane’ equation from the university of Okara, Punjab, Pakistan. The respondents were approached using simple random sampling and interviewed using face to face interview schedule. The data was collected using a structured questionnaire. The analysis was administered through SPSS-20.The study explored that parental illiteracy is associated with the high criminal and negative orientation of students towards beggars. It was also explored that females and respondents from rural background have low negative orientation towards beggars. However, males and respondents from urban background have medium criminal orientation and low positive orientation towards beggars, respectively. The study is useful for the government of Punjab, Pakistan campaign and policy for anti-begging. The study introduced the geometrical model of youth’s orientation toward begging. The study also contributed to the literature on begging by extending its domain from Law and Criminology to sociology as it incorporated social variables e.g., parents’ education, gender, etc., to explore their association with the youth’s socialization about begging. Keyswords: Begging, Crime, Education, Gender, Students Pages: 611-621 Article: 50 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)50 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)50 Download Pdf: download pdf view article Creative Commons License Relationship between Entrepreneurial Export Orientation and Export Entrepreneurship through Mediation of Entrepreneurial Capabilities Authors: Muhammad Saqib Nawaz Masood ul Hassan Abstract: Export led growth is prominent paradigm in developing world since decades. Exports play vital role in the economy by improving the level of balance of payments, economic growth and employment. Due to strategic importance of exports, organizational researchers focused on finding antecedents of export performance of the organizations. To line with this, current study aims to find the impact of entrepreneurial export orientation on export entrepreneurship through mediation of entrepreneurial capabilities in the Pakistani context. For this purpose, data was collected from 221 exporting firms of Pakistan by using questionnaire. Collected data was analyzed with the help of Smart PLS. In findings, measurement model confirmed the validity and reliability of measures of variables. Additionally, structural model provides the positive impact of entrepreneurial export orientation on export entrepreneurship. Similarly, entrepreneurial capabilities mediate the relationship between entrepreneurial export orientation on export entrepreneurship. The findings provide important implications for the managers of exporting firms to improve export performance. Keyswords: Entrepreneurial Capabilities, Entrepreneurial Export Orientation, Export Entrepreneurship Pages: 622-636 Article: 51 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)51 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)51 Download Pdf: download pdf view article Creative Commons License China Pakistan Economic Corridor: Explaining U.S-India Strategic Concerns Authors: Nasreen Akhtar Dilshad Bano Abstract: Regional and International political and economic landscape is being changed owing to China Pakistan Economic Corridor (CEPEC)-the new security paradigm has taken place-that has increased the strategic concerns of the U.S. and India. This research paper attempts to re-examine China-Pakistan relations in the new emerging geo-political compass. This paper has investigated the question that how regional, and global developments have impacted the China-Pakistan relationship? And why China – Pakistan have become partners of CPEC? In the global context, this paper assesses the emerging International Order, Indo-U. S strategic narrative vis-à-vis CPEC, and the containment of China through the new alliances and their impacts on China -Pakistan vis-à-vis the Belt Road Initiative (BRI). Quadrilateral (Quad) alliances is shaping the new strategic political and security paradigms in the world politics. Keyswords: BRI, China, CPEC, India, Pakistan, Silk Road, Strategic Concerns Pages: 637-649 Article: 52 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)52 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)52 Download Pdf: download pdf view article Creative Commons License The Structure of Domestic Politics and 1973 Constitution of Pakistan Authors: Dr. Fida Bazai Dr. Ruqia Rehman Amjad Rashid Abstract: Pakistan is located in a pivotal region. Its geo-strategic location affects its national identity as a nation state. Unlike Europe in South Asia security dilemma, proxy warfare and nuclear arms race are consistent features of the regional politics. The identity of Pakistan as security-centric state gives its army disproportional power, which created institutional imbalance that directly affected constitutionalism in the country. The constitution of Pakistan is based on principles of civilian supremacy and separation of power but in reality Pakistan’s army is the most powerful institution in country. This paper argues that the structure of Pakistani politics; created institutional imbalances by the disproportionate distribution of resources is the key variable in creating dichotomy. The structure of domestic politics is based upon the principles of hostility to India, use of Islam for national unity and strategic alliances with major powers to finance defense against the neighboring countries. Keyswords: Constitutionalism, Identity, Islam, South Asia Pages: 650-661 Article: 53 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)53 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)53 Download Pdf: download pdf view article Creative Commons License National Integration and Regionalism in Pakistan: Government’s Strategy and Response toward Regionalist Demands 1947-77 Authors: Najeeb ur Rehman Mohammad Dilshad Mohabbat Muhammad Wahid Abstract: The countries of South Asian region have pluralistic societies with different language, religious, and ethnic identities. Pakistan is no exception who is facing the challenge of regionalism since its inception. Different ethnic groups have been consistently raising their voices for separatism or autonomy within the frame work of an existing territorial state. The issues of provincialism, ethnicity, and regionalism is posing a serious challenge to the integrity of the country. This paper aims to explore the causes of the regionalism in Pakistan and intends to analyze the policies and strategies of different political governments which they launched to tackle this all important issue. The paper follows the historical method of research and analyzes different types of qualitative data to conclude the finding of the research. The paper develops the theory of “Regionalists Demand and Government Response” which shows how different regionalist forces put their demands and how the governments react on these demands. It recommends the grant of greater regional autonomy to the regionalists to enhance internal security and to protect the country from disintegration. Keyswords: Demands, Ethnicity, Government Strategy, National Integrity, Nationalism, Regionalism Pages: 662-678 Article: 54 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)54 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)54 Download Pdf: download pdf view article Creative Commons License Fostering Entrepreneurial Mindset through Entrepreneurial Education: A Qualitative Study Authors: Saira Maqbool Dr. Qaisara Parveen Dr. Muhammad Hanif Abstract: Research on entrepreneurial mindset has flourished in these recent years. Its significance lies in a critical suspicion and its matters for inventive behavior. Entrepreneurship joined with innovative abilities, seen as one of the most wanted in this day and age. This study aims to determine the perceptions about entrepreneurial mindset, its importance, and the role of entrepreneurship education and Training in developing the entrepreneurial mindset. This is a qualitative study based on interviews conducted by professors of Pakistan and Germany. The analysis was determined through content analysis. The results determine that 'Making Entrepreneurial Mindset' assists with seeing better all parts of business venture, which will undoubtedly influence their view of business venture, pioneering abilities, and mentalities. Keyswords: Entrepreneurship Education, Entrepreneurial Mindset Pages: 679-691 Article: 55 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)55 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)55 Download Pdf: download pdf view article Creative Commons License Benefits of Implementing Single National Curriculum in Special Schools of Lahore city for Children with Intellectual Disability: Teachers’ Perception Authors: Dr. Hina Fazil Khurram Rameez Sidra Ansar Abstract: Single national curriculum (SNC) is an important issue across the Punjab Province of Pakistan. Making and implementing SNC is not only focusing the education of normal pupils, but also focusing students with disabilities (SWD). The field of special education experienced an increased discussion of curriculum for students with intellectual disabilities (SID). The present research aimed to know the benefits to implement first stage of single national curriculum for students with Intellectual disability and to know the differences about the benefits between public and private schools regarding SNC for students with ID based on demographic characteristics. Likert type researchers-made questionnaire with reliability) Cronbach alpha .922) was used. 90 special educationists from public and private schools were chosen through random sampling technique. The findings raised some benefits such as: SNC will bridge the social and economic disparities which will increase the acceptance of ID students. It was recommended that SNC should include areas of adaptive skills, motor, and vocational skills to get involved in work activities. Keyswords: Benefits, Children with Intellectual Disability, Single National Curriculum Pages: 692-703 Article: 56 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)56 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)56 Download Pdf: download pdf view article Creative Commons License Last Rituals and Problems Faced by the Hindu Community in Punjab: A Case Study of Lahore Authors: Sabir Naz Abstract: Lahore is the provincial capital of Punjab, where a sizeable population of the Hindus has been residing there since the inception of Pakistan. There had been many crematoriums in the city but with the passage of time, one after another, disappeared from the land after partition of the Sub-continent. Those places were replaced by commercial or residential sites. There is also a graveyard in the city which is in the use of Hindu Valmik Sect. However, it was encroached by some Muslims due to very small size of population and indolence of the Hindus. Later on, the encroachments were removed by the District Government Lahore in compliance of order of the Supreme Court of Pakistan. Presently, there is a graveyard as well as a crematorium in the city. The community remained deprived of a place to dispose of a dead body according to their faith for a long period which is contravention with the guidelines of the Quaid-e-Azam, founder of the nation Keyswords: Crematorium, Graveyard, Hindu community, Last Rituals Pages: 704-713 Article: 57 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)57 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)57 Download Pdf: download pdf view article Creative Commons License Estimating Growth Model by Non-Nested Encompassing: A Cross Country Analysis Authors: Benish Rashid Dr. Shahid Razzaque Dr. Atiq ur Rehman Abstract: Whether models are nested or non-nested it is important to be able to compare them and evaluate their comparative results. In this study six growth models have been used for analyzing the main determinants of economic growth in case of cross countries, therefore by using these six models we have tested them for non-nested and nested encompassing through Cox test and F-test respectively. Data from 1980 to 2020 were used to analyze the cross country growth factors so therefore, the current study looked at about forty four countries with modelling these different comparative studies based on growth modelling. So, we can make these six individual models and we can estimate the General Unrestricted Model with the use of econometric technique of Non-Nested Encompassing. By evaluating the data using the Non-Nested Encompassing econometric technique, different sets of economic variables has been used to evaluate which sets of the economic variables are important to boost up the growth level of the country. And found that in case of nested model or full model it is concluded that model with lag value of GDP, trade openness, population, real export, and gross fix capital formation are the main and potential determinants to boost up the Economic Growth in most of the countries. Keyswords: Cross Country, Economic Growth, Encompassing, Nested, Non-nested Pages: 714-727 Article: 58 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)58 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)58 Download Pdf: download pdf view article Creative Commons License Assessment of Youth Buying Behaviour for Organic Food Products in Southern Punjab: Perceptions and Hindrances Authors: Ayousha Rahman Asif Yaseen Muhammad Arif Nawaz Abstract: This research examined the cognitive antecedental effects on organic food purchase behaviour for understanding the perceptions and hindrances associated with purchasing organic food products. Theory of Planned Behaviour (TPB) was adopted as a theoretical framework. A total of 250 young consumers in the two cities of Southern Punjab, Pakistan was randomly sampled and data were collected via a face-to-face survey method. Partial least square technique was employed to test the model. The results showed that attitude towards organic food purchasing motivated when moral norms were activated to consume organic food products. Further, environmental knowledge moderated the relationship of organic food purchase intentions and behaviour significantly. The findings highlighted the importance of moral norms as a meaningful antecedent that could increase the TP-based psychosocial processes if consumers have sufficient environmental knowledge. Therefore, farmers, organic products marketers, government administrators, and food retailers should take initiatives not only to highlight the norms and values but also when promoting organic food production and consumption. Keyswords: Environmental Knowledge, Organic Food Purchase Behaviour, Personal Attitude, PLS-SEM, Subjective & Moral Norms Pages: 728-748 Article: 59 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)59 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)59 Download Pdf: download pdf view article Creative Commons License An Analysis on Students Ideas about English and Urdu as Medium of Instructions in the Subjects of Social Sciences studying in the Colleges of the Punjab, Pakistan Authors: Ashiq Hussain Asma Amanat Abstract: The worth and usefulness of English education as a foreign language is of great concern to language rule and planning (LRP) researchers compared to teaching their native language globally in higher education. The study under research examines the perspectives of two similar groups of the final year students of at Higher Education Institutions of Pakistan. The first group consists of art students who received the Urdu medium of instruction (UMI), and the second group received the English medium of instruction (EMI). An empirical methodology was carried out in the present year, students answered questionnaires to find out the benefits and challenges of learning subject-based knowledge, what subject-based knowledge means to them, and their understanding of language as a teaching language. Interviews were conducted with the selected group of students who wished to participate in research. Additional information is available from the tests and results obtained in the two equivalent courses. Although many similarities have been identified between the two groups, the overall knowledge of disciplinary knowledge of English medium instruction students was not very effective, while that of UMI students was very effective. It explains the implications of the findings to continue the language rule as policy experience for teaching in higher education institutions. Keyswords: English as Medium of Instruction (EMI), Higher Education Institutions (HEIs), Urdu as Medium of Instruction (UMI) Pages: 749-760 Article: 60 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)60 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)60 Download Pdf: download pdf view article Creative Commons License Environment and Women in Kurt Vonnegut’s ‘Happy Birthday Wanda Juny’: An Eco- Critical and Feminist Analysis Authors: Dr. Muhammad Asif Safana Hashmat Khan Muhammad Afzal Khan Janjua Abstract: This is an Eco-feminist study of Vonnegut’s ‘Happy Birthday Wanda Juny’ and focuses on how both women and environment are exploited by patriarchy. Ecofeminism critiques masculine dominance highlighting its role in creating and perpetuating gender discrimination, social inequity and environmental degradation. Women suffer more because of power disparity in society. Environmental crises affect women more than men because of their already precarious existence and subaltern position. There is affinity between women and nature are victims of climate change and other environmental hazards. Cheryl Glotfelty introduced interdisciplinary approach to the study of literature and environment. Literary ecology as an emerging discipline explores the intriguing relationship between environment and literature. Ecofeminism draws on feminist critique of gender inequality showing how gender categories inscribed in power structure exploit both women and nature. Francoise d‘Eaubonne coined the term ecofeminism to critique the prevalent exploitation of both women and environment. Ecofeminism asserts that exploitation of women and degradation of the environment are the direct result of male dominance and capitalism. Ecofeminism argues for redressing the plight of women and protection of environment. Vonnegut’s play ‘Happy Birthday Wanda June’ was written at a time when the movement for the right of women and protection of environment were gaining momentum. The play shows how toxic masculinity rooted in power and capitalism exploit both women and environment. Keyswords: Eco-Feminism, Eco-Criticism, Ecology, Environment, Exploitation Pages: 761-773 Article: 61 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)61 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)61 Download Pdf: download pdf view article Creative Commons License Critical Analysis of Social Equity and Economic Opportunities in the Light of Quranic Message Authors: Prof. Dr. Muhammad Yousuf Sharjeel Mahnaz Aslam Zahida Shah Abstract: This study critically evaluated the key verses of Surah Al-Baqarah -the second chapter of Quran, a sacred scripture of Islam- which specifically relates to social equity opportunities and a code of conduct in the context of economics. The Quran claims that it is a book which explains every situation; therefore, the aim of this study remained to extract those verses of Surah Al-Baqarah which can guide us in Economics. The authentic and approved Islamic clerics and their translations were consulted for the interpretations of the Holy verses. The researchers chiefly focused and studied Surah Baqarah with regards to social equity and economic opportunities. The translations were primarily in the regional language Urdu so the interpretations must not be related exactly equitable in English. The study engaged the document analysis research strategy. This study is only an endeavour to decipher Holy Quran’s message from Allah for the mankind so it must not be considered as the full and complete solution to the all the economic issues, challenges and opportunities. Ahadees and the saying of the Holy prophet were referred to where ever required and available. The researcher also considered the Tafasir (detail intellectual interpretations) of the Quran done by the well-known scholars of Islam for the verses studied therein and any statements and/or material - such as ideas, studies, articles, documentation, data, reports, facts, statistics etc. For the study, data was collected and analyzed qualitatively. On the basis of the study, recommendations were also primed. Keyswords: Economic Issues and Challenges, Social Equity, Surah Al-Baqarah, Al Quran Pages: 774-790 Article: 62 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)62 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)62 Download Pdf: download pdf view article Creative Commons License A Critical Discourse Analysis of Dastak by Mirza Adeeb Authors: Muhammad Afzal Dr. Syed Kazim Shah Umar Hayat Abstract: The present research aims to explore ideology in Pakistani drama. The drama, “Dastak”, written by Mirza Adeeb, has been taken for exploration ideologically. Fairclough’s (1992) three-dimensional model has been used for analyzing the text of the above-mentioned drama which includes textual, discursive practice and social practice analyses. The linguistic and social analyses of the drama reveal the writer’s ideology about socio-cultural, conventional and professional aspects of life. The study has also explored the past and present states of mind of Dr. Zaidi, the central and principal character of the drama, Dastak. The text implies that the writer has conveyed personal as well as social aspects of his times through the drama of Dastak. Keyswords: Dastak, Drama, Ideology, Semiotics Pages: 791-807 Article: 63 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)63 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)63 Download Pdf: download pdf view article Creative Commons License Linking Job Satisfaction to Employee Performance: The Moderating Role of Islamic Work Ethics Authors: Dr. Shakira Huma Siddiqui Dr. Hira Salah ud din Khan Dr. Nabeel Younus Ansari Abstract: The most pervasive concern in public sector organizations is declining employee performance and workforce of these organizations are less satisfied with their jobs. The aim of this study is to investigate the impact of Job Satisfaction on employee’s performance and how Islamic work ethics moderates the above mentioned direct relationship in the public sector organizations of Pakistan. The data were collected from the sample of 193 permanent employees working in public sector organizations through stratified sampling technique. The results revealed that employees Job satisfaction is significantly related to higher performance. Further, the findings indicated that Islamic work ethics moderates the relationship between job satisfaction and employee performance. The present research has some theoretical and empirical implications for academicians, policymakers, especially of public sector organizations, for the improvement of performance of their workforce. Keyswords: Employee Performance, Islamic Work Ethics, Job Satisfaction, Person-Environment Fit Theory Pages: 808-821 Article: 64 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)64 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)64 Download Pdf: download pdf view article Creative Commons License Semantics of Qawwali: Poetry, Perception, and Cultural Consumption Authors: Rao Nadeem Alam Tayyaba Khalid Abstract: Semantics is about meanings and meanings are arbitrary and shared. Understanding qawwali context requires comprehension of semantics or process of meaning creation and meaning sharing among the qawwal party and the audience. This interactive activity might frequently be hindered when interrupted by subjective meanings creation during cultural consumption. Qawwali is a cultural tradition, its semantics are conditioned by axiological premises of poetry and perceptions which are transforming. The previous researches revealed that qawwali is associated with religion which provides the religious message by singing hamd and naat. It was a means to experience Divine; therefore, semantics are multi-layered and often crossroad with values and subjective experiences. It is novel due to its ritual of Sama. It has the therapeutic power that helps mentally disturbed people and they find refuge. This study is exploratory having a small sample size of twenty purposively selected audiences. This phenomenological inquiry used ethnographic method of conversational interviews at selected shrines and cultural spaces in Islamabad. The results indicate that qawwali is a strong refuge for people facing miseries of life and they attend Sama with a belief that attending and listening will consequently resolve their issues, either psychological or physiological. They participate in Sama which teaches them how to be optimistic in a negative situation; this paper brings forth this nodal phenomenon using the verbatim explanations by the interlocutors. Semantics of Qawwali are conditioned and some of these elements are highlighted including poetry and axiology based perceptions and cultural consumption of a cultural realm. Keyswords: Cognition, Culture, Poetry, Qawwal, Qawwali, Semantics Pages: 822-834 Article: 65 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)65 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)65 Download Pdf: download pdf view article Creative Commons License Political Economy of Smuggling: The Living Source for the Natives (A Case Study of Jiwani-Iran Border, Baluchistan) Authors: Abdul Raheem Dr. Ikram Badshah Wasia Arshed Abstract: This study explores the political economy of smuggling on Jiwani-Iran border. The natives are majorly involved in illegal transportation of goods and objects, therefore; the study sets to explain how significant smuggling for the local people is. It describes the kinship role in reciprocity of their trade and transportation. The qualitative methods such as purposive sampling and interview guide were employed for data collection. The research findings revealed that local people were satisfied with their illegal trading which is depended largely on their expertise and know-how of smuggling at borders. They disclosed that their total economy was predominantly based on smuggling of stuff like drugs, diesel, oil, gas, petrol, ration food from Iran, and human trafficking. They also enjoyed the privilege of possessing Sajjil (Iranian identity card), thus; the dual nationality helped them in their daily business and rahdari (border crossing agreement), enabling them to travel to Iran for multiple purposes. Keyswords: Drugs, Human, Navigation, Political Economy, Reciprocity, Smuggling, Trafficking Pages: 835-848 Article: 66 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)66 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)66 Download Pdf: download pdf view article Creative Commons License The Vicious Circles of System: A Kafkaesque Study of Kobo Abe’s The Woman in the Dunes Authors: Imran Aslam Kainat Azhar Abstract: This paper analyses the Kafkaesque/Kafkan features of Kobo Abe’s novel The Woman in the as formulated by Kundera in “Kafka’s World.” For Kundera, in a Kafkaesque work human existence is bleakly represented through intermingling of tragedy and comedy in an indifferent world dominated by hegemonic systems. The Kafkaesque is characterised by the following: World is a huge forking labyrinthine institution where the man has been thrown to suffer its complexities, confrontation with the labyrinth makes his existence meaningless because freedom is a taboo in no man’s land, he is punished for an unknown sin for which he seeks justification from the superior authorities, but his efforts are viewed as ludicrous or comic despite the underlying sense of tragedy. (5) The Kafkaesque tendency to present tragic situation comically is also explored in Abe’s novel. The paper studies the effect of higher authorities exercising their power over man and the inscrutability of cosmic structures continuously undermining human freedom in nightmarish conditions. The paper establishes Kobo Abe in the literary world as a writer who portrays the hollowness and futility of human lives with a Kafkaesque touch. Keyswords: Authority, Institutions, Kafka, Kafkaesque, Kafkan, Kobo Abe, Kundera, The Trial, The Woman in the Dune Pages: 849-861 Article: 67 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)67 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)67 Download Pdf: download pdf view article Creative Commons License Subjectivity and Ideological Interpellation: An Investigation of Omar Shahid Hamid’s The Spinner’s Tale Authors: Hina Iqbal Dr. Muhammad Asif Asia Saeed Abstract: Louis Althusser’s concept of interpellation is a process in which individuals internalize cultural values and ideology and becomes subject. Althusser believes that ideology is a belief system of a society in which ideological agencies establish hierarchies in society through reinforcement and discrimination for cultural conditioning. These agencies function through ideological state apparatuses. These ideological agencies help to construct individual identity in society. The undesirable ideologies promote repressive political agendas. The non-repressive ideologies are inhaled by the individuals as a natural way of looking at the culture and society. This research seeks to investigate Omar Shahid Hamid’s novel The Spinners Tales through the lens of Althusser’s ideology and interpellation. This study examines how the characters of Shahid’s novel inhaled ideology and became its subjects. This research also depicts the alarming effects of cultural hegemony that creates cultural infidelity and hierarchies between the bourgeoisie and proletariat classes. Keyswords: Cultural Hegemony, Ideological State Apparatus, Ideology, Interpellation, Repressive Factors Pages: 862-872 Article: 68 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)68 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)68 Download Pdf: download pdf view article Creative Commons License Blessing in Disguise: Recommendations of Indian Education Commission (1882) and Christian Missionaries’ Educational Policy in the Colonial Punjab Authors: Mohammad Dilshad Mohabbat Muhammad Hassan Muhammad Ayaz Rafi Abstract: Woods Education Despatch is considered to be the Magna Carta of Indian Education. It controlled the Indian education field till the establishment of Indian Education Commission, 1882. The Despatch provided space to Christian missionaries by promising government’s gradual withdrawal from the education in favour of missionaries. It also facilitated the missionaries by offering system of ‘grants on aid’ to the private bodies. Consequently, the missionaries fancied to replace the government institutions in the Punjab and initiated their efforts to increase the number of their educational institutions. They tried to occupy the educational field by establishing more and more educational institutions. But after the Recommendations of the Indian Education Commission 1882, a change in their policy of numeric increase of educational institutions is quite visible. With the turn of the century, they are found to be eager to establish a few institutions with good quality of education. This paper intends to analyse different factors behind the change of their policy of quantitative dominance to qualitative improvement. It also attempts to evaluate how their change of policy worked and what steps were taken to improve the quality of their educational institutions. Following the historical method qualitative data comprising educational reports, missionaries’ autobiographies, Reports of missionaries’ conferences, and the other relevant primary and secondary sources has been collected from different repositories. The analysis of the data suggests that the attitude of the administration of the education department and the recommendations of Indian Education Commission were the major driving forces behind the change of missionaries’ educational policy in the 20th century. The missionaries, after adopting the new policy, worked on the quality of education in their institutions and became successful. Keyswords: Christian Missionaries, Indian Education Commission, Missionary Schools, Numeric Increase, Quality of Education. The Punjab, Woods Education Despatch Pages: 873-887 Article: 69 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)69 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)69 Download Pdf: download pdf view article Creative Commons License Basic Life Values of Prospective Special Education Teachers Authors: Dr. Maria Sohaib Qureshi Dr. Syeda Samina Tahira Dr. Muhammad Irfan Arif Abstract: Future teachers' preconceived values about how to live their lives and how that affects the lives of their students were the focus of this study. Descriptive research was used by the researchers. The study was carried out by using Morris's Ways to Live Scale. Researchers used this scale to study prospective special education teachers' gender, social status, personal relationships, aesthetics and mental approach using purposive sampling method. Descriptive and inferential stats were used to analyse the data collected from those who participated in the study on basic life values of prospective teachers. Results indicated that being social and sympathetic are the most important values among prospective special education teachers. It was also found that male and female prospective special education teachers living in urban and rural areas had no significant differences in their basic life values. Keyswords: Special Education, Teacher, Values Pages: 888-896 Article: 70 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)70 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)70 Download Pdf: download pdf view article Creative Commons License Perception of Dowry: Effects on Women Rights in Punjab Authors: Dr. Bushra Yasmeen Dr. Muhammad Ramzan Dr. Asma Seemi Malik Abstract: Dowry is a common tradition in south Asian countries, especially in Pakistan and India. Daughters became curses and liability for parents causing serious consequences. For control, there are legal ban/restrictions (Dowry and Wedding Gifts (Restriction) Act, 1976; Amendment in Act, 1993) on its practice in Pakistan. Despite the legal cover, the custom has been extended. Dowry amount seems to be increasing due to changing lifestyle and trends of society. To understand males’ and females’ perceptions about dowry; impacts of dowry; why dowry is essential; and how it is affecting women’s rights and eventually affecting women’s autonomy. A qualitative study was conducted. Data was collected by using unstructured interviews from males and females including social activists, economists, and married couples about wedding expenses, demands, society pressure, men’s support, and perception against dowry especially with regards to women’s rights and autonomy. The study concluded heavy dowry especially in terms of furniture, electronics, kitchenware, car, furnished houses, and cash highly associated with women’s development and their rights. General people’s perception showed that dowry is no longer remained a custom or tradition in Asian countries. It is just a trend and people follow it as a symbol of respect for parents and women as well. Keyswords: Dowry, Effects, Impacts Of Dowry, Perceptions, Women Autonomy, Women Rights Pages: 897-909 Article: 71 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)71 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)71 Download Pdf: download pdf view article Creative Commons License NCOC-An Emblem of Effective Governance: An analysis of Pakistan’s Counter Strategy for Covid-19 as a Non-Traditional Security Challenge Authors: Dr. Iram Khalid Abstract: COVID -19 affected the world unprecedentedly. Lack of capacity and poor standards of governance caused nontraditional security challenges to Pakistan too. The NCOC is the central nerve center to guide the national response to COVID-19 by Pakistan and can be best analyzed in the light of the decision-making theory of Naturalist Decision Making (NDM). The study points out the effective role performed by NCOC at policy formation through a more prosaic combination of science, data, decision making and execution of decisions at the level of federalism. The study highlights the changing patterns of government’s approach during the pandemic at various levels. Pakistan faced economic, political and social crisis during this phase. This study uses a survey and key informant interviews as the source of analysis for qualitative data collection. By applying the decision- making theory, the paper extends that there is a need to use a model to balance the existing gap within the system, to meet challenges. The study suggests a coordinating approach among various units and center; that might raise the level of performance to meet the nontraditional security challenges with innovation, creativity and boldness. Keyswords: COVID-19, Decision Making Theory, Governance, Nontraditional Threats, Strategy Pages: 910-930 Article: 72 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)72 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)72 Download Pdf: download pdf view article Creative Commons License Comparative Implications of Wednesbury Principle in England and Pakistan Authors: Safarat Ahmad Ali Shah Dr. Sara Qayum Arzoo Farhad Abstract: Wednesbury principle is one of the most important and useful grounds of the Judicial Review. Judicial review is a remedy provided by the public law and is exercised by the superior and higher courts to supervise administrative authorities' powers and functions. The main objective of the judicial review is to ensure the fair and transparent treatment of individuals by public authorities. The ground of the judicial review, i.e., Unreasonableness or irrationality or popularly known as Wednesbury Unreasonableness was introduced by lord Greene in the Wednesbury Corporation case in 1948. Initially, the scope of this ground of judicial review was very narrow and was allowed only in rare cases. However, with the development of administrative law and Human rights, it also developed. Its development resulted in different controversies and issues about the application of this ground. The main issue is about its encroachment in the jurisdiction of other branches of the government i.e., the parliament and executive. The free and loose application of this principle results in confusion and conflict between different organs of the government. The present paper is based on the implications of the limitations on the ground of Wednesbury Unreasonableness both on the judicial and administrative bodies in Pakistan to avoid the chaos and confusion that results in the criticisms on this ground of judicial review. Keyswords: Administrative Authorities, Critical Analysis, Illegality, Judicial Review, Pakistan, Wednesbury Unreasonableness Pages: 931-946 Article: 73 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)73 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)73 Download Pdf: download pdf view article Creative Commons License Water Sharing Issues in Pakistan: Impacts on Inter-Provincial Relations
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+			TRogers
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+		Journal of Development and Social Sciences
+		JDSS
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+		2709-6262
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+			2
+			IV
+			April 2008. 2008
+			Pakistan Social Sciences Research Institute (PSSRI)
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+	B&nm=test&type=MultiPublishing&mod=PublishingTitles&mid=7155F7796 F354F21B1183937D847D6DF&AudId=29CB3DCAC7E94A08B642EC371FE6E70B&tier= 4&id=68836B06CCE143B199D4E7B0> (accessed: Apr. 29
+	Rogers, T.: Word from My Mother. D Magazine, Dallas, Tex., April 2008. Available at: <http://www.dmagazine.com/ME2/Audiences/dirmod.asp?sid=5C1933F1D01C4247A724B AB13C6E670B&nm=test&type=MultiPublishing&mod=PublishingTitles&mid=7155F7796 F354F21B1183937D847D6DF&AudId=29CB3DCAC7E94A08B642EC371FE6E70B&tier= 4&id=68836B06CCE143B199D4E7B0> (accessed: Apr. 29, 2008).
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+		Flight-Test Evaluation of the Tool for Analysis of Separation and Throughput
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+			LilingRen
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+			John-Paul B.Clarke
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+		10.2514/1.30198
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+		Journal of Aircraft
+		Journal of Aircraft
+		0021-8669
+		1533-3868
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+			45
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+			2008
+			American Institute of Aeronautics and Astronautics (AIAA)
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+	Ren, L.; and Clarke, J.-P. B.: Flight-Test Evaluation of the Tool for Analysis of Separation and Throughput. J. Aircraft, vol. 45, no. 1, 2008, pp. 323-332. DOI: 10.2514/1.30198.
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+		Contrast and Comparison of Metroplex Operations: An Air Traffic Management Study of Atlanta, Los Angeles,New York, and Miami
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+			LilingRen
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+			John-PaulClarke
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+			DavidSchleicher
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+			SebastianTimar
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+			AdityaSaraf
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+			DonaldCrisp
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+		
+		
+			TarynLewis
+		
+		
+			TerenceThompson
+		
+		10.2514/6.2009-7134
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Los Angeles, New York, and Miami
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 21-23, 2009
+		
+	
+	Ren, L.; Clarke, J.-P.; Schleicher, D.; Timar, S.; Crisp, D.; Gutterud, R.; Lewis, T.; and Thompson, T.: Contrast and Comparison of Metroplex Operations -An Air Traffic Management Study of Atlanta, Los Angeles, New York, and Miami. AIAA 2009-7134, 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Hilton Head, S.C., Sept. 21-23, 2009.
+
+
+
+	
+		
+			LRen
+		
+		
+			DCrisp
+		
+		
+			EMcclain
+		
+		
+			DSignor
+		
+		
+			DSchleicher
+		
+		
+			RGutterud
+		
+		
+			TLewis
+		
+		
+			J.-PClarke
+		
+		
+			TThompson
+		
+		
+			ASaraf
+		
+		
+			STimar
+		
+		
+			LWang
+		
+		
+			FRomli
+		
+	
+	
+		Atlanta Large TRACON (A80) Site Survey Report
+		
+	
+	Contract No. NNX07AP63A, unpublished
+	Ren, L.; Crisp, D.; McClain, E.; Signor, D.; Schleicher, D.; Gutterud, R.; Lewis, T.; Clarke, J.-P.; Thompson, T.; Saraf, A.; Timar, S.; Wang, L.; and Romli, F.: Atlanta Large TRACON (A80) Site Survey Report. NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished.
+
+
+
+	
+		
+			LRen
+		
+		
+			ASaraf
+		
+		
+			TThompson
+		
+		
+			LMullick
+		
+		
+			KStefanidis
+		
+		
+			DSchleicher
+		
+		
+			TLewis
+		
+		
+			NArora
+		
+		
+			LWang
+		
+		
+			DCrisp
+		
+		
+			J.-PClarke
+		
+	
+	
+		Metroplex Operations and The Metroplex Problem: A Literature Review
+		
+	
+	Contract No. NNX07AP63A, unpublished
+	Ren, L.; Saraf, A.; Thompson, T.; Mullick, L.; Stefanidis, K.; Schleicher, D.; Lewis, T.; Arora, N.; Wang, L.; Crisp, D.; and Clarke, J.-P.: Metroplex Operations and The Metroplex Problem: A Literature Review. NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished.
+
+
+
+	
+		Airport Configuration Prediction, Phase 2 SBIR Final Report. Contract No. NNA05BE66C
+		
+			LStell
+		
+		
+			Nov. 2006
+			Metron Aviation, Inc
+			Herndon, VA
+		
+	
+	Stell, L.: Airport Configuration Prediction, Phase 2 SBIR Final Report. Contract No. NNA05BE66C, Metron Aviation, Inc., Herndon, VA, Nov. 2006.
+
+
+
+	
+		Airport Configuration Planner with Optimized Weather Forecasts
+		
+			LStell
+		
+		No. NNX07CA12P
+		
+			July 2007
+			Metron Aviation, Inc
+			Herndon, VA
+		
+	
+	Phase 1 SBIR Final Report. Contract
+	Stell, L.: Airport Configuration Planner with Optimized Weather Forecasts, Phase 1 SBIR Final Report. Contract No. NNX07CA12P, Metron Aviation, Inc., Herndon, VA, July 2007.
+
+
+
+	
+		
+			SynergyConsultants
+		
+		
+		Seattle-Tacoma International Airport Greenhouse Gas Emissions Inventory -2006. Port of Seattle
+		Seattle, Wash
+		
+			Jan. 2009. Jan. 20, 2009
+		
+	
+	Synergy Consultants: Seattle-Tacoma International Airport Greenhouse Gas Emissions Inventory -2006. Port of Seattle, Seattle, Wash., Jan. 2009. Available at: <http://www.portseattle.org/downloads/community/environment/greenhousegas08.pdf> (accessed: Jan. 20, 2009).
+
+
+
+	
+		Benefit-Cost Analysis of a 2022 Point-to-Point ATM Concept
+		
+			DavidSchleicher
+		
+		
+			AlexHuang
+		
+		
+			BrianKiger
+		
+		
+			KRamamoorthy
+		
+		10.2514/6.2004-5410
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Providence, R.I.
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 16-19, 2004
+		
+	
+	Schleicher, D.; Huang, A.; Kiger, B.; and Ramamoorthy, K.: Benefit-Cost Analysis of a 2022 Point-to-Point ATM Concept. AIAA Paper 2004-5410, AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence, R.I., Aug. 16-19, 2004.
+
+
+
+	
+		
+			DSchleicher
+		
+		
+			AHuang
+		
+		
+			PDavis
+		
+		TR07258-01
+		Future NAS Flight Demand Generation Tool, Phase-II SBIR Final Report
+		Campbell, Calif
+		
+			Feb. 2007
+		
+	
+	NNA05BE65C, Sensis Seagull Technology Center
+	Schleicher, D.; Huang, A.; and Davis, P.: Future NAS Flight Demand Generation Tool, Phase-II SBIR Final Report. TR07258-01, Contract No. NNA05BE65C, Sensis Seagull Technology Center, Campbell, Calif., Feb. 2007.
+
+
+
+	
+		Demand Loading Analysis for a 3X NextGen Future
+		
+			DavidSchleicher
+		
+		
+			EricWendel
+		
+		
+			AlexHuang
+		
+		10.2514/6.2007-7714
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 18-20, 2007
+		
+	
+	Schleicher, D.; Huang, A.; and Wendel, E.: Demand Loading Analysis for a 3X NextGen Future. AIAA 2007-7714, 7th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Belfast, Northern Ireland, Sept. 18-20, 2007.
+
+
+
+	
+		
+			JSorenson
+		
+		
+			BKiger
+		
+		
+			RKelley
+		
+		
+			MJackson
+		
+		No. NAS2-02076
+		Capacity Increasing Concept Massive Point-to-Point (PTP) and On-Demand Air Transportation System Phase 3 Concept and Scenarios
+		
+			Seagull Technology, Inc., Campbell, Calif
+			2004. Dec. 15, 2004
+		
+	
+	NASA Contract
+	Sorenson, J.; Kiger, B.; Kelley, R.; and Jackson, M.: Capacity Increasing Concept Massive Point-to-Point (PTP) and On-Demand Air Transportation System Phase 3 Concept and Scenarios (2004). TR04231-07, NASA Contract No. NAS2-02076, Seagull Technology, Inc., Campbell, Calif., Dec. 15, 2004.
+
+
+
+	
+		
+			DSchleicher
+		
+		
+			TBLewis
+		
+		
+			RGutterud
+		
+		
+			LWong
+		
+		
+			J.-PClarke
+		
+		
+			DCrisp
+		
+		
+			TThompson
+		
+		
+			ASaraf
+		
+		
+			BSliney
+		
+	
+	
+		SCT Site Survey Report
+		
+	
+	Contract No. NNX07AP63A, unpublished
+	Schleicher, D.; Lewis, T.B.; Gutterud, R.; Wong, L.; Clarke, J.-P.; Crisp, D.; Thompson, T.; Saraf, A.; and Sliney, B.: SCT Site Survey Report. NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished.
+
+
+
+	
+		
+			DSchleicher
+		
+		
+			LRen
+		
+		
+			RGutterud
+		
+		
+			STimar
+		
+		
+			DCrisp
+		
+		
+			TBLewis
+		
+		
+			J.-PClarke
+		
+		
+			ASaraf
+		
+	
+	
+		Miami Site Survey Report
+		
+	
+	Contract No. NNX07AP63A, unpublished
+	Schleicher, D.; Ren, L.; Gutterud, R.; Timar, S.; Crisp, D.; Lewis, T.B.; Clarke, J.-P.; and Saraf, A.: Miami Site Survey Report. NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished.
+
+
+
+	
+		Assessment of the Potential Benefits of an Ideal Integrated Metroplex-Wide Departure Planner
+		
+			AdityaSaraf
+		
+		
+			DavidSchleicher
+		
+		
+			KatyGriffin
+		
+		
+			PeterYu
+		
+		10.2514/6.2009-7038
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Campbell, Calif.
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 30, 2008
+		
+	
+	Saraf, A.; and Schleicher, D.R.: SLDAST Phase I Experiment Assessment Report: Integrated Metroplex Planner. Sensis Corporation, Campbell, Calif., Sept. 30, 2008.
+
+
+
+	
+		Assessment of the Potential Benefits of an Ideal Integrated Metroplex-Wide Departure Planner
+		
+			AdityaSaraf
+		
+		
+			DavidSchleicher
+		
+		
+			KatyGriffin
+		
+		
+			PeterYu
+		
+		10.2514/6.2009-7038
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, S. C.
+		
+			American Institute of Aeronautics and Astronautics
+			September 21-23, 2009
+		
+	
+	Saraf, A.; Schleicher, D.R.; Griffin, K.; and Yu, P.: Assessment of the Potential Benefits of an Ideal Integrated Metroplex-wide Departure Planner. AIAA-2009-7038, 9 th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Hilton Head, S. C., September 21-23, 2009.
+
+
+
+	
+		Characterization of and Concepts for Metroplex Operations: Metroplex Issues Map
+		
+			DRSchleicher
+		
+		
+			ASaraf
+		
+		
+			TBLewis
+		
+		
+			RGutterud
+		
+		
+			GeorgiaTechTeam
+		
+	
+	
+		NASA Metroplex NRA Project Report
+		
+			Aug. 28, 2009
+		
+	
+	Contract No. NNX07AP63A, unpublished
+	Schleicher, D.R.; Saraf, A.; Lewis, T.B.; Gutterud, R.; and Georgia Tech Team: Characterization of and Concepts for Metroplex Operations: Metroplex Issues Map. NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished, Aug. 28, 2009.
+
+
+
+	
+		Ralph Iovinelli, AWA/FAA
+		
+			Faa
+		
+		10.2514/6.2009-7133
+		
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Washington, D.C.
+		
+			American Institute of Aeronautics and Astronautics
+			2008. Jan. 2009. Jan. 20, 2009
+		
+	
+	FAA: "Terminal Area Forecast (TAF), 2008. FAA, Washington, D.C., Jan. 2009. Available at: <http://aspm.faa.gov/main/taf.asp> (accessed: Jan. 20, 2009).
+
+
+
+	
+		
+			STimar
+		
+		
+			TBLewis
+		
+		
+			RGutterud
+		
+		
+			LRen
+		
+		
+			DCrisp
+		
+		
+			ASaraf
+		
+		
+			BLevy
+		
+		
+			DRappaport
+		
+		
+			KStefanidis
+		
+		
+			J.-PClarke
+		
+		
+			TThompson
+		
+		
+			DSchleicher
+		
+	
+	
+		New York Site Survey Report
+		
+	
+	Contract No. NNX07AP63A, unpublished
+	Timar, S.; Lewis, T.B.; Gutterud, R.; Ren, L.; Crisp, D.; Saraf, A.; Levy, B.; Rappaport, D.; Stefanidis, K.; Clarke, J.-P.; Thompson, T.; and Schleicher, D.: New York Site Survey Report. NASA Metroplex NRA Project Report, Contract No. NNX07AP63A, unpublished.
+
+
+
+	
+		
+			NtscVolpe
+		
+		
+			Boeing
+		
+		No. VNTSC-DTS20-PDP-001
+		Logistics Management Institute, Flight Transportation Associates, and FAA: A Preliminary Design Process for Airspace Systems: Initial Assessment -Chicago Case Study Final Report
+		Washington, D.C.
+		
+			Department of Transportation
+			Oct. 19, 2000
+		
+	
+	Report
+	Volpe NTSC, Boeing, Logistics Management Institute, Flight Transportation Associates, and FAA: A Preliminary Design Process for Airspace Systems: Initial Assessment -Chicago Case Study Final Report. Report No. VNTSC-DTS20-PDP-001, Department of Transportation, Washington, D.C., Oct. 19, 2000.
+
+
+
+	
+		The Airspace Operations Laboratory (AOL) at NASA Ames Research Center
+		
+			ThomasPrevot
+		
+		
+			NancySmith
+		
+		
+			EverettPalmer
+		
+		
+			JoeyMercer
+		
+		
+			PaulLee
+		
+		
+			JeffreyHomola
+		
+		
+			ToddCallantine
+		
+		10.2514/6.2006-6112
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Moffett Field, Calif
+		
+			American Institute of Aeronautics and Astronautics
+			June 2006
+			1
+		
+	
+	Executive Summary
+	VAMS Project Office: Virtual airspace modeling and simulation system-wide concept report. Volume 1: Executive Summary, NASA Ames Research Center, Moffett Field, Calif., June 2006.
+
+
+
+	
+		Estimating Airport System Delay Performance. USA/Europe Air Traffic Management R&D Seminar, the 4th
+		
+			JWelch
+		
+		
+			RLloyd
+		
+		
+			Dec. 2001
+			Santa Fe, N. M.
+		
+	
+	Welch, J.; and Lloyd, R.: Estimating Airport System Delay Performance. USA/Europe Air Traffic Management R&D Seminar, the 4th, Santa Fe, N. M., Dec. 2001.
+
+
+
+	
+		Airspace Design Tool User Notes. Internal technical memorandum, Metron Aviation
+		
+			THWhite
+		
+		
+			JChan
+		
+		
+			JDifelici
+		
+		
+			SAugustine
+		
+		
+			MGraham
+		
+		
+			TThompson
+		
+		
+			2004
+			Dulles, Va
+		
+	
+	White, T.H.; Chan, J.; DiFelici, J.; Augustine, S.; Graham, M.; and Thompson, T.: Airspace Design Tool User Notes. Internal technical memorandum, Metron Aviation, Dulles, Va., 2004.
+
+
+				
+			
+		
+	
diff --git a/file152.txt b/file152.txt
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+
+	
+	
+		
+I. IntroductionC onflict prediction is an integral part of maintaining safe separation between aircraft in the National Airspace System.This task is currently handled completely by human controllers, but as air traffic demand continues to rise, automation will play a more prominent role in conflict detection and possibly resolution.To enable this transition, it is important to develop automation tools that can operate reliably and with a high degree of accuracy in real-world settings.One of the primary challenges in predicting conflicts using automation in a realistic setting is dealing with the effects of uncertainty.Automated conflict probes often utilize some type of trajectory generator to build a predicted trajectory, and this predicted trajectory never exactly matches what the aircraft will actually fly.Depending on the type and magnitude of the uncertainty, as well as the capabilities of the trajectory predictor, these errors can range from relatively minor errors, such as differences in turn modeling, to major issues like intent errors that can greatly affect the accuracy of trajectory predictions. 1 These errors, in turn, affect the ability of the conflict probe to detect conflicts and to suggest resolutions that are free from conflict.There are two primary ways to address the issue of errors in the trajectory prediction: improve the accuracy of the trajectory generator, or compensate for the error by adapting the conflict probe.For this work, the focus is on compensating for errors, rather than trying to improve trajectory predictions.Specifically, the authors are detecting and resolving conflicts at greater than the legal standard for separation of five nmi horizontally and 1,000 ft vertically using a geometric conflict detection scheme.While improving trajectory prediction accuracy is beneficial for detecting conflicts when using automated conflict probes, 2,3,4,5 and validating the accuracy of a trajectory prediction is seen as a necessary part of the future National Airspace System 6 (NAS), improving trajectory prediction performance often requires some form of equipage on the aircraft and/or data sharing, which can make the solution more difficult and expensive to implement in actual operations.There are alternative approaches that try to use adaptive algorithms to improve the accuracy of trajectory predictions, particularly during climb, by adapting either the modeled aircraft thrust 7,8 or weight. 9These approaches show promise, but are not yet mature.On the mitigation side, there are many studies that look at using increased separation criteria, or "buffers," to deal with uncertainty or error.Some examples include studies examining enlarged horizontal detection ranges in the presence of cruise speed errors, 10 wind prediction errors, 11 and maneuver-initiation time errors. 12One common theme through much of this work is a primary focus on horizontal errors and the effectiveness of using horizontal buffers or probabilistic conflict detection schemes to account for those errors.Some simulation test beds, such as the Center-TRACON Automation System (CTAS) 13 use a vertical buffer for aircraft that are transitioning altitudes, but that buffer is often on the order of hundreds of feet, which is not enough to account for uncertainties during the descent phase.
+II. BackgroundThe authors have examined the effectiveness of a buffer to aid automated conflict detection and resolution in the presence of trajectory prediction errors using a geometric conflict detection algorithm, but that work was focused on using buffers in the horizontal plane of up to two nmi. 14In that work, the authors looked at a range of uncertainties and tried to determine what effects these uncertainties had on missed-alert rates, false-alert rates, and losses of separation (LOS) using automated conflict detection and resolution tools in a non-real-time simulation.Missed alerts are conflicts that should have been detected but are not, and false alerts are conflicts that are predicted to occur but do not.That study found, among other things, that there was a very high rate of missed detections and LOS cases near an aircraft's top-of-descent (TOD) point.Additionally, increasing the horizontal separation for these cases did not have a noticeable effect on the number of LOS observed.Follow-on, unpublished work revealed the difficulty the conflict probe had in the vertical plane due to the very small legal vertical separation requirement.With many aircraft descending fast enough to completely pass through the legal separation in under 20 seconds, it does not take much uncertainty to create a situation that results in an LOS using standard vertical separation.This danger can be reduced by asking the aircraft to provide intent information about its planned descent, such as the anticipated TOD point or its desired descent profile, but an aircraft might not exactly fly the stated profile anyway.As an example, there can be an error on the order of a few nautical miles between the Flight Management System's (FMS) predicted and actual TOD point. 15Additionally, depending on the airspace class, aircraft in level flight can pass over another level aircraft 1,000 feet below and be perfectly legal and safe.It is only when aircraft are transitioning altitudes that detections in the vertical plane become a concern.One solution is to simply clear all of the airspace beneath an aircraft that might be descending soon.This would ensure safety, but could decrease airspace capacity and increase the total delay experienced by aircraft near either their own or some other aircraft's TOD point.Therefore, it was decided to design vertical conflict detection buffers that would provide just enough warning to ensure safe separation, while minimizing the amount of extra airspace that would need to be cleared in addition to the amount required by the legal separation standard.The question of "How much warning is enough?" is one that is not totally answered.For this study, the vertical separation buffers have an altitude range that covers roughly four minutes of descent at the nominal predicted descent rate for each aircraft.The exact sizes of the buffers, therefore, vary from flight to flight.Four minutes was chosen as the look-ahead time for this study as a reasonable minimum that should allow a resolution tool to resolve a potential conflict before it becomes an LOS.
+III. Simulation Environment
+A. ACES and AAC AutoresolverThe simulation test bed used for the study is the Airspace Concepts Evaluation System (ACES). 16This is a non-real-time simulation that uses a four-degree-of-freedom model to create aircraft trajectories based off performance data and stored flight plan information.The aircraft performance data are derived from the Base of Aircraft Data (BADA) 17 and the flight plans are created from the filed flight plans for days in the National Airspace System (NAS).For the current study, the traffic scenario for all the data runs consisted of the flight plans for 9,272 flights across the US, representing about four hours worth of takeoffs during the busiest part of a day in 2005.The wind data is RUC data recorded from a day in May of 2002.The simulation includes a conflict probe that uses knowledge of those flight plans to check for conflicts along an aircraft's predicted trajectory.Conflict resolution is handled by the Autoresolver, 18 which is a component of the Advanced Airspace Concept 19 (AAC).The simulation only examines aircraft during their flight through Center airspace, from departure fix to arrival fix, and does not examine flights inside terminal airspace.The current version of AAC attempts to resolve conflicts at roughly eight minutes until predicted LOS, referred to as its action time, though if both aircraft are headed to the same arrival fix and within 20 minutes of that fix, AAC can attempt to resolve the conflict at 20 minutes to predicted LOS.Typically, AAC tries to issue a resolution that is free of conflicts for up to four minutes beyond its action time.
+B. Trajectory PredictionIn this study, two trajectories are created for each aircraft.The first is the "real" trajectory, which is the one that the aircraft will actually fly.From that trajectory a "perturbed" trajectory is created, which includes the prediction errors being tested (figure 1(a) has an example of this).Every minute, the perturbed trajectory is sent to the conflict detection algorithm and then on to the AAC Autoresolver if a resolution is required.
+IV. Experiment SetupThe study consists of two sections.The first section examines the performance of the vertical buffer in terms of conflict detection only.This allows the vertical buffers to be examined in repeatable data sets using multiple error types, with every case having the exact same number of actual conflicts because each aircraft will fly the same "true" trajectory in each run.The second study uses the vertical buffers for both detecting and resolving conflicts.Both studies use the same error types.The legal separation requirement used for all runs is defined as five nmi of horizontal separation and 1,000 ft of vertical separation.For conflict detection, a six nmi horizontal range is used for all cases.When issuing resolutions, the Autoresolver attempts to obtain seven nmi of horizontal separation.The simulations that were run without a vertical buffer use 1,000 ft vertically for the entire flight, while the cases with vertical buffers use a specialized buffer near TOD and during descent, and 1,000 ft elsewhere.The vertical buffers used in this study are described in detail in the next subsection.
+A. MethodTable 1 shows the five types of trajectory uncertainty used in this study.The error rages were chosen to be roughly in line with values used in other studies, though they were chosen to be slightly larger overall.Cruise speed, descent speed, and TOD location are modeled as uniform distributions around zero.In this simulation, "descent speed" includes an error in the predicted descent Mach number and descent CAS.Wind speed errors are modeled as a prediction that is 25% stronger than the actual wind, as read from a RUC wind file, with the direction unchanged.The aircraft fuel weight is used to adjust the aircraft's weight, and is modeled as a uniform distribution applied to predicted aircraft fuel weights around a nominal value.The references for the error ranges are included in the table, while a more detailed description of how the errors are implemented in ACES can be found in previous work by the authors.
+Error SourceError Range Top of Descent Location roughly +/-10 nmi 20,21 Descent Speed +/-10% The conflict detection portion of this study examined six error configurations and had two vertical buffer settings, for a total of 12 data runs.The test matrix for this portion of the study is shown in table 2. Four of the errors were examined individually (TOD location, descent speed, cruise speed, and wind speed), with a fifth case that had all of the errors, including weight, enabled together.Aircraft weight was not examined on its own because previous work has shown it to have a mild, though non-zero, impact on the aircraft trajectory near TOD. 14A case with no error was also examined to establish the baseline behavior without trajectory prediction errors.All the error configurations had one simulation run with the enhanced vertical buffers disabled and one with them enabled.The resolution portion of the study consisted of five simulations with all the errors enabled.The test matrix for this part of the study is shown in table 3. The first three runs used the full error range with vertical buffers disabled, set to the full size they were in the detection runs, and set to 80% of full size.The last two runs used 50% of the values shown in table 1, and included a case with no vertical buffer and one with a buffer set to 50% of the full value used in the detection runs.This last pair of runs was used to roughly simulate how effective a vertical buffer combined with improved trajectory predictions would be.The vertical conflict detection buffer consists of two parts (see figure 1(a) and figure 1(b)).The first part was a buffer around the predicted top of descent point.This buffer was constructed by assuming the aircraft might descend as much as four minutes early or late.Using the predicted average descent for each aircraft, this buffer is extended along the descent for four minutes and should provide a minimum of three minutes warning.The second part of the vertical detection buffer is implemented after the aircraft has started to descend and is shown in figure 1(b).This buffer is created at the aircrafts current position during each conflict detection cycle and extends forward along the predicted trajectory.The buffer takes the predicted descent rate at the temporal midpoint of its remaining descent, creates a "fast-descent" and "slow-descent" profile, and extends those four minutes into the future from the aircraft's current position.The fast-descent profile assumes the aircraft is descending 400 fpm faster than predicted, while the slow descent profile assumes a descent rate 200 fpm slower than predicted.These values were based on results from preliminary data  collected for this study.These two buffers comprise the "full" buffer case.The simulations with reduced buffer size simply scaled the early/late descent time and fast/slow climb rate by a percentage value.The look-ahead time was four minutes along the aircrafts predicted descent.Alternative look-ahead times and buffer shapes will be examined in a future study.
+B. Conflict Detection MetricsThe main metrics being used in the detection part of the study are the number of missed and false alerts for a specific predicted time until LOS.A missed alert is defined as a case where there is a loss of separation along an aircraft's true trajectory that is not detected by looking at the perturbed, predicted trajectory.This is recorded by the time until the aircraft would actually have a loss of separation.Depending on the error type, there are generally more missed detections when the aircraft are still 20 minutes apart than when they are closer.In this study, we are most concerned with the missed detections that occur with 3 minutes or less until the time of first loss of separation.These late detections can be very difficult to solve, as neither aircraft has much time to move out of the way.In addition, even though these conflicts do not always result in a loss of separation, they are cases that could more easily become losses, depending on the situation in the surrounding airspace and the capabilities of the person or automation attempting to resolve the conflict.False alerts are defined similarly to missed, except that false alerts are cases where the perturbed predicted trajectory detects a potential conflict that the true trajectory reveals will not actually occur.Furthermore, for all cases in this study, the legal separation requirement (five nmi horizontally and 1,000 ft vertically) is used to determine whether or not a conflict actually occurred.This means that using any enlarged conflict detection criteria will produce false alerts, even with zero trajectory prediction error.These false alert cases do not impact safety directly, but they can have a large impact on efficiency, as a high rate of these alerts means that many aircraft are being moved to resolve conflicts that would not have actually occurred.This, in turn, adds to the delay for aircraft flying through the area.Therefore, even though some non-zero value should be expected any time an enlarged detection criteria is used, it is desirable to keep this rate as low as possible without degrading safety.
+C. Conflict Resolution MetricsFor the portion of the study looking at resolutions, the primary metrics are the number of losses of separation, the number of resolutions issued, and the total delay for aircraft in flight due to conflict resolution maneuvers.The average delay per resolution is also reported.The LOS metric is the driving one for this study, as it represents failures of the system that could affect safety.It is defined as any case where two aircraft in enroute airspace pass within the legal separation requirement of each other.It should be noted that LOS cases are expected in this simulation, because the vertical buffers presented here are only a partial solution aimed at significantly reducing or eliminating the number of losses seen in the descent phase of flight.Also, the Autoresolver is only the first level of the multi-layered AAC, so LOS cases in this study could be more accurately described as conflicts that would not be resolved by Autoresolver, and would fall through to the next layer of an overall system.Analysis of those other systems is beyond the scope of the current work, so for simplicity, conflicts that the Autoresolver fails to resolve will be called LOS cases.The number of resolutions and total delay are ways to quantify the effect of the vertical buffers on system efficiency, as compared to a system with no buffers.Any changes to the system that improve robustness will likely have efficiency penalties, but keeping track of the number of extra resolutions and the amount of extra delay allow for comparisons between options, both now and in future work.
+V. Results
+A. Conflict DetectionThe goal of the first part of the study is to check how many conflicts are detected at least three minutes before predicted loss of separation.As a reminder, a "missed alert" is an alert where the flown "truth" trajectory predicts a loss of separation while the perturbed predicted trajectory does not.A "false alert" is the case where the perturbed predicted trajectory identifies a conflict that would not have occurred, based on the "true" flown trajectory.Also as a reminder, conflicts were only detected, not resolved in this portion of the study, so it is possible for a single conflict to produce multiple false alerts and/or missed alerts.Figure 2 details the percentage of potential conflicts that were missed conflict alerts, plotted as a function of time until loss for each of the five error cases.The chart on the left uses the standard legal separation vertically for conflict detection, while the chart on the right includes the vertical detection buffers.Both charts use the same horizontal detection range of six nmi, and both charts show all error types, with the case that had no trajectory prediction errors included as a reference.The case without the vertical buffer illustrates the problem caused by these uncertainties.While there are fewer missed detections as the time to loss decreases, many of those missed detections persist until there is very little time to resolve them.This is especially true for the descent speed and top-of-descent position errors which do not really show a decrease in the number of missed alerts until there is less than five minutes until LOS.These two errors help drive the curve for the "all error" case up, so that at four minutes to actual LOS for the all-error case, 8.7% of conflicts are missed by the detection algorithm without the vertical buffer.The right chart shows the effectiveness of the current iteration of the vertical detection buffer.It should be noted that these are missed alerts for all phases of flight, so there are some in climb or cruise that vertical detection buffers for descent simply will not address, especially in the case with all the errors combined.There is a noticeable drop in missed alerts for all error types due to the addition of the buffers, especially with less than five minutes until the loss of separation.To continue the example from the previous paragraph, enabling the buffer drops the missed detection value at four minutes to LOS to 2.4% for conflicts in all phases of flight.Considering that the vertical buffer will not directly impact conflicts that do not involve at least one arriving aircraft, that reduction is significant.Looking at an error that directly affects arriving aircraft, the number of missed detections at eight minutes to LOS for TOD error drop from 7.5% without the vertical buffer, to 1.2% with the buffer enabled.At four minutes, there 4.7% of conflicts are missed with TOD error and no vertical buffer and 0.1% missed for the same error with vertical buffers enabled.These results further strengthen the position that conflicts involving aircraft descending into their arrival fix make up a large portion of the conflicts that are difficult to detect, and that a vertical buffer can largely mitigate this.Figure 3 shows the percentage of alerts that were false alerts for the conflict detection study.This figure shows that adding vertical buffers has a significant effect on false alerts, especially below 10 minutes until predicted LOS.As these were the results with no conflict resolutions implemented, every aircraft flew the same true trajectory in both the left and right charts.Therefore, the overall increase should be entirely attributable to the added vertical detection buffer.To give an example of the scope of the increase, in the case with all errors, at 8 minutes until predicted LOS, the percentage of detections that were false alerts is 30% without the vertical buffers and 53% with the buffers enabled, a jump of 23%.The case without error saw the biggest jump, with the percentage of false alerts moving from 17% without the vertical buffers to 49% with buffers, or a jump of 32%.The magnitude of these increases implies that there was a lot of traffic around aircraft that were near or past their TOD point.This, in turn, implies that there are many aircraft that could be at risk if an aircraft deviates much from its predicted trajectory near TOD.The net result of a large increase in the number of resolutions issued due to these false alerts is supported by results presented later in the paper.The fact that there are so many more detections also means that it will likely be difficult to find a strategy for mitigating uncertainties in trajectory prediction for this level of trajectory prediction error that does sharply increase the number of false alerts or add significant delay, as there are simply many aircraft in relative proximity during the descent phase for aircraft that are arriving.
+B. Conflict ResolutionThe results with conflict resolutions enabled are discussed in this section.The primary metrics analyzed are the number of losses of separation, the number of resolutions issued, and the total delay and delay per resolution.This section will explore the effectiveness of the vertical buffers and the penalty for using the buffer in terms of the amount of extra delay created and number of resolutions issued.Figure 4 shows the losses of separation, categorized by flight phases of the two aircraft involved, for the simulations with and without the vertical buffers.All cases detect conflicts at six nmi horizontally, and attempt to obtain at least seven nmi horizontally when issuing resolution maneuvers.The left group of columns are cases where at least one aircraft was climbing, the second group is the case where both aircraft were roughly in their cruise segment, the third group is for cases where one aircraft was an arriving aircraft descending into the Terminal area, and the fourth group is the special case where one aircraft was a descending arrival and the other was a departing aircraft still climbing to cruise altitude.The main point of this plot is to show that simply by using increased vertical separation criteria near and after an aircraft's top of descent point, one can dramatically decrease the number of times uncertainty induces an LOS.However, these buffers alone are not enough to completely remove the problem.The case without the vertical buffer once again emphasizes the difficulties the conflict detection and resolution algorithms have with descending aircraft, with 209 of the 276 total LOS cases involving at least one aircraft that was descending near its arrival airport, as shown by the right two blue columns.When the buffer is enabled, the number of total LOS cases drops to 49, while the number of losses involving arriving aircraft drops to 12 (right two light blue columns combined).Ideally, the number of LOS for arriving aircraft would be zero with the vertical buffer enabled, but there were a few cases that slipped through.While it should be possible to keep increasing the size of the vertical buffer to cover all of those cases, it is worth investigating a different approach for dealing with those last few losses of separation.The idea that it might not be efficient to continually increase the vertical buffer to remove all LOS cases came from the results of the 80% buffer run, shown as the green columns in the figure.This scenario actually produced slightly better results than the full buffered case as far as dealing with LOS cases occurring for arriving aircraft, for reasons that are not clear.Unfortunately, due to timing constraints, exploring the causes of these losses of separation involving arriving aircraft that are not resolvable even with the vertical buffers is beyond the scope of this paper.However, this analysis will be done in future work, as accounting for these cases is necessary for making a system that is truly robust to trajectory prediction errors.One interesting result was the decrease in the number of LOS cases between two aircraft in cruise for both buffer cases.This could simply be the result of these buffers being used for temporary, in cruise descents to avoid conflicts, but further analysis would be required before it could be claimed as a benefit.As the focus of this study is on LOS cases involving an arriving aircraft, the examination of cruise LOS cases will be also deferred to a future study.The number of resolutions as well as the delay per resolution are summarized in table 4. It is immediately obvious that the addition of these vertical buffers produces a steep increase in both the number of resolutions as well as the total delay experienced by aircraft in the system, with the number of resolutions issued increasing by 53% and the total delay by 106%.However, one has to consider that the unbuffered case also had a very large number of losses of separation that need to be addressed, so some penalty is likely unavoidable.The 80% buffer results are more promising, showing an appreciable decrease in the number of resolutions and amount of delay added to the system, increasing the number of resolutions by 42% and the total delay by 69%.The fact that this 80%buffer was able to perform just as well as the full buffer in regards to accounting for losses of separation involving arriving aircraft was a major point.It strengthened the idea that alternative options might be best suited to dealing with the few losses that the vertical detection buffer does not catch, as there seems to be diminishing returns in regards to catching LOS cases when increasing the vertical buffer beyond a certain size.Planned future work includes developing alternative methods for dealing with these last few LOS cases, as well as exploring the use of smaller buffers.This could lead to a system with no LOS involving arriving aircraft and with less system-wide delay caused by resolution maneuvers than would be possible using the enhanced vertical buffers alone.This section describes a pair of simulations run with all of the trajectory prediction error ranges set to 50% of their full values, to roughly simulate the effects of improving trajectory prediction accuracy.The first run used no vertical buffer, while the second used a vertical buffer that was also set to 50% of the full value, to take advantage of the reduced error range.As trajectory accuracy improves, one would expect to see fewer losses, a smaller buffer requirement, and more efficiency in terms of the number of resolutions and the delay per resolution, though it is difficult to predict the amount of savings without simply collecting the data.The results of these runs are shown in figure 5, along with the original, full error case with no vertical buffer.It is immediately apparent that, even without the addition of the vertical buffer, cutting the trajectory prediction error in half cuts more than half of the losses off separation in all flight regimes.Furthermore, the addition of a vertical buffer (also half the size of the first one tested in this study), cuts the overall number of LOS cases from 117 in the unbuffered case to 23, and the cases involving arriving aircraft from 87 to 3.Table 5 shows the results of the half-error cases, with the full-error case (no vertical buffer) provided for reference.Compared to the half-error, no vertical buffer case, implementing the half-sized vertical buffer increased the number of resolutions issued by 42% and increased the total delay due to resolution maneuvers by 82%.These increases are in line with the percentages seen for implementing the vertical buffers in table 4, though the overall numbers are lower because of the reduced number of resolutions and delay in the half-error case without vertical buffers.This illustrates the effectiveness of combining the approaches of improving trajectory prediction accuracy and implementing vertical buffers, as the overall number of LOS cases, the number of added resolutions, and the increase in delay are all significantly reduced when the buffer is implemented in the half-error case.
+VI. Future WorkAs stated previously, these vertical buffers are only a first step to making a system that can reliably predict and resolve all conflicts in the presence of uncertainty.The major problem area considered prior to this work was when one or more aircraft were descending towards their arrival fix.However, uncertainties and their resulting trajectory prediction errors can lead to losses of separation in climb and cruise, as well.Additionally, even our relatively large vertical buffers were not sufficient to completely deal with trajectory prediction errors during descent.The next step is to explore ways to remove those last few LOS cases for arriving aircraft.Additionally, more work needs to be done examining different vertical buffer sizes and lookahead times.Following that, the focus will shift to using buffers and perhaps an adaptive climb algorithm for removing LOS cases during climbs, and then figuring out a way to remove LOS cases in cruise as efficiently as possible.Increasing the range of types of uncertainty is also part of the planned work, with the eventual goal of producing a system that can be made robust to varying levels of trajectory prediction uncertainty in all phases of fight.A secondary goal is to do that while limiting the decrease in system efficiency in terms of delay caused by resolution maneuvers.
+VII. ConclusionsThe results of this study point to vertical buffers as an effective first step for detecting potential losses of separation involving aircraft descending into terminal airspace in the presence of trajectory prediction errors.Results showed that errors involving predictions during the descent phase are difficult to detect with much more than a few minutes until loss with just a conventional horizontal buffer.Further, implementing vertical separation buffers can be an effective technique for reducing the number of cases that are missed by the detection algorithm, especially for errors in TOD location and descent speed.Results also showed that enabling the vertical buffers decreased the number of missed alerts with four minutes to LOS from 8.7% for all errors without the vertical buffers to 2.4% with buffers.For TOD errors, the missed alerts at 4 minutes until LOS dropped from 4.7% without the vertical buffer to 0.1% with the buffers.Enabling the buffers also increased the percentage of detections that were false alerts by around 20% to 30% for predicted LOS times in the eight minute range for all error types.When AAC was allowed to resolve detected conflicts, implementing the full vertical buffer produced a significant reduction in the number of losses of separation in the simulation.The number of LOS cases involving arriving aircraft was reduced from 207 in the case with full error and no vertical buffer to 12 when the full-sized vertical buffer was used.Setting the vertical buffer to 80% of the full size actually reduced the number of LOS for arriving aircraft compared to the full buffer, dropping the number of cases to 10.In terms of the other metrics, the full buffer increased the number of resolutions issued by 53% and the amount of delay accumulated by aircraft executing resolution maneuvers by 106%, while using the 80% buffer increased the number of resolutions by 41% and the delay by 69%.These results imply that vertical buffers can be effective in reducing the number of LOS cases involving arriving aircraft, but there is a point beyond which increases to the buffer size results in larger delay and more resolutions with no real reduction in the number of LOS cases, and alternative methods for catching the remaining LOS cases for arriving aircraft need to be developed.The study with 50% uncertainty showed that improvements in trajectory prediction significantly improve the ability of the vertical buffer to account for all LOS cases with arriving aircraft.Additionally, the system as a whole runs more efficiently with less resolutions and total delay when there is reduced uncertainty.While this is expected, it suggests that a joint approach of reducing trajectory prediction error and building robust detection schemes is likely the most viable way to achieve a system that has no LOS cases in the presence of multiple trajectory prediction errors.Figure 1 .1Figure 1.Vertical conflict detection buffer (a) before and (b) after the top-of-descent.
+Figure 2 .2Figure 2. Missed alerts for multiple uncertainties using conflict detection only.
+Figure 3 .3Figure 3. False alerts for multiple uncertainties using conflict detection only.
+Figure 4 .4Figure 4. Losses of separation with all uncertainty enabled.
+Figure 5 .5Figure 5. Losses of separation with 50% uncertainty enabled.
+14 Table 1 .141Trajectory Prediction Error Source and Range.
+Table 2 .2Test Matrix: Conflict Detection.Error TypeEnhanced Vertical Buffer SettingNo ErrorDisabled; Full BufferTop of Descent LocationDisabled; Full BufferDescent Speed (CAS and Mach)Disabled; Full BufferCruise SpeedDisabled; Full BufferWind SpeedDisabled; Full BufferAll Errors (including Weight)Disabled; Full Buffer
+Table 3 .3Test Matrix: Conflict Detection and Resolution.Error TypeBuffer SizeEnhanced Vertical Buffer SettingAll ErrorsFull Error Range Disabled; Full Buffer; 80% BufferAll Errors50% Error RangeDisabled; 50% Buffer
+Table 4 .4Resolution efficiency metrics by vertical buffer, full uncertainty.ConfigurationResolutions IssuedTotal Delay, minAverage Delay per Resolution, sAll Error; No Buffer12,574(Base)3,844(Base)18.3All Error; Full Buffer 19,180 (Base+53%) 7,912 (Base+106%)24.8All Error; 80% Buffer 17,794 (Base+42%) 6,492 (Base+69%)21.9
+Table 5 .5Resolution efficiency metrics by vertical buffer, 50% uncertainty.ConfigurationResolutions IssuedTotal Delay, minAverage Delay per Resolution, sAll Error; No Buffer12,574(Full)3,844(Full)18.350% Error; No Buffer9,860(Base)2,866(Base)17.450% Error; 50% Buffer 13,967 (Base+42%) 5,202 (Base+82%)22.3
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
+			Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5644
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+		Aeronautical Air-Ground Data Link Communications
+		Nice, France
+		
+			John Wiley & Sons, Inc.
+			2010
+			
+		
+	
+	McNally, D., Mueller, E., Thipphavong, D., Paielli, R., Cheng, J., Lee, C., Sahlman, S., and Walton, J., "A Near-Term Concept for Trajectory-Based Operations with Air/Ground Data Link Communication," 27th Iternational Congress of the Aeronautical Sciences, Nice, France, 2010.
+
+
+
+	
+		Relative Significance of Trajectory Prediction Errors on an Automated Separation Assurance Algorithm
+		
+			TALauderdale
+		
+		
+			ACone
+		
+		
+			ABowe
+		
+		
+			2011
+			Berlin, Germany
+		
+		
+			USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	Lauderdale, T. A., Cone, A., and Bowe, A., "Relative Significance of Trajectory Prediction Errors on an Automated Separation Assurance Algorithm," 9th USA/Europe Air Traffic Management Research and Development Seminar , Berlin, Germany, 2011.
+
+
+
+	
+		Predictability of Top of Descent Location for Operational Idle-Thrust Descents
+		
+			LaurelLStell
+		
+		10.2514/6.2010-9116
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Berlin, Germany
+		
+			American Institute of Aeronautics and Astronautics
+			2011
+		
+		
+			USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	Stell, L. L., "Prediction of Top of Descent Location for Idle-thrust Descents," 9th USA/Europe Air Traffic Management Research and Development Seminar , Berlin, Germany, 2011.
+
+
+
+	
+		Build 4 of the Airspace Concept Evaluation System
+		
+			LarryMeyn
+		
+		
+			RobertWindhorst
+		
+		
+			KarlinRoth
+		
+		
+			DonaldVan Drei
+		
+		
+			GregKubat
+		
+		
+			VikramManikonda
+		
+		
+			SharleneRoney
+		
+		
+			GeorgeHunter
+		
+		
+			AlexHuang
+		
+		
+			GeorgeCouluris
+		
+		10.2514/6.2006-6110
+		No. AIAA 2006-6110
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		
+			American Institute of Aeronautics and Astronautics
+			2006
+		
+	
+	Meyn, L., Windhorst, R., Roth, K., Drei, D. V., Kubat, G., Manikonda, V., Roney, S., Hunter, G., and Couluris, G., "Build 4 of the Airspace Concepts Evaluation System," AIAA Modeling and Simulation Technologies Conference and Exhibit, No. AIAA 2006-6110, 2006.
+
+
+
+	
+		User Manual for the Base of Aircraft Data (BADA) Revision 3.8
+		
+			ANuic
+		
+		
+			April 2010
+			EUROCONTROL Experimental Centre
+		
+	
+	Tech. Rep. 2010-003
+	Nuic, A., "User Manual for the Base of Aircraft Data (BADA) Revision 3.8," Tech. Rep. 2010-003, EUROCONTROL Experimental Centre, April 2010.
+
+
+
+	
+		Automated conflict resolution, arrival management, and weather avoidance for air traffic management
+		
+			HErzberger
+		
+		
+			TALauderdale
+		
+		
+			Y-CChu
+		
+		10.1177/0954410011417347
+	
+	
+		Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
+		Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
+		0954-4100
+		2041-3025
+		
+			226
+			8
+			
+			2010
+			SAGE Publications
+			Nice, France
+		
+	
+	Erzberger, H., Lauderdale, T. A., and Cheng, Y., "Automated Conflict Resolution, Arrival Management and Weather Avoidance for ATM," 27th Iternational Congress of the Aeronautical Sciences, Nice, France, 2010.
+
+
+
+	
+		Concept for Next Generation Air Traffic Control System
+		
+			HeinzErzberger
+		
+		
+			RussellAPaielli
+		
+		10.2514/atcq.10.4.355
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			10
+			4
+			
+			2004-212828 , 2004
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	NASA/TP-
+	Erzberger, H., "Transforming the NAS: The Next Generation Air Traffic Control System," NASA/TP-2004-212828 , 2004.
+
+
+
+	
+		Trajectory Modeling Accuracy for Air Traffic Management Decision Support Tools
+		
+			SMondoloni
+		
+		
+			MPaglione
+		
+		
+			SGreen
+		
+		
+			2002
+		
+	
+	The 23th Congress of the International Council of the Aeronautical Sciences (ICAS
+	Mondoloni, S., Paglione, M., and Green, S., "Trajectory Modeling Accuracy for Air Traffic Management Decision Support Tools," The 23th Congress of the International Council of the Aeronautical Sciences (ICAS), 2002.
+
+
+
+	
+		Predictability of Top of Descent Location for Operational Idle-Thrust Descents
+		
+			LaurelLStell
+		
+		10.2514/6.2010-9116
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, Texas
+		
+			American Institute of Aeronautics and Astronautics
+			2010
+			11
+			12
+		
+	
+	Stell, L. L., "Predictability of Top of Descent Location for Operational Idle-Thrust Descent," AIAA Aviation Technology, Integration, and Operations Conference, Fort Worth, Texas, 2010. 11 of 12
+
+
+
+	
+		Downloaded by NASA AMES RESEARCH CENTRE on April 17
+		10.2514/6.2012-564422
+		
+		
+			2013
+			American Institute of Aeronautics and Astronautics
+		
+	
+	American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org | DOI: 10.2514/6.2012-5644 22
+
+
+
+	
+		
+			RACoppenbarger
+		
+		
+			GKanning
+		
+		
+			RSalcido
+		
+		Real-Time Data Link of Aircraft Parameters to the Center-TRACON Automation System (CTAS)," 4th USA/Europe ATM R&D Seminar
+		Santa Fe, New Mexico
+		
+			2001
+		
+	
+	Coppenbarger, R. A., Kanning, G., and Salcido, R., "Real-Time Data Link of Aircraft Parameters to the Center-TRACON Automation System (CTAS)," 4th USA/Europe ATM R&D Seminar , Santa Fe, New Mexico, 2001.
+
+
+
+	
+		Accuracy of RUC-1 and RUC-2 Wind and Aircraft Trajectory Forecasts by Comparison with ACARS Observations
+		
+			BarryESchwartz
+		
+		
+			StanleyGBenjamin
+		
+		
+			StevenMGreen
+		
+		
+			MatthewRJardin
+		
+		10.1175/1520-0434(2000)015<0313:aorarw>2.0.co;2
+	
+	
+		Weather and Forecasting
+		Wea. Forecasting
+		0882-8156
+		1520-0434
+		
+			15
+			3
+			
+			2000
+			American Meteorological Society
+		
+	
+	Schwartz, B. E., Benjamin, S. G., Green, S. M., and Jardin, M. R., "Accuracy of RUC-1 and RUC-2 Wind and Aircraft Trajectory Forecasts by Comparison with ACARS Observations," Weather and Forecasting, Vol. 15, No. 3, 2000, pp. 313-326. 12 of 12
+
+
+
+	
+		Robust Conflict Detection and Resolution around Top of Descent
+		
+			AndrewCone
+		
+		
+			AishaBowe
+		
+		
+			ToddLauderdale
+		
+		10.2514/6.2012-5644
+		
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2013
+		
+	
+	American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org | DOI: 10.2514/6.2012-5644
+
+
+				
+			
+		
+	
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+I. Introductionurrent forecasts predict a steady growth in the demand for air travel across the National Airspace System (NAS), which in its present form is already impacted by capacity constraints.As one of those en-route capacity constraints is controller cognitive workload, research in both Europe and the U.S. into automated separation assurance systems is being conducted as a means of increasing the en-route capacity to help meet the anticipated growth in demand.Among the inherent challenges is understanding how such systems perform in the presence of uncertainty.Most studies to date focus on the overall performance of automated separation assurance tools using deterministic trajectory models, 1,2,3 though some recent studies look at the effects of specific sources of uncertainty, such as wind, weight, and pilot response delays in these models. 4,5None of these studies, however, is a sensitivity study that can inform system designers of the operating limits of their prototype separation assurance systems.The present work seeks to provide such feedback.This paper describes an experiment that studies the sensitivity of an automated separation assurance tool to the presence of horizontal trajectory uncertainty.This study is limited to the effects of horizontal uncertainty only, and horizontal separation standards in particular, so as not to overlap with concurrent work focusing on vertical uncertainty. 6By evaluating a range of horizontal separation standards and traffic demand levels, a general picture of the cost/benefit landscape in terms of the performance of the automated separation assurance tool could be created.This would help answer some basic questions about what the presence of uncertainty would mean for a system like this.First, assuming five nmi as a nominal standard, what is the benefit of decreasing horizontal separation assurance standards, and how do those benefits scale with traffic demand level?It is logical to assume that lower horizontal separation requirements, which would correspond to less trajectory uncertainty, would produce some benefits to the system.The scope of those benefits, however, is currently unknown.Second, again assuming five nmi as a nominal standard, what is the cost of adding extra buffer to the required horizontal separation standard, and how does it scale with traffic demand level?This would correspond to less stringent requirements for things like aircraft equipage, which might translate to significant cost savings for an airline.However, if the cost in terms of such things as delays and resolution efficiency becomes large enough, then an argument for requiring improved equipage becomes easier to make.Additionally, if the current standard of five nmi remains unchanged, the implementation of an automated separation assurance tool would almost certainly require the addition of some buffer to account for uncertainty effects.But is there a point at which the safety benefit of an expanded buffer is overwhelmed by the attendant performance costs?This study examines the effects of trajectory uncertainty in the strategic environment on the performance of a specific automated separation assurance tool."Strategic" is defined as 2 to 20 minutes until predicted loss of separation.The automated separation assurance tool evaluated in this study is the Advanced Airspace Concept, or AAC. 7This is a centralized, ground-based concept for a conflict detection and resolution system.It assumes the existence of data communications to support the regular downlink of position and intent information and periodic uplink of trajectory amendments.In this simulation we do not model trajectory uncertainty explicitly, as we are more interested in the collective effect of all uncertainties on the performance of an automated separation assurance tool than a specific source of uncertainty.Instead, we vary the horizontal separation standard as a proxy for all sources of trajectory uncertainty and study the effects those adjustments have on the performance of the algorithms.Performance is defined by safety and efficiency metrics.
+II. Technical ApproachThe experiment was run in the Airspace Concepts Evaluation System (ACES) 8 simulation environment, using the Advanced Airspace Concept (AAC) as the ground-based automated separation assurance tool.ACES is a fasttime simulation capable of simulating the entire NAS, though for this experiment it was decided to use a single center, Cleveland Center, for simplicity's sake.AAC, as mentioned in the introduction, was the automated conflict resolver used in this study.It received a list of conflicts from the conflict detection algorithm in ACES every two minutes, and sent back resolutions in the form of flight plan updates.The version of AAC used in this study utilized what is called the "multiple resolver."This means that AAC did not take the first successful resolution it found for a specific conflict.Instead it would try to find a variety of resolutions, such as a left turn, right turn, path offset, altitude adjustment, and/or speed adjustment, then pick the "best" of the successful resolutions to send back to ACES as the actual resolution maneuver."Best," in this case, meant the least delay resolution.This tended to drive AAC to vertical maneuvers when they were available, as they often have little impact on delay.This version of AAC also had a scheduling algorithm that was used in arrival cases.This algorithm was a "first come, first serve" type that sought to keep aircraft headed into a terminal fix at least one minute apart.This helped ensure a relatively smooth flow of traffic into these fixes.In one sense, the scheduler preconditions the arrival traffic, which makes the separation assurance algorithm's job much simpler than it would be without the scheduler.However, Traffic Flow Management is a concept that is already seeing acceptance in the field, and it seems a logical assumption that such technology will be available for integration with an automated separation assurance tool when a system such as AAC begins to see use in the real world.With that said, it must be noted that the results of the study, especially in the arrival traffic environment, would be quite different without the presence of some kind of scheduling algorithm.The conflicts in this study can be broken into two main categories: arrival conflicts and everything else.The nonarrival cases include overflights in addition to cases where one aircraft is passing through an arrival stream.Arrival conflicts, also called "merging arrivals," are defined by Farley as "projected conflicts between two aircraft on arrival to the same arrival fix and within 20 minutes flying time of that fix." 1 For these data, the definition of arrival is expanded to include all aircraft heading for the same arrival airport.It might be asked why the arrivals case is treated separately when it involves such a limited portion of the data set.The reason is that the arrival conflict cases are often quite complicated to solve, as not only are aircraft often in a descent for at least part of the conflict, but the fact that they are so near to their final fix can limit the maneuvering options available, as some of them would not allow the aircraft to make it back to its flight plan in time to hit that fix.Additionally, there are often other aircraft in the immediate airspace around the terminal, which increases the likelihood that a trial resolution might be rejected because it would cause a secondary conflict.For those reasons, arrival conflicts are treated as their own subset of conflicts, while everything else is grouped in the non-arrival bin.The conflict detection algorithm also treats these two groups of conflicts differently, scanning ahead 8 minutes for non-arrival conflicts, 20 for arrivals.Finally, aircraft that are maneuvered by the scheduler are not included in either conflict bin.Because the scheduler is integrated with AAC, it can use the conflict resolution logic to check potential scheduling maneuvers to ensure that they are conflict free before sending the maneuver off to ACES and the aircraft in question.The experiments involved changing the required horizontal separation for the conflict detection and resolution algorithms.The horizontal separation radii used for the detection algorithm in this study were four, five, six, and nine nmi.The case with a horizontal separation radius of five nmi for the conflict detection algorithm was treated as the baseline, as that radius is the current minimum standard.As such, four and six were used to note the effects of variations around this case.Nine nmi was chosen mostly to stress system.We wanted an upper end to our study that was larger than was likely to be seen in a real world implementation, while not being so large as to be completely beyond the scope of possibility.A two nautical mile buffer was added to the conflict detection radius for the resolution logic in all cases, as in the work by Farley, Kupfer, and Erzberger 1 .This was done to account for the real world likelihood that a resolved conflict may reoccur during the course of the resolution due to uncertainties such as wind.For a 10 minute resolution trajectory, two nmi was seen to greatly reduce the chance of this occurring.Adding too many miles to this buffer might adversely affect the performance of AAC, while too few might not adequately reduce the chance of a conflict reoccurring.Therefore, the baseline case, for example, uses five nmi of horizontal separation for detection and seven for resolution.Cases were run in series on the same computer using the same version of ACES and AAC.Additional conditions included using 960 feet of vertical separation for the conflict detection algorithm for all cases.The vertical separation of just under 1,000 feet was used for coding concerns.There was no buffer added to the vertical separation criterion, so the same value was used for both the detection and resolution algorithms.The traffic data for the 1X case were based on 24 hours of traffic from a single day; April 19, 2007.The traffic demand sets for 2X and 3X cases were created from this initial day using AvDemand. 9These demand sets are the same ones used in Kupfer's recent work 2 .Due to constraints within the simulation, such as the lack of any control at the terminal level, the arrival traffic was manually set to 1.5X for hub airport traffic for both the 2X and 3X cases.This was done by examining all of the aircraft in the 2X and 3X cases that were arriving at hub airports in the NAS, and reducing that number in the input file until it matched a rate of 1.5 times the rate in the 1X case.There were 7,714 total active flights in the 1X case, 15,066 in 2X, and 19,686 in 3X.The four main metrics of interest were the number of resolutions, the number of unresolved conflicts, the average delay per resolution, and the average number of attempts required to find a resolution.Comparing the number of resolutions and the number of unresolved conflicts helps give us an idea of how complicated the airspace became and how well the system performed overall.The overall point of a separation assurance system is to maintain adequate separation between aircraft, whatever that separation is deemed to be.The average delay helped us understand how efficiently the system ran.Less delay per resolution means less disruption to the originally filed flight plans, which can also help with things such as minimizing fuel burn for a flight.The fourth metric was the number of attempts required to find a resolution.This metric is used to describe "workload," which in this paper refers to how much effort is required to find a solution for an individual conflict.There are a limited set of possible resolutions available for AAC to use to solve a given conflict.As the number of attempts required to find a successful resolution increases, the likelihood of seeing unresolved conflicts or high delay resolutions also increases.In practical terms, the behavior of this metric can indicate how close AAC is to the edge of its operating envelope.
+III. ResultsThe following table summarizes the general results of this sensitivity study.As mentioned in section II, the two independent variables were required horizontal separation for the conflict detection algorithm and the traffic demand level.The three main metrics of interest were the number of resolutions and unresolved conflicts, the average delay per resolution, and the average number of attempts required to find a resolution.The results in Table 1 clearly show that increasing both density and required horizontal separation results in a greater number of conflicts, a larger average delay per resolution, and more resolution attempts to find a solution.There are a few interesting points from this table.First would be the non-linear scaling of the number of resolutions with both traffic demand level and the separation criteria.Moving from five nmi to six, for example, increases the number of resolutions issued for all demand levels by roughly 30%.However, changing the radius from five nmi to nine produces an almost 140% increase in the number of conflicts at 1X and 2X, and over 150% at 3X. Holding separation radius fixed while moving traffic demand levels has a similar effect.The point to note from this column is that the horizontal separation radius and traffic demand level have mostly independent effects on the number of resolutions.It was also encouraging to note that the failure cases only appear in the three cases with the highest resolution counts.For the overall numbers, the percentage of successful resolutions from AAC was over 99.97% for all tested demand levels and horizontal separation radii.The second point is that the average delay per resolution follows a generally upward trend for both increasing horizontal separation standards and traffic demand level.There is a notable exception to this when moving from four nmi to five at three times traffic density.This will be examined in more detail later in this paper.For now, it should be pointed out that the gross effect of increasing both the horizontal separation standards and the traffic demand level is to increase delays, and that increasing both of these factors together can result in some significant delay increases.The last point on this table is that the number of resolution attempts required to find a suitable resolution is much more sensitive to required horizontal separation than for changes in traffic demand level.However, it should be noted that the nine nautical mile case seems to show slightly more sensitivity to demand than the other cases.This result further supports the idea that the resolution algorithm is relatively stable at lower separation radii, though once the separation radius passes a certain point, the effects of the demand level begin to impact the performance of the algorithm.
+A. Non-Arrival ConflictsThe data for the non-arrival conflicts are shown below in Table 2.As mentioned previously, non-arrival conflicts consist of all conflicts that are not arrival conflicts.This table shows the general data for the non-arrival conflicts.The first area of interest is the resolution and unresolved conflict count.Firstly, there were a handful of unresolved conflicts that were filtered out of these cases, all of which had less than two minutes to loss of separation when first detected.These cases were caused by boundary issues within the code, such as an aircraft popping into the center airspace from a TRACON and immediately being in conflict with an aircraft descending into its final fix.As AAC was designed and intended to operate solely in a strategic environment, and as conflicts with less than two minutes to loss of separation were seen as below the lower limit of a strategic resolver, those conflicts were considered beyond the scope of this study and were filtered out.The result was a single case in which AAC was unable to resolve a conflict in the non-arrival environment.This one case was difficult as it involved both a climbing and descending flight, and had the climber passing through an arrival stream.The only other point to make about the resolution and unresolved conflict counts is the evident fact that the number of conflict resolutions required increased with increases in both traffic demand level and horizontal separation.However, the rate at which the numbers increased moving from 2X to 3X was smaller than expected.This could be partially the result of the manual reduction of the arrival traffic to 1.5X.Though the reduced arrival rate was implemented to deal with some artifacts of the simulation near the final fix, the aircraft it removed would have otherwise been flying through the airspace and possibly getting into non-arrival conflicts on their way to their final fix.The fact that the airspace in question was Cleveland Center helps mitigate this somewhat, as that center has a large number of overflights, which needed no reduction.Still, the reduced overall traffic is a major suspect for the reason the number of resolutions is not higher in the 3X, non-arrival cases.The second area of interest on this chart is the average delay per resolution, which does not vary much for a given horizontal separation radius, though that variation is a little larger for the six and nine nautical mile cases than the four and five.Additionally, for a fixed demand level, every extra mile of required horizontal separation increases the average delay per resolution by about three seconds.This holds true for all demand levels in the non-arrival environment.This shows that, for the non-arrival conflicts, the conflict resolver has a small sensitivity to both demand level and separation radius.While it is true an increase of a few seconds is a huge percentage increase in average delay for some of the lower horizontal separation radius cases, we felt that the absolute change in delay was a better indication of sensitivity.This result confirms the idea that, at least for non-arrival conflicts, AAC should be able to efficiently maintain separation almost regardless of the required horizontal separation and the traffic demand level.Finally, we looked at the average resolution attempts required to find a successful resolution.These data give an indication of how hard AAC is working to find a solution for a problem.The results show little variation for a fixed horizontal separation radius, though the 1X cases take fewer attempts than the 3X cases for any given radius.Increasing that horizontal separation radius seems to add about one resolution attempt to each conflict per nautical mile added.All of these trends support the assertion that AAC is perfectly capable of handling all of the test cases we ran in the non-arrival environment.From the benefit's point of view, the resolution attempts data do not give much support to the case for smaller separation standards.While decreasing the required horizontal separation radius from five to four nmi results in fewer conflicts, once the conflict gets to the resolution algorithm, a smaller radius is not saving the algorithm much work.This result is most likely due to the multiple resolver logic.This code lets AAC attempt to find a handful of successful resolutions before choosing one.That strategy not only improves the likelihood that for a given conflict AAC will be able to find an efficient solution, but it also tends to even out the number of resolution attempts for conflicts.We believe this is more than a fair trade, as the extra work AAC might go through to find multiple solutions is more than offset by the extra efficiency of the solutions obtained.2 show examples of how the delays break down for a given demand set.In Fig. 1 we see the average delay per resolution maneuver for different classes of maneuvers.It is no surprise that most of the high delay maneuvers are horizontal maneuvers.This is part of the reason that AAC tends to choose vertical maneuvers when it can, as they usually have significantly less delay per resolution than horizontal maneuvers.Resolution maneuvers using speed increases, though limited to an increase of 15 knots or less, are also used to save time when available.However, the number of non-arrival conflicts that are solvable with speed adjustments are quite limited, as evident in Figure 2.The general trends of Figure 1 are not unexpected.One example would be the delay per resolution maneuver for horizontal resolution maneuvers, which increases by about 5 seconds per nautical mile added or subtracted to the required horizontal separation radius.The average delay per resolution maneuver for vertical maneuvers shows no real trend, though the average values for the 4 radii are within about a 4 second range.The fact that speed resolution maneuvers show less benefit as the horizontal radius increases is interesting, as it implies that increases in separation radius lessen the effectiveness of these maneuvers.However, the difference between the average delays of the highest and lowest cases is only about 6 seconds, which is a small variation for the basis of a conclusion.Finally, the "Direct-To" maneuvers, as with the vertical ones, do not show any definite trend.They average between about 68 and 77 seconds of time savings for each maneuver, with the highest savings per maneuver at nine nmi and the lowest at six.Those "Direct-To", or "D2", maneuvers are one of the more interesting features of these figures.These are resolution maneuvers that send an aircraft to some fix further along in its flight plan.These maneuvers result in both the conflict being resolved and the aircraft saving time by skipping over part of its original flight plan.Technically, these maneuvers take place in the horizontal plane.However, unlike the other horizontal resolution maneuvers, which also take an aircraft off of its original flight, D2 maneuvers do not try to immediately bring an aircraft back to its flight plan once the conflict is cleared.It was this fundamental difference in approach, as well as the vastly differing delay per resolution results, that justified separating D2 maneuvers from other horizontal resolutions.As these D2 maneuvers can be difficult to find, especially in more crowded airspace, there are not many as a percentage of the overall number of resolutions.Nevertheless, even at very high separation radii these maneuvers save a lot of time on a per resolution basis.Of course, as the horizontal separation radius increases, the number of D2s available decreases, so the time savings as applied to an average delay per resolution decreases even though the data show the time savings per D2 is higher at nine nmi than it is at four.These maneuvers are the reason there was a net negative delay per resolution for the 1x, four nautical mile case, as a handful of D2 maneuvers can accumulate a very large times savings.In fact, in every case we ran, D2 maneuvers averaged around a minute of time savings per maneuver.These two figures also show a few interesting trends in the choices AAC makes about which resolution maneuvers to use.The first would be that the percentage of resolutions that use vertical maneuvers is relatively
+Horizontal Separation Radius (nmi) Percentage of Resolutions (%)Horizontal Vertical Speed Direct-To
+Figure 2. Resolution breakdown by maneuver type for non-arrival resolutions at 1X traffic demand levelconstant In addition, the relative simplicity of these maneuvers helps keep the delay per resolution on the scale of around 7 seconds.However, the number of horizontal maneuvers is greatly increased as the horizontal separation radius increases.The increase in these maneuvers is the result of the decrease in the number of D2 and speed change maneuvers.It is this increase in the percentage of horizontal maneuvers as much as the increased delay per horizontal maneuver that is causing the large increases in the average delay per resolution for the overall results of non-arrival conflicts.We expected that increased separation radii would result in more delay for horizontal maneuvers, if only because the aircraft must fly farther to get clear of a conflict.What we did not expect was that AAC would be forced to use so many more traditional horizontal maneuvers as an additional result of increasing horizontal separation radius.In the end, we can say that it is quite probable that the sensitivity in AAC to required horizontal separation is increased as a result of the use of D2 and speed change maneuvers.Without these maneuvers, there would likely be far more horizontal maneuvers in all cases, and the average delay per resolution would be far less sensitive to this parameter.This result also points out that reducing the number of horizontal resolution maneuvers can have a significant impact on the average delay per resolution.While this is a relatively apparent result, it does emphasize that further improvement upon AAC's average delay per resolution will need to successfully reduce the number of resolution maneuvers in the horizontal plane other than D2's.Whether or not this high emphasis on vertical resolution maneuvers in non-arrival conflicts is desirable from viewpoints other than resolution efficiency is beyond the scope of this paper.Figure 3 shows a breakdown of the number of resolution attempts required to find a successful resolution for a conflict in the non-arrival environment.Each bin is 5 resolution attempts wide.This chart is a visual confirmation that the shift in the average number of attempts required for a resolution is due more to a shift in the center of the distribution than a shift in the shape of the curve.The resolution attempts curve is certainly flattening out, and would most likely continue to flatten if the horizontal separation radius were further increased.However, as it is unlikely that the required horizontal separation would be increased to over nine nmi in a real world situation, the behavior of AAC beyond that point is of reduced interest.In the case where AAC was beginning to struggle, we would expect the distribution to be much more flattened out, with significantly more cases requiring large numbers of resolution attempts.These curves, then, can be used as a check on how close AAC is to the edge of acceptable performance.As such, we can say that these curves support the assertion that AAC is stable for all of the separation radii tested for cases based on our selected day of traffic.
+B. Arrival ConflictsThe arrival conflicts consist of conflicts between two flights that are headed to the same airport.In this area, AAC has some additional functionality to help handle aircraft in this difficult environment.Specifically, it utilizes a first come, first serve arrival scheduler.The scheduler tries to maintain at least one minute of separation between flights as they pass into the terminal area.This forces a lot of interaction between the conflict detector and the scheduler logic, if for no other reason than to limit the number of cases in which both sets of logic are trying to address the same aircraft at the same time.The results of this from an AAC point of view are much smoother operations when dealing with arrivals.Unfortunately, from an analysis point of view, this makes truly separating the conflict data from the scheduler data difficult if not impossible.Perhaps in later versions of this code there will be ways to more easily separate the cases.As it currently stands, however, the scheduler and arrival conflict logic need to be looked at together, at least in the realm of conflicts and failures.The way conflicts are defined is important to note, because the scheduler and arrival conflict resolution logic is the same at its core.Thus the general method of addressing a problem is the same.In both cases, the logic will try to satisfy some criteria by using flight plan amendments.In the case of an arrival conflict, the goal is to maneuver so that separation is not lost.The scheduler, on the other hand, is dealing with a single aircraft and seeks to maneuver it in such a way that there is at least a minute between all aircraft headed for the same fix.The scheduler also checks to ensure that all separation standards are maintained through its resolutions.The difference between the arrival conflict and a scheduled aircraft is that a failure of the conflict resolution logic in the arrival environment is certain to result in a loss of separation.For the scheduler, however, a failure simply means that the algorithm could not find a time slot for the aircraft without exceeding some maximum allowable delay.While this type of failure will likely result in a loss of separation, it does not guarantee that there will actually be one.This is all complicated by the interaction between flights that have failed to schedule and the conflict detector logic.At the time of this paper, that aspect of the logic was still being examined.A last point to make when looking at the arrival data in general is the reminder that the density of arrival traffic was manually lowered to about 1.5X for all of the 2X and 3X cases.As such, though the direction of trends might be accurate for a full 2X or 3X run, the rate of change that results from increasing traffic demand level is most likely underestimated for all of our numbers.The first thing to notice about Table 3 is resolution and unresolved conflict data.As mentioned earlier, the exact resolution count must be taken with a grain of salt because of the complicated interaction between arrival conflicts and aircraft that have been through the scheduler and the reduced number of arrival aircraft in the 2X and 3X demand sets.Still, it is useful to look at the cases that were qualified as arrival conflicts to see how the conflict resolution algorithm performs in this environment.Also, the fact that there were unresolved conflicts in multiple cases should be noted.The fact that all of these unresolved conflicts happen in the most difficult cases implies that AAC might be nearing the edge of its current performance envelope.The average delay per resolution is where the first few points can be made.In the four, five and six nautical mile horizontal separation cases, there was around a 30 second increase when moving from 1X to 3X for a fixed radii.The nine nautical mile cases showed a 52 second increase in the average delay per resolution moving when comparing 1X to 3X.In addition, every nautical mile increase in the horizontal separation for a given demand level resulted in roughly 10 seconds being added to the average delay per resolution.However, when moving from six to nine nmi the change per nautical mile was closer to 13 seconds for 1X and 2X demand levels, and 20 seconds per nautical mile for 3X.Thus, the efficiency of AAC's conflict resolution maneuvers in the arrival environment is sensitive to both changes in traffic demand level and required horizontal separation.This sensitivity is expected to be even more pronounced if full 2X and 3X arrival traffic is used.Unfortunately, the near linear trends observed in the data would probably not hold for these cases.The last point is the average resolution attempts per resolution.Again, this is a metric that lets one get a feel for the workload of the conflict resolution algorithm in terms of the difficulty solving an individual conflict.Cases with higher resolution attempt values are more likely to see high delay resolutions or unresolved conflicts.For these data, except for the nine nautical mile cases, the data showed this parameter was more sensitive to required horizontal separation than traffic demand level.For the nine nautical mile cases, there was some increased sensitivity to demand level.This is not a surprise when we consider the other data for the arrival case, which shows that nearly all of the parameters show increased sensitivity for these cases.These data point to arrival conflicts being significantly more sensitive to both changes in demand level and required horizontal separation than the non-arrival cases.For the arrivals in general, changing horizontal separation and traffic demand had a noticeable effect on the average delay for a resolution, though only changes in the horizontal separation seemed to affect the average number of resolution attempts required.Additionally, the increased sensitivity shown in the nine nautical mile arrival cases implies that the system might have a limit somewhere above the range of variables we tested.Most likely, increasing the horizontal separation radius beyond nine nmi should produce ever increasing trends until AAC is no longer able to adequately handle the traffic.However, as nine nmi is already a horizontal separation radius that is considerably larger than today's standards, it is unlikely that AAC would be tasked to handle cases like those in the real world.As such, all one can say from these data is that, for the reduced arrival traffic cases which were examined, AAC showed it was quite capable of handling conflicts safely, with the possible exception of the nine nautical mile, 3X case.and5 show the average delay per resolution for the arrival cases and the percentage of resolutions that used each class of maneuver, respectively.The first thing to note is the absence of D2 maneuvers that played such a large role in the non-arrival environment.With aircraft so close to their final fixes in the arrival cases, these D2 maneuvers do not work nearly as well, so they are not used.One of the other things to keep in mind with these charts is that the conflict detection logic is looking ahead 20 minutes to scan for arrival conflicts, which gives AAC more time to find a solution and more options for finding a solution than it would have with the same time window the non-arrival logic uses.With all this in mind, one of the notable features is the very large increase in the delay of horizontal maneuvers due to increases in horizontal separation radius.Many of these resolutions have large times to first loss, and a resolution algorithm that is trying to minimize delays.Even so, when forced to use a horizontal maneuver in the arrival environment the delay is much more sensitive to increases in horizontal separation than it was in the nonarrival cases.The increase in the arrival case is on the order of 20 seconds per nautical mile, as opposed to the roughly 5 second per nautical mile increase seen in the non-arrival cases.This shift is likely due to the extra complexities involved with moving an aircraft horizontally in the arrival environment while avoiding secondary conflicts from all the other aircraft sharing the airspace.Figure 5 shows that, like the non-arrival cases, increasing the horizontal separation radius drives up the number of horizontal resolutions used.A second point of note in these figures is the relatively small role that vertical maneuvers play in the arrival cases.With the low number of vertical resolution maneuvers used (Fig. 5), the variation in the average delay per resolution with changes in horizontal separation radius for those maneuvers is easier to understand.With so few vertical maneuvers, a few outliers can significantly affect the average, which is part of what happened here.Even so, the changes in the average delay per resolution for these maneuvers were much larger than anticipated.Part of the reason there are so few vertical maneuvers in these cases could be because of the number of speed resolutions.It was not surprising that AAC would prefer to use speed resolutions when possible, as they usually have such low delay.However, the fact that AAC found so many successful speed resolutions in the arrival environment was initially unexpected.Some probable explanations include the large amount of time AAC has to deal with conflicts in the arrival environment.With up to 20 minutes to examine a conflict, the time the aircraft has to respond is significantly more than in the non-arrival cases.As such, it is easier to find speed resolutions in these cases, whereas those resolutions might not maneuver an aircraft fast enough in a conflict with only 6 minutes to first loss instead of 16.It should also be remembered that there is a scheduler operating on many of the arriving flights.As such, even though the airspace might be more crowded than in a general, non-arrival case, there should be more order in the arrival cases.Fortunately, this is a situation one could expect in a real world implementation, as traffic flow management tools and arrival schedulers are already in use in some centers.As such, it seems logical to assume that any real world implementation of AAC would be able to take advantage of such tools, as well.It is also notable Figure 6 shows what the 1X breakdown of the arrival attempts required to find a resolution looks like for the 1X case.It is immediately evident that the shift in the mean is larger than it was in Fig. 3.
+IV. DiscussionThe non-arrival cases showed very little sensitivity to either traffic demand levels or required horizontal separation.In the general sense, this implies that AAC is stable, at least for these settings and this day of traffic.This is the kind of behavior one would want from a code being used to help handle a safety critical system with a wide range of possible scenarios.These results also show some of the benefits of reducing required horizontal separation.Though there is not much difference in the average delay per resolution moving from five to four nmi, there is a 20-25% reduction in the number of issued resolutions for all demand levels.That works out to roughly 550 fewer resolutions in the non-arrival environment at 3X.That reduction in the number of resolutions combined with the lower delay per resolution works out to 119 minutes of overall delay savings for moving from five to four nmi.The arrival case was sensitive to both demand level and required horizontal separation.In these cases there were some unresolved conflicts in the highest demand and separation radius cases.While a strategic resolver is expected to have a tactical, safety-critical backup, it is still desirable to keep the number of unresolved strategic conflicts as low as possible.AAC showed better than a 99.8% success rate in all cases.Of greater impact is the large spread in average delay per resolution, which changes from about 19 seconds for the case most representative of today's NAS to 1 minute and 59 seconds in the 9 nautical mile, 3X case.While it is unlikely that the real-world would see such a large horizontal separation radius coupled with such a dense traffic level, the data show a definite negative impact to resolution efficiency with increasing demand and/or required horizontal separation.In addition, moving from five to four nmi of horizontal separation appears to have some benefit in the arrival environment, saving over 10 seconds per resolution even at just 1X.Recall, however, that the arrival traffic in the "2X" and "3X" cases were reduced to roughly 1.5X.If a full 2X or 3X scenario were run, it is likely that the differences between all of the horizontal separation radii would increase for the two higher demand levels.That would also increase the benefit of using a smaller radius in arrival cases.Unfortunately, the relatively small number of arrival cases compared to non-arrival cases means that the overall benefit of a reduction in horizontal separation standards is likely to remain small, though it could be significantly more in a center with a greater percentage of arrival traffic.The average amount of work AAC had to do to find a successful resolution algorithm increased substantially in the worst-case scenarios.This increase in the number of resolution attempts in those scenarios implies that those unresolved conflicts are probably not outliers, and that the number of unresolved conflicts is likely to keep increasing if AAC is stressed with horizontal separation requirements greater than nine nmi in a heavy arrival scenario.This is a concern, as it implies that AAC could have trouble finding resolutions in higher density arrival scenarios that use demand sets with full arrival traffic as opposed to the 1.5X arrival sets used in this study.In the end, however, even though AAC had to struggle to find resolutions in the most demanding cases and was not able to find particularly efficient conflict resolutions, it was nevertheless able to resolve virtually all of the detected conflicts.The data raise a number of interesting questions.For example, how can speed maneuvers, which are generally more efficient and more favorable from a flight operations perspective, be used more often in non-arrival cases?If we increased the look-ahead time horizon for the conflict detection algorithm to 20 minutes, as it is for arrival conflicts, would that allow AAC to use more speed resolutions and fewer path stretches?The extra time would certainly come with the cost of increased uncertainty in the trajectory, which would likely require larger separation criteria and increase the number of conflicts detected, but would there be enough reduction in the number of path stretch maneuvers to justify the change?
+V. ConclusionOverall, AAC was able to safely handle conflicts over the entire range of horizontal separation radii and traffic demand levels tested.In non-arrival cases, AAC was also able to consistently find efficient conflict resolutions for a wide range of traffic demand levels and horizontal separation radii.In arrival cases, AAC showed reduced efficiency and increased single conflict workload as both the horizontal separation and traffic demand levels were increased.In the non-arrival environment, AAC had little sensitivity to either traffic demand levels or horizontal separation radii in regards to the number of unresolved conflicts, the average delay per resolution maneuver, and the number of resolution attempts required by the algorithm to find a successful resolution.Non-arrival delays showed no real trend for demand level increases, though the 3X cases had between 3 and 5 seconds extra delay per resolution.Each increase of one nautical mile of horizontal separation produced around 3 seconds of extra delay and 1 more resolution attempt per resolution in all cases for a given demand level.In the arrival environment, the average delay per resolution and number of resolution attempts required to successfully resolve a conflict were sensitive to both separation radius and demand levels.The number of unresolved conflicts metric was slightly sensitive to large horizontal separation radii and high demand levels.AAC was still able to safely separate all traffic for every case except the nine nautical mile cases at 2X and 3X and the six nautical mile case at 3X.For those three cases, AAC still resolved over 99.8% of all conflicts.Each step up in demand level increased the average delay per resolution by 10 to 30 seconds, with the larger increases generally appearing in the higher separation radius cases.Each increase of one nautical mile resulted in roughly 10 extra seconds of delay per resolution maneuver except for the 3X case moving from six to nine nmi of separation.In that situation, each extra nautical mile added around 20 seconds of delay.The number of resolution attempts required for a given resolution increased by about 2 per nautical mile added, except in the 2X and 3X cases moving from six to nine nmi which had showed an increase of roughly 4 attempts per resolution.Overall, these results will support work analyzing the overall costs and benefits of using a certain horizontal separation standard in the NAS.These data should give an approximation of anticipated AAC behavior to researchers who choose to examine something other than the current separation standard.The data should also help those who wish to do trade studies involving horizontal separation to understand the AAC portion of the equation.Figure 1 .1Figure 1.Non-arrival delay breakdown for all horizontal separation radii at 1X traffic demand level
+Figure 3 .3Figure 3. Histogram of resolution attempts required to find a successful resolution for nonarrival conflicts at a 1X traffic demand level
+Figure 4 .4Figure 4. Arrival delay breakdown for all horizontal separation radii at 1X traffic demand level
+Figure 5 .5Figure 5. Resolution breakdown by maneuver type for arrival resolutions at 1X traffic demand level
+Figure 6 .6Figure 6.Histogram of resolution attempts required to find a successful resolution for non-arrival conflicts at a 1X traffic demand level
+Table 1 . Summary of general results1TrafficRequiredAverageDemandHorizontalResolutionsUnresolvedAverage Delay perAttempts perLevelSeparation (nmi)ConflictsResolution (min:s)Resolution1x4547000:0717.82x41822000:1118.23x42317000:2918.41x5714000:1119.82x52472000:1320.13x53021000:2520.21x6951000:1521.62x63115000:2121.73x64035100:3222.01x91701000:2925.62x95827200:4028.13x97688200:5928.9
+Table 2 . Summary of non-arrival results2TrafficAverage DelayAverageDemandRequired HorizontalResolutions Unresolvedper ResolutionAttempts perLevelSeparation (nmi)Conflicts(min:s)Resolution1x44760-00:0120.32x41675000:0320.13x42140000:0320.81x5602000:0221.32x52236000:0621.53x52709000:0521.71x6782000:0522.12x62759000:1122.53x63515000:0922.41x91395000:1624.82x94936100:2425.83x96129000:2125.3
+Table 3 . Summary of arrival results3TrafficAverage DelayAverageDemandRequired HorizontalResolutions Unresolvedper ResolutionAttempts perLevelSeparation (nmi)Conflicts(min:s)Resolution1x471000:0824.72x4147000:2224.93x4177000:3425.31x5112000:1927.22x5236000:2826.93x5312000:4828.71x6169000:3029.62x6356000:4228.73x6520100:5630.11x9306001:0735.22x9891101:2240.43x91559201:5941.9
+		
+		
+
+			
+AcknowledgmentsThe authors would like to acknowledge Paul Borchers of NASA, who was instrumental in the initial stages of this experiment.
+			
+
+			
+
+				
+
+
+	
+		Automated Conflict Resolution: A Simulation Evaluation Under High Demand Including Merging Arrivals
+		
+			ToddFarley
+		
+		
+			MichaelKupfer
+		
+		
+			HeinzErzberger
+		
+		10.2514/6.2007-7736
+		AIAA-2007-7736
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			Sept 18-20, 2007
+		
+	
+	Farley, T., Kupfer, M., Erzberger, H., "Automated Conflict Resolution: A Simulation Evaluation Under High Demand Including Merging Arrivals," AIAA-2007-7736, AIAA Aviation Technology Integration and Operations (ATIO) Conference, Belfast, Northern Ireland, Sept 18-20, 2007.
+
+
+
+	
+		Automated Conflict Resolution -A Simulation Based Sensitivity Study of Airspace and Demand
+		
+			MKupfer
+		
+		
+			TFarley
+		
+		
+			YChu
+		
+		
+			HErzberger
+		
+	
+	
+		26th International Congress of the Aeronautical Sciences (ICAS)
+		Anchorage, Alaska
+		
+			Sept 15-19, 2008
+		
+	
+	Kupfer, M., Farley, T., Chu, Y., Erzberger, H., "Automated Conflict Resolution -A Simulation Based Sensitivity Study of Airspace and Demand," 26th International Congress of the Aeronautical Sciences (ICAS), Anchorage, Alaska, Sept 15-19, 2008.
+
+
+
+	
+		Safety Performance of Airborne Separation: Preliminary Baseline Testing
+		
+			MariaConsiglio
+		
+		
+			SherwoodHoadley
+		
+		
+			DavidWing
+		
+		
+			BrianBaxley
+		
+		10.2514/6.2007-7739
+		AIAA-2007-7739
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			Sept 18-20, 2007
+		
+	
+	Consiglio, M., Hoadley, S., Wing, D., Baxley, B., "Safety Performance of Airborne Separation: Preliminary Baseline Testing," AIAA-2007-7739, AIAA Aviation Technology Integration and Operations (ATIO) Conference, Belfast, Northern Ireland, Sept 18-20, 2007.
+
+
+
+	
+		Automated Separation Assurance in the Presence of Uncertainty
+		
+			DMcnally
+		
+		
+			DThipphavong
+		
+	
+	
+		26th International Congress of the Aeronautical Sciences (ICAS)
+		Anchorage, Alaska
+		
+			Sept 15-19, 2008
+		
+	
+	McNally, D., Thipphavong, D., "Automated Separation Assurance in the Presence of Uncertainty," 26th International Congress of the Aeronautical Sciences (ICAS), Anchorage, Alaska, Sept 15-19, 2008.
+
+
+
+	
+		Impact of Pilot Delay and Non-Responsiveness on the Safety Performance of Airborne Separation
+		
+			MariaConsiglio
+		
+		
+			SherwoodHoadley
+		
+		
+			DavidWing
+		
+		
+			BrianBaxley
+		
+		
+			DanetteAllen
+		
+		10.2514/6.2008-8882
+		AIAA-2008-8882
+	
+	
+		The 26th Congress of ICAS and 8th AIAA ATIO
+		Anchorage, Alaska
+		
+			American Institute of Aeronautics and Astronautics
+			Sept 15-19, 2008
+		
+	
+	Consiglio, M., Hoadley, S., Wing, D., Baxley, B., Allen, D., "Impact of Pilot Delay and Non-Responsiveness on the Safety Performance of Airborne Separation," AIAA-2008-8882, 26th International Congress of the Aeronautical Sciences (ICAS), Anchorage, Alaska, Sept 15-19, 2008.
+
+
+
+	
+		Analysis of a Multi-Trajectory Conflict Detection Algorithm for Climbing Flights
+		
+			DavidThipphavong
+		
+		10.2514/6.2009-7021
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, South Carolina
+		
+			American Institute of Aeronautics and Astronautics
+			Sept 21-23, 2009
+		
+	
+	to be published
+	Thipphavong, D., "Analysis of a Multi-Trajectory Conflict Detection Algorithm for Climbing Flights," AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Hilton Head, South Carolina, Sept 21-23, 2009. (to be published)
+
+
+
+	
+		Automated Conflict Resolution for Air Traffic Control
+		
+			HErzberger
+		
+	
+	
+		25th International Congress of the Aeronautical Sciences
+		
+			2006
+		
+	
+	Erzberger, H., "Automated Conflict Resolution for Air Traffic Control," 25th International Congress of the Aeronautical Sciences, 2006.
+
+
+
+	
+		Build 4 of the Airspace Concept Evaluation System
+		
+			LarryMeyn
+		
+		
+			RobertWindhorst
+		
+		
+			KarlinRoth
+		
+		
+			DonaldVan Drei
+		
+		
+			GregKubat
+		
+		
+			VikramManikonda
+		
+		
+			SharleneRoney
+		
+		
+			GeorgeHunter
+		
+		
+			AlexHuang
+		
+		
+			GeorgeCouluris
+		
+		10.2514/6.2006-6110
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Campbell, California
+		
+			American Institute of Aeronautics and Astronautics
+			2006. 2007
+		
+	
+	Meyn, L., Windhorst, R., Roth, K., Drei, D. V., Kubat, G., Manikonda, V., Roney, S., Hunter, G., and Couluris, G., "Build 4 of the Airspace Concepts Evaluation System," AIAA Modeling and Simulation Technologies Conference and Exhibit , 2006. 9 Sensis Corp. AvDemand User Guide, Campbell, California, 2007.
+
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+				
+			
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+	
diff --git a/file154.txt b/file154.txt
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+
+	
+	
+		
+Nomenclature
+II. BackgroundThe second FAA-sponsored Sense-and-Avoid (SAA) Workshop 5 defined SAA as "the capability of a UAS to remain DWC from, and avoid collisions with, other airborne traffic.SAA provides the intended functions of self separation (SS) and collision avoidance (CA) compatible with expected behavior of aircraft operating in the NAS."The SS function is intended to be a means of compliance with the regulatory requirements (14CFR Part 91, §91.111 and previously described §91.113) to "see and avoid" and remain "well clear" of other aircraft.The UAS community has transitioned to using the term "detect and avoid" rather than "sense and avoid" after publication of the workshop report and with no change in meaning.The rest of this paper uses DAA for consistency.
+A. Loss of DAA Well ClearThe SAA workshop defined "well clear" as the state of maintaining a safe distance from other aircraft that would not normally cause the initiation of a collision avoidance maneuver by the UAS or any other aircraft. 5A set of definitions of well clear was proposed by a recent FAA report, 5 a dedicated U.S. government workshop on well clear, 2 and variations on methods utilized by the Traffic Collision and Avoidance System (TCAS II). 6,7The UAS Executive Committee Science and Research Panel-coordinated research efforts by NASA, the Massachusetts Institute of Technology Lincoln Laboratory, and the U.S. Air Force Research Laboratory to compare the performance metrics and potential effect on the NAS when using these well clear definitions.Based on this work, the definition of DAA well clear shown in Eqn.(1) was recommended to RTCA Special Committee (SC)-228 and the FAA.After incorporating feedback from both organizations, a consensus on the definition of DAA well clear for UAS was reached.According to this definition, loss of DAA well clear -which is different than the subjective "well clear" in 14 CFR Part 91, §91.111 and §91.113-is an event in which a UAS is in close proximity with another aircraft such that the following three conditions are concurrently true:
+Current Vertical Distance ( h d )The DWC definition has a spatial threshold in the vertical dimension known as * h d to which the current vertical separation ( 21 h d h h ) between the two aircraft is compared.
+Horizontal Miss Distance (HMD)The DWC definition also has a spatial metric in the horizontal dimension known as horizontal miss distance (HMD), which is defined as the projected separation in the horizontal dimension at predicted closest point of approach (CPA) using linear (constant velocity) extrapolation:In the example illustrated in Figure 1 and described in the paragraph above it, HMD is the cross-track distance between the UAS and the manned aircraft because the former is flying due east while the latter is flying due west.
+Modified Tau ( mod
+ )The DWC definition also has a temporal separation metric known as "modified tau" or mod  that estimates the time to CPA between two aircraft.Modified tau is adopted from the collision detection logic of TCAS II 6 that is on board manned aircraft.Modified tau is based on the concept of "tau" (  ), which is calculated as the ratio of slant range ( r ) between aircraft to their slant range rate ( r ) and measured in seconds:As described in the TCAS II Manual, 6 one issue with the tau metric is that the calculated tau can be large even when the physical separation between two aircraft is small if the rate of closure is low (e.g., two flights are flying at approximately the same speed, on the same heading, offset by a small distance).In such a situation, the calculated tau value does not assure adequate separation because a sudden trajectory change that increases the closure rate (e.g., a turn) may cause loss of DWC.To provide protection for these types of situations, a modified alerting threshold referred to as "modified tau" was developed for use in TCAS II.Modified tau uses a parameter known as "distance modification" (DMOD) to provide a minimum threat range boundary encircling the UAS.Modified tau ( mod  ) is defined as follows using horizontal range and horizontal range rate and measured in seconds:                   mod is a constant,
+B. Near Mid-Air Collision (NMAC)An NMAC is defined as an event in which two aircraft are within 500 feet horizontally and 100 feet vertically of each other: 6
+III. Design of Experiment
+A. Problem StatementThis experiment aimed to determine what criteria should be included in an equation that would define when vertical maneuvers should be allowed and when they should be suppressed for DAA systems performing well clear recovery.The initial hypothesis from discussion in SC-228 was that vertical maneuvers should be suppressed when vertical rate error exceeded some threshold value, initially set to 200 fpm.Preliminary work 8 focused on determining the effect of differing vertical rate error thresholds on the severity of a loss of DAA well clear.Results showed that vertical rate error alone was not a reliable indicator for when vertical well clear recovery (WCR) maneuvers should be allowed or suppressed.This paper summarizes the follow-on work that spurred SC-228 to develop the equation currently in the MOPS.This follow on work determined that, in addition to the vertical rate error present in a sensor, the climb and descent rate available to a UAS during WCR maneuvers could influence the severity of a loss of DAA well clear.
+B. Simulation PlatformThis study used a fast-time simulation with a generic resolver developed at NASA as a DAA system. 9The simulation includes a model for selecting a maneuver from the guidance options offered and a model for pilot delays associated with evaluating and executing maneuvers in this domain.Aircraft were modeled using a kinematic trajectory generator that accepts aircraft performance constraints and produces trajectories that satisfy those constraints.The maneuver itself was chosen by a part of the guidance algorithm, which used in this simulation as a proxy for the decision pilots would make when presented with vertical and horizontal DAA WCR guidance.This algorithm would select a vertical or a horizontal maneuver based on maximizing the predicted minimum separation, then send that maneuver to the UAS.The vehicle had a simple pilot-delay module that adds 6 seconds of delay before executing a maneuver.These delays were based on some unpublished, internal analysis performed on data from human-in-the-loop testing done in a previous study. 10If the maneuver guidance was changed during the delay, a new maneuver would be chosen while the UAS continued executing any previous guidance.The delay would then be added again before beginning the new maneuver.
+C. Sensor ModelSensor errors were simulated based on a sensor model and fusion tracker developed by Honeywell and provided to NASA under contract * .This model was further tuned to match data from one of the flight tests performed previously in the course of the overall UAS research. 11It added "noise" to the UAS's current position and speed (both vertical and horizontal), as well as the intruder's detected position and speeds.The only sensor model used in this test was the air-to-air radar, as the intruders were all non-cooperative.This radar also had field-of-regard limits that were based on the actual radar used in NASA flight testing.In this case, those limits are +/-110 degrees for azimuth and +/-15 degrees for elevation.More information on this sensor and tracker is provided in Ref. 12.
+D. Encounter SetsA series of pairwise encounters between a single UAS and a non-cooperative VFR aircraft was simulated in two mirrored groups of 54,000 encounters each.The first group allowed the DAA system and pilot model to select and fly vertical maneuvers to regain well clear when the DAA system determined that vertical maneuvers were the preferred solution.The second group of encounters forced the guidance algorithm to use only a horizontal maneuver to regain well clear.This allowed for a clear comparison between identical encounters in which a vertical maneuver was selected versus suppressed.Each encounter consisted of a UAS and a non-cooperative VFR aircraft, referred to as an "intruder."The UAS began the encounter flying north at 9000 feet at a speed of either 50 or 200 knots.These speeds were based on anticipated airspeeds from the MOPS for UAS operating below 10000 feet mean sea level.The intruder's airspeed was either 70 or 170 knots.The upper intruder airspeed was taken from the MOPS as a nominal high airspeed for non-cooperative VFR aircraft below 10000 feet mean sea level, while the lower airspeed was chosen to represent very slow, non-cooperative fixed wing aircraft that could potentially operate at these altitudes.The intruder crossed the UAS's path at one of nine horizontal locations and five vertical distances with the intruder's heading angles varying from 0 degrees (north) to 180 degrees (south) in 45-degree steps.The crossing point was set to 70 seconds from the start of the simulation.The actual starting location of the intruder and the unmitigated closest point of approach was set by the simulation to meet each defined crossing point.This allowed a variety of intruder geometries to be tested.Further, the UAS's maximum turn rate, climb rate, and descent rate were varied independently, to determine which UAS performance parameters, if any, were primary factors in determining whether vertical resolutions produced more separation than horizontal maneuvers.The UAS was only permitted to maneuver after it received well clear recovery guidance from the DAA algorithm.The intruder did not maneuver in this simulation.UAS performance parameters-airspeed, vertical rate, turn rate-as well as ranges of intruder states and encounter geometries are documented in the full encounter matrix in Table 1.The experiment was designed to allow a direct comparison between the two sets of encounters (with vertical maneuvers allowed and with vertical maneuvers suppressed) because the errors seen by the guidance algorithm would be the same for encounters with the same initial conditions, and would only begin diverging when the UAS began its well clear recovery maneuver.This allowed the simulation tests to be broken into the two sets of 54,000 encounters discussed earlier.The trade-off for using this method was that a study of the effects of heading angles and similar parameters would be out of scope.For this simulation, every encounter that had the same initial states would see the same error distribution up until well clear recovery (WCR) was triggered and the UAS began maneuvering, regardless of the UAS's maneuver performance.Consideration was given to utilizing multiple simulation runs with differing noise parameters or random seeds, but such work was deemed out of scope for this study.
+E. Performance MetricsThe primary metric was the Severity of Loss of DAA Well Clear (SLoWC), which is a measure of the minimum separation during an encounter.This metric was developed for the DAA MOPS.It estimates penetration into the DAA well-clear zone, and it accounts for vertical and horizontal separation.More details are given in Appendix B. The resulting SLoWC ranges from 0% to 100%, with 0% indicating Well Clear and 100% representing zero horizontal and vertical separation, that is, a collision.As an example, for a head-on, co-altitude encounter, moving from a horizontal miss distance of 4000 feet to 3000 feet increases the SLoWC by roughly 23%, while moving from 1000 feet to a horizontal miss distance of 0 feet increases the SLoWC by about 26%.The minimum value of SLoWC at which an NMAC can occur is around 70% and is dependent on the encounter geometry.Another metric that is not explicitly measured but is a major factor in the results is the number of NMACs (as defined in Section IIB).When an NMAC occurs, there is a risk of a collision.The goal of Well Clear Recovery is to regain well clear separation and avoid an NMAC.The analysis presented in this paper begins with the encounters where a vertical maneuver was chosen (in the data sets in which they were allowed).Those could be compared to the encounters with identical initial conditions in the data set where only horizontal maneuvers were allowed, as every encounter geometry tested was present in both sets.These pairs of encounters were checked to see if both the vertical and horizontal maneuvers began at the same time.Those encounter geometries with both a horizontal maneuver and a vertical maneuver at the same simulation time step were the only ones included in the primary analysis.This parity was seen as the fairest way of examining the effect of allowing (not requiring) vertical maneuvers, and assured that the initial horizontal and vertical maneuvers had the same estimated time to the closest point of approach (CPA) when the maneuvers were initiated.Otherwise there was the potential that one of the maneuvers would start significantly closer to the CPA, resulting in an inflated SLoWC.To eliminate these guidance changes, one would need to reduce sensor noise or improve smoothing without sacrificing the speed at which the algorithm reports unexpected intruder maneuvers-track accuracy, smoothness and processing times are tradeoffs of the tracker design.Note that DAA MOPS only have requirements on track accuracy and processing time.There are further tradeoffs in radar system design, with additional complications due to the frame of reference.For these air-to-air radars, an intruder's vertical position and rate must be determined by relative altitude estimation based on an elevation angle.Comparisons of these tradeoffs are out of scope for this study.Table 1.Encounter matrix with 54,000 encounters.Full matrix was run twice: one test set allowed vertical maneuvers to regain well clear while the other test set did not.In all encounters, UAS were initialized flying level at 9000 feet heading due north.Each intruder was initialized 70 seconds before a defined crossing point.The UAS only maneuvered when it received Well Clear Recovery guidance from the DAA system.
+IV. ResultsThe first subsection below illustrates the vertical-rate error that was present in the simulation at the moment WCR guidance was triggered.This is to provide some context for the separations presented later in the results.The second subsection discusses the maximum SLoWC observed during a WCR maneuver for two different UAS performance levels.
+A. Vertical-Rate ErrorFigure 2 illustrates the vertical-rate error from the air-to-air radar and tracker model at the point when WCR guidance was triggered.Comparing this error to the loss of well clear definition's vertical separation threshold of 450 feet and a nominal time from WCR guidance to minimum separation of 40 seconds helps frame the difficulty accounting for this error during WCR.The range of these errors is one of the reasons early work in this area focused on when and how often to suppress vertical WCR maneuver guidance.This vertical-rate error is not as problematic when dealing with cooperative intruders, as they are expected to be equipped with both a Mode C or Mode S transponder, and ADS-B.
+B. Maximum Severity of Loss of DAA Well ClearFigure 3 shows a comparison between the maximum Severity of Loss of DAA Well Clear (SLoWC) for the scenario where vertical maneuvers were allowed and chosen, and the scenario where only horizontal maneuvers were permitted, as described in Section IIID. Figure 3a) shows the percentage of encounters with certain SLoWC values, in bins of 10, for vertical and horizontal maneuvers for a UAS that could climb and descend at 2000 fpm. Figure 3b) shows the same results for a UAS that can only climb and descend at 500 fpm.For both of these figures, it is desirable to have taller bars to the left, as that corresponds to more encounters with a low SLoWC (more minimum separation).In these results, it can be seen that both the 2000-fpm and 500-fpm UAS have more horizontal encounters below a SLoWC of 20%, while there are more vertical encounters above a SLoWC value of 30%.This corresponds to an overall observation that horizontal maneuvers, on average, produced a lower value of SLoWC than vertical maneuvers.In other words, there was more net separation when horizontal maneuvers were employed in encounters where vertical maneuvers were preferred.For perspective, the median difference in SLoWC between horizontal and vertical maneuvers for the 2000-fpm UAS was 11%.An 11% difference in SLoWC corresponds to a distance of roughly 50 feet vertically for a head-on, co-altitude encounter with a horizontal CPA of zero.As the vertical or horizontal CPA increases, the vertical separation represented by that difference in SLoWC will also slightly increase.An 11% difference in SLoWC for a head-on, co-altitude encounter also corresponds to a difference in horizontal separation of 500 for low horizontal CPA values, and roughly 800 feet for higher horizontal CPA values.For the UAS that was limited to climb and descent rates of 500 fpm, the difference in median SLoWC was 13%.There is also a slight difference in the percentage of encounters that are over the line representing a SLoWC value of 70%.This value marks the threshold of a high-risk area, as NMACs are possible above this value.The actual SLoWC of an NMAC will be dependent on the individual encounter geometry.The 2000-fpm UAS showed almost no difference between the number of encounters that had high-risk SLoWC values when vertical or horizontal maneuvers were used.For the 500-fpm UAS, however, horizontal maneuvers produced a lower percentage of encounters with SLoWC values over 70% than vertical maneuvers.A preliminary investigation into some of the effects of sensor errors 8 also revealed that lower sensor errors combined with UAS with high climb and descent rates had fewer encounters with high-risk SLoWCs when they employ vertical maneuvers than they did when they were forced to use horizontal maneuvers.This led to the hypothesis that higher performance UAS should be allowed to use vertical maneuvers in some cases, even though vertical maneuvers have a higher average SLoWC.Further analysis also suggests that as vertical-rate error decreases, higher-performance UAS are likely to experience fewer encounters with SLoWC over 70% if they can use vertical maneuvers.Though this is not explicitly in the figure, it should be noted that the number of encounters where vertical maneuvers were preferred by the generic algorithm discussed in Section IIIB is higher for UAS with high climb and descent performance.In the case of the UAS that could climb and descend at 2000 fpm, vertical WCR maneuvers were preferred in 75% of the total encounters.For the UAS that was restricted to climb and descent rates of 500 fpm, vertical maneuvers were preferred in only 30% of the total encounters.The results presented in this subsection focused on the encounters where vertical maneuvers were preferred.This was to help answer the question of whether or not vertical maneuvers should be suppressed, and if not, when should they be allowed.These results were part of the data set that led to the adoption of the equation given in Appendix A. There was a general trend in all of the data that showed UAS with higher vertical rate performance available for WCR maneuvers had less difference between the SLoWC of horizontal and vertical maneuvers than vehicles with lower performance.There was also a larger difference between vertical and horizontal maneuvers when vertical-rate error was larger.This equation allows vertical maneuvers when the UAS can reach vertical rates that exceed the projected errors associated with the intruder.This will ensure that vertical maneuvers are only offered when they are safe, and will scale with technology, as improved air-to-air radar performance will allow vertical maneuvers to be utilized for WCR more often.These results led to a discussion about potential cases when vertical maneuvers should be allowed.The idea of relying upon a fixed sensor-error threshold to determine when vertical maneuvers should be allowed did not fit with the data, particularly when considering the chance of high-risk encounters.SC-228 eventually came to a consensus that UAS vertical rate performance should be considered in addition to sensor error, and that the correct approach would be to compare the performance of the UAS with the intruder's potential position.This would allow UAS to use vertical maneuvers when their performance levels allowed them to avoid the intruder's potential position, based on the intruder's currently estimated state and the sensor errors.The equation that came out of that SC-228 discussion is given in Appendix A.
+C. Direct Comparison between Horizontal and Vertical ManeuversFigure 4 shows how SLoWC changes for an encounter when vertical maneuvers are suppressed.Similar to the previous chart, for the data set that allowed vertical maneuvers, each encounter where a vertical maneuver was chosen was stored.From there, the identical encounter from the data set with only horizontal maneuvers was stored.If the maneuvers from each data set were executed at the same time, the difference between the resulting SLoWC values was stored.These paired encounters are identical up until the point the UAS begins maneuvering.In effect, each data point represents the change in SLoWC for an individual encounter when a horizontal maneuver is used in place of a vertical maneuver.Positive values indicate a decrease in the SLoWC metric (more separation) when a horizontal maneuver is used instead of a vertical maneuver.Negative numbers denote an increase in SLoWC (less separation) when a horizontal maneuver is used instead of a vertical maneuver.The vertical dashed line in the center is simply to separate the areas where vertical maneuvers had more separation and horizontal maneuvers had more separation.As implied earlier, the generic DAA system preferred a vertical maneuver in each of these encounters.The overall trend shows that, on average, SLoWC was lower when horizontal maneuvers were employed, regardless of vertical rate performance.This is consistent with results in Section IVB.Comparing between the results for the 500-fpm and 2000-fpm UAS, the data also show that the 500-fpm UAS had a higher percentage of encounters where a horizontal maneuver had a lower SLoWC by 30% or more (the 500-fpm UAS data are the yellow bars).The 2000-fpm UAS (the blue bars) had a higher percentage of encounters where the vertical maneuver had a lower SLoWC by 20% or more.These results were expected, as a UAS with higher climb and descent rates available when doing vertical maneuvers should be able to gain more vertical separation than a UAS with poor vertical acceleration over the same amount of time.What was not expected, however, was that the overall percentage of encounters where vertical maneuvers had a lower SLoWC than horizontal maneuvers would be similar across all UAS performance levels.Data show that, for 38% of the encounters for the 2000-fpm UAS, the vertical maneuver had a lower SLoWC than the horizontal maneuver.For the 500-fpm UAS that number was 39%.There were a few more UAS performance levels tested in preliminary work, and they also had a similar percentage of encounters where vertical maneuvers had less separation.Some of that could be attributed to chance, as there is always a likelihood that a vertical maneuver will produce more separation than a horizontal maneuver, even with large vertical rate uncertainty.However, the fact that the percentage of encounters where this was true was seemingly insensitive to the vertical performance of the UAS raised a question of whether there could be another important factor influencing the data.It is possible that some encounter geometries may be more difficult to resolve with horizontal maneuvers, even in the presence of large intruder vertical rate errors.One type of encounter was found where this seems to be true, and it is discussed in Section VB, below.It is possible that there are more encounter geometries, but a full analysis of encounter geometries would require a different analysis approach and was out of scope for this work.
+V. Discussion
+A. Severity of Loss of DAA Well ClearThere are multiple potential causes for why suppressing vertical maneuvers would lead to more severe losses of well clear when attempting to regain DWC.One potential reason would be conflict geometries that are difficult to resolve with horizontal maneuvers.An example of this type of encounter is in the next section.It is also possible that it is more difficult to recover from a poor initial maneuver choice when the only option is a reversal, due to the other dimension being suppressed.The difficulty is predicting when it would be worth attempting a vertical resolution.Exploring the encounter set in more detail was out of scope for this paper, but could lead to better understanding of when vertical maneuvers are more likely to result in increased separation and reduced NMAC risk.However, even with that knowledge, it is the author's opinion that it would be difficult for algorithms that are only using state-based trajectory predictions and trajectory smoothing to reliably determine when vertical resolutions could be beneficial, outside of using an equation such as the one in Appendix A. To further reduce the number of severe losses of well clear using vertical maneuvers will likely require adjustments on the algorithmic side, or waiting for sensor errors to be drastically reduced.For algorithms, it is possible that adding tools that can utilize trajectory prediction error distributions, or heuristics that can help the algorithm determine when and how to respond to large shifts in vertical rate predictions, could produce systems that have a lower NMAC risk while allowing more vertical maneuvers.However, such algorithmic design and testing is out of scope for this work.The current requirements in the MOPS allow vertical maneuvers when the vertical rate achievable by the UAS compares favorably with the vertical-rate error and position of the intruder.That seems adequate for current systems, and could be implemented with a generic DAA algorithm as long as there is knowledge of the sensor system and its characteristic errors.However, data gathered for this study showed there are enough encounters in which the suppression of vertical maneuvers resulted in lower separation to warrant further study, and that the average minimum separation achieved should not be the sole indicator for determining the best maneuver for preventing NMACs in a future system.
+B. Horizontal Maneuver AmbiguityThis section highlights a type of encounter where vertical-rate error can strongly affect the separation obtained during a vertical maneuver.This is due to the vertical-rate error changing the predicted co-altitude point during a crossing conflict with an intruder that is changing altitude.These cases do not necessarily lead to a dangerous maneuver or an NMAC, but they can make those events more likely if the wrong turn direction is chosen.A few of these cases appeared in the data, but the encounter matrix used for this study had a limited number of these encounters.Additionally, the trajectories from the cases where an incorrect initial turn direction led to a turn reversal were complicated, as the guidance algorithm was trying to find the correct trajectory through some large variations in sensor noise.Therefore, to highlight the class of encounter, rather than the way a specific algorithm attempted to recover from an incorrect initial turn, this paper will cover these encounters with a general example.Figure 5 shows an example encounter where vertical-rate error has a large effect on the suitability of a horizontal maneuver as well as vertical.In this example, there is an intruder that is estimated at 1300 feet above the UAS, descending at a predicted rate of 1500 fpm, and is projected to pass above the UAS if no action is taken.The horizontal closure rate is 135 knots, and the vertical-rate error is +/-1000 fpm.Further assume there are 45 seconds until the horizontal CPA.In this example, the ground speed of the intruder is the same for all descent rates.The exact ground speeds and encounter angle are not specified, as the specific encounter geometry is not what is being highlighted.The encounter is a crossing conflict, though the heading difference does not have to be exactly 90 degrees.If the descent rate is 1500 fpm, as shown with the blue line, the intruder will reach a horizontal separation of zero in 45 seconds with 175 feet of vertical separation.If the intruder is descending at 2500 fpm, as shown with the grey line, it will reach co-altitude in just over 31 seconds with over 3000 feet of horizontal separation, and will reach zero horizontal separation with 575 feet of vertical separation.Therefore, if the intruder is descending faster than predicted, the UAS should turn away from the intruder.If the intruder is descending at 500 fpm, as shown with the orange line, it would not reach co-altitude for 156 seconds, and would cross zero horizontal separation with 925 feet of vertical separation.Turning towards the intruder would be the correct option in this case, though the UAS might not need to maneuver at all to avoid a loss of well clear.Encounters like this could make vertical maneuvers preferable even if the predicted separation is not as high as it is for horizontal maneuvers and the level of vertical-rate error is large.In the above encounter, if the UAS can climb above the altitude at which the intruder would be in 45 seconds (intruder was descending at the minimum rate), an NMAC could be prevented, regardless of the intruder's actual descent rate.However, a horizontal turn, even though the potential separation achieved might be higher, could result in a more dangerous scenario if the UAS chooses the wrong initial turn direction.In the case of a turn towards the intruder with the intruder descending faster than predicted, the UAS could find itself in a similar situation to the initial encounter, only with significantly less time until CPA.With a turn away from an intruder that is descending more slowly than predicted, the UAS could put itself into a "chase scenario" where the UAS will need to either outrun the intruder or rely on the intruder to make a maneuver of its own.This is further complicated by the UAS radar's field of regard.If the UAS turns in front of a non-cooperative intruder, there is a risk that it will lose sight of the intruder, depending on the conflict geometry and field-of-regard value.This highlights the field of regard as a potential complication for horizontal maneuvers.This could become an issue depending on encounter geometry, relative ground speeds, and the horizontal maneuver selected by the UAS.However, an exploration of this was out of scope for the current work.These encounters could also benefit from guidance algorithms that use risk-based or heuristic approaches to mitigating sensor errors, rather than a more traditional approach that is built on a primary trajectory prediction.This would likely become less important as air-to-air radar vertical-rate errors decrease, but for current error levels it can be difficult to make the safest decision based only on a predicted trajectory and an error distribution.
+VI. ConclusionsThis study examined two sets of 54,000 encounters in which the UAS was forced into a predicted loss of DAA well clear.In one set, the UAS was allowed to choose vertical maneuvers for WCR guidance; in the other set, the UAS was restricted to horizontal guidance.Results of the study showed that the average separation was higher when vertical maneuvers were suppressed, in terms of the "severity of loss of DAA well clear" metric.However, it was noted that a UAS' climb and descent rate available for WCR maneuvers influenced the percentage of encounters that were high risk (potential NMAC).Specifically, the percentage of high-risk encounters when using vertical maneuvers decreased as the UAS climb and descent performance increased, and the sensor vertical rate errors decreased.Accordingly, it is suggested that UAS should be allowed to maneuver vertically when their vertical performance is high enough to avoid the potential positions of the intruder (based on the intruder's estimated position, vertical rate, and the characteristic vertical rate error of the sensor on the UAS).This conclusion led to the equation in the MOPS, which is shown in Appendix A.Additionally, in roughly 38% of the encounters, the severity of an encounter increased when a horizontal maneuver was used in place of a vertical maneuver.This percentage was seemingly independent of UAS climb and descent performance, and led to a preliminary investigation of other potential factors that could make vertical maneuvers preferable to horizontal maneuvers, even with large levels of vertical rate error.One potential factor was certain conflict geometries that are intrinsically difficult to safely resolve with horizontal maneuvers, due in large part to the effect of vertical-rate error.One such case highlighted in this paper is a crossing conflict with an intruder that is changing altitude, where the vertical-rate error can have a large effect on the left/right turn decision.In some of these cases, a vertical maneuver might not offer as much separation on average but potentially could have less risk of an NMAC in the worst case.Overall, the results support the equation developed by RTCA Special Committee 228 for the DAA MOPS as a solution that is implementable now.This equation allows for vertical maneuvers if the UAS's performance exceeds the non-cooperative intruder's vertical-rate error, estimated vertical position and estimated vertical rate.However, for future solutions it is recommended that crossing conflicts with a descending or climbing intruder be investigated, as well as the stability of a maneuver (likelihood of a second maneuver being required) and NMAC risk.There is also a potential need to improve horizontal guidance algorithms for difficult encounters, such as through heuristics or risk-based methods, to more intelligently chose a target heading when large errors make it difficult for a traditional trajectory prediction to find a stable solution.
+Appendix A: Equation for Suppressing Vertical ManeuversThis is the equation from the MOPS.It is used to determine when to provide vertical maneuver guidance to regain well clear in relation to non-cooperative intruders.When the following inequality is true, vertical maneuver guidance must not be offered.h E  is the 95% metric threshold of the relative vertical position accuracy as estimated by the DAA tracker.
+_ own maneuver h is the planned vertical velocity of the maneuver that will be used by the UAS.By default, 500 feet per minute should be used as the minimum allowable performance unless the applicant specifies that a larger value is used by guidance to regain DWC.Different values may be used for climbs and descents, in which case the inequality above should be checked for each value.Where S is a horizontal distance threshold defined byFigure 1 Figure 1 .11Figure1illustrates the variables and parameters used to define well clear for UAS, each of which will be described in detail in this section.The asterisked parameters are thresholds and the non-asterisked variables are measured or projected values.The dashed objects are projections of the aircraft.This schematic illustrates an encounter between a UAS flying level heading east and a manned aircraft flying level heading west.
+Downloaded by NASA AMES RESEARCH CENTER on June 9, 2017 | http://arc.aiaa.org| DOI: 10.2514/6.2017-4382
+Figure 2 .2Figure 2. Vertical rate error from the radar and tracker at the time when the UAS began maneuvering to regain well clear.
+Figure 3 .3Severity of Loss of DAA Well Clear for UAS maximum vertical rate of a) 2000 fpm and b) 500 fpm.Horizontal maneuvers had lower SLoWC on average for both the 2000 fpm and 500-fpm UAS.The 500-fpm UAS had fewer encounters with high risk SLoWC (over 70) when using horizontal maneuvers.There was no change in the number of high risk SLoWC encounters when horizontal maneuvers were used for the 2000-fpm UAS.
+Figure 4 .4Figure 4. Change in SLoWC when a horizontal maneuver is forced compared to the SLoWC when a vertical maneuver was used.On average, encounters have lower SLoWC when horizontal maneuvers are used in place of vertical.This change in SLoWC is more pronounced for the 500-fpm UAS.However, the percentage of total encounters where vertical maneuvers have lower SLoWC is roughly the same for both 2000-fpm and 500fpm UAS.
+Figure 5 .5Figure 5. Intruder descent profile in terms of horizontal and vertical distance from the UAS.This shows the progression of vertical separation based on horizontal separation between UAS and an intruder up until the horizontal separation is zero.Each line represents a possible intruder descent rate based on an error range of +/-1000 fpm and a predicted intruder descent rate of 1500 fpm.
+ownhis the current vertical velocity of the UAS as estimated by the DAA tracker.tracker h  is the current relative vertical velocity between the intruder and UAS as estimated by the DAA tracker.It is equal to the intruder's vertical velocity minus the UAS's vertical velocity.tracker h  is the current relative vertical position between the intruder and UAS as estimated by the DAA tracker.The relative vertical position is equal to the intruder's vertical position (altitude) minus the UAS's vertical position (altitude).
+
+ is the 95% threshold of the relative vertical velocity accuracy as estimated by the DAA tracker.1 2CPA E t h  h  E  _ own maneuver hown htracker h  CPA ttracker h  where(4)tis the estimated time to closest point of approach.CPAh E
+Note that the equation for HMD is different than in equation 1.This is because setting HMD to negative infinity when tcpa is less than zero would result in HMDPen values of negative infinity, as well.The above definition ensures the value of HMDPen is continuous throughout the Loss of Well Clear.The VertPen is defined as   * 2 2 * mod mod 1 , ( ) 4 2 S MAX DMOD r DMOD r          whereDMOD* mod  4000 ft, 35 sec,i rhorizontal range,i rhorizontal range rate(8)The HMDPen is defined asHMDPen MIN , 1  HMD DMOD  whereHMD     (x d v t rx CPA 22 ) ( ) for for y ry CPA xy d v t rt CPA CPA t   0 0(9)* h  12 , 1 d VertPen MIN where h    H d  h h * and 450 ft H (10)
+			Downloaded by NASA AMES RESEARCH CENTER on June 9, 2017 | http://arc.aiaa.org| DOI: 10.2514/6.2017-4382
+			*  The Honeywell DAA tracker is a sub-TRL6 tracker that is in its own iterative development cycle.This is one instantiation of the tracker with expected improvements in later versions to meet the developing DAA requirements and help with better alerting and guidance performance.Downloaded by NASA AMES RESEARCH CENTER on June 9, 2017 | http://arc.aiaa.org| DOI: 10.2514/6.2017-4382
+		
+		
+
+			
+AcknowledgementsThe authors would like to acknowledge the work put in by Eric Mueller, Matt Edwards, Ted Lester, and the rest of the SC-228 discussion group that assisted in the development the equation that was presented to and accepted by SC-228 for adoption into the MOPS.
+			
+
+			
+Appendix B: Severity of Loss of DAA Well Clear (SLoWC)The value of Severity of Loss of DAA Well Clear (SLoWC) that is reported in the results section is the maximum SLoWC for each encounter.The value of SLoWC for each point of that encounter is determined by the following equation: This equation was developed for the SC-228 MOPS.(1where the Fernandez-Gausti's squircle is defined asThe RangePen is defined as , 1 r RangePen MIN S     (7)			
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+			DStevens
+		
+		NASA TM-2015-219392
+		
+			2015
+		
+	
+	Mueller, E. R., Isaacson, D., and Stevens, D., "Air Traffic Controller Acceptability of Unmanned Aircraft System Detect and Avoid Thresholds," NASA TM-2015-219392, 2015.
+
+
+
+	
+		Evaluating Alerting and Guidance Performance of a UAS Detect-And-Avoid System
+		
+			SMLee
+		
+		
+			CPark
+		
+		
+			DThipphavong
+		
+		
+			DRIsaacson
+		
+		
+			CSantiago
+		
+	
+	
+		Sense and Avoid (SAA) for Unmanned Aircraft Systems (UAS)
+		SAA Workshop Second Caucus Report
+		
+			2016-219067, 2016. Jan. 2013
+		
+	
+	Federal Aviation Administration
+	Lee, S. M., Park, C., Thipphavong, D., Isaacson, D. R., Santiago, C., "Evaluating Alerting and Guidance Performance of a UAS Detect-And-Avoid System," NASA TM-2016-219067, 2016. 5 Federal Aviation Administration, "Sense and Avoid (SAA) for Unmanned Aircraft Systems (UAS)," SAA Workshop Second Caucus Report, Jan. 2013.
+
+
+
+	
+		Minimum Operational Performance Standards (MOPS) for Traffic Alert and Collision Avoidance System II (TCAS II) version 7.1
+		
+			IncRtca
+		
+	
+	
+		DO-185B
+		
+			Jun. 2008
+		
+	
+	RTCA, Inc., "Minimum Operational Performance Standards (MOPS) for Traffic Alert and Collision Avoidance System II (TCAS II) version 7.1," DO-185B, Jun. 2008.
+
+
+
+	
+		A TCAS-II Resolution Advisory Detection Algorithm
+		
+			CesarMunoz
+		
+		
+			AnthonyNarkawicz
+		
+		
+			JamesChamberlain
+		
+		10.2514/6.2013-4622
+		AIAA Paper 2013-4622
+	
+	
+		AIAA Guidance, Navigation, and Control (GNC) Conference
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 2013
+		
+	
+	Munoz, C., Narkawicz, A., and Chamberlain J., "A TCAS-II Resolution Advisory Detection Algorithm," AIAA Guidance, Navigation and Control Conference, AIAA Paper 2013-4622, Aug. 2013.
+
+
+
+	
+		UAS Well Clear Recovery against Non-Cooperative Intruders using Vertical Maneuvers
+		
+			AndrewCCone
+		
+		
+			DavidPThipphavong
+		
+		
+			SeungManLee
+		
+		
+			ConfesorSantiago
+		
+		10.2514/6.2017-4382
+	
+	
+		17th AIAA Aviation Technology, Integration, and Operations Conference
+		
+			American Institute of Aeronautics and Astronautics
+			20160012017. Oct. 2016
+		
+	
+	Cone, A., Thipphavong, D., Lee, S. M., Santiago, C., "Effect of Vertical Rate Error on Recovery from Loss of Well Clear Between UAS and Non-Cooperative Intruders" NASA Technical Report 20160012017, Oct. 2016
+
+
+
+	
+		The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Detect-And-Avoid (DAA) Systems
+		
+			MichaelAbramson
+		
+		
+			MohamadRefai
+		
+		
+			ConfesorSantiago
+		
+		10.2514/6.2017-4485
+		NASA/TM-2017-219507
+	
+	
+		17th AIAA Aviation Technology, Integration, and Operations Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2017
+		
+	
+	9 Abramson, M., Refai, M., Santiago, C., "The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Unmanned Aircraft Detect-And-Avoid Systems," NASA/TM-2017-219507, 2017
+
+
+
+	
+		The Impact of Suggestive Maneuver Guidance on UAS Pilot Performing the Detect and Avoid Function
+		
+			RobertCRorie
+		
+		
+			LisaFern
+		
+		
+			JayShively
+		
+		10.2514/6.2016-1002
+	
+	
+		AIAA Infotech @ Aerospace
+		
+			American Institute of Aeronautics and Astronautics
+			2016
+		
+	
+	Rorie, R.C., Fern, L., and Shively, R.J. "The impact of suggestive maneuver guidance on UAS pilots performing the detect and avoid function." AIAA Infotech@ Aerospace, 2016.
+
+
+
+	
+		UAS Integration in the NAS Project: Flight Test 3 Data Analysis of JADEM-Autoresolver Detect and Avoid System
+		
+			CGong
+		
+		
+			MWu
+		
+		
+			CSantiago
+		
+	
+	
+		NASA/TM
+		
+			2016-219441, Dec. 2016
+		
+	
+	Gong, C., Wu, M., and Santiago, C., "UAS Integration in the NAS Project: Flight Test 3 Data Analysis of JADEM- Autoresolver Detect and Avoid System," NASA/TM-2016-219441, Dec. 2016.
+
+
+
+	
+		An Alternative Time Metric to Modified Tau for Unmanned Aircraft System Detect And Avoid
+		
+			MinghongGWu
+		
+		
+			VibhorLBageshwar
+		
+		
+			EricAEuteneuer
+		
+		10.2514/6.2017-4383
+	
+	
+		17th AIAA Aviation Technology, Integration, and Operations Conference
+		Reston, VA
+		
+			American Institute of Aeronautics and Astronautics
+			2017
+		
+	
+	submitted to AIAA Aviation Conference
+	Wu, M. G., Bageshwar, V. L., Euteneuer, E. A., "An Alternative Time Metric to Modified Tau for Unmanned Aircraft System Detect-And Avoid," submitted to AIAA Aviation Conference, AIAA, Reston, VA., 2017
+
+
+				
+			
+		
+	
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+
+	
+	
+		
+I. Nomenclature
+II. IntroductionDetect-and-avoid (DAA) systems serve as a critical module in enabling integration of Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS).A DAA system provides surveillance, alerts, and maneuver guidance to keep a UAS "well clear" of other aircraft [1,2].In the United States, simulation tests as well as flight tests have provided supporting information for defining a DAA Well Clear [1,3] (DWC) and requirements for the alerting and maneuver guidance performance [4][5][6][7][8], on which surveillance requirements were derived and based.The parameters used to define DWC are explained in more detail in section II of this paper.Prototype DAA algorithms have also been developed for alerting and maneuver guidance (referred to as guidance in this paper) research [9][10][11].These developments enabled the RTCA Special Committee 228 (SC-228) to publish the Minimum Operational Performance Standards (MOPS) for DAA systems [12] and air-to-air radar [13] in 2017.The corresponding Technical Standard Orders (TSO), TSO-C211 and TSO-C212, were published by the Federal Aviation Administration (FAA) in October 2017.These standards, referred to as the Phase 1 MOPS, target UAS operations transitioning into, out of, or through class D, E (up to 18,000 ft MSL), and G airspace to or from class A airspace.A DAA system, according to the Phase 1 MOPS, contains surveillance components of Automatic Dependent Surveillance-Broadcast (ADS-B) In, airborne active surveillance that can interrogate transponders of nearby aircraft, and air-to-air radar that can detect aircraft with or without transponders.Traffic Alert and Collision Avoidance System (TCAS) II [14] is an optional component.Phase 2 work for extending the MOPS to additional UAS categories and operations is underway.One of the Phase 2 objectives seeks an alternative DWC for UAS with non-cooperative aircraft, i.e., visual flight rules (VFR) aircraft without a broadcasting transponder.The DWC in the Phase 1 work was selected with considerations of interoperability with TCAS-II.To avoid triggering TCAS's resolution advisories during an encounter which leads to DAA maneuvers, the DWC was defined to encompass a vast majority of the TCAS alerting volume [1].The resulting DWC is deemed very safe but may be unnecessarily large for encounters of UAS with noncooperative aircraft, which TCAS-II cannot detect and therefore need not be considered.Additionally, the airborne radar required under Phase 1 is heavy and consumes considerable power, which makes it difficult for smaller UAS to operate in compliance with the Phase 1 MOPS.A smaller DWC for non-cooperative aircraft would allow these UAS to equip with a lower-power, lighter weight airborne radar, provided the UAS restricted their operational speeds to values between 40 and 100 kts true airspeed (TAS).Four candidate DWCs were proposed for additional analysis in terms of their alerting performance and safety metrics [15].Another Phase 2 objective considers extending the maximum UAS true airspeed defined in RTCA Document 365 (DO-365) from the current limit of 200 kts to 291 kts for Phase 1 compliant UAS.A true airspeed of 291 kts is roughly equivalent to 250 kts indicated airspeed at around 10,000 ft MSL.This 250-knot IAS value is notable because it is the FAA-imposed upper bound for aircraft flying under 10,000 ft MSL.If the Phase 1 MOPS is extended to 291 kts for these large UAS, updated supporting information must be provided that considers higher closure rates between UAS and non-cooperative VFR traffic.The Phase 1 airborne radar, documented in RTCA DO-366 [13], serves as the sole surveillance component for detecting non-cooperative intruders.This large, high-power radar shall detect a large non-cooperative intruder such as a King Air at a distance of 6.7 nmi.Both objectives described above have the effect of changing the required surveillance volume of the Phase 1 radar.If the required alerting time can be assumed to be independent of a DWC, a reduced DWC can potentially reduce the required surveillance volume.On the other hand, increasing the maximum UAS speed would have the effect of increasing the required surveillance volume so as to ensure sufficient alerting times for encounters with higher closure rates.As a direct support of SC-228 Phase 2 MOPS work, this paper analyzes the combination of increased maximum UAS speed (291 kts TAS) and a reduced DWC size on the trade space for the alerting timeline and the radar's surveillance volume.DWC serves as an independent variable, with each of the four candidate values and the Phase 1 DWC tested individually.Results will inform the SC-228 of recommendations to surveillance requirements.This paper is organized as follows: Section II provides additional background information of DWC, alerting, and guidance.Section III describes the experiment plan.Results are given in Section IV and additional discussion given in Section V. Section VI concludes this work.
+III. Background
+A. Anatomy of a DWC DefinitionThe DWC definitions discussed in this paper are defined by a group of four parameters.These parameters are: horizontal miss distance (HMD), modified tau (τmod) and its associated distance modifier (DMOD), and current altitude separation (h).The horizontal miss distance is the horizontal distance between the UAS and another aircraft at the projected horizontal closest-point-of-approach (CPA).This projection is a state-based "dead-reckoning" trajectory that assumes both the UAS and the other aircraft will continue in their current direction of travel from their current relative position with their current relative vertical and horizontal velocities.The altitude separation is calculated at the aircrafts current time.When two aircraft are diverging, τmod is set to infinity.For two aircraft that are converging, τmod and the associated DMOD are shown in the equation below.Note that the range (r) used is the horizontal range without a vertical component, and the rate-of-change of the range () is negative when aircraft are converging.𝜏 𝑚𝑜𝑑 = { -(𝑟 2 -𝐷𝑀𝑂𝐷 2 ) 𝑟𝑟̇, 𝑟 > 𝐷𝑀𝑂𝐷 0, 𝑟 ≤ 𝐷𝑀𝑂𝐷(1)The value of DMOD for each DWC definition in this paper is set to the value of the HMD threshold to avoid undesirable oscillations of alerts arising from the concavity of the DWC volume [20].Additionally, when r is less than DMOD, the value of τmod is set to zero.This ensures that when the UAS detects another aircraft violating the HMD criteria, the τmod criteria will also be violated.
+B. Alternative Detect-and-Avoid Well Clear DefinitionThe original Phase 1 DWC was created as a single DWC definition for all encounters between a UAS and another aircraft.As discussed in the Introduction, that decision led to the size of the Phase 1 DWC as it needed to safely interact with TCAS II, which could be equipped on many cooperative aircraft.TCAS-II is a system that is designed to be a "last resort."As such, when a TCAS-II Resolution Advisory (RA) appears to a pilot, the pilot is expected to follow that advisory, no matter what other guidance the pilot might be receiving from other sources.The decision to make DAA alerts occur before potential TCAS-II RA's was to avoid potentially confusing situations in which a UAS pilot received a TCAS-II RA calling for a climb followed by a DAA alert asking for a descent.This requirement for TCAS compatibility pushed the radar range out to 6.7 nmi, as discussed in the Introduction, and made it too heavy and/or large for some of the smaller UAS.The four alternative DWC definitions analyzed in this paper were candidate DWCs for encounters between noncooperative VFR traffic and UAS.These candidates were proposed based on analysis [15] of encounters representing operations of UAS that are smaller and cannot carry a Phase 1 compliant radar.The primary metrics considered were unmitigated collision risk and maneuver initiation range.
+C. DWC Candidate DefinitionsThe DWC definitions discussed in this paper fall into two categories.The first is the DWC definition that was accepted by the FAA at the conclusion of "Phase 1" work by SC-228.This is referred to in the paper as the "Phase 1 DWC."The second category consists of four candidate DWC volumes that were being tested for implementation in "Phase 2," and are discussed in detail in Ref. 16.The threshold values of the DWC definition parameters for each of the five DWC's are given in the table below.In order for a loss of DAA well clear (LoDWC) to be declared, all of the parameter thresholds must be violated at the same time.Note that for DWC2, which has a τmod threshold of zero, LoDWC will only occur when the UAS and another aircraft are physically within the HMD and h thresholds.The candidate DWC definitions use smaller parameters than the Phase 1 DWC for HMD and τmod, but keep the same threshold value of h.This is in large part because the current airspace is structured such that 500 feet is the minimum vertical separation that aircraft are generally allowed to have below 10,000 feet MSL.So if the threshold value of h was increased to 500 feet, there would be a risk of legitimate traffic that was already adequately separated, according to convention, triggering a DAA alert and pilot response.Due to some preliminary, unpublished study results that showed limited benefit to alerting metrics, an expected adverse effect on safety metrics, and discussions with subject matter experts from SC-228, the threshold value of h value is not decreased in the candidate definitions, either.
+D. MotivationAs discussed previously, part of the motivation for this work came from discussions about whether the Phase 1 DWC definition was necessary for non-cooperative VFR encounters, as that DWC was sized primarily by cooperative VFR constraints.Additionally, using one of the candidate DWC definitions from Phase 2 would enable the use of a single DWC definition for non-cooperative VFR encounters for all UAS.It should be noted that this investigation is only for the non-cooperative DWC definition.The DWC definition for cooperative VFR encounters is already the same for both Phase 1 and Phase 2 UAS, due to the relatively low cost, size, weight, and power requirements of cooperative sensors.It should also be noted that, even if one of the candidate DWC definitions was adopted for noncooperative VFR encounters by Phase 1 UAS, those UAS would still be expected to carry a Phase 1 compliant radar.As stated in the Introduction, using one of the smaller candidate DWC definitions could potentially decrease the required surveillance volume of the Phase 1 radar, increase the maximum allowable Phase 1 UAS airspeed at altitudes below 10,000 ft MSL, or some combination of both.However, prioritizing the decrease in radar range or the increase in allowable UAS speed is beyond the scope of this paper, and would be determined by SC-228.In order to apply one of the candidate DWC definitions from Phase 2 to Phase 1 compliant UAS, it must be shown that the reduced DWC will not adversely affect the safety and alerting metrics.The effect of the reduced DWC on safety metrics is covered in another paper [17].This paper uses simulation results that do not include any pilot actions to maintain DWC, referred to as "unmitigated" simulations, to determine the effects of a smaller DWC on alerting metrics for the Phase 1 UAS operations.This paper describes part of an effort to identify the effects of modifying the DWC for Phase 1 compliant UAS in an encounter with non-cooperative VFR traffic.The specific focus of this paper is on the alerting metrics.
+IV. Experiment PlanThe experiment that underpins this paper leveraged the simulation architecture that was used in a previous study [16].The data come from a fast-time simulation that used a full day of NAS-wide real VFR traffic data and projected UAS missions and trajectories.The UAS missions and trajectories are filtered to capture only the trajectories that are between 500 feet AGL and 10,999 feet MSL.
+A. Encounter GenerationThe VFR traffic was extracted from ground-based radar tracks recorded on 21 non-contiguous days in 2012 by the 84th RADAR Evaluation Squadron.The tracks contain non-cooperative tracks and cooperative tracks from vehicles "squaking" a 1200 transponder code (standard transponder setting for cooperative VFR aircraft).These were processed to remove measurement noise and produce continuous trajectories, which were fed into the simulation.It is estimated that roughly 15% of VFR traffic is non-cooperative [12], but due to the low number of non-cooperative VFR tracks that could be extracted, some of the lower speed cooperative VFR tracks were used as surrogates for noncooperative traffic in this study.This substitution was made in the previous study [16], and is considered reasonable due to the similarity in the airspeed, turn rate, and accelerations of both cooperative and non-cooperative VFR aircraft when true airspeeds are below 170 knots [18].The speed distributions of the UAS and VFR traffic are shown below in Fig. 1a and Fig .1b, respectively, while the altitude distributions of the UAS and VFR traffic are shown in Fig. 1c and Fig. 1d.The UAS traffic is from a NAS-wide set of trajectories created from demand estimations and potential mission profiles built from opinions of subject matter experts [19].All UAS speeds were considered, but the UAS altitude was restricted to values between 500 ft AGL and 10,999 ft MSL.10,000 ft MSL is the upper bound at which a UAS could encounter non-cooperative VFR traffic but is raised to 10,999 ft MSL to include a few UAS missions flying slightly above 10,000 ft MSL.These two sets of trajectories were run through a NAS-wide, fast-time simulation called the Java Architecture for DAA Extensibility and Modeling (JADEM) [9] to extract encounters that feature one UAS and one non-cooperative VFR flight that are close enough to potentially trigger the DAA alerting logic.Each encounter has only one pair of aircraft, and thus are referred to as "pairwise encounters" for the rest of this paper.In total, there were 94,093 of these pairwise encounters extracted for this study.Of those encounters, 505 led to a near mid-air collision, which is defined by two aircraft passing within 500 ft horizontally and 100 ft vertically of each other.Those 94,093 pairwise encounters were then run, unmitigated, through each point of the simulation test matrix to determine the effects of different DWC definitions, air-to-air radar ranges, and radar horizontal and vertical fields of regard on a group of alerting metrics.The alerts themselves were generated by the open-source Detect-and-AvoID Alerting Logic for Unmanned Systems (DAIDALUS) [19].DAIDALUS can produce alerts at three levels.In order of increasing severity, they are Preventive, Corrective, and Warning alerts.For Preventive alerts, no action is required by the UAS pilot.For a Corrective alert, the pilot is expected to maneuver, but needs to coordinate with an air-traffic controller before maneuvering to avoid the other aircraft.For a Warning alert, the pilot is expected to maneuver to avoid the other aircraft as soon as possible, without a coordination requirement.DIADALUS can also provide guidance for the pilot during these alerts, but these studies were unmitigated and so no maneuver was executed upon guidance.The conflict zone the alerting and guidance protects is based on each DWC with its HMD threshold buffered by a factor of 1.519.The buffer gives the system a few seconds to alert against aircraft suddenly maneuvering towards the UAS.Corrective and warning alerts are issued if an intruder is predicted, with a constant velocity assumption, to enter the conflict zone within 60 and 30 seconds, respectively.
+B. Variables and MetricsThe independent variables in this study are DWC type, radar range, radar bearing limits, and radar azimuth limits.The results are further broken down by UAS speed range, to highlight specific metrics that are strongly affected by UAS speed.As a final filter, only the encounters that led to an unmitigated loss of DWC are included in the metrics.This is to ensure that each alert that is processed is one that would have required a pilot's input to avoid.Note that the Phase 1 MOPS requires a radar with 15 degrees elevation (vertical field of regard) and 110 degrees bearing (horizontal field of regard).The test matrix is shown below in Table 2.In all, there were 60 test cases, with each test using a single test value from each of the four test parameters.The dependent variables that were examined included the average alert time for Corrective and Warning alerts (time between the first alert and a LoDWC), the range from the UAS at which an alert first occurred, and the relative heading and elevation angles of the two aircraft at the time of first alert.Only the Corrective alerts are included in this paper, as they occur farther away from the UAS and drive the air-to-air radar requirements.The metrics for Warning alerts showed similar trends to the metrics for Corrective alerts.Preventive Alerts, which are included in the MOPS for Phase 1 UAS, are not included in this study, as they do not require pilot action.Sensor uncertainty, DAA guidance, and pilot action times are not considered in this paper, but will be examined in future studies.
+V. Data and ResultsAs stated in the previous section, the results presented in this study focus primarily on the Corrective alerts and the effect of the independent variables on the Corrective alert timeline.This is because Corrective alerts, which still require pilot action, have significantly longer average alerting time requirements than Warning alerts, and are impacted by restricted radar range more heavily than warning alerts.The field-of-regard limitations had similar effects on both the Corrective and Warning alerts, so the Corrective alert data are shown for continuity.The only exception is Table 3, below, which includes average Warning alert time.Table 3 shows the average alert times for both corrective and warning alerts for the four candidate definitions as well as the Phase 1 DWC.The standard error of the averages is also included.In this table, the air-to-air radar had full horizontal field of regard (+/-180 degrees) and full vertical field of regard (+/-90 degrees).The UAS true airspeed values were between 40 and 200 knots TAS.Examination of the corrective alerts show that limiting the radar range to 4 nmi shortens the average corrective alert time for all DWCs, and that DWC2 had the longest average corrective time of all DWCs at all radar ranges.Overall, these results show that removing the τmod component completely from a DWC definition (DWC2) seems feasible.These results also show that only DWC2 and the Phase 1 DWC met the MOPS requirements for average corrective and warning alert times (55 seconds for corrective, 25 seconds for warning).Figure 2a and 2b show the cumulative distribution of the range at the point of first corrective alert for each encounter.The vertical axis is the cumulative probability that a given encounter will have its maximum potential corrective alert time at a certain range.This does not mean that the encounter will have the desired 55 seconds of corrective alert time, as a sudden maneuver could limit the "maximum potential value" of a corrective alert to just a few seconds, depending on the encounter geometry and relative velocities of the UAS and VFR aircraft.These curves are generated from data that has a maximum radar range of 8nmi, and has full horizontal and full vertical fields of regard.This allows the comparison of DWC performance based exclusively on what the range value was when the first corrective alert was recorded.The difference in the smoothness of the curves in Fig. 2a versus Fig. 2b is due to a difference in the number of pairwise encounters in each data set, as each DWC's curve only includes encounters that lead to a LoDWC.For Fig. 2a, the number of pairwise encounters varies from 7,402 (DWC3) to 23,735 (Phase 1 DWC), while for Fig. 2b, the number of encounters is between 233 (DWC3) to 1074 (Phase 1 DWC).As an example of how to interpret this data, look at the Phase 1 DWC curve in Fig. 2a.The curve crosses a probability of 0.6 at 4 nmi, which means that 60% of the encounters that lead to a LoDWC will have their maximum potential corrective alert duration (actual value varies by encounter) if the radar range is 4 nmi, while the other 40% of the encounters will have some truncation of their alerting timeline.As these data are from an encounter set that includes maneuver VFR traffic and maneuver UAS, it does not show which encounters will have at least 55 seconds of corrective alert time.Instead, these figures, as well as Fig. 3 and Fig. 4 below, show what percentage of corrective alerts will have their timelines truncated, regardless of what the maximum value might be.Figure 2a shows data for the UAS that are flying between 40 and 200 knots TAS.These results show that all of the candidate DWC definitions have a lower radar range requirement than the Phase 1 DWC, with DWC2 requiring the least range.This is an expected result considering the reduced HMD and τmod requirements of the candidate DWC's.Examining the Phase 1 DWC curve in combination with the MOPS requirement of 6.7 nmi for radar range shows that close to 97% of corrective alerts will alert at the maximum possible distance if the minimum radar range is used with the Phase 1 DWC.If DWC2 was implemented for these encounters, the range at which 97% of the corrective alerts would be expected to have their maximum potential timeline is 5 nmi.This implies the radar range requirement could be relaxed from 6.7 nmi for Phase 1 UAS if DWC2 was adopted for non-cooperative VFR traffic without increasing the percentage of encounters that have less than the maximum possible corrective alert duration.Figure 2b shows the data for UAS that are flying between 200 and 291 knots TAS.These higher-speed UAS would be required to reduce their speed to 200 knots TAS under the current MOPS.Examining the Phase 1 DWC curve shows this is a logical restriction, as less than 80% of encounters have their maximum potential corrective alert duration with this DWC definition and the current radar minimum range of 6.7 nmi.The relative sparsity of the data compared to the curve in Fig. 2a is due to the very limited number of UAS that are flying above 200 kts TAS in this data set (see Fig. 1a).It is not possible to determine what the required radar range would need to be increased to so that 97% of corrective alerts have their maximum potential duration, as the tested radar range for this study only went as high as 8 nmi.However, by using one of the smaller candidate DWC definitions, it should be possible to maintain the original 97% maximum potential duration corrective alert rate without significantly increasing the radar range requirement.In fact, the current requirement of 6.7 nmi combined with DWC2 would allow UAS flying between 200 and 291 knots TAS to maintain the same percentage of corrective alerts that are at maximum duration as UAS that are using the current Phase 1 DWC with true airspeeds restricted to below 200 knots.Overall, these two figures imply that changing the DWC from the Phase 1 definition to one of the candidate DWCs, and their smaller alerting volume, could allow for reduced radar range requirement or increased allowable UAS speed or some combination of both without sacrificing alerting time.Figure 3 shows that both the bearing and elevation range curves are relatively independent of the DWC definition.These curves show the percentage of LoDWC encounters that achieve their maximum potential corrective alert timeline if the radar is limited to the specified bearing or elevation range.The number of pairwise encounters in these data varies between 7,402 and 23,735, depending on DWC type.As discussed in Table 2, the angular ranges specified on the x-axis of Fig. 3a and Fig. 3b are the symmetric ranges either to the left and right, or above and below the centerline of the UAS.As an example, a bearing range of 90 on Fig. 3a means the air-to-air radar can track targets up to +/-90 degrees horizontally from the direction of travel.The requirements in the current MOPS call for +/-110 degrees of horizontal and +/-15 degrees of vertical field of regard.These results show that the percentage of corrective alerts that had their timeline truncated due to horizontal and vertical field-of-regard constraints was not significantly affected when one of the new candidate DWCs was used instead of the Phase 1 DWC.Note that Fig. 3a shows that +/-110 degrees of horizontal field of regard allowed maximum possible corrective alert duration in 93% encounters.3a. Figure 4 shows the strong effect that UAS speed had on the realtive bearing at which the first alert occured.Fig. 4a shows the relative bearing at first corrective alert when the UAS had a true airspeed between 40 and 100 knots, while Fig. 4b shows the same plot but with the true airspeed between 100 and 150 knots.As in previous figures, the difference in the smoothness of the curves is due to the difference in the number of pairwise encounters in each speed bin.The number of pairwise encounters for Fig. 4a is between 6,292 and 19,549, while for Fig. 4b it is between 532 and 2,175, depending on the DWC being tested.The major difference in the slopes of the two figures is because in Fig. 4a, the UAS was generally slower than the VFR traffic it encounted, while in Fig. 4b the UAS was generally faster.This is because the non-cooeprative VFR traffic airspeed peaked around 100 knots, as was shown in Fig. 1b.When a UAS is flying slower than the non-cooperative VFR traffic, it is much more likely that the VFR aircraft will aproach the UAS from the side or slightly behind.This difference in encounter gemoetry is why the field-of-regard of the radar, rather than its range, seems to have the largest impact on the alerting timeline for UAS flying below 100 knots TAS.This same effect was noticed when the low C-SWaP UAS were studied, as they were a subset of the UAS in Fig. 4a.The data for UAS true airspeeds above 150 knots are very similar to Fig. 4b.Overall, Fig. 4 shows that, in order to allow maximum potential corrective alert duration in 93% of encounters, a horizontal field of regard of +/-125 degrees is required for UAS below 100 kts TAS, and around +/-50 degrees is required for UAS at and above 100 kts TAS.
+VI. Discussion and Future WorkPrevious work has led to a recommendation that the Low C-SWAP radar have a horizontal field of regard of +/-140 degrees from centerline [16], instead of the Phase 1 value of +/-110, with the goal of allowing 95% of encounters to have their maximum possible alert duration.While the aggregate data presented in Fig. 2a suggest that +/-110 degrees is sufficient to allow 93% of encounters to have their maximum possible corrective alert duration, the data in Fig. 3a implies that the low-speed Phase 1 UAS, which make up a majority of the UAS flying below 10,000 feet, would require closer to +/-125 degrees to allow 93% and +/-135 degrees to allow 95% of encounters to have their maximum possible corrective alert duration.These Phase 1 UAS flying below 100 kts TAS are the primary drivers of the horizontal field-of-regard requirement for the agreggated data set of Phase 1 UAS flying between 40 and 200 kts TAS.For these low-speed UAS, the horizontal field of regard, rather than the range of the radar, appears to have the largest impact on the alerting timeline.To that end, should SC-228 decide to pursue allowing shorter radar ranges for Phase 1 UAS, it will likely be beneficial to also investigate increasing the radar horizontal field of regard requirement, as it currently only allows 91% of the corrective alerts for Phase 1 UAS flying below 100 kts TAS to have their maximum possible duration.The work presented in this paper is aimed at understanding the effects of changing the DWC definition for noncooperative aircraft encountered by Phase 1 UAS on alerting metrics.To fully understand the overall effects of changing the DWC definition, a few more pieces are needed, including the effect on safety metrics, the impact of sensor uncertainty, and how the new DWC would affect pilots and air traffic controllers.A study examining the impact of sensor noise and error on the alerting and guidance when using a smaller DWC is currently in software development stages.There are also plans to explore pilot-related questions such as pilot response times and pilot acceptability in future human-in-the-loop studies.
+VII. ConclusionThis paper examined four candidate DWC definitions as potential replacements for the DWC definition currently in MOPS for use with non-cooperative VFR aircraft.These candidate definitions were taken from "Phase 2" studies, which were focused on small UAS that could not carry the radar required in the Phase 1 MOPS.The alerting metrics studied in this paper suggest that using the new, candidate DWC definitions from Phase 2 would not adversely affect the alerting metrics of a Phase 1 UAS when encountering non-cooperative VFR traffic.Further, by using DWC2 (HMD of 2200 feet, h of 450 feet, and τmod of 0), the radar requirement from Phase 1 could potentially be reduced to 5 nmi while keeping the same percentage of maximum duration corrective alerts as the current, Phase 1 DWC.Alternatively, if the radar requirement is kept at 6.7 nmi, it may be possible to increase the allowable UAS speed range from 200 knots TAS to 291 knots TAS without adversely affecting the alerting timeline.This would more closely align with the FAA's limit of 250 knots indicated airspeed at 10,000 feet MSL.Whether allowing increased UAS speeds below 10,000 feet or reducing radar range requirements has higher priority is a decision for SC-228.DWC2 either matched or exceeded the alerting metric requirements from Phase 1, and had the lowest radar range requirement of all candidate DWC definitions.That definition is recommended by the authors for any modification of non-cooperative DWC in the Phase 1 MOPS.Additionally, DWC2 was selected by RTCA SC-228 in March 2019 as the DWC for encounters between Low C-SWaP UAS and non-cooperative VFR aircraft.Therefore, selecting DWC2 for Phase 1 UAS would also create a single DWC definition for UAS, regardless of size, in an encounter with a noncooperative VFR aircraft between 500 ft AGL and 10,000 ft MSL.rules h = altitude separation between two aircraft r = current horizontal range between two aircraft ̇ = rate of change of horizontal range between two aircraft τmod = modified tau
+Fig 1 .1Fig 1. Histograms with probability of true airspeeds for a) UAS and b) non-cooperative VFR flights, and probability of altitude for c) UAS and d) non-cooperative VFR flights in the simulation.
+Fig 2 .2Fig 2. Cumulative distribution of range at the point of first corrective alert for UAS flying with a true airspeed a) between 40 and 200 knots, and b) between 200 and 291 knots.
+Fig 3 .3Fig 3. Cumulative distribution of a) relative bearing and b) relative elevation at the point of first corrective alert for a UAS with true airspeed between 40 and 200 knots.
+Fig. 4 Figure 444Fig. 4 Relative bearing at the position of first corrective alert for UAS with a radar range of 8 nmi and a true airspeed a) between 40 and 100 knots, and b) between 100 and 150 knots Figure 4 further parses the data presented in Fig.3a.Figure4shows the strong effect that UAS speed had on the realtive bearing at which the first alert occured.Fig.4ashows the relative bearing at first corrective alert when the UAS had a true airspeed between 40 and 100 knots, while Fig.4bshows the same plot but with the true airspeed between 100 and 150 knots.As in previous figures, the difference in the smoothness of the curves is due to the difference in the number of pairwise encounters in each speed bin.The number of pairwise encounters for Fig.4ais between 6,292 and 19,549, while for Fig.4bit is between 532 and 2,175, depending on the DWC being tested.The major difference in the slopes of the two figures is because in Fig.4a, the UAS was generally slower than the VFR
+Table 1 . DAA well-clear definitions and threshold parameter values DAA Well Clear Label HMD and DMOD threshold (ft) τmod threshold (s) h threshold (ft)1Phase 1 DWC400035450DWC 1200015450DWC 222000450DWC 3150015450DWC 4250025450
+Table 2 . Test matrix2Test ParameterUnitsTested ValuesDWC Typen/aPhase 1 DWC, DWC1,DWC2, DWC3, DWC4Radar rangenautical miles4, 6, 8Horizontal field of regarddegrees from centerline +/-110, +/-180Vertical field of regarddegrees from centerline +/-15, +/-90
+Table 3 . Average alert times for corrective and warning alerts for UAS flying between 40 and 200 kts TAS and radar with full horizontal field of regard and full vertical field of regard Range (nmi) DWC Label Corrective Alert Time (s) Standard Error of the Mean (Corrective) Warning Alert Time (s) Standard Error of the Mean (Warning)34Phase1DWC48.040.1834.010.1304DWC151.890.2432.650.1564DWC257.540.2135.270.1324DWC349.300.2830.810.184DWC449.360.2232.390.156Phase1DWC56.000.1735.280.136DWC154.050.2432.650.166DWC258.410.2135.270.136DWC351.150.2830.800.186DWC453.530.2232.690.158Phase1DWC57.000.1735.280.138DWC154.150.2432.650.168DWC258.410.2135.270.138DWC351.220.2830.800.188DWC453.890.2232.690.15
+			Research Engineer, Aviation Systems, AIAA Member
+			Research Engineer, Aviation Systems, AIAA Member
+			Senior Research Scientist, Senior AIAA Member
+		
+		
+			
+
+				
+
+
+	
+		Defining Well Clear for Unmanned Aircraft Systems
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+			StephenPCook
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+		AIAA Infotech @ Aerospace
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+		FAA Position on Building Consensus Around the SARP Well-Clear Definition
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+	Murphy, J. R., Hayes, P. S., Kim, S. K., Bridges, W., and Marston, M., "Flight Test Overview for UAS Integration in the NAS Project," AIAA Atmospheric Flight Mechanics Conference, AIAA SciTech, 2016.
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+		Evaluating Alerting and Guidance Performance of a UAS Detect-And-Avoid System
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+		Analysis of alerting performance for detect and avoid of unmanned aircraft systems
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+	Smearcheck, S., Calhoun, S., Adams, W., Kresge, J., and Kunzi, F., "Analysis of Alerting Performance for Detect and Avoid of Unmanned Aircraft Systems," IEEE/ION Position, Location and Navigation Symposium (PLANS), 2016, pp. 710-730.
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+		The Impact of Suggestive Maneuver Guidance on UAS Pilot Performing the Detect and Avoid Function
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+	Rorie, R. C., Fern, L., and Shively, J., "The Impact of Suggestive Maneuver Guidance on UAS Pilot Performing the Detect and Avoid Function," AIAA InfoTech @ Aerospace Conference, AIAA SciTech, 2016.
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+	Fern, L., Rorie, R. C., Pack, J. S., Shively, R. J., and Draper, M. H., "An Evaluation of Detect and Avoid (DAA) Displays for Unmanned Aircraft Systems: The Effect of Information Level and Display Location on Pilot Performance," Proceedings of 15th AIAA Aviation Technology, Integration, and Operations Conference, 2015.
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+		The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Detect-And-Avoid (DAA) Systems
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+		17th AIAA Aviation Technology, Integration, and Operations Conference
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+	Abramson, M., Refai, M., and Santiago, C., "A Generic Resolution Advisor and Conflict Evaluator (GRACE) in Applications to Detect-And-Avoid (DAA) Systems of Unmanned Aircraft," Proceedings of the 17th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 2017.
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+		DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems
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+	Muñoz, C., Narkawicz, A., Hagen, G., Upchurch, J., Dutle, A., Consiglio, M., and Chamberlain, J., "DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems," 34th Digital Avionics Systems Conference (DASC), IEEE/AIAA, 2015, pp. 5A1-1.
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+		Development, Integration and Testing of a Stand-alone CDTI with Conflict Probing Support
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+	Suarez, B., Kirk, K., and Theunissen, E., "Development, Integration and Testing of a Stand-Alone CDTI with Conflict Probing Support," Infotech@ Aerospace 2012, 2012, p. 2487. doi:10.2514/6.2012-2487, URL https://arc.aiaa.org/doi/abs/10.2514/6.2012-2487.
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+		Minimum Operational Performance Standard (MOPS) for Helicopter Hoist Systems
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+	Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) Systems, DO-365, RTCA. Inc., 2017.
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+		AeroMACS minimum operational performance standards (MOPS) compliance field trials for Hitachi prototype
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+		2015 Integrated Communication, Navigation and Surveillance Conference (ICNS)
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+			IEEE
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+	Minimum Operational Performance Standards (MOPS) for Air-to-Air Radar for Traffic Surveillance, DO-366, RTCA. Inc., 2017.
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+		Fixed-wing group petitions FAA for advisory circular
+		10.1016/s0740-8315(88)80079-2
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+		Hospital Aviation
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+			Elsevier BV
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+	"Introduction to TCAS II Version 7.1," Federal Aviation Administration (FAA), 2011. URL http://www.faa.gov/documentLibrary/media/Advisory_Circular/TCAS%20II%20V7.1%20Intro%20booklet.pdf.
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+		Well Clear Trade Study for Unmanned Aircraft System Detect And Avoid with Non-Cooperative Aircraft
+		
+			MinghongGWu
+		
+		
+			AndrewCCone
+		
+		
+			SeungmanLee
+		
+		
+			ChristineChen
+		
+		
+			MatthewWEdwards
+		
+		
+			DevinPJack
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+		2018 Aviation Technology, Integration, and Operations Conference
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+			American Institute of Aeronautics and Astronautics
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+	Wu, M. G., Cone, A. C., Lee, S., Chen, C., Edwards, E. W. M., and Jack, D. P., "Well Clear Trade Study for Unmanned Aircraft System Detect and Avoid with Non-Cooperative Aircraft," 18th AIAA Aviation Technology, Integration, and Operations Conference, 2018.
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+		Detect and Avoid Alerting Performance with Limited Surveillance Volume for Non-Cooperative Aircraft
+		
+			MinghongGWu
+		
+		
+			SeungmanLee
+		
+		
+			AndrewCCone
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+		10.2514/6.2019-2073
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+		AIAA Scitech 2019 Forum
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+			American Institute of Aeronautics and Astronautics
+			2019
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+	Wu, M. G., Cone, A. C., Lee, S., "Detect and Avoid Alerting Performance with Limited Surveillance Volume for Non- Cooperative Aircraft," AIAA SciTech Forum, 2019.
+
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+	
+		Defining Well Clear Separation for Unmanned Aircraft Systems Operating with Noncooperative Aircraft
+		
+			ChristineChen
+		
+		
+			MatthewWEdwards
+		
+		
+			BilalGill
+		
+		
+			SamanthaSmearcheck
+		
+		
+			TonyAdami
+		
+		
+			SeanCalhoun
+		
+		
+			MinghongGWu
+		
+		
+			AndrewCone
+		
+		
+			SeungManLee
+		
+		10.2514/6.2019-3512
+	
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+		AIAA Aviation 2019 Forum
+		
+			American Institute of Aeronautics and Astronautics
+			2019
+		
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+	Chen, C. C., Gill, B., Edwards, M. W. M., Smearcheck, S., Adami, T., Calhoun, S., Wu, M. G., Cone, A. C., Lee, S., "Defining Well Clear Separation for Unmanned Aircraft Systems Operating with Noncooperative Aircraft," 19 th AIAA Aviation, Technology, Integration, and Operations Conference, 2019.
+
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+	
+		Extended Airspace Encounter Models for Unmanned Aircraft Sense and Avoid Safety Evaluation
+		
+			AndrewJWeinert
+		
+		
+			EricHarkleroad
+		
+		
+			JohnGriffith
+		
+		
+			MatthewWEdwards
+		
+		10.2514/6.2013-5049
+		
+	
+	
+		AIAA Infotech@Aerospace (I@A) Conference
+		Lexington, Massachusetts
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 2013
+		
+		
+			MIT Lincoln Laboratory
+		
+	
+	Tech. rep.
+	Weinert, A. J., Harkleroad, E. P., Griffith, J., Edwards, M. W., and Kochenderfer, M. J., "Uncorrelated Encounter Model of the National Airspace System, Version 2.0," Tech. rep., MIT Lincoln Laboratory, Lexington, Massachusetts, Aug. 2013. URL www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.pdf&AD=ADA589697.
+
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+	
+		UAS Demand Generation Using Subject Matter Expert Interviews and Socio-economic Analysis
+		
+			SricharanKAyyalasomayajula
+		
+		
+			RohitSharma
+		
+		
+			FrederickWieland
+		
+		
+			AntonioTrani
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+			NicolasHinze
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+			ThomasSpencer
+		
+		10.2514/6.2015-3405
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+		15th AIAA Aviation Technology, Integration, and Operations Conference
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+			American Institute of Aeronautics and Astronautics
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+	Ayyalasomayajula, S., Sharma, R., Wieland, F., Trani, A., Hinze, N., and Spencer, S., "UAS Demand Generation Using Subject Matter Expert Interviews and Socio-Economic Analysis," Proceedings of the AIAA Aviation Conference, 2015.
+
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+	
+		Formal Analysis of Extended Well-Clear Boundaries for Unmanned Aircraft
+		
+			CésarMuñoz
+		
+		
+			AnthonyNarkawicz
+		
+		10.1007/978-3-319-40648-0_17
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+		Lecture Notes in Computer Science
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+			Springer International Publishing
+			2016
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+		
+I. IntroductionS the popularity of unmanned aerial vehicles continue to grow over the coming years, so will the potential number of aircraft lacking transponders in the national airspace.Therefore, alternative solutions for automated tracking of multiple unknown targets needs to be explored.To ensure safe separation between aircraft, it is critical for decision makers in the air traffic management system, such as air traffic controllers, to have accurate information about the vehicles they are responsible for, including: location, speed, and heading.While this information can easily be attained for conformant aircraft equipped with transponders, this feat poses a challenge for non-conformant aircraft and those lacking a transponder.As small vehicles, such as unmanned aerial vehicles, become more prevalent, many may not have the ability to report state information to relevant decision makers.Similarly, this may also be the case for vehicles that are non-conformant or have malicious intent.For vehicles lacking the ability, or intentionally refusing to report vehicle information, it can be difficult to reliably and consistently estimate the state of the aircraft using ground-based sensors.For these unknown aircraft, often it is beneficial to resort to tracking methods that use imperfect location data provided by radars to infer information about the moving aircraft and track them over time.This process, called tracking, is an elementary problem in air traffic control -but a problem rife with opportunity for exploitation by intelligent systems.Typically, tracking is broken down into several areas of research, including, data association, sensor fusion, state estimation, track identification, and track deletion.While many approaches and techniques 1,2,3 have been used to track aircraft using only radar data, many require some amount of a priori knowledge, such as, the number of vehicles in the airspace, vehicle model information, or the initial vehicle locations.Furthermore, many are used for post-processing or have track identification information available to aid with data association.In an effort to overcome these limitations, a real-time tracking solution that does not require any a priori information is presented.In a study conducted by Reid 1 , a Monte Carlo method was used to simulate the tracking of vehicles over a large number of test cases.Although this study showed promising results, some assumptions limit the application of this work, including, the use of only one radar source, a priori knowledge of some of the vehicle states, and not accounting for maneuvering targets.A similar strategy for clustering data to simplify the solution space and for track identification was used in a study conducted by Park, et al. 2 Here, a minimum spanning tree method was proposed to cluster data without having prior knowledge of how many vehicles existed in the airspace.This solution proved effective despite lacking this information.If the number of vehicles were known a priori, the problem of clustering points would be drastically simplified.Once data were clustered and associated to a track, a Kalman filter was used to estimate the state of each aircraft.While the methods used in this study are useful for tracking in simulation tools, the approach could not be applied to real-time tracking systems.In another study conducted by Chan, et al. 3 , a hybrid fuzzy logic and α-β gain filter was used for state estimation.The primary focus of their study was to track vehicles that are highly dynamic using an approach that would increase state estimation accuracy while minimizing the number of track losses when compared to a traditional two-stage Kalman filter.One drawback to using this two-stage Kalman filter approach is that the vehicle mode must be identified using a maneuver detector or an acceleration detector.Thus, many times the Kalman filter would have delayed transitions between each mode once a new flight mode was detected.Using the fuzzy-gain filter, the system does not make assumptions about process, system, or measurement noise; whereas, a Kalman filter relies on this information, coupled with a dynamic model to estimate the current state of each target.While the fuzzy-gain filter produced favorable results, the system requires identity information, or flight IDs, for each target to distinguish between different vehicles.Since the origin of all data points are known, clustering of data and data association was unnecessary, drastically simplifying the problem of tracking.In this paper, the authors aim to develop an automated tracking system that addresses the following topics: track creation/clustering, track identification/establishment, sensor fusion, state estimation, data association, and track deletion.In particular, a nearest neighbor spanning tree is used for clustering, a maximum a posteriori probability (MAP) estimation is used for sensor fusion, a Kalman filter is used for state estimation, and a multi-Gaussian membership function is used for data association.As opposed to the many approaches discussed above, and in most other literature, the focus of this paper is on developing a real-time tracking solution for an airspace that contains: vehicles that are highly evasive, aircraft with unknown vehicle models, an unknown number of vehicles, multiple data sources, and no a priori information.Therefore, the proposed approach is a viable option for tracking non-conformant aircraft in a highly congested airspace while solely utilizing radar data.Results show a comparison of track estimation accuracy between using the sensor fusion technique alone and the application of a Kalman filter to see if the additional step provides improved estimates.
+II. Simulation Environment
+A. Data Acquisition and Simulation AssumptionsSince the main objective of this research was to allow the radar-based tracking algorithm to be performed in real time, true track data were obtained to help increase the tracking simulation speed.Once these data were gathered, it was imported into the radar tracking simulation environment where the algorithm could be developed and tested.To obtain the aircraft data, a simulation was developed to allow several aircraft to travel throughout a designated airspace while traversing a series of waypoints.The airspace was selected to be 10 km by 10 km in size, or 100 km 2 .At the start of each simulation, the user could indicate how many vehicles would be active in the airspace, how many waypoints each vehicle should capture before leaving the simulation area, and the total amount of time the simulation will run.Once a vehicle captures all its assigned waypoints, it departs from the designated airspace and a new vehicle enters with random initial conditions.Therefore, throughout the entirety of the simulation, the number of aircraft within the designated airspace remains constant; however, the tracking algorithm must be able to detect when an aircraft leaves or enters the simulation area.For each time step in the simulation, that is every 0.1 seconds, the position data for each aircraft is recorded.Each aircraft model was developed to be a vehicle traveling at constant speed in level two-dimensional flight.Therefore, a kinematic model was used to model the dynamics of each aircraft.Each vehicle was constrained to a maximum speed of 60 m/s and can turn with a maximum load factor of 3.5.Therefore, the maximum turn rate for each vehicle can be found by using Eq.(1), where  ̇ is the maximum turn rate,  is the gravity constant,  is the aircraft speed, and  is the load factor.𝜓 ̇𝑚𝑎𝑥 = 𝑔 𝑉 √𝑛 2 -1(1)For more information about the various aspects of this simulation environment refer to Ref. 4. Once the data acquisition is complete, the aircraft position data will be stored in an external file so that it can be used in the radar tracking simulation environment.In this study, five different simulation scenarios were tested.In each scenario, the number of aircraft and the total amount of simulation time was varied.For each successive scenario, the simulation time or the total number of active vehicles were increased to make tracking vehicles more difficult.Table 1 lists the parameters for all five simulation cases.Here, the "Total Vehicles" refers to the total number of aircraft that appear in the airspace over the entirety of the simulation.
+B. Radar SensorsIn this study, two radars were used to track all vehicles in the radar tracking simulation environment.To model the radar sensors, functions were created to return noisy radar position data given the true aircraft position.This was done by first finding sources for actual radar sensor standard deviations 5 .The values used for the radar sensor standard deviations are shown in Table 2.  2) and (3).𝑅 𝑛 = 𝑅 + 𝜎 𝑅 𝑟𝑎𝑛𝑑𝑛 (2)Where  is the actual range of the vehicle from the radar source,   is the standard deviation in range,  is the random number generation function, and   is the noisy returned range value.𝜃 𝑛 = 𝜃 + 𝜎 𝜃 𝑟𝑎𝑛𝑑𝑛(3)Similarly,  is the angle used to describe the actual vehicle location with respect to the radar source,   is the standard deviation in angle, and   is the noisy returned angle value.Using these equations, the generated values in the data acquisition simulation can be converted into noisy radar data for use by the tracking algorithm.The noisy data are then converted from the respective radar reference frame to the global frame with the following equations:𝑋 1 = 𝑅 𝑅 1 cos(𝜃 𝑅 1 ) + 𝑋 𝑅 1(4) 1 =   1 sin(  1 ) +   1 (5)   where   1 is the range measurement given by radar 1,   1 is the angle measurement given by radar 1,   1 and   1 are the known x and y positions of radar 1 in the global frame, and  1 and  1 are the Cartesian position measurements in the global frame.These equations are applied to every radar to find global Cartesian coordinates for all range and angle measurements (i.e.  and   for radar ).Note that for all results discussed, both radars swept every three seconds.It was also assumed that the radars acted more like directed ranging in that they have instantaneous measurements for all objects in the airspace at the end of the three second sweep.Increasing the fidelity of the radar models by including the differences in measurement timestamps due to sensor sweep rates would increase the realism of the measurements.However, it would not change the calculation frequency, as the measurements would be stored until the calculation was performed.
+III. Tracking AlgorithmTo create the tracking algorithm that must solve many different types of problems, such as track management, data association, and state estimation, the problem was broken down into several subsystems.By doing so, the design approach can be simplified and modifications to each subsystem can be independent.The various subsystems for the tracking algorithm, including clustering techniques, data association, track establishment (i.e.track validity), sensor fusion, current state estimation, and track deletion, have been shown in Figure 1.In this figure, the overall flow diagram of the proposed solution can be found.
+Figure 1. Tracking Algorithm Flow DiagramIn Figure 1, each block represents a different subsystem of the tracking algorithm.The arrows connecting the blocks represent the sequence of steps from when an input is received, and how the new data is processed.Here, an input is considered to be a set of position data points that were obtained from the radar returns in a given time step.When the tracking system is initialized, no computation will occur until one or more radars have supplied an input.However, once the first input is received, the algorithm will commence and continue to run with each new set of radar returns.On the first iteration of this algorithm, it is obvious that no previous tracks have been identified or created, thus the algorithm will "Create New" clusters, denoted by the "N" arrow connecting the "Create New OR Add To" block to the "Initial Clustering" block.Therefore, the initial clustering algorithm is used to determine which data points most likely belong to the same vehicle.Once the data points have been clustered, a temporary track ID will be assigned to each potential vehicle.Next, the algorithm will use designated criteria to determine whether each cluster is a "valid" track.A valid track thus represents a true vehicle return from the radar.Once completed, each cluster of data points will run through a sensor fusion algorithm to obtain a single fused state estimation for that time step.Given this fused value, if the algorithm is on the third iteration or higher for a particular track ID, a Kalman filter is used to estimate the current state of each vehicle.Whereas, if the algorithm is on the first or second iteration, the fused value is used as the best estimate of the current state.Finally, the algorithm will decide whether any temporary or valid tracks have gone stale.In this study a stale track means that no new data points have been associated to that particular track for a set period of time.If this is the case, the aircraft will be removed from the list of vehicles that are being tracked.This process is then repeated for every set of new radar data returns.If one or more track ID(s) have already been assigned, it must check to see if the new data belongs to an already existing track, or if it needs to be assigned to a new track all together.To determine this, a data association platform is used.For points that are assumed to be associated to a given track, the "Initial Clustering" block will be bypassed and the algorithm will follow the "A" arrow from the "Create New OR Add To" block to the "Establish Track?" block.
+A. Initial ClusteringWhen new data points are not assigned to a previously existing track, or no tracks have been created yet, a clustering algorithm is employed to determine which data points likely belong to the same vehicle.To accomplish this, a Nearest Neighbor (NN) spanning tree was used.An example of how this NN spanning tree method distinguishes which data points belong to which aircraft, and how many aircraft are present, can be seen in Figure 2.
+a) NN Spanning Tree b) Clustered Tracks Figure 2. Initial Clustering Algorithm ExampleOnce a set of data points are imported into this subsystem, it first calculates the distance between all pairs of points.With this information, it finds which two data points are located farthest apart, shown by the circled points in Figure 2 a).Next, one of these two data points are randomly selected and the selected point will serve as the NN spanning tree starting location, depicted as the green circle in the figure.From this selected point, the algorithm will find the closest point to this starting location, and connect the two points with a line.The system will then repeat this process of connecting the current point to the closest point that has not yet been visited until all points are included in the tree.An example of a completed NN spanning tree can be seen in Figure 2 a).Computationally, a NN spanning tree can be created in quadratic time.With this NN spanning tree, the algorithm will now determine which connected points belong to the same cluster.To accomplish this, the initial clustering algorithm looks to see if two connected points are too far apart from one another, or if the change in angle between edges connecting three consecutive points is too large.These criteria are defined by the user, and can be modified depending on the accuracy of the radar sources and the types of aircraft expected to be in the airspace of interest.Once the criteria are checked for each edge on the NN spanning tree, the algorithm will delete the edges between the points that violate these thresholds.A final product of the initial clustering process can be seen in Figure 2 b).These clusters represent individual vehicle tracks.While this technique was utilized for a two-dimensional case in this study, the three-dimensional case would utilize the same approach.
+B. Data AssociationIf initial clusters have already been assigned, the tracking system must check to see if any new data points should be associated to an already existing vehicle track or if they should be assigned to a new vehicle track.To accomplish this, a data association system was created to determine the degree of membership for each new point to each existing track.For this study, a multi-Gaussian function was used that assigns a degree of membership to each radar return based on the last reported position and speed for each particular aircraft.Using the last known state information of each aircraft, we can find the maximum distance it could have traveled between the last reported state and the current time step.Therefore, if an aircraft has traveled the exact distance it was expected to travel, and a new data point was found exactly at this predicted distance, it would be assigned a degree of membership of one to that particular track.However, if the point is too close to, or too far from, the predicted location of the vehicle, it would be assigned a degree of membership less than one.Therefore, zero membership signifies there is no chance that the data point could belong to that track.
+a) 2D Gaussian Membership Function b) 3D Gaussian Membership Function Figure 3. Gaussian Membership Function for Data AssociationIn Figure 3 a), the multi-Gaussian function has been shown.As one can see from the figure, when a new data point lies at exactly one on the x-axis (i.e.exactly where it was expected to be based on the previous time step information), it is assigned a degree of membership of one.However, if it lies closer or farther than it is expected to be at that time step, the degree of membership is reduced.Due to the fact that aircraft can have a large change in heading between consecutive time steps, the degree of membership for the case where a point is closer than expected has not been punished as much as the case where it lies too far.If, for example, an aircraft is traveling straight on the previous time step, but then immediately turns hard to its right, when the next radar return is obtained the distance between the two consecutive locations of the aircraft could be quite close to one another.Therefore, we do not want to disassociate data points when an aircraft has turned unexpectedly.Thus, it assigns a higher membership for points closer than expected, and assigns a comparatively lower membership for points located farther than expected.One possible drawback to this approach is for cases where new vehicles popup in the airspace.This scenario may cause the data association metric to breakdown and miss-assign popup vehicle points to already existing nearby tracks.In Figure 3 b), a three-dimensional plot of the degree of membership has been shown.This represents the threedimensional case that will associate points lying in front of the previous aircraft location to its degree of membership.While the true function associates points in all directions, the points lying behind the aircraft have been excluded to help the reader visualize the plot trends.Here, the axes on the horizontal plane represent the location of the new data point with respect to the previous point.Therefore, the point (0, 0) on the horizontal plane represents the exact location of the previous data point.Similar to the 2D representation, the height of the plot represents the membership value.To determine which raw radar points belong to which track IDs, if any, each new data point is assigned a degree of membership to each existing cluster.Once all memberships have been assigned, an assignment algorithm is used to allocate points to existing clusters.First, the assignment algorithm will step through each point and see if it has membership to only one possible existing cluster.If membership exists to only one cluster, and the membership is higher than 0.5, the point will be assigned to that particular cluster.With the remaining points, the algorithm will then step through each existing cluster and see if exactly two points have a degree of membership greater than 0.1 for that cluster.Since only two radar sources were used throughout the simulation, if a cluster has only two remaining possible points, the points would be assigned to that cluster.Next, the algorithm will step through each remaining radar point to see if it has a degree of membership much higher for one particular cluster than all remaining clusters.If this membership difference is above 0.5, the point will be assigned.Lastly, the algorithm finds the maximum value of all remaining membership values, if this maximum is higher than 0.1it will be assigned accordingly.Points that did not meet the threshold criteria are assigned to existing tracks.Thus, these points will be clustered using the "initial clustering" techniques described in the previous section immediately following the data association step.These new clusters are thus assigned new track IDs.
+C. Establish TrackAfter the tracking algorithm has assigned new data points to existing tracks, or has created new tracks, it will now determine if a track is "valid."A track is valid when it is believed to represent an actual aircraft.Therefore, in a realworld scenario this aircraft shall be reported to all other aircraft in its vicinity, or to an air traffic controller, to ensure no aircraft collide with one another.To determine if a temporary track ID should be reassigned to a valid track ID, two criteria are checked and must be met.First, the algorithm checks to see how many data points have been assigned to that particular track.Next, it will calculate the lifetime of the track.This is done by looking at the most recent time stamp for a set of data points that have been assigned to that track and the first ever recorded time stamp for that track.If the lifetime is greater than a user-defined threshold, 15 seconds, and has more than enough data points assigned to it, the track will become valid.
+D. Sensor FusionOnce the raw measurements have been associated to a track, the values need to be fused in order to provide an overall best estimate for the measured vehicle location.To fuse the measurements, the posterior probability distributions of the measurements as given by the normal distribution parameters in Table 2 were maximized to yield the best overall estimation of the true location.The maximized posterior (MAP) estimate for the range using two radar sources is given in the following equation:𝑅 𝑀𝐴𝑃 = 𝜎 𝑅 2 2 𝜎 𝑅 1 2 +𝜎 𝑅 2 2 𝑅 1 + 𝜎 𝑅 1 2 𝜎 𝑅 1 2 +𝜎 𝑅 2 2 𝑅 2(6)where   is the overall fused range value in the global frame,  1 and  2 are the range measurements in the global frame for radars 1 and 2 respectively, and   1 and   2 are the standard deviations in range for radars 1 and 2 respectively.For the general case where  radars exist, the MAP estimate can be found using Eq. ( 7).𝑅 𝑀𝐴𝑃 = ∑ ( ∏ (𝜎 𝑗 2 ) 𝑛 𝑗=1 ∑ [∏ (𝜎 𝑞 2 ) 𝑛 𝑞=1 ] 𝑛 𝑝=1 𝑅 𝑖 ) 𝑛 𝑖=1,  ≠  and  ≠ To find   , the range from each respective radar in the global frame,   and   (Eqs.( 4) and ( 5)) were used to create the relationship shown in Eq. ( 8).𝑅 𝑖 = 𝑋 𝑖 cos(tan -1 ( 𝑌 𝑖 𝑋 𝑖 ))(8)After finding the MAP estimate for range in the global frame, the same must be done for angle.This is shown in the following equation:𝜃 𝑀𝐴𝑃 = 𝜎 𝜃 2 2 𝜎 𝜃 1 2 +𝜎 𝜃 2 2 𝜃 1 + 𝜎 𝜃 1 2 𝜎 𝜃 1 2 +𝜎 𝜃 2 2 𝜃 2 (9)where   is the overall fused angle value in the global frame,  1 and  2 are the angle measurements in the global frame for radars 1 and 2 respectively, and   1 and   2 are the standard deviations in angle for radars 1 and 2 respectively.Again, this can be represented for  radars using the general form shown in Eq. (10).𝜃 𝑀𝐴𝑃 = ∑ ( ∏ (𝜎 𝑗 2 ) 𝑛 𝑗=1 ∑ [∏ (𝜎 𝑞 2 ) 𝑛 𝑞=1 ] 𝑛 𝑝=1 𝜃 𝑖 ) 𝑛 𝑖=1,  ≠  and  ≠  (10) Similar to   ,   was found using   and   .This is shown in Eq. (11).𝜃 𝑖 = tan -1 ( 𝑌 𝑖 𝑋 𝑖 )(11)Now that the measurements are fused into global values for  and , they can be converted back to Cartesian with the following equations:𝑋 𝐹 = 𝑅 𝑀𝐴𝑃 cos(𝜃 𝑀𝐴𝑃 )(12)  =   sin(  )The above data fusion equations were tested in a scenario where radar 1 was placed at the location (200, 9000) [m] and radar 2 was placed at (6000, 1000) [m].The standard deviations in range and angle for both radars were the same as in Table 2. Using these values and a normal distribution, each radar measured the location of an object placed at (5000, 5000) [m] 50,000 times (Eqs.( 2) through ( 5)).The results of the raw measurements and the fused values and are shown in Figure 4.
+a) Raw Radar and Fused Outputs b) Histogram of Distance Error Figure 4. Distribution Plots for 50,000 Runs of the Same PointAs can be seen in Figure 4 a), the fused value appears to have a much smaller distribution about the actual value.With these data points, the distance from each point, raw and fused, to the actual value was calculated.These distance values were plotted in a histogram to compare the accuracy of the raw values verses the fused values.This histogram demonstrating the distribution of results can be seen in Figure 4 b).Here, it is clear that the distribution of the fused values is more representative of the actual location of the object.Both the mean error and the standard deviation for the fused distribution is less than either single radar distribution.
+E. Current State EstimationIn order to provide the best estimate for the current vehicle position, a Kalman estimation algorithm was developed.The Kalman estimator uses a general aircraft model combined with fused sensor measurements to give an overall best estimate for the new aircraft position.The aircraft model used is a kinematic model given by Eqs. ( 14), (15), and (16), and assumes each aircraft is constrained to 2D level flight.𝑥 𝑖 = 𝑥 𝑖-1 + 𝑣 𝑖-1 cos(𝜓 𝑖-1 ) 𝑑𝑡(14)  =  -1 +  -1 sin( -1 )  (15)𝜓 𝑖 = 𝜓 𝑖-1 + 𝜓 ̇𝑖-1 𝑑𝑡(16)In the above equations,  represents the current time step,  -1 represents the previous time step,  is the time step increment,  is speed,  ̇ is angular velocity,  is heading angle, and  and  are Cartesian positions.From these equations and using an Euler method integration step, the state space representation was created and is shown with the following equations.[𝑥 𝑖 𝑦 𝑖 𝜓 𝑖 ] = [ 1 0 0 0 1 0 0 0 1 ] [ 𝑥 𝑖-1 𝑦 𝑖-1 𝜓 𝑖-1 ] + [ cos(𝜓 𝑖-1 ) 𝑑𝑡 0 sin(𝜓 𝑖-1 ) 𝑑𝑡 0 0 𝑑𝑡 ] [ 𝑣 𝑖-1 𝜓 ̇𝑖-1 ](17)The measurement equation was then created and is shown in Eq. ( 18).𝑧 = [ 1 0 0 0 1 0 ] [ 𝑥 𝑖-1 𝑦 𝑖-1 𝜓 𝑖-1 ] (18)Where  is a vector of the fused measurements,   and   .The state space representation was then used to create a Kalman estimator using the common algorithm 6 .This estimator was used by the algorithm on each iteration, for each existing track, after the third time step.This is due to the fact that the first value for  ̇ is only available on the third time step.Before the third time step, only the fused sensor values are used for state estimation.
+Figure 5. Vehicle Track Showing Actual Path (green), Radar Returns (black), Kalman Estimates (blue)In Figure 5, an example of the Kalman estimation for a track based on the associated radar values and model predictions is shown.It should be noted that since the measurements are taken every 3 seconds and the vehicle can turn more than 90 degrees in that time, less weight was put on the model predictions than the measurement values in the filter.This resulted in the Kalman estimated path more closely following the fused measurements while still taking the assumed model into account.This could be improved by adding more radars that sweep at different times or increasing the measuring frequency of the current radars.Computing the Kalman estimated value is not computationally expensive.The Kalman estimation can be computed in linear time, proportional to the number of fused data inputs, and each point can be processed on the order of 10 -5 seconds.
+F. Delete TrackSimilar to the criteria used when determining when a track should be deemed valid, a time threshold is used to determine when a track has gone "stale."A track becomes stale when the vehicle is no longer detected by radar for an extended period of time.In practice, this could happen if a vehicle were to crash, land, or leave the designated airspace.In addition, a stale track can be one that never became valid.Therefore, this method can also be used to throw out data points that should have belonged to a particular aircraft, but did not meet the data association criteria due to a bad radar data return; that is, too much uncertainty existed in the radar return to be assigned to the correct track.To determine if an aircraft track has gone stale and should be deleted from the list of candidate clusters to which new points can be assigned, the current time step minus the last recorded data return time stamp must meet a userdefined threshold.For this study, a threshold of 15 seconds was used.Therefore, if a track has not been assigned a new data point for 15 seconds, the track will be deleted and deemed stale.While this threshold was sufficient for all cases tested in this study, this threshold would need to be modified to account for different vehicle platforms, the amount of time between consecutive data returns, and the probability of detection for each radar source.
+IV. ResultsTo briefly depict the tracking simulation environment, a snapshot from data set 4 can be seen in Figure 6.In Figure 6 a), the true aircraft position data are shown.To help visualize where the vehicle has been in the past, a trail of each vehicle position has been depicted.In Figure 6 b), a snapshot of the current time step radar returns is shown.This is the data that would be imported into the tracking algorithm for this particular time step.In Figure 6 c), a trail of radar returns is shown.Lastly, Figure 6 d) displays the tracking algorithm output.Here, each track ID has been plotted with a different track color, and has been superimposed on top of the raw radar data returns.As one can see, all data points have been successfully associated to their correct tracks, and all tracks are distinguishable the tracking algorithm.To analyze the performance of the tracker, one can examine the comparison between the number of true vehicles in the airspace versus the number of tracks assigned by the tracking algorithm.For this study, the ratio between the assigned tracks and the true number of vehicles was used as a figure of merit.To compute this ratio, the total number of assigned tracks was calculated at the conclusion of the simulation.This total number of tracks was then divided by the true number of vehicles that existed in the airspace throughout the entirety of the simulation.Therefore, a value less than 1 implies an under-assignment of tracks; that is, the tracking algorithm did not identify some new data points as new vehicles.If a value of greater than 1 was found, this means that an over-assignment of tracks has occurred.An example for when this may occur is when the tracking algorithm loses an aircraft, deeming it as a stale track.If at a later time the algorithm recognizes this aircraft, it will be assigned a new flight ID; thus, the number of tracked vehicles is now more than the true number of vehicles in the airspace.This ratio is referred to as the Correctly Identified Tracks (CIT) ratio.For each simulation case, the CIT ratio was recorded and can be found in Table 3.In addition to using the CIT ratio as a performance measure for the tracking algorithm, four additional parameters were considered.As the traffic density of the airspace increases, so does the likelihood of two aircraft coming within close proximity of one another.Many times tracking algorithms will miss-assign data points associated with these aircraft when a radar return that resonated from one aircraft, is thought to have come from another nearby aircraft.Throughout all simulations, the tracker will tally the total number of instances where this occurs.The results of this "Miss-Association" metric can be found in Table 3.As previously mentioned in the simulation environment description, when a vehicle exits the airspace and a new vehicle appears with random initial conditions, no restriction is placed on where the new vehicle will popup.Therefore, in many instances the new vehicle would appear very close to where the exiting vehicle was expected to be.When this would occur, the data association algorithm would sometimes assign the popup vehicle data points to the exiting vehicle's track ID.Thus, resulting in an under-assignment of track IDs.Instances of this nature could occur in physical systems where an aircraft lands or exits the sensor range, while another vehicle takes off or is picked up by radar.Due to this possible occurrence in practice, the simulation was not restricted to omit these cases.The total number of instances where a popup aircraft was not correctly identified is shown in Table 3 as the "Missed Popups" metric.There were also instances where a popup vehicle appeared near an exiting aircraft and was correctly assigned a new track ID.However, due to being close in proximity, on the next iteration the algorithm miss-assigned the popup vehicle's points to the exiting vehicle's track ID.These instances are known as the "Lost Popups" in Table 3.In these cases, the CIT Ratio is not affected since the new point was initially identified and assigned a new track ID, thus yielding the same total number of tracks.A final metric used to evaluate the algorithm performance was the number of vehicles that were thought to have gone stale prior to actually leaving the airspace.If for example, an aircraft is traveling along and the data-association system does not assign new data points to its track (i.e.assigned to another existing track or to a new track altogether), the aircraft track has the potential of going stale if no future data points are assigned.Thus, the track has been lost.Another example of how a track can be prematurely lost is if a vehicle exiting the airspace is near another active vehicle.Not to be confused with the lost popup metric, the exiting vehicle is near another already active vehicle, as opposed to an exiting vehicle being near a popup vehicle.In these cases, the data association may assign the vehicle that has left the airspace the data points from the other nearby aircraft.Therefore, the nearby aircraft would not be assigned any new data points and would go stale; whereas, the exiting vehicle would remain an active track, following the other vehicle's path.These cases are referred to as the "Lost Track" cases below.By referencing the CIT Ratio column in Table 3, it can be seen that throughout all simulations there were no overassignments of flight IDs.Thus, the algorithm did not miss-assign new points that truly belonged to an already existing track, to a new track ID.However, in data set 5 the tracking algorithm miss-assigned points to an existing track ID when they truly belonged to a new track ID, resulting in a CIT Ratio less than one.In all instances, this underassignment of tracks was a result of a missed popup scenario.While this result is undesirable with respect to system performance and reliability, this was expected to occur.In data sets 1-4, the tracking algorithm correctly identified all tracks, never had any miss-associations, and had no tracks go stale prior to their mission completion.However, in data sets 2-4, each simulation had one instance where a popup vehicle was originally correctly identified, but was then assigned to the exiting vehicle's ID.When the total number of aircraft was drastically increased in data set 5, the tracking algorithm began to have more difficulty.Throughout the simulation, the tracking algorithm missed four popups in total.Due to these missed popups, the total number of identified tracks was four less the total number of true tracks, thus yielding a CIT ratio of 0.98.Due to the increased density of the airspace, missing popups or losing popups after being identified was expected to occur.When a vehicle exits the airspace, or a new one pops up, the probability of having another nearby aircraft is increased.As seen in Table 3, on three instances a track was lost prior to completing its mission.As previously described, there were two options for how a track could be lost.First, if data points that belong to a track are not assigned correctly, but are instead assigned to a new cluster, an over-assignment of vehicles would occur.The second scenario is when an exiting vehicle is near another active track.If the exiting vehicle cluster steals the nearby aircraft radar points, the true vehicle will no longer have future data points to be assigned.Thus, the track will go stale.In this second scenario, the total number of tracks are retained, thus, the CIT ratio is not affected.Throughout testing, all lost tracks were due to the second scenario described above.On one occasion two vehicles in close proximity caused a miss-association of data points in data set 5. When two active vehicles were crossing one another's paths, the two of the four radar returns were miss-assigned.Although this only happened on one occasion, this was marked as two missed data associations.As a result, both the fused radar values and the Kalman filter values had a larger amount of error than all other estimations.In all cases where this "Miss-Association" occurred, the algorithm did not indefinitely mix up the track IDs.Once they crossed each other's path, the data points were again correctly assigned.One way to fix this miss-assignment of data points would be to allow the algorithm to consider which points came from which radar source.If such information was available, the tracking algorithm could be sure not to assign two points to a vehicle that resonated from the same radar source.To evaluate the performance of the sensor fusion and state estimation systems, the position and heading error was calculated for all aircraft and all data sets.In Figure 7, the results for data set 5 are displayed as histograms.In Figure 7 a), the raw radar position error for each source and the fused radar position error is shown.It can be seen that the fused radar error is consistently less when compared to using either radar source individually.The shape of the histogram is skewed to the left, insinuating that lesser errors are found more often than larger errors.This same trend holds for the Kalman state estimation error, as seen in Figure 7 b).In Figure 7 c), the fused radar error and the Kalman error has been shown on the same histogram plot.It can be seen that the fused values consistently have slightly less error than the Kalman values.Lastly, in Figure 7 d), a histogram of the heading error calculated by the Kalman state estimation is shown.This distribution implies that the heading cannot be predicted with high accuracy.This inaccuracy is due to the vehicles having a relatively high turn rate when coupled with the slow sensor sweep rate.These factors make the reachable solution space quite large, making it difficult for the Kalman filter to accurately predict the true heading value.The results for all other data sets were similar in nature.The mean and standard deviation for all errors and all data sets can be seen in Table 4.As seen from the above table, the results for each data set were quite consistent.In all cases, Radar 1 and Radar 2 had the highest amount of position error.Once passed through the sensor fusion system, the fused radar values had less total mean error and had a lower standard deviation.Thus, the aircraft true location could be more accurately represented.After the fused values were found, the algorithm used a Kalman filter to predict the aircraft location and heading.In all cases, the Kalman filter had slightly more average error and a slightly higher standard deviation.The increased error in the Kalman values is due to the way the Kalman filter was constructed.Specifically, more reliance was placed on the fused radar values because there was little knowledge about the aircraft model.By increasing the weight on the fused values, all Kalman values were close to the original fused point location.However, by not having placed much trust on the vehicle model, errors were added to the system.In all scenarios tested, the radar sources were located relatively close to all vehicles in the operational airspace, thus, the raw radar errors were fairly small.As aircraft begin traveling farther away from the radar sources, we would expect the fused radar values to have a higher amount of error.Thus, in these instances we would expect the combined sensor fusion and Kalman filter system to outperform the sensor fusion system if used by itself.The sensor fusion values could estimate the position of the aircraft, but not the heading of the vehicle.However, adding the Kalman filter allowed the heading to be estimated.When aircraft were traveling in a fairly linear fashion the state estimation technique was highly effective.However, as aircraft began to turn, due to the aggressiveness of their navigation controllers, having a large maximum turn rate, and a low radar sweep rate, the heading estimation begins to break down.If data were available at a faster rate or if the vehicles were limited in turn rate, we would expect the Kalman filter to have greater performance, especially in the area of heading estimation.
+V. ConclusionThe tracking method proposed in this study was complete and robust, capable of tracking every vehicle with a mean error less than 8.7 m.Although data points were occasionally associated to incorrect tracks, the methods proposed in this study were sufficient to show this approach could be used if some optimization and tuning were imposed.Throughout testing, the sensor fusion system was marginally superior to using the fusion and Kalman estimation hybrid system.For all cases, the difference between the two system errors was less than 10%.This slight increase in error for the hybrid system was likely due to the low confidence placed on the vehicle model used for the Kalman filter, and the low sensor sweep rate.Nonetheless, both architectures minimized the true distance errors and imposed better confidence for data association.This work serves as a good baseline for future studies, and would require some additional research and refinement prior to implementation.One area for future work would be in the data association algorithm.At this time, the userdefined thresholds have not been optimized to account for various vehicle models and/or sensor sweep rates.In addition, further research would need to be conducted to deal with aircraft that can hover or are highly evasive.These types of models pose a great challenge and would need to be overcome using an adaptive membership association based on the detected vehicle dynamics.Lastly, in this study the radar sensors do not incorporate a non-unity probability of detection.Integrating this element may require additional tuning for the track identification/deletion and data association algorithms.Once more intelligent algorithms for data association, track validity, and track deletion are created, more complex scenarios with fewer assumptions can be tested.By increasing the traffic density in a given area, the radar returns become more difficult to associate to existing or new tracks.Thus, in areas of an airspace expecting higher traffic flow densities, such as those found near airports, higher precision radars should be used to identify vehicles.In doing so, a weighted average system can be imposed to put more weight on data returns from radars that are closer to the identified object verses returns from farther radars with higher uncertainty.a) True Aircraft Tracks for Specified Time Window b) Radar Returns for Current Time Window c) Radar Returns for Specified Time Window d) Estimated Tracks for Specified Time Window Figure 6.Tracking Visualization Example
+
+
+
+
+
+Table 1 . Simulation Cases DATA SET Max Vehicles Total Vehicles Simulation Time (hrs)11230.2522162332524108125401561
+Table 2 . Radar Parameters Parameter Symbol Value2Range𝜎 𝑅4.37 mAngle𝜎 𝜃0.002 radThese values were then used in combination with the normal distribution pseudorandom number generationfunction in MATLAB to generate a model that would return normally distributed values. Thus, these normallydistributed values were found using Eqs. (
+Table 3 . Data Association Metrics DATA3SETCIT RatioMiss-AssociationsMissed PopupsLost PopupsLost Tracks11.00000021.00001031.00001041.00001050.982453
+Table 4 . Aircraft Position and Heading Errors4Position Error (m)Heading Error (degrees)DATA SETStatisticRadar 1Radar 2FusedKalmanKalman1Mean Std. Dev.11.5 8.99.2 6.57.7 4.98.4 4.978.4 49.22Mean Std. Dev.11.5 8.59.4 6.97.9 4.78.5 4.884.5 51.33Mean Std. Dev.11.7 8.59.3 6.87.9 4.78.5 4.886.1 53.44Mean Std. Dev.12.0 8.89.2 6.88.0 4.98.6 5.087.5 53.25Mean Std. Dev.12.0 8.89.1 6.68.0 4.98.6 5.188.3 52.2
+			Downloaded by NASA AMES RESEARCH CENTER on January 9, 2017 | http://arc.aiaa.org| DOI: 10.2514/6.2017-1133
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+	
+		An algorithm for tracking multiple targets
+		
+			DReid
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+	Reid, D., "An Algorithm for Tracking Multiple Aircraft," IEE Transactions on Automatic Control, Vol. 24, Issue 6, Dec. 1979, pp. 843-854. doi: 10.1109/TAC.1979.1102177.
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+		Radar Data Tracking Using Minimum Spanning Tree-Based Clustering Algorithm
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+			2011
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+	Park, C., Lee, HT., Musaffar, B., "Radar Data Tracking Using Minimum Spanning Tree-Based Clustering Algorithm," 11 th AIAA Aviation Technology, integration, and Operations (ATIO) Conference, Sept. 2011. doi:10.2514/6.2011-6825.
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+	Chan, C. C. K., Lee, V., Leung, H., "Radar Tracking for Air Surveillance in a Stressful Environment Using a Fuzzy-Gain Filter," IEEE Transactions on Fuzzy Systems, Vol. 5, Issue 1, Feb. 1997, pp. 80-89. doi:10.1109/91.554452.
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+		UAS Collision Avoidance, Navigation, and Target Assignment in a Congested Airspace Using Fuzzy Logic
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+			BrandonCook
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+		10.2514/6.2015-2031
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+		AIAA Infotech @ Aerospace
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+			American Institute of Aeronautics and Astronautics
+			Jan. 2015
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+	Cook, B., Arnett, T., Rich, B., Kivelevitch, E., "UAS Collision Avoidance, Navigation, and Target Assignment in a Congested Airspace Using Fuzzy Logic," AIAA SciTech 2015 -AIAA Infotech @ Aerospace, Jan. 2015. doi:10.2514/6.2015-2031.
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+		A New Approach to Linear Filtering and Prediction Problems
+		
+			REKalman
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+		10.1115/1.3662552
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+		Journal of Basic Engineering
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+	Kalman, R. E., "A New Approach to Linear Filtering and Prediction Problems," Journal of Basic Engineering, Vol. 82, Issue 1, March 1,1960, pp. 35-45. doi:10.1115/1.3662552.
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+I. INTRODUCTIONAs the popularity of using small Unmanned Aircraft Systems (sUAS) for various applications continues to increase in the coming years, so will the number of aircraft operating in low altitude airspace.With this increase in air traffic density, it is imperative to accurately identify and track all vehicles.This becomes increasingly evident when operators, both commercial and hobbyists alike, begin flying their vehicles beyond visual line of sight (BVLOS).In these scenarios, accurate state estimation is vital, regardless of whether a human traffic manager or an automated system is responsible for keeping aircraft separated from one another or from physical structures.Due to the fact that several sensor sources can provide position estimations for a single aircraft, these data must be interpreted and combined into a single position estimate.If all data were provided to an air traffic manager or sUAS operator, the abundant information could be overwhelming and difficult to interpret.Furthermore, it may be difficult for a human operator to identify readings that are inaccurate or contradictory, which may lead to poor decisions.Although many solutions have been presented to take the data from multiple sources and fuse the values into a single estimation, these solutions are typically constrained to specific systems and are not adaptive based on the accuracy and number of sensors being used [1,2].In this study, a novel adaptive sensor fusion technique that can be used in real-time operations is proposed.In particular, three sensor types are used to identify where several sUAS are located: GPS, radar, and an onboard detection suite.For the onboard and radar sources, a preliminary Maximum a Posteriori (MAP) estimator is used to combine the various readings from similar sensor types to a single estimation.After a single reading from each sensor type is found, a Fuzzy Logic based system is used to determine the confidence in each reading to create a weighted average position estimate.
+II. PROBLEM DESCRIPTIONGiven position data for several sUAS, obtained from several GPS, radar, and onboard detection sensors, one must be able to provide an accurate estimate of where the vehicle is located using sensor fusion.The amount, and accuracy, of information available to the sensor fusion platform is determined by each sensors' specifications.In this study, the source of each data point is known, as well as, to which vehicle the data belongs.In the following sections, a description of each sensor and its performance is shown.
+A. Sensor PlatformsIn this study, three sensor platforms, or types, were used to provide raw position data of vehicles in three-dimensional space: GPS, radar, and onboard sensors.Each of these sensor platforms vary in performance and reliability.Thus, a vehicle may be identified by one or more sensor types.Whereas the GPS platform produces only one position estimate for each vehicle, for both the radar and onboard sensor platforms more than one source may be within range to sense the vehicle.For example, if multiple vehicles are within close proximity, their onboard detection sensors will each measure the position of one another.To model the various sensors for simulation, functions were created to provide noisy position estimates of a vehicle given its true position.To accomplish this, the perceived location of each vehicle was found using each sensors' respective performance (i.e. standard deviation in error).By using the standard deviation in error for each sensor platform and the built-in randn function in MATLAB, a normally distributed pseudorandom number generator, a noisy output could be found.Because the GPS system estimates the position of the vehicle in three-dimensional space, the standard deviations in error for the lateral and vertical planes were used to generate perceived vehicle position estimates in Cartesian space.For both the ground based radar and the onboard sensor packages, the vehicle position is estimated by converting the range, azimuth, and elevation measurements to Cartesian space.The standard deviation in error for each sensor package is shown in Table I.In addition, the maximum sensing range for the radars and onboard sensors are shown.The error values for the radar and GPS platforms were found in [3] and [4], respectively.Because this study focuses on sUAS applications, the radar maximum sensing range was limited to 2000 m.However, since the radar source is designed to detect larger objects at a greater distance, the probability of detection of an sUAS at this range would be low.By increasing the sensing range the error associated with all measurements also increases.Therefore, this large detection range allowed the sensor fusion system to be tested under high uncertainty conditions.The onboard detection sensor standard deviation values were not selected from any particular sensor package.These onboard sensor values were selected such that they provide an optimistic estimation of a nearby vehicle.Thus, the solution presented in this study needs to be tuned to the sensor packages available.With these standard deviations in error, the measured location of each vehicle can be found using the following equations.For the GPS, the measurement values were found using (1) through (5).
+𝛼 = 2𝜋 𝑟𝑎𝑛𝑑   𝑟 = 𝜎 𝑟 𝑟𝑎𝑛𝑑𝑛 Where  is the built-in MATLAB function to find a random number between zero and one,   is the standard deviation in the lateral plane,  is the random standard deviation function, and  and  are the noisy returned measurements of the angle and range, respectively.The noisy measurement in the lateral plane, found using (1) and ( 2), can be converted to Cartesian space (  ,   ,   ) using (3), (4), and (5).Here, , , and  represent the true location of the vehicle in Cartesian space,   is the standard deviation in altitude, and  is the random standard deviation function.𝑥 𝑚 = 𝑥 + 𝑟 cos 𝛼(3)  =  +  sin  (4)𝑧 𝑚 = 𝑧 + 𝜎 𝑧 𝑟𝑎𝑛𝑑𝑛 (5)Similarly, the measurements for both the radar and onboard detection sensor types can be found using (6) through (11).Here, ,  , and  are the true range, azimuth angle, and elevation angle, respectively, used to describe the vehicle location with respect to the sensor source,   ,   , and   are the standard deviations in range, azimuth, and elevation, respectively, and   ,   , and   are the noisy measured range and angles with respect to the sensor location.𝑅 𝑚 = 𝑅 + 𝜎 𝑅 𝑟𝑎𝑛𝑑𝑛(6)  =  +    (7)𝜀 𝑚 = 𝜀 + 𝜎 𝜀 𝑟𝑎𝑛𝑑𝑛(8)To convert the measured values (  ,   , and   ) from their respective spherical reference frame to the global Cartesian frame, (9) through (11) are used.Here,   ,   , and   represent the position of the sensor source in Cartesian space and   ,   , and   represent the measured position of the vehicle in Cartesian space.𝑥 𝑚 = 𝑥 𝑠 + 𝑅 𝑚 cos 𝜀 𝑚 cos 𝜃 𝑚    𝑦 𝑚 = 𝑦 𝑠 + 𝑅 𝑚 cos 𝜀 𝑚 sin 𝜃 𝑚    𝑧 𝑚 = 𝑧 𝑠 + 𝑅 𝑚 sin 𝜀 𝑚   B.
+Simulation EnvironmentTo test the effectiveness of the Fuzzy Logic based sensor fusion system, a simulation environment was used to compare the raw sensor measurements to the fused position estimates.In this study, one or more vehicles were randomly placed within a 2 km by 2 km area.On each corner of the area boundary, a radar source was placed, each with a maximum sensing radius of 2 km.After the vehicle location(s) were set, each sensor package would run independently to measure the location of each vehicle.If a vehicle was located outside of a particular sensor's range, it would not be recognized by that sensor.Thus, some vehicles may be identified by all three sensor sources and others by a combination of radar and only one other source.Due to the locations and ranges of each radar, each vehicle is always identified by at least two radar sources.A depiction of the simulation space is shown in Fig. 1.The various arcs depict the sensor ranges of the radar sources.All simulations conducted constrain the vehicles to be within the areas shaded in yellow due to symmetry.In this figure, the numbers two through four represent the number of radars that can reach that particular region.For this study, 135 cases, each varying in vehicle position and sensor availability, were tested.For each combination of available sensor platforms, a total of 45 cases were tested.For each set of 45 cases, 15 belonged to each designated region shown in Fig. 1.A breakdown of the cases can be seen in Table II.In practice, not all sUAS will be self-reporting its GPS information to a ground based station, or broadcasting its location via a transponder to all surrounding vehicles.Thus, some vehicles that are placed within the airspace will not be identified via GPS.For scenarios involving only radar and GPS, all cases involved only one sUAS.For these scenarios, 15 total cases were evaluated in each radar configuration (two, three, and four radars).These 15 cases were a result of testing five different vehicle locations, each tested at three different altitudes.For scenarios involving only radar and onboard sensors, the available radars again varied, however, for each radar configuration there were between two and four vehicles (whose onboard sensors could sense one another), yielding three different scenarios per radar configuration (nine in total).These nine scenarios were each evaluated for five different vehicle location sets.Lastly, for the scenarios involving all thee sensor types, the same cases as previously described for the onboard and radar case were used, with GPS enabled.For each of the cases shown in Table II, 1,000 independent measurements from each sensor source were obtained, each with randomized noise.The error between the true vehicle location and the measured vehicle location was recorded.These raw measurements were then passed through the sensor fusion package and the results of the final position estimations were compared against the raw measurement values.
+III. PROPOSED SOLUTIONTo accurately estimate the state of an sUAS given measurements from several sensor sources, a sensor fusion package based on fuzzy logic was developed.This sensor fusion package considers the number of sensor types and determines how much confidence one should place in each measurement.If, for example, a GPS measurement is obtained, and is known to have low uncertainty, whereas, a radar measurement is expected to have high uncertainty, one would place more confidence in the GPS measurement accuracy.Therefore, the level of confidence for each measurement is taken into consideration to output a single position estimation.In practice, each sensor's accuracy should be found through sensor testing/calibration and/or obtained from manufacturer hardware specifications.Prior to using the fuzzy solution to calculate measurement confidence, two additional steps are taken.The first is to reduce the number of measurements being examined by the fuzzy system.To do this, the algorithm first takes all the radar and onboard data, separately for each sensor type, and combines the measurements into a single estimate for each.Thus, if for example all four radar sources identify an sUAS, the four measurements are combined into a single value.To do this, a Maximum a Posteriori (MAP) estimator was used.Once the measurements for each vehicle were reduced to a single value for each sensor type, the types of sources available for each vehicle were identified.Since a radar measurement was guaranteed for all vehicles in all scenarios, there were three possible sensor type combinations: GPS and radar, onboard and radar, and all three types.In this study, for each measurement recorded, the sensor type and sensor performance is known.In addition, after all measurements are recorded they are processed simultaneously.Therefore, if sensors sample data at different rates, measurements would be stored until the fusion system can process all data simultaneously (i.e.fuse the data at slowest available sensor's sample rate).
+A. Maximum a Posteriori EstimatorIf multiple measurements for the same vehicle were obtained from a similar source (e.g.multiple radars or multiple onboard sensors) a Maximum a Posteriori (MAP) estimator was used to combine the multiple measurements into a single position estimate for that sensor type.To accomplish this, the posterior probability distributions of the measurements, as given by the normal distribution parameters in Table I, were maximized to yield the best overall estimation as perceived by that sensor type.Because the standard deviations for each measurement are measured in the local spherical frame, to calculate the MAP estimate one must first convert each individual measurement to the global spherical frame, as shown in (12) through (15).Θ 𝑖 = tan -1 ( 𝑦 𝑖 𝑥 𝑖 )(12)Ε 𝑖 = tan -1 ( 𝑧 𝑖 𝑟 𝑖 ) (13)𝑟 𝑖 = 𝑦 𝑖 sin 𝜃 𝑖(14)Where Θ  , and Ε  are the measurements for the  ℎ radar (or onboard sensor) source in the spherical global frame,   ,   , and   are the raw measurements in the Cartesian global frame ((9) through ( 11)), and   is the two-dimensional range of the measured value on the x-y plane, found using (14).Then using (15), the range in the spherical global frame,   , can be found.𝑅 𝑖 = 𝑧 𝑖 sin Ε 𝑖(15)Using the above global representation for each measurement, the MAP estimate for  radars (or  onboard sensors) can be found using (16).Here,   represents the MAP estimation found using the measurements of interest (  ) and their respective standard deviations (  ).So, this equation can be used to calculate the MAP estimate of the range (  ), azimuth (Θ  ), and elevation (Ε  ) in the global frame by using the respective individual measurements and standard deviations for range (  ,   ), azimuth (  ,   ), and elevation measurements (Ε  ,   ).𝑄 𝑀𝐴𝑃 = ∑ ( ∏ (𝜎 𝑞 𝑗 2 ) 𝑛 𝑗=1 ∑ [∏ (𝜎 𝑞 𝑟 2 ) 𝑛 𝑟=1 ] 𝑛 𝑝=1 𝑄 𝑖 ) 𝑛 𝑖=1,  ≠  and  ≠  (16)Once calculated, the measurement can be converted to the Cartesian frame using (17) through ( 19), where   ,   , and   are the final , , and  MAP values, respectively.𝑥 𝑀𝐴𝑃 = 𝑅 𝑀𝐴𝑃 cos Ε 𝑀𝐴𝑃 cos Θ 𝑀𝐴𝑃(17)  =   cos Ε  sin Θ  (18)𝑧 𝑀𝐴𝑃 = 𝑅 𝑀𝐴𝑃 sin Ε 𝑀𝐴𝑃(19)Although the above MAP estimation helps decrease the measurement uncertainty for each sensor type, the following fuzzy fusion technique can be achieved without finding the MAP estimate.If not found, the sensor confidence levels shown in (20) would need to be modified.In particular, the confidence values for each sensor type would need to be divided by the number of raw measurements obtained from that particular sensor type.Doing so will satisfy the constraints in (20).
+B. Fuzzy Sensor FusionOnce the measurements from each source have been reduced (if necessary) to only one estimate per sensor type, the fuzzy sensor fusion package is employed.This fuzzy approach is used to determine how much confidence one should place in each sensor type's estimate.Due to each sensor having variation in its performance for both the lateral (x-y plane) and vertical (z) directions, the sensor confidence was calculated separately for each.Thus, if for example two sensor types are available and one is relatively more accurate in its altitude estimation, but the other is more accurate in its lateral position estimation, one could vary the confidence on each estimation accordingly.Overall, the confidence values for each sensor type are used to create a weighted average of the measurements.Therefore, the final estimation of the vehicle position given  sensor types can be described by (20).Where  ⃗  is the final fused position estimation in threedimensional Cartesian space,   ,   , and   , are measurements from the  ℎ sensor type, and    and    are the lateral and vertical confidence, respectively, for the  ℎ sensor type.In this study, three separate fuzzy systems were developed to help simplify the construction of the sensor fusion system.Overall, each fuzzy system is constructed in a similar manner and is governed by the same fuzzy architecture, where each differs is in the inputs, outputs, and rule bases.Each of the fuzzy systems are of Mamdani-type and have the following architecture: triangular membership functions, fuzzy partitioning, normalized inputs and outputs, minimum "and" method, minimum implication method, sum aggregation, and centroid defuzzification.For the scope of this paper, the fuzzy partitioning is such that the membership functions are structured where the end points of one membership function coincide with the center points of the neighboring membership functions.Due to this fuzzy partitioning, and the fact that each Fuzzy Inference System (FIS) input and output contains three membership functions, for all possible inputs exactly two rules will be activated.Thus, the third rule yields an output of zero membership.This can be verified by referencing Fig. 2.Each FIS consists of a single input with four outputs.Depending on the FIS being used, the input to the system is based on the normalized distance a sensor is from the sUAS.If, for example, one or more onboard sensors detect a single sUAS, the average range the detected vehicle is away from each sensor is used.However, if no vehicles are close enough to a particular sUAS to sense it with their onboard sensors, the input will instead be based on the distance from the radar sources.Here, instead of using the average distance, the input will be the range to the closest radar source.The four outputs of each FIS are dependent on the sensor platforms available.If only two sensor types are available, the FIS outputs would be the confidences in the lateral and vertical estimations for each sensor type.Given this common architecture, examples of input and output membership functions are shown in Fig. 2. In the left inset, the input (normalized distance) is described by three membership functions: Close, Medium, and Far.Regardless of the source used to describe the distance, the input domain will always lie between zero and one.Similar to the input, each output (sensor confidence) is also described by three membership functions: Low, Medium, and High.These two outputs represent the confidence level placed on two sensor types along the same direction (either lateral or vertical).Since the sum of the confidence values for each direction must be equal to one, as seen in (20), the domain for each output must satisfy the constraint described by (21).Where  1 and  2 are the domains for outputs one and two, respectively.Therefore, in this example, the first input domain lies between 0.55 and 0.85, and the second output has a domain between 0.15 and 0.45.Thus, 0.55 + 0.45 = 1 and 0.85 + 0.15 = 1 , satisfying (21).This property will hold for all domains that satisfy this constraint due to the structure of each FIS and the input output relationships described by the rule bases, shown in Tables IV andVI.Since a centroid defuzzification technique is used, and two rules are always active for all input values, the FIS output can never reach the bounds of the output domain.Thus, the actual minimum and maximum outputs of the FIS are limited by the relationship shown in (22).Here,   and   are the minimum and maximum values of the  ℎ output domain (  ), respectively.Out ∈   :𝑎 𝑖 +𝑐 𝑖 6 ≤ Out ≤ 𝑎 𝑖 +5𝑐 𝑖 6 (22)The rule bases of each respective FIS are shown in Tables III, V, and VII.In addition, the domains of each output for each respective FIS are shown in Tables IV, VI, and VIII.Here, the minimum and maximum values for each output are also shown.In Tables III through VIII, the following shorthand is used:• Normalized distance: [Dist] • GPS confidence
+a) GPS and RadarIf an sUAS is equipped with an onboard GPS system, and is also detected by two or more ground based radars, the FIS rules shown in Table III are used.Here, the input to the system would be the minimum normalized distance the vehicle is sensed from all radar sources.To normalize the distance input, the true range is divided by 1464.2 m.This is 50 m greater than the distance from the center of the simulation area to any of the four radars.b) Onboard and Radar If an sUAS does not have an onboard GPS system, but is recognized by both a ground based radar and at least one other vehicle, the following FIS is used.The input to the system is the normalized average range the vehicle is from all other sUAS that sense that particular vehicle.This distance is normalized by taking the true average value and dividing it by 150 m.This normalization value was selected after testing and tuning the fusion system.This value is near the range where the onboard sensor errors become exceptionally large (i.e. less accurate).c) GPS, Onboard, and Radar If an sUAS has GPS onboard, is detected by surrounding vehicles, and identified by the ground based radars, then this FIS will be employed.Here, the input to the FIS is again the normalized average range separating the vehicles, as sensed by the onboard sensors.This average range was normalized by taking the true average value and dividing it by 180 m (also determined after testing/tuning).Unlike the previously described FISs, a total of six confidence values need to be assigned.To accomplish this, it was decided that two of the six values, one for each direction (lateral and vertical) would be held constant, regardless of the input value.Thus, the FIS still only needs to compute four confidence values.For this study, G(xy) and R(z) were held constant at 0.5.These were selected due to the low uncertainty associated with the GPS estimation in the lateral plane, and the relatively low uncertainty with radar measurements in the vertical plane.Recalling (20), we need to ensure that the sum of all three confidences must be equal to one, for both the lateral and vertical directions.Thus, the domain of each output must consider how much confidence has already been placed in the sensor that is held constant.For this fuzzy system, the domain of the outputs must satisfy (23).Here,  1 and  2 are the domains for outputs one and two (for the same direction), respectively, and  is the constant confidence level as defined by the designer.IV.RESULTS In each of the 135 configuration cases shown in Table II, 1,000 independent measurements were evaluated.For each case, the error for each independent measurement was recorded.To visualize the error distributions, each sensor type's estimate and the final fused estimate was plotted on a histogram.In Fig. 3, an example histogram is shown.This histogram shows the results of a sample trial where all four radar sources are available and three sUAS can sense the vehicle with onboard sensors.Here, the measurement error was broken down into the lateral error, vertical error, and the total error.As seen from this figure, the fuzzy fusion estimation more accurately modeled the true vehicle location than all other sources.In all cases, the fused mean error is lesser than all other estimations, and has a lesser standard deviation.Although not explicitly shown, in all cases the MAP estimations displayed were more accurate than the individual raw measurement values.To evaluate the performance of the proposed fusion technique, the mean and standard deviation of the error for each configuration was computed and compared against the sensor MAP values.The results of all 135 cases have been described in Fig. 4. Here, the mean and standard deviation of the error for each sensor type combination is shown.Whereas the left inset in Fig. 4 segregates the data by the number of radars, the middle and right insets combine the results for all numbers of radars and instead segregates the data by number of sUAS.As it can be seen from these graphs, the fused value had a lower mean and standard deviation than any single sensor MAP estimation in all cases.In addition, as the number of sUAS increased, the mean and standard deviation of the error decreased.This means that as the number of vehicles sensing one particular vehicle increased, so did the accuracy of the position estimation.Lastly, using all three sensor types resulted in the best fused estimation.
+V. CONCLUSIONIn this study, we have demonstrated a novel approach to estimate the location of an sUAS using a Fuzzy Logic based sensor fusion technique.The presented fusion system produced position estimates that had lower mean error and standard deviation when compared to using a Maximum a Posterior estimator for each sensor platform.Overall, this approach could be applied to any number of sensor sources with varying reliability and performance, and be used in real-time operations.At this time, the fuzzy system parameters were developed by hand with no additional tuning.To improve its performance, we wish to develop a Genetic Algorithm to train each FIS.In addition, we would like to incorporate this system into a vehicle tracker.Therefore, as vehicles move in space throughout time, the tracker would need to identify the vehicles and use data association to assign new data to tracks.Adding this time component would allow this fuzzy solution to be used for stateestimation of the speed and heading of all sUAS.Lastly, we are interested in using a quaternion approach to estimate vehicle locations.This approach may have a beneficial reduction in the computational complexity of the proposed system.Fig. 1 .1Fig. 1.Simulation Area
+Fig. 22Fig. 2 Example of Fuzzy Inference System Structure
+in lateral and vertical measurements, respectively: [G(xy)] and [G(z)] • Onboard sensor confidence in lateral and vertical measurements, respectively: [O(xy)] and [O(z)] • Radar confidence in lateral and vertical measurements, respectively: [R(xy)] and [R(z)] • Domain and Output bounds, respectively: [] and [Out] • Inputs: Close [C], Medium [M], and Far [F] • Outputs: Low [L], Medium [M], and High [H]
+
+TABLE II.SENSOR SPECIFICATIONSTypeParameterStd. Dev.Sensor RangeRange (R)4.37 m2000 mRadarAzimuth (𝜃)0.002 radN/AElevation (𝜀)0.002 rad0.5236 radRange (R)1.00 m200 mOnboardAzimuth (𝜃)0.175 radN/AElevation (𝜀).0175 radN/AGPSLateral (𝑟) Altitude (z)3.10 m 3.90 mN/A N/A
+TABLE IIII.SIMULATION CASES# Radars# UAS# CasesGPS On?115Yes22 35 5Yes/No Yes/No45Yes/No115Yes32 35 5Yes/No Yes/No45Yes/No115Yes42 35 5Yes/No Yes/No45Yes/No
+TABLE IIIIII.GPS AND RADAR FIS RULESInputOutputsRule #DistG(xy)R(xy)G(z)R(z)1CLHLH2MMMMM3FHLHLTABLE IV.GPS AND RADAR FIS OUTPUT DOMAINSG(xy)R(xy)G(z)R(z)𝓓[0.55, 0.85][0.15, 0.45][0.2, 0.5][0.5, 0.8]𝐎𝐮𝐭[0.6, 0.8][0.2, 0.4][0.25, 0.45][0.55, 0.75]
+TABLE V .VONBOARD AND RADAR FIS RULESInputOutputsRule #DistO(xy)R(xy)O(z)R(z)1CHLHL2MMMMM3FLHLHTABLE VI.ONBOARD AND RADAR FIS OUTPUT DOMAINSO(xy)R(xy)O(z)R(z)𝓓[0.325, 0.775] [0.225, 0.675] [0.2, 0.425][0.575, 0.8]𝐎𝐮𝐭[0.4, 0.7][0.3, 0.6][0.25, 0.4][0.6, 0.75]
+TABLE VIIVII.GPS, ONBOARD, AND RADAR FIS RULESInputOutputsRule #DistO(xy)R(xy)O(z)G(z)1CHLHL2MMMMM3FLHLHTABLE VIII. GPS, ONBOARD, AND RADAR FIS OUTPUT DOMAINSO(xy), R(xy)O(z)G(z)𝓓[0.175, 0.325][0.125, 0.275][0.225, 0.375]𝐎𝐮𝐭[0.2, 0.3][0.15, 0.25][0.25, 0.35]
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+I. IntroductionRAFFIC management decisions and automation supporting those decisions currently lack accurate departure demand information.Future departure demand is typically predicted using the scheduled departure times of individual flights.These times have been shown to be poor estimates of actual departure times, especially during periods of high delays when traffic management is most necessary. 1Errors in predicted departure demand affect national flow management as well as traffic management at the regional -Air Route Traffic Control Center (ARTCC) and Terminal Area Approach Control (TRACON) -and airport level.A flight's actual takeoff time is revealed when the aircraft takes off and is detected by terminal area radar.Prior to discovering that the flight is airborne, departure demand is predicted first using scheduled times from the Official Airline Guide (OAG) and then filed times after the flight operator files flight plan information.This information can be supplemented by airline-provided updates to flight times via the Collaborative Decision Making (CDM) process.Departure predictions are currently inaccurate, as shown later in this paper, with flights departing both early and late relative to the predictions.Improved predictions of when each flight will depart (or will be ready and want to depart) would directly improve traffic management decisions.For departures from most airports, there is no advance notice when flights will take off earlier than scheduled.When flights take off later than scheduled, only traffic managers in the FAA Air Traffic Control Tower (ATCT) know whether or not the flight has left the parking gate and, therefore, whether the late flight will take off shortly or not for at least the flight's taxi time when it has not yet left the gate.In a few airports, electronic flight strip (EFS) 2 systems, including the Departure Spacing Program (DSP), provide information to TRACON and ARTCC controllers and traffic managers about which aircraft are moving on the airport surface and their progress toward their departure runways.Research is also in progress under the CDM Surface Management Sub-Team § to use airport surface surveillance, such as the Airport Surface Detection Equipment -Model X (ASDE-X), to improve predictions of takeoff times using information about whether or not the flight has left the parking gate and its taxi progress toward a runway.These techniques provide a somewhat earlier observation of aircraft movement than waiting for the terminal area radar to detect the flight after takeoff.How far in advance the takeoff time prediction is improved depends on the flight's taxi time; the longer the taxi time, the earlier the takeoff time prediction is improved.However, these techniques provide little benefit prior to aircraft leaving their parking gates.Moreover, the required EFS or ASDE-X systems are only available at a small number of airports.Furthermore, these techniques provide little benefit for airport surface traffic management.On the airport surface, knowledge of departure demand means knowing when aircraft will push back from their parking gates.Whether a flight has left its gate may not be known to controllers and traffic managers in the ATCT until the flight reaches a handoff spot between ramp control and ATCT control.Surface geometry and local agreements about how responsibility is divided between the ATCT and air carrier or airport authority ramp towers or stations can further complicate this prediction.In these cases, surface surveillance systems such as ASDE-X may not provide coverage of the ramps not controlled by the ATCT.At other airports, knowledge that a flight represents actual demand on the airport surface starts when aircraft call for pushback (i.e., clearance to leave their parking gate).Currently, no advance warning is available when a flight will push back from its gate earlier than scheduled.If a flight is late relative to its schedule, traffic managers in the ATCT usually have no information about how late the flight will be.Air carriers who are CDM participants may modify the estimated gate departure times, but the accuracy of these modifications vary between the air carriers.
+II. ApproachThis paper describes an approach that improves the prediction of gate push back, or Out times, thereby improving the knowledge of departure demand at longer prediction horizons.Moreover, the approach could be applied at every airport without installing expensive sensors or automation.The approach uses data provided by the air carrier or National Airspace System (NAS) user.These data describe when certain intermediate milestones in preparing for departure are completed.Statistics for when these pre-departure events are expected prior to the actual Out time are compiled in advance from large amounts of historical data.These statistics are then applied in realtime to improve Out time predictions.Results show significant potential benefits.Some uncertainty in a flight's gate departure time is unavoidable, for example if an aircraft experiences an unexpected mechanical problem shortly before planning to push back.In these cases, the predicted Out time error may be very large, though no larger than the error would be under today's systems.The departure predictability for many flights may be improved by using information about the status of the departure preparations.This paper presents an algorithmic approach for applying pre-departure event times to improve block out, or gate push back, time predictions for individual flights.The paper then describes the application of this method using Aircraft Communication Addressing and Reporting System (ACARS) messages from a large domestic air carrier at a busy airport.ACARS messages that mark several pre-departure events were used in this study.The paper presents results that show the benefit of applying this pre-departure information using the described algorithm to two existing automation systems that predict Out times -the Surface Management System (SMS) and the Enhanced Traffic Management System (ETMS).][4][5] ETMS is an FAA automation system widely used for national and regional traffic management. 6he algorithm proposed in this paper could be used by an FAA automation system such as the ETMS.Although pre-departure event information exists for many flights, the information is currently not easily available.In addition to demonstrating the value of this information to traffic management systems, and an algorithm for improving departure demand predictions, a goal of this paper is to motivate progress toward a standard method for FAA traffic management systems to gain access to these air carrier data.Currently, a mechanism already exists for flight operators to submit improved gate departure time estimates to ETMS; however, most operators do not have more accurate estimates.Therefore, flight operators could use the proposed algorithm and provide ETMS with improved gate departure time estimates using the existing mechanism.§ http://cdm.fly.faa.gov/Workgroups/surface.html 3 Figure 1 illustrates the current process for estimating gate push back time in SMS. Figure 2 captures a possible implementation of the Out time prediction algorithm discussed in this paper.The algorithm could be implemented within SMS, and the improved estimations of gate push back times could be sent to ETMS using the existing mechanism.This paper is organized as follows.The next sections describe the data that were used in this study and an analysis of the accuracy of currently available gate departure time predictions.The following sections present the algorithm for applying pre-departure events to improve gate departure time predictions and the process used to select the algorithm parameters.Lastly, results of applying the algorithm to improve SMS and ETMS gate departure time predictions are presented and conclusions and opportunities for future research are listed.
+III. Data CollectionThe data sources for this analysis were SMS log files from Louisville (SDF) airport and ETMS data.The SMS log files contain both the SMS predictions of gate departure times and the ACARS messages.The ACARS Out message was used for the actual Out time.The SMS log files and the ETMS data both contain the original scheduled data; SMS receives Aircraft Situation Display to Industry (ASDI) data which are a subset of the ETMS data.A total of 36 SMS log files were used for this study, covering portions of time from June through August, 2006.Typically 4 SMS log files cover one day.ETMS data were processed for the same time periods.This research utilized two tools developed by Mosaic ATM, Inc.The Surface Operations Data Analysis and Adaptation (SODAA) tool ingests SMS log files and raw surface surveillance data into a database and facilitates researchers' construction and visualization of database queries. 7,8An ETMS parser and database allows ETMS orig files to be loaded into a database for research queries.Five pre-departure events -Crew on Board (ACARS Initialization), Crew on Board (Flight Plan Upload), Cargo Door Closed, Load Complete, and Crew Door Closed -were studied.These events were chosen since they are the ACARS events recorded by the particular air carrier prior to gate push back.The times for each of these events were obtained from ACARS messages recorded in the SMS log files.The Crew on Board (ACARS Initialization) message is automatically generated by the aircraft when the pilots power on the ACARS system.This is generally one of the first things the pilots do after entering the aircraft and, therefore, represents a reliable measure of when the crew boards the aircraft.The Crew on Board (Flight Plan Upload) message is generated when the initial flight plan information is received by the aircraft via ACARS.The Door Closed times come from messages automatically sent by the aircraft whenever the aircraft door is closed.The Load Complete message is manually sent and, therefore, susceptible to human error.The Load Complete event is generally expected after the Cargo Door Closed event and before the Crew Door Closed event, but these events may occur in different orders.Merging the SMS and ETMS data required matching the flights across the two different data sources.For a flight to be used in this analysis, the flight was required to be found in both the SMS and ETMS data.Moreover, the flight must have had at least one Crew on Board, Crew or Cargo Door Closed, or Load Complete ACARS message and have had an ACARS Out message.Lastly, the flight must have had an original scheduled gate departure time in at least one of the data sources.After merging the data sets, a total of 872 flights were studied.The goal was to generate a sufficient flight set, not to perfect the flight matching.A number of flights were discarded that could have been included using different processing.For example, flights split across two SMS log files were discarded in  4 the present approach.Note that all flights were from a single major air carrier and were departures from the single studied airport.Several queries were run against the SODAA and ETMS databases to produce tables of data: ACARS messages, SMS Out time predictions, ETMS messages, actual Out times (from the ACARS Out message), and original scheduled Out times.Several simple programs were written to process and merge the data into the format necessary for running the Out time prediction algorithm.
+IV. Baseline DataTo measure the improvements that ACARS data can provide, the current prediction accuracy attained without the use of ACARS data is calculated as a baseline.Three current Out time prediction sources are studied -OAG, ETMS, and SMS.The OAG scheduled time is equivalent to the initial ETMS scheduled time and the initial SMS gate time of departure, but the ETMS gate time may subsequently change as a result of CDM messages, flight plans, and time-out delay logic.The following examines these three baselines and compares them to understand which data source provides the best predictions of Out times at various prediction horizons (i.e., amounts of time prior to the actual Out time).
+A. OAG Original Scheduled Gate Departure TimeThe OAG scheduled departure time, which is a gate departure time, not a scheduled takeoff time, is often used as a prediction of the flight's block out time.It can be the only prediction of gate departure time for many hours until a flight plan is filed.The first ACARSprovided Out time is used as truth against which the prediction accuracy is measured.** Figure 3 illustrates the inaccuracy of the scheduled gate push back times for those flights included in this study.Flights rarely push back any earlier than 15 minutes prior to their scheduled time, but they can push back quite a bit later, as illustrated in the right tail of the distribution.For this data set, flights pushed back on average 14 minutes later than their scheduled times.Figure 4 plots the percentage of flights for which the OAG scheduled departure time was within a specified accuracy window of the ACARS Out time.The OAG data are static, provided 24 hours or more prior to the scheduled departure time.Therefore, there is no dependence on prediction horizon.However, the graph is drawn as a function of time prior to ACARS Out time for consistency with later graphs.Each data series in the graph represents a prediction accuracy window.For example, the OAG scheduled departure time is within +/-5 minutes of the ** A complete analysis of the ACARS data is beyond the scope of this paper.In some situations, more than one ACARS Out message may be sent for a flight.
+5actual Out time for only 25% of the flights.Wider accuracy windows capture larger percentages of flights in the expected way.But even at the widest accuracy window shown, +/-60 minutes, not all of the flights are captured.A total of 4.4% of the flights push back more than +/-60 minutes from their OAG scheduled time.
+B. Enhanced Traffic Management SystemThis section describes the use of the ETMS Estimated Gate Time of Departure (EGTD) as a prediction of Out time.A variety of ETMS messages may be received prior to a flight leaving its parking gate and can modify the EGTD.These messages include:• F -Filed flight plan • N -Airline-provided flight modification (CDM participants only)• B -Time-out delay logic triggered by ETMS once the current time is equal to or later than the estimated runway time of departure • A -Amended flight plan • E -Control Times Issued • Q -Airline-provided flight creation (CDM participants only) These updates should improve the prediction accuracy at shorter prediction horizons.Figure 5 plots the percentage of flights for which the ETMS EGTD was within the specified window of the actual Out time, as a function of time prior to actual Out time.The ACARS Out time was again used as the actual Out time.Each data series in the graph represents a prediction accuracy window.For smaller accuracy windows (i.e., +/-5 minutes and +/-10 minutes), the prediction accuracy improves as the prediction horizon decreases.However, for larger accuracy windows, the prediction accuracy initially improves with decreasing prediction horizon but then worsens for the shortest prediction horizons.The underlying flight data were analyzed to determine the cause of the prediction errors worsening in the last 10 minutes prior to the actual Out time.The air carrier used in this study provided new "N" messages, or airline-provided CDM flight modification times, to ETMS close to the actual Out time.These schedule updates tended to predict a gate departure time later than when the flight actually departed.Different carriers likely have different processes and systems for submitting flight modification data to ETMS and, therefore, this characteristic may be isolated to the air carrier studied in this paper.However, our hypothesis based on this observation is that the prediction accuracy using ETMS data is not necessarily a reflection on ETMS processes and logic but rather a reflection on the quality of the data that airlines submit to ETMS.
+C. Surface Management SystemThe last data source used as a baseline for Out time predictions was SMS.Like ETMS, SMS relies on a scheduled time for its initial estimate of Out time.SMS receives this scheduled time from ASDI or directly from the air carrier.SMS receives some schedule updates directly from the air carrier.Otherwise, SMS does not modify the estimated Out time unless the current time reaches the estimated Out time and the flight has not yet pushed back.SMS will then adjust the estimated Out time to remain equal to the current time until the flight pushes back.This is typically referred to as time-out delay logic.The SMS time-out delay logic runs every 10 seconds.Figure 6 plots the percentage of flights for which the SMS estimated Out time was within a specified accuracy window of the ACARS Out time, as a function of the prediction horizon.Out prediction times improve on average as the prediction horizon is reduced.The ASDI data used by SMS do not receive the updates to the ETMS EGTD.
+6Therefore, the effect of the air carrier's schedule updates seen in the ETMS performance is not seen in the SMS performance.
+D. Comparison of Data SourcesComparison of the three current data sources for predicting Out times revealed that no one data source provides the most accurate predictions at all prediction horizons.SMS and ETMS both outperform OAG scheduled data.SMS is, on average, always more accurate in predicting Out times than using the OAG scheduled time.SMS improves on OAG scheduled data through the time-out logic and some schedule updates provided by the air carrier to SMS.Since many more flights depart later than scheduled as opposed to earlier, the time-out delay logic is important and makes a significant impact on prediction accuracy at short prediction horizons.The biggest difference between SMS and OAG scheduled data is for the shortest prediction horizons and smallest accuracy windows.SMS predicts almost 60% more flights within the +/-10 minute window than OAG scheduled data alone.However, SMS does have a larger standard deviation of prediction errors and root mean square error (RMSE) than the OAG scheduled data for longer prediction horizons.This is due to poor estimates in the schedule updates provided by the air carrier at longer time horizons.ETMS consistently provides better predictions (in terms of the percentage of flights within an accuracy window) than the OAG scheduled data for prediction horizons less than one hour.As the prediction horizon increases, the benefit of using ETMS data over just the OAG scheduled data declines.At prediction horizons between 60 and 90 minutes (depending on the accuracy window), the OAG scheduled data become a better predictor than ETMS.Since ETMS starts with the OAG scheduled data, some ETMS data, subsequent to the scheduled data but more than an hour before the actual Out time, must cause this change in prediction accuracy.The ETMS data were studied to find the cause, and it seems to be a result of how the air carrier submits flight modification messages to ETMS.Though individual CDM 'N' messages are sent for flights close to departure time, the air carrier also sends bulk 'N' messages for large groups of flights at particular points in time during the day.These are usually for flights that are not expected to depart for many hours in the future.These updates to gate departure times are actually slightly less accurate, on average, than the OAG scheduled gate departure times.Figure 7 shows the differences in prediction accuracy between SMS and ETMS.A positive value indicates that SMS is more accurate than ETMS for that accuracy window and prediction horizon.Although the results vary with the accuracy window used, the trend is that SMS is, on average, more accurate for prediction horizons greater than 60 minutes and less than 30 minutes.However, for prediction horizons between 30 minutes and 60   minutes, ETMS provides better predictions.The exact prediction horizons at which the crossover occurs depends on the accuracy window.For longer prediction horizons, SMS outperforms ETMS because SMS is using the original scheduled data while ETMS is using updates that are actually worsening the prediction.For short prediction horizons, SMS outperforms ETMS due to the SMS time-out delay logic.Since many more flights depart later than scheduled as opposed to earlier, the time-out-delay logic is important and makes a significant impact on prediction accuracy at short prediction horizons.Although ETMS also has time-out delay logic, there are two main differences between the SMS and ETMS logic that make SMS more accurate.First, SMS applies the time-out delay logic every 10 seconds; ETMS applies the time-out delay logic every 5 minutes.Consequently, ETMS can predict a flight's Out time as being as much as 5 minutes before the current time.Second, the SMS time-out delay logic is triggered once a flight misses its gate departure time.ETMS does not start applying its time-out delay logic until the flight has missed its runway time of departure.Thus, for SDF, where taxi times are 14 minutes on average † † , the SMS time-out delay logic is applied 14 minutes sooner than in ETMS.In the middle prediction horizons, the updated gate times that ETMS receives through messages such as filed flight plans, flight plan amendments, and airline flight modifications -which are not available through the ASDI data that SMS uses -allow ETMS to outperform SMS.
+V. Block Out Time Prediction AlgorithmThis section describes the Out Time Prediction Algorithm that applies pre-departure event data -ACARS messages in the current study -to improve ETMS and SMS Out time estimates.The algorithm is based on the observation that each type of pre-departure event will occur during a range of time prior to gate departure.The algorithm is described as follows: 1) For each type of pre-departure event, the algorithm assumes the event occurs y minutes prior to actual Out time, where y is a random number described by a normal distribution.The pre-departure events studied in this paper are five ACARS messages: ACARS Initialization Crew on Board, Flight Plan Information Received Crew on Board, Cargo Door Closed, Crew Door Closed, and Load Complete.2) For each event type, a sample set of data is analyzed to estimate the distribution (i.e., mean and standard deviation) for the amount of time prior to the actual Out time that the event is expected to occur.3) A confidence interval is used to define the range of times relative to the actual Out time in which the event is expected to occur.The confidence interval is defined by min and max values, illustrated in Figure 8.For example, if the mean value is 20 minutes prior to the actual out time, the standard deviation is 5 minutes, and a 95% confidence interval is used, then the minimum value would be 10 minutes (2 times the standard deviation from the mean) and the maximum value would be 30 minutes.These min and max times are relative to the actual Out time.Note that this approach does not require the distribution be normal; the min and max values may be selected without requiring symmetry relative to the mean.4) When an ACARS message is received for a flight, the algorithm evaluates whether the current Out time prediction should be changed.The algorithm compares the difference between the event time and the current Out time prediction to the min and max values for that event type.a) If the current Out time prediction minus the event time is less than (or equal to) max and greater than (or equal to) min, the current Out time prediction is not changed.b) If the event time is later than min before the current Out time prediction, then the Out time prediction is moved later -to be the event time plus min, as shown in Figure 9.For each event type, the algorithm has three parameters -min, max, and x -that must be selected from analyzing historical pre-departure event data.Note that these parameters may be different for different air carriers or airports.Future research will study the robustness of the algorithm to these parameters and the set of characteristics (airport, flight operator, aircraft type, time of day, etc.) that should be used to define sets of parameters.The algorithm was prototyped in Java and tested against the data used to generate the baseline prediction accuracy.The following section describes the process of selecting the algorithm parameters.The subsequent section compares the prediction accuracy using the algorithm with the baseline cases.
+VI. Identifying Algorithm ParametersThis section discusses the approach used to select the algorithm parameters.Each of the five ACARS message types evaluated as pre-departure events require three parameters: the minimum and maximum bounds on the expected range and the time-out delay parameter.In addition, a time-out delay parameter is needed for use prior to any pre-departure event occurring.The distribution of the event time relative to the actual Out time was studied for each message type, to understand whether the time of the event relative to the actual Out time is sufficiently consistent and far enough before the actual Out time for the event to be a useful predictor of actual Out time.For example, on average the Cargo Door Closed event occurred 13.5 minutes prior to the actual Out time, with a standard deviation of 17.3.Figure 12 shows a histogram of the differences between the Cargo Door Closed times and actual Out times.Significant opportunity exists for future enhancements to the algorithm to better handle outliers.In the current analysis, Cargo and Crew Door Closed and Load Complete messages for a   9 flight were only used if a Crew on Board message had previously been received, which resulted in a "filtered" data set.Based on analysis of the ACARS data, four of the ACARS message types were selected for use in the algorithm.The ACARS Initialization Crew on Board (M40) message was discarded as having too large a standard deviation.Figure 13 graphs the mean and standard deviation of each of those message types.As expected, the closer an event typically happens to the actual Out time, the smaller the standard deviation.Several different metrics are useful for evaluating prediction accuracy -the percentage of flights with predictions within n minutes of the actual Out time, the mean of the prediction errors, the median of the prediction errors, the standard deviation of the prediction errors, and the RMSE.The RMSE is a frequently-used measure of the differences between values predicted by a model or an estimator and the values actually observed from the thing being modeled or estimated.All of these statistics can be measured at times prior to the actual Out event.Comparing two runs of the algorithm and judging which produced better predictions requires a single scalar statistic.Instead of choosing one prediction horizon, an additional statistic was defined that combined the RMSE at various prediction horizons (nPH = number of prediction horizons), weighting each prediction horizon depending on the relative importance of accuracy in predictions that amount of time in advance.This composite RMSE is defined in Eq. ( 1).(The weighting values which were used are shown in Figure 14.The highest weight was given to the time horizon of 10 minutes prior to actual Out as opposed to 5 minutes prior.A good estimate at 10 minutes tends to result in a good estimate at 5 minutes, and our goal was to achieve strong estimates far enough in advance to support improved airport surface planning.Further work includes analyzing the results achieved using different weighting factors.The approach to find the best value for each of the time-out delay parameters was to set all other parameters used by the algorithm to values that would not change the predicted Out time.The other time-out delay parameters were set to 0, as were the min values for each event type.The max values were set to infinity.The one exception is the min parameter for the event type being evaluated for the time-out delay parameter.The min parameter should always be greater than or equal to the time-out delay parameter.For the event type being evaluated, the time-out delay parameter was set to various values, running the algorithm for each value.If the event type also had a min parameter, the min parameter was set to the same value as the time-out delay parameter.The value of the parameter that minimized the composite RMSE statistic was selected.For each event type, the approach used to find the best values for the min and max parameters was to set all of the parameters for the other event types to values that would not change the predicted Out time.The time-out delay parameters were set to 0, as were the min values for each other event type.The max values for each other event type were set to infinity -a value much larger than any possible value of the difference between the actual Out time and an ACARS event time.For the event type being evaluated, the time-out delay parameter was set to the best value found in the prior step.The min value was initially set equal to the time-out delay parameter.The value of the max parameter was varied, running the algorithm for each value.The value of the max parameter that minimized the composite RMSE statistic was selected.Once the best value for the max parameter was found, this was held constant and the min parameter was varied until the best value was found.Other methods for selecting the parameters were also explored, either by evaluating the parameters in a different order or by setting previously evaluated parameters to their optimal value instead of 0. Though different methods yield different parameters, when the algorithm was run using these different parameters, the composite RMSEs which resulted were not significantly different (e.g., 17.30 vs. 17.28).This is an area that requires further research in order to determine the optimal method for finding all algorithm parameters.
+VII. ResultsAfter determining the best values for each of the algorithm parameters, the Out time prediction algorithm was run using these parameters and the results compared to the baseline cases.Figure 15 graphs the RMSEs (for various prediction horizons) for the original scheduled data alone, the SMS predictions alone, and the SMS predictions improved with ACARS data and the Out time prediction algorithm.The application of ACARS data through the Out time prediction algorithm improves the Out time predictions at all prediction horizons.Figure 17 shows the composite RMSE values for each data source.The application of ACARS data using the proposed algorithm provides a 36% improvement to the SMS predictions and a 73% improvement to the OAG Schedule.How each individual parameter affects the results of the algorithm has not been studied yet.Future work will include a sensitivity analysis to understand the robustness of the algorithm to parameter choices.Figure 16 graphs several statistics for each data source -the mean, median, RMSE, and standard deviation of the prediction errors made at 10 minutes prior to the actual Out times.All statistics illustrate an improvement in prediction errors.Figure 18    The benefit of applying ACARS pre-departure data to improve ETMS gate departure time predictions was also studied.In addition to the 3 previously discussed baseline cases (the OAG scheduled data alone, ETMS data alone, and the SMS predictions alone), an additional baseline case -SMS and ETMS combined -was created in order to understand the benefits of replacing the ASDI data feed with the ETMS data feed in SMS.In addition to the prior test case (SMS improved with ACARS data), two additional test cases were studied -ETMS improved with ACARS data, and SMS and ETMS combined and improved with ACARS data.The same algorithm parameters that were selected to apply the ACARS data to the SMS data were used for both of the additional test cases.Since those parameters were tuned based on the SMS data, the prediction accuracy results achieved using these parameters with the ETMS data will not necessarily result in the best possible prediction accuracy.Figure 19 graphs the Out time prediction RMSEs (as a function of prediction horizon) for each of the data setsthe 4 baseline cases and 3 test cases.The SMS with ACARS dataset performs as well or better than all other datasets at every prediction horizon.The addition of ETMS data to the SMS data did not provide any additional benefits.Figure 20 charts the composite RMSEs for each of the datasets.Theoretically, the ETMS data should provide additional benefits to both the SMS and SMS with ACARS data cases.As discussed earlier, the cause was determined to be a characteristic which may be unique to the air carrier from which the present dataset was obtained.The CDM messages sent by the air carrier into ETMS were less accurate than the ETMS data prior to the CDM messages.Different results would be expected if the estimates of Out time provided by the air carrier via CDM messages were more accurate.12 Figure 21 graphs the percentage of flights, at each prediction horizon for which the predicted Out time is within +/-5 minutes of the actual Out time.The three data sets that apply ACARS data all show a dramatic improvement in prediction accuracy over the data sets that do not incorporate pre-departure event data.
+VIII. ConclusionThis paper presented an algorithm for using pre-departure event data to improve gate departure time predictions.The methodology using several ACARS messages was applied to improve SMS and ETMS Out time predictions, improving SMS predictions in terms of composite RMSE by 36%.The approach is flexible and could be used with other pre-departure event data and to improve other existing gate departure time predictions.Data from a single flight operator at a single airport was studied in detail.The initial results presented in this paper demonstrate significant potential for the proposed technique.This research is being continued and a variety of future objectives have been identified.First, we will perform a sensitivity analysis to understand how dependent the prediction improvement is on the algorithm parameters and, moreover, how dependent the optimal parameters are on the data set.As part of this robustness analysis we will study additional data sets for several air carriers at multiple airports.One challenge in wide application of the approach is that although the pre-departure event data exists broadly, it exists in different forms and is not available  13 from a single source.Future work will include identifying how standard pre-departure events/data is and determining whether other events and possibly other data sources may be more appropriate for some flight operators or airports.For example, pre-departure clearance delivery time obtained from an EFS system may be a useful alternative or additional data source.The method is also not limited to gate departure time predictions.Future work may apply the methodology to improve the prediction of takeoff times at airports that do not have and will not receive detailed surface surveillance systems but that may have EFS.Figure 2 .2Figure 2. Possible implementation of the out time prediction algorithm within SMS with improved estimated gate push back times being provided to ETMS.
+Figure 1 .1Figure 1.Current process for estimating gate push back times in SMS.
+Figure 3 .3Figure 3. Histogram of prediction errors for scheduled data.
+Figure 4 .4Figure 4. Gate push back time prediction accuracy of scheduled times for various accuracy windows.
+Figure 5 .5Figure 5. Gate push back time prediction accuracy of ETMS data for various accuracy windows.
+Figure 6 .6Figure 6.Gate push back time prediction accuracy of SMS data for various accuracy windows.
+Figure 7 .7Figure 7. Prediction accuracy differences between SMS and ETMS data.
+77
+Figure 8 .8Figure 8. Definition of Min and Max Values.
+Figure 9 .9Figure 9. Application of late ACARS message.
+Figure 10 .10Figure 10.Application of early ACARS message.
+Figure 11 .11Figure 11.Time-out delay logic.
+Figure 12 .12Figure 12.Distribution of actual Out time minus event time for cargo door closed events.
+Figure 13 .Figure 14 .1314Figure 13.Mean and standard deviation of Out time minus event time by message type.
+10
+graphs the percentage of flights at each
+Figure 16 .16Figure 16.error statistics at 10 minutes prior to Out.
+Figure 15 .15Figure 15.RMSE for SMS vs. SMS with ACARS Data.
+Figure 19 .19Figure 19.RMSE of Out time predictions over all datasets.
+Figure 18 .18Figure 18.Prediction accuracy within +/-10 minutes window.
+Figure 21 .21Figure 21.Prediction accuracy within +/-5 minutes window.
+Figure 20 .20Figure 20.Composite RMSE each data source.
+		
+		
+
+			
+AcknowledgmentsThis work was conducted by Mosaic ATM, Inc. under a Phase 2 Small Business Innovation Research (SBIR) contract funded by NASA Ames Research Center.
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+	Brinton, C. R., Atkins, S. C., and Hall, A., Analysis of Taxi Conformance Monitoring Algorithms and Performance, Proceedings of the Integrated Communication, Navigation and Surveillance Conference, Dulles, Virginia, 2007.
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+IntroductionImproving the efficiency of flight operations is a defining objective of the Next-Generation Air Transportation System (NextGen).To reduce fuel consumption, emissions, and noise during arrival operations, a continuous descent at low engine power is desired -preferably at idle throttle setting from cruise altitude to the final approach fix -that can be planned and executed through the airplane's Flight Management System (FMS).Such an arrival profile is referred to as an Optimized Profile Descent (OPD).The challenge is to perform OPD operations during busy traffic conditions where airspace and runway capacity are limited.Indeed, NextGen must respond to a traffic demand that is projected to double by the year 2025 [1].Today, OPDs are typically prevented or disrupted during busy traffic conditions by controller actions taken to separate, schedule and sequence aircraft for terminal airspace entry and landing.These frequent, tactical control actions include temporary altitude assignments, speed changes, lateral vectoring, and airborne holding.While such actions serve well to manage throughput and separation, they impede an otherwise continuous, low-power descent to the runway.These tactical control actions not only create inefficient flight profiles, but they also prevent a shared understanding of intended arrival trajectories between controllers and pilots.Over the past decade, numerous initiatives have been launched in the U.S. and abroad to study the benefits and implementation of OPDs.Although some activities -such as the early continuous-descent-approach trials at London's Heathrow airport [2] -have relied upon conventional aircraft equipage, most have leveraged modern FMS guidance, navigation and control technologies.Examples include flight trials conducted at Lousiville in 2004 and Amsterdam in 2006 [3,4].In these studies, area navigation (RNAV) routes were combined with vertical profile constraints to create a set fixed, published procedures.Due to the inflexible nature of these procedures, however, they could not be adapted to account for dynamic separation and throughput constraints.As a result, they were mostly restricted to periods of low traffic density, which limited their full benefits potential.In recent years, OPD initiatives such as those in daily use at Los Angeles have sought to address the high-density traffic problem by coupling RNAV procedures with redesigned airspace.These techniques segregate arrival flows to avoid conflicts with over flights, and they rely on legacy controller decision making to establish inter-arrival spacing at designated control points.While beneficial, the application of these procedural techniques tends to be limited to specific arrival directions, atmospheric conditions, and runway configurations.Furthermore, without predictive automation, controllers must apply conservative spacing buffers at control points, which can limit runway throughput.To mitigate this problem, Ren and Clarke [5] have developed a stochastic technique that calculates the minimum inter-arrival spacing required at a control point along the descent profile to allow OPD operations in heavier traffic.This capability, however, is limited to off-line processing due to its computational complexity and relies on controllers upstream to precisely deliver aircraft to the control point without compromising the OPD.Without additional automation, the ability of controllers to precisely deliver airplanes to control points will vary largely with skill level, resulting in indeterminate benefits.Other approaches for allowing OPD operations in busy traffic have focused primarily on flight-deck automation.Flight trials at Stockholm demonstrated the ability of FMS-equipped aircraft with Required-Time-of-Arrival (RTA) capabilities to meet assigned landing times while performing OPDs [6].Tailored Arrivals at San Francisco, Los Angeles and Miami have proven the feasibility of issuing fixed 3D profile clearances over data link for automated guidance and control through the FMS [7].Without accompanying ground automation to strategically tailor trajectories for separation and throughput, however, the chances of an uninterrupted OPD in busy traffic using airborne capabilities alone are limited.To better accommodate efficient arrivals during busy traffic, NASA, in collaboration with the FAA and Boeing, is developing the Efficient Descent Advisor (EDA) as a near-term technology for NextGen.EDA provides controllers with strategic maneuver advisories that allow aircraft to fly idle-thrust descents while maximizing throughput and avoiding conflicts, even during periods of peak demand.The paper first describes the concept and technology behind EDA as near-term controller tool.An overview of recent simulations and field tests is then provided, followed by key results from those activities pertaining to EDA concept validation and prototype design.
+Automation Overview
+Operational ConceptThe concept behind EDA as a near-term (2015 to 2018) capability for NextGen is referred to as Three-Dimensional Path Arrival Management (3D-PAM).Under 3D-PAM, EDA provides controllers in the Air Route Traffic Control Center (ARTCC) with comprehensive clearance advisories that can be issued by voice, which satisfy a time-based metering schedule computed by the currently deployed Traffic Management Advisor (TMA).TMA specifies the time required for each airplane to cross a meter fix located at the TRACON boundary for optimal arrival throughput [8].To compute solutions, EDA models descent trajectories that can be flown at idle thrust through the FMS, thereby enabling a fuel-efficient OPD.In the process of computing maneuver advisories that meet the TMA schedule, EDA checks for and attempts to avoid conflicts with other traffic along the arrival trajectory to the meter fix.3D-PAM is a trajectory-based concept, since it relies on predictions computed over strategic time horizons of up to 25 minutes.Over these time horizons, EDA solutions affect multiple airspace sectors within the ARTCC.This is markedly different from today's sectorbased arrival operations where each controller develops a solution that primarily affects only the portion of flight within their own sector boundaries.By attempting to avoid conflicts in a strategic manner while solving the meet-time problem, EDA decreases the chance that a controller will have to interrupt an OPD trajectory to manage separation downstream.In looking for conflict-free solutions, EDA only considers adjusting the trajectory of the arrival aircraft for which a meet-time solution is being generated.Because of this inherent constraint and the requirement to meet a precise arrival time at the meter fix, EDA cannot always compute a conflict-free solution.In such cases, EDA provides controllers with an advisory that minimizes the number and severity of predicted downstream conflicts.Its important to note that when using EDA for 3D-PAM, the controller retains full responsibility for separation assurance.Furthermore, EDA is intended to work with, rather than replace, automation for general conflict detection and resolution, such as that described in [9] and [10].For 3D-PAM operations, the majority of control actions required for an uninterrupted OPD to the runway are assumed to occur in ARTCC airspace with the assistance of EDA.With EDA enabling precise delivery of aircraft to the meter fix, TMA can potentially compute schedules that depend on little or no further delay absorption in the TRACON, thereby allowing aircraft to continue along uninterrupted glide paths to the runway.In 3D-PAM operations, it is assumed that after crossing the meter fix, aircraft can continue to the runway along a pre-defined RNAV path, flown using the FMS with Required Navigation Performance (RNP) criterion for lateral containment.This general concept is illustrated in Fig. 1, showing an airplane that has received an EDA speed and path-stretch clearance to the meter fix.The initial 3D-PAM concept is focused towards commercial air carrier operations in which airplanes are equipped with a "3D FMS", i.e., one that provides both lateral (LNAV) and vertical (VNAV) guidance and control.Pilots enter EDA clearances into the FMS, which then guides and controls the airplane along its computed arrival path.With the assumption of an idle-thrust descent, EDA speed and path clearances, together with meter-fix crossing restrictions built into the Standard Terminal Arrival Route (STAR), are sufficient for the FMS to compute the location of Top-of-Descent (TOD).Although 3D-PAM relies on voice-based communications for near term application, the concept and automation can be readily adapted to accommodate data-link communications in the future.With data link, more intricate clearances can be issued, resulting in potentially more efficient arrival solutions in the presence of complex traffic, airspace and weather constraints.
+Functional DescriptionThe primary elements of EDA are shown in Fig. 2. At its core, EDA relies upon a Trajectory Synthesizer (TS) to generate accurate 4D predictions for each aircraft in the airspace.For a more complete description of EDA functions and algorithms, refer to Coppenbarger, et al. [11].Before computing an advisory for an arriving flight, EDA first calls the TS to compute the airplane's Estimated Time of Arrival (ETA) at the meter fix.To compute the ETA, the TS uses the airplane's active flight plan together with stored information specifying its nominal descent speed.If the absolute difference between the airplane's ETA and Scheduled Time of Arrival (STA) computed by TMA differs by more than a set tolerance (currently set to 20 sec) EDA computes a meettime maneuver advisory.This computation process relies upon repeated calls to the TS while iterating on speed and path degrees of freedom to absorb any required delay.EDA only invokes path stretching for delays that are too large to be absorbed with changes to cruise and descent speed alone [11].Having solved the meet-time problem, EDA then checks for any traffic or airspaceboundary conflicts along the trajectory to the meter fix.In the event of a conflict, EDA further iterates on speed and path in its attempt to generate a conflict-free solution that satisfies all spatial and temporal constraints.In resolving conflicts, changes to the path geometry alone are considered prior to changes to both speed and path.Upon finding a successful trajectory solution, EDA displays the required speed and path parameters in the form of a clearance advisory to the controller.Since the user interface represents a key result of recent simulations, it is described in detail in the "results" section of this paper.
+Development and Testing ApproachEDA research and development has proceeded incrementally through a combination of simulations and field tests.Over the past two years, efforts have focused on developing a prototype as a basis for transferring technology to the FAA in support of 3D-PAM.Recent efforts have relied on a series of high fidelity, human-in-the-loop simulations to iterate on the concept and design of EDA as decision-support tool for the radar (R-side) ARTCC controller.The objective is to produce a working prototype upon which design and performance specifications can be based.By relying on highfidelity simulations with traffic scenarios and airspace conditions that represent end-state operations, the EDA concept and prototype can be matured to the greatest extent possible prior to pursuing more costly and intrusive field evaluations.The remainder of this section describes the simulations used to study and develop the EDA prototype.The field test recently completed at the Denver ARTCC to collect data for modeling trajectory-prediction uncertainty for use in future simulations is also described.Key findings from these activities are discussed in Section 4.
+SimulationsFort Worth ARTCC Simulations A series of simulation experiments involving Fort Worth ARTCC (ZFW) were completed in 2005 to demonstrate EDA automation and potential benefits.Results from these early 'proof-of-concept' simulations showed the potential of EDA to substantially improve flight efficiency and reduce controller workload.These benefit findings were used to launch the current 3D-PAM development effort.The simulations involved the high-altitude and low-altitude arrival sectors in northeast ZFW airspace, illustrated in Fig. 3. Controller participants were presented with traffic scenarios initialized with aircraft track and flight plan data captured during actual ZFW arrival rushes.Scenarios with TMA and EDA were compared against baseline scenarios in which controllers were provided with TMA only.In all scenarios, TMA schedules and metering delays were presented to controllers ARRIVALS IN CONSTRAINED AIRSPACE using both graphical timelines and metering lists.In EDA scenarios, only the controller working the high-altitude airspace (sector 42) was provided with automation for meeting TMA arrival times.EDA provided controllers with metering solutions involving combinations of cruise speed, descent speed and path stretching.
+Denver ARTCC SimulationsMore recently, in 2009, simulations were conducted with controllers and subject-matter experts from Denver ARTCC (ZDV) to develop EDA for 3D-PAM.The purpose of these activities was to validate the 3D-PAM concept, assess controller workload distribution, and obtain end-user design feedback for improving EDA functions and user interface.Controllers were provided with a high-fidelity display (pictured in Fig. 4), simulating an end-state implementation of EDA on the FAA's Display System Replacement (DSR).
+Field TestingTo assess the accuracy of current EDA trajectory predictions and to provide a basis for modeling real-world prediction uncertainty in upcoming simulations, a field test was conducted at Denver ARTCC in September 2009.Pre-scripted (i.e., non automated) EDA speed-profile clearances were issued to revenue flights operated by United and Continental airlines that approached Denver along published STARs from the northeast and northwest.Eligible aircraft types were the Boeing 737-300 and -800, Boeing 757-200, and Airbus 319/320, all of which were equipped with a 3D FMS.Pilots were issued bulletins describing the test procedures prior to flight.Their participation, however, was entirely voluntary.If they chose to participate, pilots completed a data sheet to record aircraft weight and wind forecasts used for FMS input.FMS-estimated arrival times at waypoints and TOD were also recorded on the data sheet.Over a three-week period, approximately 400 flights participated in the trials.After discarding flights that had either interrupted OPDs or incomplete pilot-recorded data, 270 flights remained for post-test data analysis.Selection wasGround-based predictions using intent data derived from flight plans and EDA clearances were compared against flown trajectories.Initial results from this analysis are presented in Section 4. 4.In addition to the commercial flights, a single FAA Global-5000 business jet participated in the field test.This airplane was issued both speed and path-stretch clearances.Since the Global-5000 FMS does not compute a performance-based VNAV path based on idle thrust, a fixed inertial flight-path angle of -2.5° was chosen to specify its descent trajectory from TOD to the meter fix.
+Results and DiscussionKey results gathered across the aforementioned simulation and field experiments are now categorized and presented.These general findings are accompanied by a discussion of their significance to the evolution of EDA.
+User InterfaceEDA's user-interface design was continuously improved upon with feedback from controllers, and therefore represents a key result of the human-in-the-loop simulations.The state of the Graphical User Interface (GUI), procedures and phraseology described below is that resulting from the December 2009 3D-PAM simulation.
+GUIThe primary elements of the EDA GUI are illustrated in Fig. 6.Airplanes crossing the TMA freeze horizon that required a delay to meet their scheduled arrival time at the meter fix were presented with the symbol "EDA" at the bottom of their data block.This symbolreferred to as the EDA portal -was displayed in a cyan color to allow it to stand out from other information in the data block.EDA portals were displayed whenever the required delay for an airplane exceeded a preset tolerance of 20 seconds.Numerical data showing scheduled arrival times and delay values were presented in a meter list similar to that used in today's TMA operations.Because of the added functions and GUI provided by the EDA tool, controllers seldom needed to refer to TMA-timeline information during the 3D-PAM simulations.When ready for an advisory, controllers simply clicked on the EDA portal.Upon clicking, a window containing the advisory opened up, as shown in Fig. 6.The horizontal trajectory prediction associated with the advisory -including the predicted location of the airplane's TOD -was presented on the controller's display along with the advisory window.The controller could adjust the location of the advisory window to minimize display clutter, thereby establishing the future default location of the window.For simplicity, only a single EDA advisory window could be opened at any one time.Furthermore, to minimize workload during busy conditions, controllers asked that only a single, ready-to-issue clearance advisory be presented in the window at any one time, representing the best solution that EDA could find within traffic and airspace constraints.In this way, no manual manipulation was needed by the controller to generate a viable solution, and no choosing from among multiple options was required.The controller could leave the advisory window open for as long as necessary to evaluate the suitability of the solution.However, if the window was left open for more than 60 sec (an adjustable parameter) a 'refresh' button within the window appeared, allowing for an update at the controller's discretion.Any advisory updates were based on trajectory predictions using the latest surveillance data available for that airplane.Controllers insisted that no advisory updates occur without their request, since they might be in the process of issuing a verbal clearance based on the previous advisory.If satisfied with the EDA advisory, the controller issued it as a clearance.After receiving acknowledge from the pilot, the controller pressed the 'Accept' button, which closed the advisory window and updated the flight plan for the airplane.Because the active trajectory prediction now incorporated the EDA-based flight intent, the predicted arrival time at the meter fix would conform to its scheduled arrival time assigned by TMA.This resulted in the EDA portal changing color from cyan to gray.If at any time the controller wished to see the advisory that was issued, they simply clicked on the gray EDA portal.If the controller chose not to issue the EDA advisory as a clearance -for example due to other traffic duties, radio interruptions, or unresolved conflicts -they simply closed the advisory window.In such event, the color of the EDA portal would remain cyan, indicating that a meet-time problem remained for that airplane.Any subsequent click on the portal resulted in a new EDA advisory.Controllers tended to reject advisories that relied on auxiliary control actions or traffic assumptions.For example, controllers often rejected an EDA advisory that was predicted to result in a downstream separation violation without compensating control action.In the event that controllers chose to accept such an advisory, they were alerted to the potential conflict situation by a red 'Accept' button in the advisory window, accompanied by information identifying the conflicts.The controller then had the option of accepting the conflicted EDA advisory -perhaps with the intent of resolving conflicts by moving other aircraft -or rejecting it by closing the window.If rejecting an initial advisory, controllers often reopened the advisory window after resolving traffic conflicts using legacy control techniques.If an advisory was accepted with a conflict, controllers requested that an indicator (e.g., framing the EDA portal with a red box) be presented in the data block to enhance their awareness.A revision of the EDA GUI to better manage unresolved conflicts is in progress, based on controller feedback obtained from the latest simulation.
+Phraseology and ProceduresA major challenge for enabling EDA to support 3D-PAM operations was designing the phraseology and procedures to allow trajectorybased clearances to be communicated by voice and managed largely in accordance with today's Federal Aviation Regulations (FARs).Although designed for voice-based communications, advisories were formatted in a manner that could support clearance delivery by data link in future.Clearance phraseology resulting from the latest EDA simulation -with validation from additional, pilot-oriented 3D-PAM simulations conducted by Boeing -is represented in the example below.Here, the sequence of communications between the controller and pilot correspond to the GUI example shown in Fig. 6.In this example, it is assumed that 'UAL 123' is flying its filed STAR, designated as TELLR1, which specifies the nominal arrival route and the speed/altitude crossing restrictions at the meter fix SAYGE.The EDA clearance consists of a cruise Mach number of 0.76, a descent calibrated airspeed of 250 kt, and a dogleg path stretch between the waypoints LBF (a.k.a.North Platte) and AMWAY, which has a turn-back point located 51 nmi from AMWAY along a magnetic bearing of 125°.Controller: "United 123, EDA clearance, maintain mach point seven-six, slash two-fivezero knots, revised routing when ready to copy" Pilot: "EDA clearance, maintain mach point seven-six, slash two-five-zero knots, ready to copy revised routing, United 123" Controller: "United 123, at North Platte proceed direct to the AMWAY one-two-five slash five-one then direct AMWAY, descend via the TELLR ONE profile" Pilot: "At North Platte proceed direct to the AMWAY one-two-five slash five-one, then direct AMWAY, descend via the TELLR ONE profile, UAL123" Once cleared via the TELLR1 profile, no further air/ground communications are typically required other than for radio frequency changes as the airplane transitions from sector to sector en route to the meter fix.The airplane simply flies the FMS-computed trajectory and TOD based on the pilot-entered EDA clearance.Controllers in downstream sectors are informed that the airplane is flying an EDA profile by the presence of the unlit EDA portal in the data block.In addition, once an EDA advisory is accepted, the letter "P" is displayed next to the altitude field in the second line of the data block, indicating that the airplane is flying a profile descent with TOD managed through the FMS.
+Concept-Related FindingsTrajectory-Based Solutions Controllers expressed a clear desire for EDA advisories that fully defined the arrival trajectory to the meter fix.As opposed to advising partial solutions, this trajectory-based approach relieved controllers from having to continuously monitor and recall the clearance status of each flight.This not only minimized controller workload, but also provided both airborne and ground-based automation with comprehensive flight-intent information once controllers and pilots accepted the EDA solution.To allow clearance delivery by voice, however, it was necessary to break the trajectory-based solution into a series of individual speed and path instructions, as previously described.Controllers were encouraged to retrieve an advisory for a flight as early as possible, once the airplane crossed the TMA freeze horizon and an EDA portal appeared in its data block.This allowed cruise-speed adjustments to have the greatest effect on arrival time, thereby minimizing the need for path stretching.In heavy traffic conditions, however, workload limitations often prevented the controller from retrieving EDA advisories as soon as they became available.For this reason, controllers stressed the need for an operational concept that does not depend on EDA clearances being issued right away.Instead, controllers asked that viable, up-to-date solutions be available upon request, i.e., at any time the portal is clicked.Similarly, controllers stressed that EDA should interoperate with any manual control actions taken to assist with scheduling, sequencing and spacing.For example, controllers might want to initiate delay maneuvers prior to an aircraft reaching the TMA freeze horizon or change an aircraft's cruise altitude to assure separation.Under these circumstances, controllers requested that EDA recognize the flight intent created by these manual control actions in any subsequent advisories.This was shown to be possible in the latest simulation in which controllers used existing DSR functions to enter manual speed and altitude clearances into the ground-based automation to update flight intent.These updated flight-intent data were then incorporated into any future EDA solutions.
+Situational AwarenessAlthough EDA offers to improve the sharing and awareness of intended arrival trajectories between controllers and pilots, human-in-theloop simulations revealed challenges to the controller's immediate situational awareness.Because EDA takes advantage of all available airspace in generating solutions, aircraft may be assigned trajectories with widely varying path geometries and speed profiles.This is unlike today's operations where controllers tend to create organized flows of traffic through arrival sectors.Although this structure limits flexibility and efficiency, it allows controllers to maintain simple mental models of the traffic to help manage their risk of losing situational awareness.In simulation, controllers commented that EDA compromised their ability to instantly assess the arrival plan, thereby requiring them to place considerable trust in the automation.To address this concern, while preserving the benefits of trajectory-based automation, functions were added to the EDA prototype to allow rapid display of predicted trajectories and review of previously accepted advisories.Concerns about situational awareness decreased over the course of each simulation as controllers gained familiarity and trust in the automation.Of particular concern to controllers was the awareness and accuracy of the airplane's predicted TOD location, which was calculated independently by airborne and ground automation rather than specified directly in the EDA clearance.Controllers suggested that in future GUI iterations, uncertainty in TOD location -perhaps out to two standard deviations -be presented graphically as a band along the predicted trajectory.Since directly relaying TOD information between the airborne and ground automation is likely impractical using voice communications, TOD awareness might instead be handled through simple procedures, such as pilots reporting when within a certain proximity of the FMS-calculated TOD.Pilots reporting to controllers when within 10 nmi of the FMS-calculated TOD proved effective during the initial EDA simulation for 3D-PAM.To help address controller concerns regarding the awareness and accuracy of horizontal trajectories, EDA path stretching was anchored between fixed start and end points.For example, in Fig. 6, the path-stretch maneuver is anchored between the published waypoints LBF and AMWAY.This implementation eliminated uncertainty associated with the airplane's turnout to the outbound path-stretch leg and provided a single stream of traffic to the meter fix for each baseline STAR.The fixed start point constraint was relaxed by EDA, however, if required to generate a solution.For example, if the airplane was past the published start point at the time the advisory was generated, the path-stretch maneuver was modeled as an immediate turnout from the nominal route.This provided greater flexibility over when an advisory could be requested for a given flight.The fixed start point was also relaxed if needed for lateral conflict avoidance.Similarly, the anchor point for path stretching was moved to the meter fix itself rather than a point upstream if needed for conflict avoidance.
+Traffic and Airspace ConflictsIn every simulation, controllers stressed the importance of avoiding traffic conflicts in any advised EDA solution.Although EDA advisories were generated only in response to a meet-time problem, it was important for the EDA solution to strategically avoid conflicts in order to prevent trajectory interruptions downstream.Controllers requested that conflict avoidance in EDA be fully automated, producing conflict-free advisories prior to display.In the event that a conflict-free solution could not be found, controllers suggested that a meet-time solution that minimized the number of conflicts be presented.In such cases, however, controllers insisted on a clear indication that a downstream conflict will occur pending issuance of the EDA solution with no future, compensating control action.In addition to avoiding conflicts, controllers stressed the importance of constraining EDA trajectories to avoid penetrating adjacent lateral sector boundaries, thereby preventing the need for point-outs and other inter-sector coordination measures.The acknowledgement of airspace-boundary constraints has now been incorporated into the current EDA prototype.
+Operations in All Traffic and WeatherAlthough EDA is currently designed for highdensity traffic conditions where arrival demand exceeds airport capacity, controllers expressed a desire to extend EDA to support all traffic levels.In addition to harmonizing procedures, this capability would allow the sharing of comprehensive arrival intent between controllers and pilots at all times of day.When no metering is required, EDA could simply advise pilots to fly their preferred arrival profile.Concepts and designs for this '24/7' capability are being investigated for study in future simulations.To facilitate '24/7' operations, controllers stressed the importance of avoiding airspace regions impacted by convective weather or other hazardous phenomena.Potential ideas and algorithms for allowing EDA to support '24/7' operations in the presence of convective weather regions can be found in [9].
+Controller WorkloadResults from the ZFW simulations identified significant potential workload benefits for controllers using EDA, attributed primarily to a reduction in maneuver-related clearances.Shown in Fig. 7 are flight tracks resulting from a busy ZFW traffic scenario, with and without the use of EDA for generating maneuver advisories.In this scenario, aircraft were scheduled by TMA to cross the meter fix with an in-trail spacing of 7 nmi, representative of a heavy arrival rush.Given the traffic demand, the required delay for each airplane induced by the flow constraint ranged from 0 to 6 min.In baseline operations without EDA (i.e., with TMA only), controllers issued frequent maneuver instructions to pilots in their efforts to absorb delay while managing separation.In the horizontal domain, the repeated use of tactical vectoring without EDA is evident by the irregular shaped paths seen in Fig. 7. Similarly, the frequent use of temporary altitude assignments -each requiring the pilot to increase engine power to maintain level flightcan be seen in the vertical tracks without EDA.Corresponding track data with EDA indicate far less maneuvering, with the majority of flights issued a single speed and path instruction near the TMA freeze horizon, resulting in a continuous descent to the meter fix.Fig. 8 shows the aircraft location within ZFW airspace corresponding to each maneuver instruction issued during a heavy-traffic simulation scenario.As seen here, EDA substantially reduces the required number of maneuver clearances, with most aircraft receiving all necessary arrival instructions shortly after crossing the TMA freeze horizon, near the high-altitude sector boundary.Over all ZFW simulation scenarios, EDA was found to reduce the number of required maneuver instructions by 70%, in comparison with a TMA-only baseline.The number of maneuver instructions required with EDA in the highaltitude and low-altitude arrival sectors was reduced by 55% and 95%, respectively.The histograms shown in Fig. 9 show the majority of aircraft receiving two or less maneuver instructions (depending on whether pathstretching was required) with use of EDA in the high-altitude sector.In the low-altitude sector, all but a few aircraft required any further maneuver instructions following initial EDA clearances upstream.Direct measures of controller workload were obtained from the 2009 3D-PAM simulations.Workload measures were based on the Modified-Bedford scale, which rates the difficulty of completing a task on a scale of 1 to 10, increasing with level of difficulty [12]. 1  Controllers were rotated through the three northeast ZDV arrival sectors (9, 16, and 15) previously shown in Fig. 5.After each rotation, each controller provided a rating for their average workload over the duration of the scenario (denoted as mean workload) and another rating for their maximum workload at any point in the scenario (denoted as peak workload).In the absence of any baseline (non EDA) test conditions, workload ratings were compared only to controller estimates of mean and peak workload for traffic conditions of similar density and complexity during actual operations in the same airspace.The results are shown in Fig. 10, averaged over all traffic 1 Difficulty ratings: easy (1-3), average (4)(5)(6), hard (7)(8)(9), and impossible (10) scenarios in the two simulations conducted in 2009.Unlike previous simulations, the December 2009 simulation included automated conflict avoidance in the EDA advisories.As seen in Fig. 10, the mean and peak workload ratings with EDA were lower than estimates of peak workload provided by controllers for the same airspace in current airtraffic operations.As expected, workload was highest in Sector 9, where the EDA clearances were issued.In general, workload was only slightly lower in Sector 16.This was attributed to the difficulty of maintaining situational awareness with airplanes flying disparate pathstretch routes advised by EDA.Workload in the low-altitude airspace (Sector 15) was far below the mean and peak workload levels estimated in current operations.Controllers explained that EDA resulted in high workload occurring primarily in the initial high-altitude sector, as opposed to current operations where high workload is experienced throughout the arrival airspace during busy traffic conditions.During simulation debriefs, controllers commented that the 3D-PAM concept with EDA appeared "very workable", and that it generally reduced their workload in comparison to today's operations.Controller trust in EDA increased steadily over the course of the simulations.Furthermore, controllers felt that issuing combined speed and path clearances by voice was feasible, given the phraseology and procedures previously described.Importantly, workload appeared less with EDA despite having only one (R-side) controller handling each arrival sector.Controllers commented that during similar traffic conditions in today's operations, both an R-side and a D-side controller would be assigned to each sector.Once a mature EDA prototype is available at the end of the 3D-PAM development cycle, additional studies will be conducted to more fully assess the feasibility of voice-based clearance delivery.These studies will compare EDA against a TMA-only baseline, as was done for the ZFW proof-of-concept simulations.Upcoming simulations will include added fidelity such as pilot requests, voice chatter, radio distortion and, most importantly, models of trajectory uncertainty.
+Trajectory Prediction PerformanceAccurate and precise trajectory predictions are essential to the success of EDA.Ground-based trajectory predictions must adequately model trajectories resulting from FMS guidance and control, including any compensating pilot inputs.The accuracy of EDA trajectory predictions is limited by uncertainty in inputs such as forecast winds, aircraft weight and aircraft aerodynamic and propulsive performance models.Data collected during the 2009 Denver field test were used to study the performance of current EDA trajectory predictions.Results were based on a sample size of 270 flights, which were issued pre-scripted EDA clearances.Together with flight plan information, these pre-scripted clearances provided the intent information necessary to allow post-flight comparisons of EDA predictions against radarderived truth data.Because of its importance, the accuracy and precision of TOD predictions were given priority.A histogram of TOD prediction errorbroken down by aircraft type -is shown in Fig. 11.The median absolute error in TOD prediction was found to be 5.4 nmi, with 47% of flights having an absolute TOD prediction error of less than 5 nmi. 2 Because of the dependency on aircraft type seen in these data, it is concluded that TOD prediction error is due largely to errors in modeling aircraft weight, thrust and drag.To investigate the affect of aircraft weight error on TOD prediction, actual weight (obtained from the pilot data sheets described in Section 3.2) was substituted for the nominal weight assumed in EDA predictions for the B-737-800.As shown in Fig. 12, pilotreported weight values reduced mean TOD prediction error from 9.7 nmi to 6.9 nmi.This analysis illustrates the potential benefit of exchanging parameters between airborne and ground-based automation for improving and harmonizing trajectory predictions.Stell [13] presents more detailed analysis of ground-based TOD prediction error based on the Denver field data.In order to maximize arrival throughput during OPD operations, EDA must effectively deliver aircraft to the meter fix in accordance with the TMA schedule.Using the data collected at Denver, arrival-time predictions generated 20 minutes upstream of the meter fix were compared with actual meter fix crossing times.Results are shown by the histogram in Fig. 13.From these data, mean absolute arrivaltime prediction error was found to be 11.5 sec across all aircraft types, with 80% of flights having an absolute error less than 20 sec.Because arrival-time prediction error did not vary significantly across aircraft type, it was concluded that this error was due mainly to errors in modeling winds, which directly affect groundspeed estimates in along-track trajectory predictions.Simulation observations suggest that current EDA along-track prediction accuracy -represented by the arrival-time performance in Fig. 13 -is likely adequate for end-state operations.Current TOD-prediction accuracy, however, is likely not adequate without changes to procedures and/or expansion of conflictavoidance buffers.In general, the required accuracy of EDA predictions depends on two factors: 1) controller acceptance and trust of the automation, and 2) retention of airspace capacity for accommodating maximum throughput.The former requirement is related to the probability of success desired by controllers in avoiding conflicts with strategic EDA clearances without any further corrective action downstream.To achieve the desired level of performance, buffers can be added to the separation minima used by EDA in its conflictavoidance solutions.Although resolution buffers can be added to compensate for almost any degree of prediction uncertainty, airspace capacity will be compromised if buffers are made too large.With these considerations in mind, a simulation is planned later this year to quantitatively define the accuracy and precision required in EDA trajectory predictions, with emphasis on TOD.
+ConclusionsA prototype of the Efficient Descent Advisor (EDA) has been developed as a controller tool for accommodating fuel-efficient descents during high-density operations where traffic and throughput constraints are prevalent.A series of high fidelity, human-in-the-loop simulations were carried out with controller participants to obtain end-user feedback critical for shaping the concept and design of EDA.Controller reaction was encouraging, suggesting that EDA has the potential to be implemented as a near-term capability with only minor changes to controller roles, responsibilities and procedures.Although EDA provides an obvious application for data link communications, simulations show that trajectory-based clearances involving speed and path can successfully be issued by voice, using the phraseology and user interface described.By providing controllers with a single, comprehensive arrival solution upstream, EDA was shown in simulation to reduce the number of required maneuver clearances by 70%, suggesting a significant potential reduction in controller and pilot workload.Controller feedback obtained during simulation was incorporated directly into the design of the EDA prototype.At the request of controllers, EDA solutions were provided to strategically avoid downstream traffic conflicts and crossing of lateral sector boundaries.Functions were added to allow controllers to quickly display trajectory informationincluding Top-of-Descent (TOD) -to preserve situational awareness during trajectory-based operations in which airplanes are flying a variety of route, altitude and speed profiles.Accurate ground-based trajectory prediction remains a key challenge for EDA implementation.Data collected during live traffic operations at Denver reveal that better predictions of TOD are required to implement EDA without procedurally sharing TOD information between controllers and pilots and/or increasing buffers for conflict avoidance in the vicinity of TOD.Simulations later this year will focus on trajectory-prediction performance in the continued effort to develop EDA for NextGen.Fig. 1 .1Fig. 1. 3D-PAM Concept
+Fig. 2 .2Fig. 2. EDA Functional Elements
+Fig. 3 .3Fig. 3. Airspace Used for ZFW Simulations
+Fig. 4 .4Fig. 4. Controllers Using EDA in ZDV Simulation
+Fig. 5 .5Fig. 5. Airspace Used for ZDV Simulations
+Fig. 6 .6Fig. 6.Example of EDA CHI Upon Advisory Request
+Fig. 7 .7Fig. 7. Flight Tracks Observed in ZFW Simulation
+Fig. 8 .8Fig. 8. Location of Aircraft When Issued Maneuver Clearances in ZFW Simulation
+Fig. 9 .9Fig. 9. Number of Maneuver Clearances Issued Per Aircraft in ZFW Simulation
+Fig. 10 .10Fig. 10.Workload Measures from ZDV Simulations
+Fig. 11 .11Fig. 11.TOD Prediction Error from ZDV Field Test
+2Fig. 12 .12Fig. 12. Improvement in TOD Prediction Using Pilot-Reported Aircraft Weight for the B-737-800
+Fig. 13 .13Fig. 13.Meter-Fix Arrival Time Prediction Error
+		
+		
+			
+Contact Author Email AddressRichard.A.Coppenbarger@nasa.gov
+Copyright StatementThe authors confirm that they, and/or their company or organization, hold copyright on all of the original material included in this paper.The authors also confirm that they have obtained permission, from the copyright holder of any third party material included in this paper, to publish it as part of their paper.The authors confirm that they give permission, or have obtained permission from the copyright holder of this paper, for the publication and distribution of this paper as part of the ICAS2010 proceedings or as individual off-prints from the proceedings.			
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+I. IntroductionCCOMMODATING efficient arrival operations under challenging traffic conditions is a key objective for airtransportation modernization efforts taking place in the United States and throughout the world. 1 Efficient operations can be described as those that maximize system throughput while minimizing fuel consumption, environmental emissions and noise.An arrival trajectory that maximizes efficiency within operational constraints is referred to as an Optimized Profile Descent (OPD).These trajectories, where possible, involve a continuous descent at low engine power from cruise altitude to the final approach fix.The ability of pilots to plan and execute continuous descent trajectories through their onboard Flight Management System (FMS) has been well demonstrated in the field.Examples include the CASSIS flight trials at Stockholm 2 and on-going Tailored Arrivals operations at San Francisco and Los Angeles. 3The challenge, however, is to conduct these operations in the presence of traffic and airspace constraints.Today, continuous descents are typically precluded or disrupted during moderate to heavy traffic conditions by controller actions required to separate, schedule and sequence aircraft for arrival.These frequent, tactical control actions include temporary altitude assignments, speed changes, lateral vectoring, and airborne holding.While such actions serve well to manage throughput and separation, they impede an otherwise continuous descent to the runway.For efficient arrival operations during busy traffic conditions, trajectories must be planned in a manner that considers capacity and airspace constraints.Scheduling automation for maximizing arrival throughput in the presence of capacity constraints was previously developed by NASA as a component of the Center TRACON Automation System (CTAS) and is now deployed at each Air Route Traffic Control Center (ARTCC) in the United States.This technology -the Traffic Management Advisor (TMA) -computes time-based metering schedules and sequences for aircraft entering terminal airspace over designated meter fixes at the Terminal Radar Approach Control (TRACON) boundary. 4Today, controllers are provided with TMA scheduling information on their radar display, but they have no automation to assist them in efficiently controlling aircraft to those schedules.Without additional automation, controllers must resort to the aforementioned tactical control techniques for meeting TMA schedules while maintaining separation.In recent years, research at NASA Ames has combined TMA scheduling at the TRACON boundary with additional, optimized scheduling within the TRACON itself for enabling efficient descents to the runway during capacity-constrained conditions. 5,6This automation concept, referred to as the Terminal Airspace Precision Scheduling System (TAPSS), requires that ARTCC controllers first deliver aircraft accurately and precisely to meter fixes located at the TRACON boundary in accordance with TMA schedules.NASA has developed the Efficient Descent Advisor (EDA) to allow ARTCC controllers to perform metering operations while avoiding conflicts and keeping aircraft on efficient arrival trajectories managed through an FMS. 7his technology was developed with the goal of near-term field deployment through a collaborative government/industry effort known as 3D-Path Arrival Management (3D-PAM).The effort began in 2008 and included NASA, FAA and Boeing as primary members of the research-transition team.This paper describes results from the final Human-in-the-Loop (HITL) simulation used to validate EDA's benefits potential and ready it for official transfer to the FAA under 3D-PAM.A brief description of the operational concept that resulted from the three-year iterative design and development effort is also presented along with a summary of the products delivered to the FAA in November 2011 to complete the transition of EDA from the research domain towards operational deployment in the Next Generation Air Transportation System (NextGen).
+II. Concept OverviewEDA leverages core elements of CTAS to develop strategic arrival solutions over time horizons of up to 30 minutes to assist radar-side (R-side) sector controllers in ARTCCs.Solutions are sought that allow aircraft to perform fuel-efficient, continuous descents at low engine power while satisfying time-based-metering restrictions at the TRACON boundary.EDA calculates trajectory predictions in search for solutions that conform to Scheduled Time of Arrival (STA) constraints at the meter fix computed by TMA.In searching for these solutions, EDA attempts to avoid conflicts along the arrival path to the meter fix in order to minimize downstream interruptions to an otherwise continuous descent.Trajectories are modeled using an idle-thrust descent for large jet transports and a fixed flight-path-angle descent for regional jets.EDA evolved over the course of 3D-PAM to support operations under all traffic levels.In light traffic, where no delay absorption is required to meet capacity constraints, the TMA STA for an aircraft will match its original Estimated-Time-of-Arrival (ETA).Using EDA for all traffic levels helps to standardize arrival procedures and allows more universal sharing of flight intent between controllers and pilots and their respective automation systems.Once computed, EDA trajectory solutions are translated into specific maneuver advisories that are presented to controllers upon their request.Advisories involve a combination of cruise-speed, descent-speed and path adjustments, as illustrated in Fig. 1.The advisories become available once aircraft have crossed the TMA freeze horizon, when STAs are no longer subject to change through automatic scheduling.The freeze horizon is set within the TMA system to between 130 nmi and 200 nmi upstream of the meter fix, depending on traffic, airspace, and environmental conditions.Once an aircraft has crossed the freeze horizon, controllers are alerted to the availability of an advisory by the "EDA" symbol that appears at the bottom of the aircraft's flight-data block.This symbol is referred to as the EDA portal.Fig. 2 illustrates the primary features of the EDA prototype as they appear on the sector controller's display.In this example, the controller has clicked on the EDA portal for a flight near the center of the screen.Once the portal is clicked, a separate window opens on the display containing the advisory information for the flight.The trial-plan route associated with the advisory is also displayed, which includes the location of the aircraft's predicted top-ofdescent (TOD) point (not visible in Fig. 2, since it lies off the map in the sector to the left).In this example, the EDA advisory has cruise speed, descent speed, and path components.Path advisories are issued for conflict avoidance or whenever speed adjustments, i.e., slow downs, are insufficient for absorbing the delay required to meet an STA.Path-stretch advisories are in the form of dogleg maneuvers that have start and end points along the aircraft's nominal flight plan.EDA ensures that path-stretch trajectories do not cross sector boundaries in order to prevent additional inter-sector-coordination workload for controllers.EDA advisories are designed to facilitate clearance delivery by voice using standard FAA phraseology and procedures.For advisories that include path stretching, the controller first issues EDA airspeed clearances to the flight deck followed by saying "advised routing when ready to copy," which prepares pilots for receiving the subsequent path-stretch clearance.Once received on the flight deck, pilots enter the EDA clearances directly into their FMS for guidance and control along the intended arrival trajectory using Required Navigation Performance (RNP) criteria if specified. 8The TOD point used for flight control is calculated by the FMS using its own trajectory prediction process that incorporates the EDA clearance information.Similar to EDA, the FMS uses a constant Mach/calibrated-airspeed profile in its descent trajectory predictions.Once controllers receive a readback of the clearance from the flight deck, they update their ground-based automation with EDA flight intent by pressing an accept button in the advisory window.At this point, controllers are given the option of including a descent authorization together with the maneuver clearance.If a descent authorization is included, the aircraft can descend at its FMS-computed TOD point without further instruction from the controller.Further details concerning the EDA user interface can be found in Ref. 7.As a flight progresses along the intended trajectory, EDA continues to monitor conformance to its meter-fix STA by computing new trajectory predictions every 12-second radar cycle.A corrective advisory is generated if an ETA update falls out of conformance with the targeted STA by a set tolerance (20 seconds for simulation described herein).Conformance monitoring provides robustness in the presence of real-world trajectory prediction uncertainty.Controllers are able to manage TOD uncertainty by delaying the authorization to descend until the aircraft is well clear of any traffic that may be of concern at lower altitudes.Separating descent authorization from the primary maneuver clearance is the preferred method of operation during busy traffic conditions.To monitor for separation assurance along the arrival trajectory once an advisory has been accepted, a strategic conflict probe is included in the concept-of-operations developed for EDA under 3D-PAM.A NASA-developed conflict-probe 9 was included as a component of the EDA prototype that was evaluated in the final HITL simulation described here.
+III. Simulation ApproachA final HITL simulation of EDA in support of 3D-PAM was completed in September 2011.The objective of the simulation was to assess the potential benefits of EDA using a mature research prototype that represented a culmination of design decisions made based on six prior HITL simulation experiments.This simulation also incorporated the latest phraseology and procedures, resulting from prior simulation studies and technical interchange, needed to communicate EDA information and coordinate actions between controllers and pilots.To measure benefits, operations with EDA were compared against baseline operations in which controllers were provided with TMA scheduling automation only, representative of today's environment.The benefits evaluated were potential improvements in time-based metering accuracy, reductions in fuel and emissions, and reductions in controller workload.The simulation was conducted in the Crew-Vehicle Systems Research Facility (CVSRF) at NASA Ames Research Center.The three controller participants included two active-duty controllers from Denver ARTCC (ZDV) and a recently retired controller from the same facility.The two active-duty controllers were representatives of the National Air Traffic Controllers Association (NATCA).In the simulation, controllers were seated at three separate radar-side sector positions covering northeast ZDV airspace (Fig. 3).Controllers communicated with pseudo pilots that managed simulated flights from a separate location in the CVSRF.Two pseudo pilots were assigned to each airspace sector -one to manage radio communications and the other to make any necessary flight control inputs based on controller instructions.For added fidelity, two simulator cabs flown by airline pilots were included in the simulation.These included a Level-D-compliant B747-400 cab and a specialized research cab that was fitted with a dynamics model so that it behaved as a B737-800.NASA's Multi Aircraft Control System (MACS) was used to provide the user interface for controllers, emulating an ARTCC radar display in the field. 10MACS also provided the user interface and aircraft dynamics model (target generator) for flights managed by pseudo pilots.The simulation system, as it pertained to EDA operations, is illustrated in Fig. 4.The independent variables of the simulation involved the automation mode (EDA versus TMA only), traffic scenario, and controller sector assignment.The two traffic scenarios contained a mix of Boeing and Airbus wide- body and narrow-body jets along with Bombardier regional jets.For both scenarios, the flow-rate constraint at the meter fix was set to 36 aircraft per hour, typical of the rate used in field operations at ZDV.In one scenario, traffic demand exceeded the meter-fix capacity constraint most of the time, while in the other, demand dipped well below the capacity constraint at times.These scenarios were designed to emulate sustained and periodic arrival rushes into Denver.Both scenarios contained numerous conflicts between arrivals and overflights.For the purpose of discussion, the scenario with higher sustained traffic demand is referred to as the heavy traffic scenario, while the other is referred to as the moderate traffic scenario.The scenarios were designed to challenge controllers without exceeding their ability to manage the airspace alone, i.e., without help from a second controller at a D-side position.Ensuring that traffic scenarios were manageable by R-side controllers alone was found in pretesting to be of particular concern for baseline operations without EDA.A wind field provided by a Rapid Update Cycle (RUC) forecast model was included in each simulation run. 11he wind forecast corresponded to the time and date (July 21, 2011) at which traffic was captured from live data to provide input to the simulation.Winds were from the southwest with an average magnitude of approximately 60 kts at 36,000 ft.The winds, along with atmospheric temperature and pressure data included in the RUC forecast, were incorporated into both EDA and FMS trajectory predictions.Controllers were rotated through each sector position using three unique seating arrangements designed to study the effect of controllers on EDA benefits.The three controller stations were arranged from right to left corresponding to ZDV Sectors 9, 16 and 15, shown in Fig. 5. Sectors 9 and 16 are adjacent high-altitude sectors, and Sector 15 is the low-altitude sector (below 24,000 ft) that feeds arrival traffic to the meter fix (SAYGE).The two parallel arrival routes shown in Fig. 5 served as 'backbones' for EDA path-stretch maneuvers.The combination of independent variables resulted in the 12-run test matrix shown in Table 1, with runs arranged to minimize learning effects that might confound results.In the TMA-only baseline runs, controllers were provided with functions and displays representative of what they have available today in the field.This included a meter list showing TMA-computed ETA, STA and predicted arrival time error (STA -ETA) rounded to the nearest minute.In the TMA-only runs, controllers attempted to deliver aircraft to the meter fix with zero minutes of error displayed in the meter list, i.e., within ±30 sec of TMAcomputed STAs, accounting for rounding.In the EDA runs, controllers attempted to deliver aircraft within ±20 seconds of STAs, consistent with the corrective-advisory error tolerance previously described.In all simulation runs, controllers were asked to maintain current separation standards for aircraft in en route airspace: 5 nmi horizontal and 1,000 ft vertical.In EDA runs, controllers were provided with a strategic conflict probe with a 25-minute look-ahead time to assist with separation assurance.In TMA-only runs, controllers were provided with a conflict-alert capability similar to that available in the field today, implemented using the EDA conflict probe but with a look-ahead time set to 3 minutes.In all simulation runs -with and without EDA -trajectory-prediction uncertainty was added to better represent real-world performance.Errors in forecast winds and aircraft weight estimates were modeled to produce TOD and meter-fix ETA errors similar to those observed in field tests at Denver ARTCC. 12In addition, the variance in the onset of heading changes associated with EDA path-stretch clearances requiring immediate turnout maneuvers was modeled using flight-deck simulation results provided by Boeing.Wind errors had an RMS of 3.8 m/s; weight errors for each flight were randomly selected from a uniform distribution with bounds at approximately ±15% of the aircraft's nominal landing weight; variance in the onset of heading changes from the flight deck was ±8 s (σ); and resulting TOD errors were ±8 nmi (2σ).
+IV. Simulation Results and Discussion
+A. Metering PerformanceTMA schedules aircraft in order to maximize landing rate without exceeding capacity constraints stipulated at the runway threshold and/or designated points along the arrival route.As described previously, the capacity constraint for the simulation was set to 36 aircraft per hour over the meter fix SAYGE for both traffic scenarios.Since EDA computed advisories to conform to TMA arrival times at the meter fix, it was not expected to directly increase the volume of aircraft delivered to the meter fix in comparison with TMA-only operations.EDA did, however, substantially improve the ability of controllers to conform to the TMA-assigned arrival sequence and schedule at the meter fix, which can improve overall arrival efficiency by reducing the need for delay buffers and corrective control actions within the TRACON.In TMA operations today, controllers often manually change the TMA-assigned aircraft sequence at the meter fix without affecting overall flow rate by performing what is know as a swap.In a swap, the STAs assigned to a pair of aircraft are switched with one another, resulting in a sequence change in the displayed meter list.This is typically performed in order to prevent an aircraft with a faster original cruise speed from having to pass another in order to meet its TMA-assigned STA.Such maneuvers -which result from the first-come-first-serve algorithm that TMA applies to initial ETA predictions to the meter fix, described in Ref. 4 -often increase controller workload due to the closer monitoring of separation needed as one aircraft passes another.Although such swaps do not usually affect throughput into the TRACON, they potentially decrease runway throughput by requiring additional spacing buffers on final approach to compensate for a large aircraft being inadvertently sequenced ahead of a smaller aircraft prior to TRACON entry, as illustrated in Fig. 6.Manual sequence adjustments in en route airspace are more likely to disrupt terminal airspace operations and affect inter-arrival spacing at the runway for flights on continuous descents, which are less tolerant to maneuvering after TOD.Table 2 shows the number of manual sequence swaps performed by controllers during each simulation run, grouped by controller seating order and traffic scenario.Although controllers were allowed to perform swaps in both TMA-only and EDA runs, no swaps were found necessary by controllers when using EDA.In contrast, the average number of swaps per run with TMA-only was 3.33 and 6.33 for the moderate and heavy traffic scenarios respectively.Based on controller commentary and observations, EDA appears to have reduced the need for sequence swaps partially due to a wider utilization of the airspace through automated path stretching for delay absorption.With aircraft on substantially different horizontal paths, controllers were able to assure separation during overtake maneuvers with less effort had aircraft been on the similar routes.The ability of controllers using EDA to more accurately and precisely deliver aircraft to the meter fix in conformance with TMA schedules was also studied in the simulation.Better schedule conformance reduces the possibility of missed arrival slots.With fewer missed slots, controllers can maximum runway throughput with less compensatory delay reserved for the terminal airspace, known as TRACON delay buffer.Reducing the intentional TRACON delay buffer minimizes average TRACON transit time, thereby accommodating a continuous descent to the runway with reduced overall flight delay and fuel consumption. 13,14imulation results showed substantial improvement in arrival-time accuracy and precision at the meter fix with the use of EDA, as shown by the error histograms in Figs.7 and8.Fig. 7 compares actual meter-fix crossing times with original scheduled times of arrival assigned by TMA prior to any swapping of aircraft pairs.Fig. 8 makes the same comparison, but against scheduled times of arrival that account for swapped aircraft pairs.
+Figure 6. Possible effect of sequence change on final approach spacing requirements Table 2. Number of aircraft sequence swaps per runThe results in Fig. 7 characterize the total metering performance over all simulation runs, capturing how well controllers matched the TMA-optimized sequence and schedule with and without the use of EDA.As indicated by summary statistics in the figure, the results show a 92% reduction in mean arrival-time error (from 26 s to 2 s) over the meter fix with a corresponding reduction in the standard deviation of arrival-time error of 79% (from 71 s to 15 s) when using EDA to conform to the original TMA sequence and schedule, i.e., without controller-initiated swaps.The results in Fig. 8 characterize metering performance while accounting for the sequence swaps that occurred in the TMA-only runs.Results show a 94% reduction in mean arrival-time error (from 32 s to 2 s) over the meter fix with a reduction in the standard deviation of arrival-time error of 64% (from 42 s to 15 s) when using EDA to conform to the final TMA schedule.The results show that even when manual sequence adjustments by the controller are accounted for, the improvement in metering accuracy with EDA is substantial.According to subject matter experts, metering accuracy achieved in the field with TMA-only is even lower than that observed in the simulation with TMA-only.In real-world operations at Denver ARTCC, meter-fix delivery accuracy is typically between 1 and 2 minutes.
+B. Fuel and EmissionsA direct benefit mechanism of EDA is its potential to improve flight-path efficiency in ARTCC airspace.In general, descent trajectories that are flown at low engine power and avoid level-off segments are most fuel efficient.For the typical problem that EDA addresses, however, where aircraft are subjected to metering constraints that require delay absorption, maximizing fuel efficiency is more complicated than simply avoiding level-off segments altogether.Indeed, leveling an aircraft off at lower altitudes is an effective means by which controllers absorb delay, since it reduces an aircraft's groundspeed without changing its airspeed.Studies show that maximum fuel efficiency under a fixed arrival-time constraint is achieved by slowing the aircraft in cruise flight towards its maximumendurance airspeed while planning a continuous descent that is initiated as early as possible upstream of the meter fix. 15This strategy -employed by EDA unless speed adjustments are needed for conflict resolution -theoretically maximizes fuel efficiency for any magnitude of required delay absorption.For large delays that require path stretching, EDA first ensures that the aircraft's descent-and cruise-airspeed profile has been minimized (in that order).Once the airspeed has been minimized, fuel consumption is largely invariant to the horizontal path that the aircraft flies to absorb any remaining delay in the fixed-flight-time problem.A first-order indication of flight-path efficiency improvements can be made by comparing the number of leveloff segments with and without the use of EDA.Fig. 9 shows the vertical tracks resulting from the simulation under the heavy-traffic scenario for each of the three rotations by which controllers were assigned to the airspace sectors.By reducing unnecessary level-off maneuvers, EDA not only offers to save fuel, but it also frees up more altitudes for controllers to perform separation assurance and accommodate overflights.The reduction in the number of leveloff segments with EDA is further shown in Fig. 10, which counts the number of level-off segments observed during descent over the entire simulation.With TMA only, the majority of flights received at least one level-off instruction from the controller.With EDA, the majority of flights -almost four times as many than with TMA-only -were able to execute an uninterrupted descent to the meter fix from cruise altitude.
+Figure 9. Vertical Flight Tracks in Heavy-Traffic Simulation ScenarioTo measure efficiency directly, the fuel consumed along the arrival trajectory was compared across flights managed with and without EDA for the same traffic scenario and controller-sector rotation.Two different methods were used to compute fuel burn estimates.The first used the aerodynamic and propulsion models intrinsic to CTAS, upon which EDA's trajectory predictions themselves are based.Since a real-time fuel depletion model was not available within either the MACS dynamics model or CTAS, this approach required using piecewise trajectory predictions to recreate flown horizontal and vertical paths. 16The second method used a technique developed by Chatterji 17 that relied on state estimation together with Eurocontrol's Base of Aircraft Data (BADA) version 3.9 to estimate fuel burn for a given flight track input consisting three-dimensional position versus time.These two techniques are referred to respectively as CTAS and BADA in the results that follow.Using both the CTAS and BADA methods, the fuel consumed by each flight was estimated from the ARTCC boundary to the meter fix in order to reflect every action taken by the controller team for metering and separation assurance.Because controllers were allowed to swap aircraft at their discretion as previously described, fuel burn comparisons between flights managed with EDA versus TMA only were grouped depending on whether either flight in a pairwise comparison was swapped or not.The average fuel savings afforded by EDA for flights that did not include swaps are shown in Fig. 11.The fuel savings, on average, were found to be higher using the BADA method than the CTAS method.More importantly, the estimated fuel savings with EDA were dependent on controller-seating order and traffic scenario.The dependence of seating order indicates that fuel benefits are a function of how each controller uses EDA relative to baseline automation; some controllers will benefit from EDA more than others, depending on how proficiently they manage arrivals using TMA-only.Furthermore, fuel benefits were generally higher in the heavy traffic scenario than the moderate scenario.This indicates that controllers benefited more from strategic EDA solutions during complex traffic conditions.In such conditions, controllers were less likely to resolve combined metering and separation problems efficiently using their more tactical, legacy techniques.This same trend is observed in the results shown in Fig. 12, which includes flights that received a sequence swap when managed using TMA only.During such swaps, some flights received a fuel penalty, as they were re-sequenced to a later slot in the TMA schedule requiring more delay absorption, while others received fuel benefits as they were moved to an earlier slot that allowed more direct routing to the meter fix.Since it includes flights with schedule swaps, Fig. 12 contains substantially more pairwise fuel-burn comparisons than Fig. 11 (198 vs. 94).Results from the CTAS and BADA methods were averaged for the remaining fuel-burn analyses presented in this paper, As expected, the mean and range of fuel savings with EDA tended to increase with aircraft size.Figs. 13 and14 show the difference in fuel burn for each flight in the simulation managed with EDA vs. TMA-only, excluding and including swaps, respectively.It can be seen here that maximum fuel savings from EDA are particularly large for the small sample of B747 and B777 flights in the simulation.Average per-flight fuel savings, over the entire simulation, are shown in Table 3 along with corresponding estimates of the reduction in carbon dioxide and nitrogen compounds.These greenhouse gas emissions scale directly with fuel burn and represent the primary pollutants from jet-engine combustion. 18
+C. Controller Workload, Acceptability, and CommunicationsBy providing advisories designed to implement metering and conflict avoidance through a single, comprehensive arrival instruction, EDA potentially reduces controller workload and the number of communications required between controllers and pilots to maneuver aircraft in busy arrival airspace.These direct benefits to controllers were evaluated in the simulation together with the overall acceptance of EDA automation by the controller team.To evaluate controller workload, subjective measurements were taken during and after each simulation run.During each run, controllers were asked to rate their workload on a scale from 1 to 6, from easiest to hardest according to the scale shown in Table 4.These real-time workload ratings were obtained from each controller at the three sector positions every five minutes over the course of each simulation run.In addition to these real-time workload ratings, each controller was asked to rate their overall workload at the end of each run on a questionnaire based on a modified version of the NASA Task Load Index (TLX) rating procedure. 19The post-run TLX questionnaire asked controllers to rate their level of mental demand, temporal demand, performance and frustration on an analog scale from low to high, as shown by the example in Fig. 15.Ratings for each workload element were then converted to a numerical scale of 1 (low) to 10 (high) and then used to compute a composite workload rating.This composite rating was a weighted-sum average of the TLX ratings based on controller responses to questions regarding the importance of each workload element relative to the others.Analysis of the real-time ratings found a slight reduction in workload with EDA in comparison with TMA-only over both traffic scenarios.The average workload rating across all controllers and traffic scenarios with EDA was rated as 1.39 versus 1.63 with TMA-only.The standard deviation in both cases was small (<0.06).The low, absolute workload ratings under both automation conditions indicated that controllers found the traffic scenarios easier to manage than expected by the designers of the experiment.Albeit small, the workload reduction with EDA was found to be statistically significant (p < 0.05) based on a repeated-measures Generalized Linear Model (GLM) analysis that examined the causal effects of the independent variables in the simulation and their interactions.GLM analysis of the real-time workload ratings also revealed that controllers found the heavy traffic scenario somewhat more difficult to manage than the moderate traffic scenario on a statistically significant basis, as expected.GLM analysis of the post-run TLX ratings also indicated that the heavy scenario was more challenging than the moderate scenario in terms the frustration workload element and the overall composite TLX workload score.Interestingly, GLM analysis revealed no statistically significant effect of the automation condition -EDA versus TMA only -on TLX workload ratings.This contrasted with the statistically significant effect of automation mode on real-time workload ratings and informal post-run and post-simulation controller comments that suggested workload was lower with EDA.To evaluate controller acceptance of the EDA automation, a series of Likert-scale 20 questions were asked following each simulation run, focusing on operational safety and perceived automation benefits.GLM analysis of responses showed that all controllers found traffic operations with EDA highly acceptable, while the majority of controllers (2 of 3) found traffic operations with TMA alone only moderately acceptable.Similarly, all controllers considered operations with EDA to be very safe, with the majority of controllers feeling that EDA improved their ability to assure separation in comparison to using TMA-only.The reduced number of communications required between controllers and pilots to meter and separate aircraft with EDA was a likely contributor to the favorable workload and acceptability ratings given by controllers for EDA.The number of required maneuver clearances -i.e., those that directly affected the trajectory of aircraft -was found to decrease on average by 60% in EDA operations compared to those with TMA-only over all simulation runs.An example of this reduction is shown Fig. 16, which maps the location of aircraft at times when maneuver clearances were issued during the heavy traffic scenario using TMA-only (run 11) vs. EDA (run 5).A histogram showing the number of maneuver clearances with and without EDA for all simulation runs is shown in Figure 17.These data show that with TMA-only it was most common for aircraft to require six maneuver instructions from controllers in ARTCC airspace for metering and separation.With EDA, however, it was most common for aircraft to receive just a single, comprehensive maneuver instruction.
+V. Technology TransitionThe results described above were reported to the FAA to help complete the transition of EDA towards operational deployment.Official technology transfer of EDA under 3D-PAM included the items listed and described in Table 5.The primary deliverable was a functional-design specification of the final EDA research prototype used to support the simulation described herein.The research prototype itself was also included as a deliverable in order to provide a reference for how specified functions were implemented in software.Items described in Table 5 are specific to those delivered by NASA.Additional deliverables from the 3D-PAM partners included a concept-of-use document developed by the FAA with input from NASA and Boeing, reports of Boeing-led simulations that focused on pilot operations, and a NAS-wide cost and benefits assessment.A NASA technical report providing a compendium of the items described in Table 5 is being prepared for future publication.
+VI. ConclusionThe Efficient Descent Advisor was iteratively designed and developed through a series of HITL simulations that included trajectory-prediction uncertainty models based on field-test data.Results from initial simulations were used to refine the concept, algorithms, user-interface and procedures behind the automation.Having developed a mature research prototype that reflected a culmination of design decisions, a final simulation was carried out to measure EDA benefits.Results showed that EDA allowed controllers to perform time-based metering operations in busy traffic with substantially improved accuracy and precision.In comparison with scheduling automation only (i.e., the Traffic Management Advisor), EDA improved meter-fix delivery accuracy by 92% and reduced the standard deviation of arrival error by 79%.EDA also substantially improved vertical flight-path efficiency, providing a fourfold increase in the number of aircraft able to fly an FMS-guided continuous descent to the meter fix.Results showed that EDA saved an average of 110 lbs of fuel per flight in ARTCC arrival airspace, with significantly greater fuel savings for larger aircraft types.Although dependent on how each controller used the EDA automation, average fuel savings and corresponding emission reductions were shown to increase significantly as traffic complexity increased.EDA was also found to reduce controller workload, likely due in part to a 60% reduction in the number of maneuver instructions required between controllers and pilots with EDA in comparison with TMAonly baseline operations.Findings from this simulation capped a three-year collaborative effort between government and industry to successfully transition EDA from the research domain towards operational deployment.Figure 1 .1Figure 1.EDA advisory components
+Figure 2 .2Figure 2. Controller display showing use of EDA in HITL simulation
+Figure 4 .Figure 3 .Figure 5 .435Figure 4. HITL simulation environment
+Figure 8 .Figure 7 .87Figure 8. Actual meter fix crossing time versus TMA schedule with swaps
+Figure 10 .10Figure 10.Number of level-off segments in descent
+Figure 12 .Figure 11 .Figure 13 .121113Figure 12.Average fuel savings per flight with EDA, including swaps
+Figure 16 .Figure 15 .1615Figure 16.Example of reduction in maneuver clearances with EDA
+Figure 17 .17Figure 17.Number of maneuver clearances per flight
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+Table 1 . Simulation Test matrix1Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5611
+Table 4 . Real-time workload scaleTable 3 . Average per-flight reduction in fuel and emissions from EDA over entire simulation Per-Flight Reductions, lbs Moderate Traffic Heavy Traffic All Traffic43Fuel60191110CO2189595346NOx1.23.92.2
+			This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5611
+			Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5611
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+		
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+			
+AcknowledgmentsThe authors thank the controller teams from Denver ARTCC -led by Greg Dyer -for their participation and feedback throughout the research and development effort.The authors also thank the FAA project manager for 3D-PAM, Dr. Charles M. Buntin, for his leadership and assistance with technology transfer.Thanks also go out to Boeing and airline partners United/Continental and SkyWest for providing invaluable expertise pertaining to flightdeck automation and procedures.
+			
+
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+		Design and Operational Evaluation of the Traffic Management Advisor at the Fort Worth Air Route Traffic Control Center
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+			SEngelland
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+		Design and Evaluation of the Terminal Area Precision Scheduling and Spacing System
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+			HNSwenson
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+			JThipphavong
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+		Berlin, Germany
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+	Swenson, H.N., Thipphavong, J., Sadovsky, A., Chen, L., Sullivan, C., and Martin, L., "Design and Evaluation of the Terminal Area Precision Scheduling and Spacing System," 9th USA/Europe ATM R&D Seminar (ATM2011), Berlin, Germany, 14-17 June 2011.
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+		Benefits of Continuous Descent Operations in High-Density Terminal Airspace Considering Scheduling Constraints
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+			MaryamKamgarpour
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+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
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+			American Institute of Aeronautics and Astronautics
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+	Robinson, III, J. E., and Kamgarpour, M., "Benefits of Continuous Descent Operations in High-Density Terminal Airspace Under Scheduling Constraints," 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Fort Worth, TX, 13-15 Sep. 2010.
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+		Development and Testing of Automation for Efficient Arrivals in Constrained Airspace
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+		Arrival Management with Required Navigation Performance and 3D Paths. 7th USA/Europe Air Traffic Management R&D Seminar
+		
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+		Field test evaluation of the CTAS conflict prediction and trial planning capability
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+			BDMcnally
+		
+		
+			RalphBach
+		
+		
+			WilliamChan
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+		Guidance, Navigation, and Control Conference and Exhibit
+		Boston, MA
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+			American Institute of Aeronautics and Astronautics
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+		MACS: A Simulation Platform for Today's and Tomorrow's Air Traffic Operations
+		
+			ThomasPrevot
+		
+		
+			JoeyMercer
+		
+		10.2514/6.2007-6556
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Hilton Head, SC
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+			American Institute of Aeronautics and Astronautics
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+			
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+	Prevot, T., and Mercer, J., "MACS: A Simulation Platform for Today's and Tomorrow's Air Traffic Operations," AIAA Modeling and Simulation Technologies Conference, Hilton Head, SC, 20-23 Aug. 2007.
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+		Accuracy of RUC-1 and RUC-2 Wind and Aircraft Trajectory Forecasts by Comparison with ACARS Observations
+		
+			BarryESchwartz
+		
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+			StanleyGBenjamin
+		
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+			StevenMGreen
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+			MatthewRJardin
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+		10.1175/1520-0434(2000)015<0313:aorarw>2.0.co;2
+	
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+		Weather and Forecasting
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+		1520-0434
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+			15
+			3
+			
+			2000
+			American Meteorological Society
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+	
+	Schwartz, B.E., S.G. Benjamin, S.M. Green, and M.R. Jardin, 2000: Accuracy of RUC-1 and RUC-2 wind and aircraft trajectory forecasts by comparison with ACARS observations. Weather. Forecasting, 15, 313-326.
+
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+		Predictability of Top of Descent Location for Operational Idle-Thrust Descents
+		
+			LaurelStell
+		
+		10.2514/6.2010-9116
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+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Berlin, Germany
+		
+			American Institute of Aeronautics and Astronautics
+			June 2011
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+	Stell, L., "Prediction of Top of Descent Location for Idle-thrust Descents," 9th USA/Europe ATM R&D Seminar (ATM2011), Berlin, Germany, 14-17 June 2011.
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+		Design Principles and Algorithms for Automated Air Traffic Management. Mission Systems Panel of AGARD and the Consultant and Exchange Program of AGARD
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+			HErzberger
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+		10.2514/6.2012-561114
+		
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+			1995. 2013
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+			Madrid, Chantillon, Moffet Field
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+	AGARD. Downloaded by NASA AMES RESEARCH CENTRE on April 17
+	Erzberger, H. (1995). Design Principles and Algorithms for Automated Air Traffic Management. Mission Systems Panel of AGARD and the Consultant and Exchange Program of AGARD (p. 31). Madrid, Chantillon, Moffet Field: AGARD. Downloaded by NASA AMES RESEARCH CENTRE on April 17, 2013 | http://arc.aiaa.org | DOI: 10.2514/6.2012-5611 14
+
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+
+	
+		Design Considerations for a New Terminal Area Arrival Scheduler
+		
+			JaneThipphavong
+		
+		
+			DanielMulfinger
+		
+		
+			AlexanderSadovsky
+		
+		10.2514/6.2010-9290
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
+		
+			American Institute of Aeronautics and Astronautics
+			Sep. 2010
+			
+		
+	
+	Thipphavong, J., and Mulfinger, D., "Design Considerations for a New Terminal Area Arrival Scheduler," 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Fort Worth, TX, 13-15 Sep. 2010.
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+	
+		Comparison of Fuel Consumption of Descent Trajectories under Arrival Metering
+		
+			TasosNikoleris
+		
+		
+			GanoChatterji
+		
+		
+			RichardCoppenbarger
+		
+		10.2514/6.2012-4818
+	
+	
+		AIAA Guidance, Navigation, and Control Conference
+		Minneapolis, MN; Indianapolis, IN
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+			American Institute of Aeronautics and Astronautics
+			Aug. 2012. Sep. 2012
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+	AIAA Guidance, Navigation, and Control Conference. submitted for publication
+	Nikoleris, T., Chatterji, G., and Coppenbarger, R., "Comparison of Fuel Consumption of Descent Trajectories under Arrival Metering," AIAA Guidance, Navigation, and Control Conference, Minneapolis, MN, 13-16 Aug. 2012. 16 Nagle, N., Trapani, A., Sweet, D., Carr. G., "Determining Trajectory Change Points from Simulation Radar Data for Use in a Trajectory Modeler, with an Application to Efficient Descent Advisor Benefits Assessment," AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Indianapolis, IN, 17-20, Sep. 2012 (submitted for publication).
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+		Fuel Burn Estimation Using Real Track Data
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+			GanoBChatterji
+		
+		10.2514/6.2011-6881
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+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, VA
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+			American Institute of Aeronautics and Astronautics
+			20-22 Sep. 2011. Jan. 2005
+		
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+	Fuel Burn Estimation Using Real Track Data
+	Chatterji, G.B., "Fuel Burn Estimation Using Real Track Data," AIAA-2011-6881, 11 th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Virginia Beach, VA, 20-22 Sep. 2011. 18 Aviation & Emissions -A Primer, FAA Office of Environment and Energy, Jan. 2005
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+		Nasa-Task Load Index (NASA-TLX); 20 Years Later
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+			SandraGHart
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+		10.1177/154193120605000909
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+		Proceedings of the Human Factors and Ergonomics Society Annual Meeting
+		Proceedings of the Human Factors and Ergonomics Society Annual Meeting
+		2169-5067
+		1071-1813
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+			50
+			9
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+			Oct. 2006
+			SAGE Publications
+			San Francisco, CA
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+	Hart, S., "NASA-Task Load Index (NASA-TLX); 20 Years Later," Proceedings of the Human Factors and Ergonomics Society (HFES) 50 th Annual Meeting, San Francisco, CA, 16-20, Oct. 2006.
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+		A Technique for the Measurement of Attitudes
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+I. INTRODUCTIONImproving the flow of operations into and out of the airport environment when demand exceeds capacity remains a key objective of the Next Generation Air Transportation System (NextGen).Whereas trajectory-based concepts and technologies have been developed for specific phases of flight and control facilities, their integration across surface and airspace domains to more fully optimize traffic flow remains a considerable challenge.Nowhere is the need for integrated solutions greater than in metroplex terminal environments where traffic to and from multiple airports compete for limited airspace resources.In these environments, flight trajectories must be coordinated in a manner that de-conflicts traffic flows and balances demand and capacity by adhering to a multitude of surface and airspace flow and separation constraints.To address the Integrated Arrival, Departure, and Surface (IADS) challenge, NASA is developing and demonstrating trajectory-based departure automation under a collaborative effort with the FAA and industry known Airspace Technology Demonstration 2 (ATD-2).ATD-2 builds upon and integrates previous NASA research capabilities that include the Spot and Runway Departure Advisor (SARDA) [1][2], the Precision Departure Release Capability (PDRC) [3], and the Terminal Sequencing and Spacing (TSAS) capability [4].Reference [5] provides a qualitative description of shortfalls targeted by ATD-2 and discusses how stakeholder feedback was used to establish the high-level performance goals of improving operational efficiency and predictability while maintaining or improving throughput.Many of the shortfalls in today's airport operations can be traced to reactive handling of departures, based primarily on the order in which pilots first call the tower for services.Without automation to coordinate aircraft movements from the gate, large queues and other forms of congestion can develop on ramps and taxiways causing delays that waste fuel and generate excess emissions.Furthermore, surface congestion creates physical constraints that limit a controller's options for re-sequencing flights for maximum throughput and compliance with Traffic-flow Management Initiatives (TMIs).Inadequate compliance with TMIs at takeoff increases the chances that costly and unpredictable tactical maneuvers will be required once airborne to satisfy airspace constraints.The purpose of this paper is to provide a quantitative assessment of operational shortfalls relevant to ATD-2 efficiency, predictability, and throughput objectives.The study is also intended to identify key performance metrics needed to measure the benefits-related impact of ATD-2 in upcoming simulations and field demonstrations.Analyses were performed for operations at Charlotte Douglas International Airport (CLT), which is the site selected for the initial field demonstrations of ATD-2 that start in 2017.The paper first provides background on the ATD-2 concept and operational characteristics at CLT. Benefit mechanisms and metrics are then described followed by the approach to the quantitative shortfalls analyses.A broad sample of benefit opportunities identified by the analyses is then provided and discussed.
+II. BACKGROUND
+A. Concept OverviewThe ATD-2 concept centers on departure scheduling that allows aircraft to taxi and climb with minimal interruption.A key principle is to allow aircraft to absorb required delay at the gate prior to engine start in order to reduce fuel burn and emissions.ATD-2 manages traffic volume on the surface while accounting for takeoff constraints and flight priorities.Scheduling solutions rely on trajectory-based taxi and climb predictions that incorporate airline flight readiness information and account for individual flight routing between allocated gates, runways, and airspace fixes.Whenever possible, scheduling accommodates airline priorities and preferences by invoking the principles of Collaborative Decision Making (CDM) [7].Takeoff constraints factored into scheduling include wakevortex separation criteria and takeoff restrictions due to strategic and local TMIs.Strategic TMIs produce specific takeoff-time restrictions in the form of Expected Departure Clearance Times (EDCTs) used mostly to control the flow of traffic to destinations impacted by weather.Local TMIs include takeoff times negotiated between Tower and Center controllers through an Approval-Request (APREQ) process.As shown in Fig. 1, local TMIs help facilitate the insertion of departures into overhead streams, prevent imbalances between en-route airspace demand and capacity, and help meter departures into arrival streams at their destination.Local TMIs can also result in Miles-in-Trail (MIT) restrictions for flights transitioning from terminal to en-route airspace.These in-trail restrictions are typically enforced at departure meter points located near the terminal boundary, as shown in Fig. 1.Such constraints are intended to prevent overloading downstream airspace and help regulate departure flows from multiple airports within a metroplex terminal environment.ATD-2 scheduling results in target times at potential control points along an aircraft's departure trajectory, which include for pushback from the gate, entry into the Airport Movement Area (AMA) at the spot, takeoff, and departure-fix crossing.Yellow ovals in Fig. 1 depict control points on the surface, while blue ovals depict control points in the airspace.The takeoff point is shown by a by a yellow and blue oval as it represents the key control point for surface and airspace integration.In the initial implementation at CLT, schedule conformance is managed primarily through the metering of pushback events from the gate by Ramp controllers and conformance with takeoff restrictions due to TMIs at the runway by Tower controllers.Although only departures are directly controlled through ATD-2 automation, arrival predictions (represented by the red trajectory in Fig. 1) are factored into scheduling to minimize surface congestion.Arrivals therefore stand to benefit indirectly through lessimpeded taxi trajectories from runways to gates.
+B. CLT Operations OverviewAs the initial site for ATD-2, CLT provides an opportunity to demonstrate the capabilities and benefits of integrated surface and airspace departure scheduling.With approximately 1,600 operations per day, CLT is the sixth busiest airport in the nation in terms of operations and the second busiest on the East Coast behind Atlanta (ATL) [6].CLT is a hub for American Airlines, which together with its regional carriers operates about 90% of commercial flights at the airport.The remaining 10% of operations is comprised of other regional carriers, mainline flights operated by Southwest, Delta, United and Jet Blue, military flights, business and general aviation, and air cargo.As the dominant carrier, American manages all ramp operations at the airport.Located midway between ATL and the Washington D.C metroplex, CLT lies beneath one of the busiest air corridors in the U.S.This location, and the fact that many flights from CLT are destined to constrained airspace and airports on the East Coast, results in departures being frequently subjected to TMIs, particularly APREQs for managing overhead stream insertion for flights headed to airports within the New York and Washington DC metroplexes.The prevalence of such TMIs make CLT a suitable site for demonstrating the airspace integration benefits of ATD-2 prior to adapting the technology to multi-airport metroplex environments.Without predictive automation to assist controllers in meeting TMIs in today's operations, departures must often absorb delay on the airport surface.This can add to existing surface congestion due to traffic volume that often exceeds available gate, ramp, taxiway, and runway capacity.Such congestion is mostly a consequence of traffic growth at CLT in recent years, which has nearly doubled in the past decade.Surface congestion in the ramp area is further exacerbated by limited gate availability, single-direction taxiways, and limited options for holding flights off the gate.In response, CLT is currently undergoing a major airport expansion effort that will add gates, a new tower, and a fourth parallel runway [8].As shown in Fig. 2, CLT currently operates with three north/south parallel runways and one diagonal runway.Triple simultaneous instrument approach procedures are authorized for the parallel runways.In south-flow configurations, aircraft typically arrive on runways 18R, 18C, and 23 and depart on runways 18C and 18L.In north-flow, aircraft typically arrive on all three parallel runways -36L, 36C, and 36R -and depart on dual-use runways 36C and 36R.Runway 05/23 is used mostly for arrivals in south-flow and often as a relief taxiway in north-flow.In the south-flow configuration where the diagonal runway is used for arrivals, departures from 18C are restricted by recent FAA rules for converging but nonintersecting runways.At nighttime, Runway 05/23 is used by both arrivals and departures for noise abatement.Noise abatement procedures also require jet traffic from 18C and 18L to fly runway heading for 2 miles prior to turning on course.
+III. BENEFIT MECHANISMS AND METRICS
+A. EfficiencyEfficiency goals pertain to more expedient trajectories during taxi and climb that consume less fuel and reduce emissions.ATD-2 offers to improve efficiency through coordinated scheduling that prevents surface congestion and assists controllers in managing TMIs.The bulk of any required delay is taken at the gate, thereby reducing fuel consumption.Following pushback, aircraft can taxi with minimal interruption, forming short takeoff queues only as necessary to keep pressure on runways for maximum throughput during peak traffic periods.Once airborne, aircraft can fly optimal profile climbs along area-navigation (RNAV) departure routes, with guidance and control aided by flight-deck automation.With improved TMI conformance on the ground, fewer airborne control actions involving path, speed, and altitude changes are potentially required.In this way, ATD-2 can provide a means for transferring required delay to flight phases where it is more efficient to absorb, i.e., from the airspace domain to the airport surface, and ultimately to the gate.Less tactical maneuvering once flights are underway can also potentially reduce workload and radio frequency congestion.ATD-2 metrics for assessing efficiency include taxi-out and taxi-in durations as well as transit times to departure meter points in the airspace.To examine efficiency independent of flight routing, transit delays can be computed by comparing actual and unimpeded times along the same taxi and departure routes.
+B. PredictabilityPredictability goals pertain to reducing the variance in actual transit times as well as improving the prediction of future aircraft locations and events.For ATD-2, this involves reducing the variance of taxi-out and climb durations for departures and the variation of taxi-in times for arrivals.For individual flights, key trajectory points in need of greater predictive accuracy are pushback from the gate and takeoff.ATD-2 aims to improve predictability through the scheduling of departures from the gate to decrease surface congestion and conform indirectly to any takeoff restrictions.Even without scheduling, ATD-2 offers to improve nominal (non-metered) trajectory prediction accuracy through the use of machine-learning methods and the incorporation of flight readiness information from airline operators [9].On an individual flight basis, better trajectory predictions can improve awareness of aircraft state and intent, leading to improved tactical decisions by controllers and flight operators.On an aggregate flight basis, improved predictions can result in better forecasting of traffic demand, leading to more efficient management of airport and airspace resources.Controllers can make more informed decisions regarding airport configuration changes, TMIs, and weather-mitigation routes; and airlines can make better decisions to avoid missed connections and preserve network integrity.Furthermore, sustained predictability improvements could allow airlines to confidently reduce scheduled block timesi.e., the gate-to-gate times in published flight schedules.These times have trended upwards in recent years as airlines strive to maintain on-time performance in the presence of increasing air-traffic uncertainty.Smaller scheduled block times can reduce operating costs and decrease the probability that flights arrive early and add to surface congestion as they compete with departures for gate resources.ATD-2 metrics for assessing predictability improvements are focused on the variance of transit times for taxi-out, climb, and taxi-in phases of flight and the degree to which aircraft comply with takeoff-time restrictions derived from TMIs.
+C. ThroughputThroughput objectives pertain to the number of departure and arrival operations using runways and the airport as a whole.Throughput can also be examined from an airspace perspective by considering the number of flights crossing a given fix or boundary.ATD-2 aims to increase, or at least maintain, departure throughput with scheduling that keeps pressure on runways and maximizes use of available airport and airspace capacity.Key ATD-2 throughput metrics for benefit and shortfall assessments include runway and departure rates and excess in-trail spacing at constrained departure fixes as possible indicator of wasted airspace capacity.IV. DATA SOURCES AND GENERAL METHOD Shortfalls were analyzed using flight-specific data obtained from a variety of government and industry data sources over a time period ranging from January 1, 2014 to April 30, 2015.Surface analysis was supported with data provided by American Airlines from their Aerobhan traffic display and management system.These data contained surface track and event data for all mainline and regional airlines operating at CLT. Aerobahn track data are obtained from the Airport Surface Detection Equipment, Model X (ASDE-X) surveillance system in place at CLT. ASDE-X provides aircraft position updates at 1 Hz in the AMA and limited locations in the ramp area, obtained by combining surveillance from a variety of sensors that include surface radar, muti-lateration sensors, and Automatic Dependent Surveillance -Broadcast (ADS-B).Aerobahn data were used to obtain taxi times specific to each gate, spot and runway combination.To further support surface analysis, the Surface Operations Data Analysis and Adaptation (SODAA) tool was used.Airspace operations analysis was performed using aircraft track and flight-plan data obtained through NASA's research version of the Center-TRACON Automation System (CTAS).Archived CTAS data files were processed for input into NASA's TCSim Route Analyzer/Constructor (TRAC) tool, which was used to perform flight time and distance analysis, identify tactical airspace maneuvers, and evaluate in-trail spacing across fixes and boundaries.For use in both surface and airspace analyses, TMI restrictions were obtained from the FAA's National Traffic Flow Management Log (NTML) and Time-Based Flow Management (TBFM) system.Data were examined to reveal shortfalls in current operations that ATD-2 aims to address through departure scheduling automation.The following results are categorized by efficiency, predictability, and throughput shortfalls to align with ATD-2 benefit objectives.Within each category, findings are further divided between surface and airspace domains.
+V. RESULTS: EFFICIENCY ANALYSIS
+A. Surface Efficiency 1) Taxi-out time, fuel, and emissionsTo estimate inefficiencies on the airport surface for aircraft taxiing for departure, taxi-out times were calculated by subtracting pushback (OUT) times from takeoff (OFF) times.For all flights in 2014, mean taxi-out time was found to be 18.8 min with 33% of flights experiencing taxi-out times greater than 20 min.As seen in Fig. 3, taxi-out times were similarly distributed between the ramp area (gate to spot) and AMA (spot to runway).On average, however, aircraft spent more taxi-out time in the ramp area (10.2 min) than in the AMA (8.8 min).Time spent in the AMA was generally lower in southflow configurations, because the terminal complex is located at the north end of the field.To compute delays during movement, estimates of unimpeded taxi times were subtracted from actual taxi times for the same gate, spot, and runway combinations.Unimpeded times were calculated as the 10th percentile of observed taxiout times.The resulting distribution of excess taxi-out time (referred to here as taxi-out delay) is shown in Fig. 4. Here, the mean taxi-out delay was found to be 7.2 min for all flights in 2014, with 11.2% of flights experiencing delays of 15 minutes or more.Excess fuel burn and emissions associated with taxi-out delays were estimated using fuel-flow rate and emission coefficients obtained from the ICAO Aircraft Emissions Databank [10].These coefficients were obtained for specific aircraft types and engine fits under standard atmospheric conditions, assuming an all-engine taxi at idle-thrust (7% total available thrust).Emission compounds -computed as a ratio to fuel burned using the ICAO coefficients -included carbon dioxide along with gas compounds that contribute to air pollution and are sensitive to engine type and thrust settings, specifically unburned hydrocarbons (HxC x ), nitrogen oxides (NO x ), and carbon monoxide (CO).Total excess fuel burn was estimated at 20,400 metric tons (83 kg per flight).Estimated excess emissions, averaged for each flight due to excess fuel combustion, are shown in Table I.Total excess carbon dioxide emissions for all taxiing departures were estimated at 62,800 metric tons.These findings suggest considerable opportunity for ATD-2 to improve taxi-out efficiency through departure metering that holds flights at the gate prior to engine start in order to manage traffic volume and satisfy airspace flow constraints.Even with ATD-2, however, it is recognized not all flights can be expected to execute an unimpeded taxi to the runway.Indeed, the ATD-2 scheduler will work to feed enough aircraft into the AMA to keep pressure on runways for maximum throughput during peak periods.Prior simulations of departure metering with SARDA, from which ATD-2 borrows much of its tactical surface-scheduling algorithm, show taxi-out delay reductions of 60% during heavy traffic conditions [11].
+2) Taxi-out efficiency factorsTo gain further insight into the causes of taxi-out delays in current operations, stopping and queuing on the airport surface is now examined along with unregulated demand and the effect of TMI constraints a) Demand exceeding capacity: A root cause of surface congestion is an excess number of departures competing for taxi and takeoff services beyond what the airport and surrounding airspace can accommodate.Contributing to this phenomenon is the peaking of airline schedules in hub-and-spoke operations and published departure times that tend to fall at the top of the hour, or at the half hour, for customer convenience and ticket sales [5].Taxi demand based on airline-published departure times was examined by counting the number of flights scheduled to push back within 10-minute, non-overlapping time windows.Fig. 5 (lower) shows a time history of taxi demand versus actual taxi operations as a function of local time-of-day, compiled by averaging the demand in each 10-minute window across the entire year 2014.The difference between airlinescheduled and actual pushback demand reflects actions taken by Ramp controllers to meter demand in an effort to prevent surface congestion.These actions involved holding companyowned flights for up to 10 minutes whenever more than 15 aircraft were away from their gates and headed for the same departure runway.Fig. 5 (lower) shows that departure demand based on airline schedules often exceeded more than 20 flights competing for taxi services from the gate within a 10-minute period.To examine unregulated demand from a runway perspective, takeoff times projected from published pushback times were obtained from Aerobahn.Fig. 5 (upper) shows this airline-scheduled takeoff demand in comparison with actual takeoff events.The difference reflects delays that had to be absorbed on the airport surface, contributing to congestion and excess fuel burn.In the ATD-2 concept, a combination of strategic and tactical surface scheduling will be used to meter departures from the gate in order to spread out demand with the aim of preventing volume-related surface congestion.Perturbations to airline-published departure times will be limited, however, in order to preserve on-time arrival performance and ensure that flight networks remain intact.Stopping on the airport surface was examined using ASDE-X surveillance data and filtering algorithms available through SODAA.For this analysis, stopping was defined by an aircraft's speed falling to zero for multiple sequential points in its trajectory time-history, followed by a sustained, non-zero velocity segment.Using this method, it was found that aircraft stopped an average of 4.5 times between gates and runways with an average stop duration of 4.1 minutes, including stopping at the spot and at designated holding areas.Some of the detected stops were the result of aircraft progressing in queues to runways.The maximum queue size experienced by each flight was approximated by counting the number of aircraft already in the AMA and headed for the same runway at the time the flight left its gate.Fig. 6 shows the resulting histogram of maximum departure queue size experienced by flights in 2014 operations.Whereas it was still common for departing flights to experience more than 15 aircraft ahead of them destined for the same runway, these larger queues occurred with less frequency, likely a result of American's departure management procedure previously described.c) Effect of TMIs on Taxi-Out Time Of considerable relevance to ATD-2 is the impact of TMIs on taxi-out delay and congestion.To examine this, flights subjected to EDCT and APREQ takeoff-time constraints and MIT spacing restrictions at departure fixes were identified.Flights subjected to combinations of these TMIs were also examined.Fig. 7 shows the effect of TMIs on taxi-out delay between gates and runways.It can be seen that flights with no restrictions experienced the least amount of taxi-out delay.Considering TMI categories independently, APREQ and MIT constraints had a similar effect on mean taxi-out delay, while EDCT constraints resulted in somewhat larger delays.In general, flights subjected to multiple constraints, although far fewer in number experienced substantially larger delays than those subjected to just one constraint type.Flights with multiple constraints where one constraint was an EDCT had the largest delays, with flights subjected to both EDCT and MIT constraints experiencing a median of more than 15 minutes of delay on the surface relative to an unimpeded transit.Analysis of stopping on the surface, using the method previously described, revealed that flights with MIT constraints stopped more frequently, but flights with EDCTs, especially when also subjected to MIT constraints, stopped longer.Flights with EDCTs stopped for an average of 7.2 minutes compared to 4.0 minutes for flights with no TMI restrictions.Those flights subjected to both EDCT and MIT experienced an average stop time of 10.8 min.With departure metering that considers TMI restrictions, ATD-2 aims to reduce the need for controllers to maneuver and hold aircraft away from the gate to meet required takeoff times.It is important to note, however, that the ability to hold aircraft at the gate is limited at CLT because demand for gates often exceeds their availability.Even with ATD-2, controllers may at times have to push departures earlier than advised in order to free up gates for arrivals, thus requiring some delay to be absorbed either in the ramp area or AMA. 3) Taxi-in time, fuel, and emissions Taxi delays for arrivals were computed using the same process previously described for departures.Mean taxi-in delay for all arrivals was 4.97 minutes, with somewhat larger mean delays in the ramp area than in the AMA (3.4 minutes vs. 2.5 minutes).Considerably higher mean taxi-in delays were found for those aircraft that had gate conflicts upon landing, presumably as they waited for gates to be vacated by departures.Mean taxi-in delay for arrivals with gate conflicts was 12.9 minutes with a standard deviation of 9.3 minutes.Flights with gate conflicts upon landing represented 6.9% of all arrivals.This number is likely low, however, since situations where gates were reassigned after landing to resolve a conflict were not discernable from the data.Total excess fuel burn due to taxi-in delays for all operations in 2014 was estimated at 16,551 metric tons, corresponding to average of 62 kg per aircraft.Using the method described previously for departures, excess taxi-in fuel burn was found to result in a total CO 2 excess of 50,977 metric tons, with an average per-flight excess in CO2, CO, NOx, and H x C x of 191 kg, 1.6 kg, 0.27 kg, and 0.22 kg, respectively.
+B. Airspace EfficiencyClimb efficiency was examined in terminal airspace to observe the maneuvering of flights off their nominal departure routing between runways and departure fixes.Through more accurate compliance with controlled takeoff times, ATD-2 aims to reduce the need for maneuvering in the airspace to satisfy TMI constraints pertaining to in-trail spacing at departure fixes and en-route meter points.For this initial analysis, which focused on terminal airspace, the most relevant TMIs were MIT spacing requirements associated with RNAV departure fixes but enforced by controllers at fixes slightly upstream along the TRACON boundary, as shown by the blue triangles in Fig. 8. Due to the complexity of generating unimpeded times, which are dependent on aircraft type and atmospheric conditions, excess along-path distance was used as a surrogate for airborne delay.For this, nominal path distance along the filed RNAV Standard Instrument Departure (SID) route was used as a datum.This simplification assumes that the majority of maneuvering to satisfy MIT requirements is accomplished through vectoring, which has been affirmed by Subject Matter Experts (SMEs).The resulting distribution of excess path distance for all departures filing RNAV SIDs between May 2014 and April 2015 is shown in Fig. 9. Here, negative values indicate that flown distances were shorter than those associated with RNAV routes.Mean excess path distance for all flights was -2.8 nmi.Path stretching in terminal airspace was evident in only 11% of departure operations.Far more common was the shortcutting of routes by controllers to provide more direct flight paths.This was most common for departures destined for fixes in the opposite direction to their runway heading.For instance, when departures took off in a north-flow configuration destined for ANDYS or BUCKL (south of CLT), the controller often vectored flights directly to these fixes, resulting in sharper turns than had these flights otherwise remained on their RNAV routes.SMEs suggested that the shortcutting of routes was motivated, in part, by sub-optimal RNAV route designs.A closer examination of airspace maneuvering revealed that 50% of flights with evidence of path stretching flew alongtrack distances that were within 2 nmi of those associated with their nominal RNAV routes.Excluding these flights left only 6% of all departures with deliberate path stretching in terminal airspace.Flights routed through MERIL, which is the departure fix most commonly associated with MIT restrictions, were most frequently path stretched.MERIL flights represented 54% of all path-stretch cases.The mean excess path distance in these cases was 4.3 nmi, which was two to three times greater than for flights routed through other departure fixes.For the same period, a search for tactical level-offs and decelerations was conducted to find further evidence of maneuvering for delay absorption in terminal airspace.For this purpose, tactical level-offs were defined as those lasting for more than one minute, not associated with procedural leveloffs for segregating arrival and departure flows or managing controller handoffs.Deceleration events were defined by nonprocedural ground-speed reductions greater than 20 knots, sustained for at least one minute.Results indicated that both types of maneuvering were rare, with tactical level-offs occurring in only 2% of departures and speed reductions occurring in only 0.4% of departures.
+VI. RESULTS: PREDICTABILITY ANALYSIS
+A. Surface Predicability 1) Takeoff time predictionAn important aspect of increasing predictability with ATD-2 is improving the accuracy and precision of takeoff predictions prior to aircraft leaving the gate.From an automation standpoint, such predictions are required not only as input to ATD-2 internal scheduling but also for external TBFM departure and arrival scheduling with which ATD-2 will interface.For 2014 operations, takeoff predictions were obtained from Aerobahn just prior to aircraft leaving the gate and compared against actual takeoff times.In current operations, takeoff predictions used for airline flight planning rely on published departure times (adjusted for flight-plan changes) together with a nominal taxi-out time assumption.In current practice, this nominal taxi time is typically a constant value that is adjusted for season but does not account for assigned gate and runway end points.Results revealed a mean airline takeoff prediction error of 6.3 min with a standard deviation of 21.4 min.The large variance is due in part to uncertainty in actual taxi-out times, which, in 2014 operations at CLT, had a standard deviation of 8.7 min.ATD-2 aims to reduce the variance of taxi-out time predictions using machine-learning algorithms, trained with historical data.Applying such algorithms to 2014 CLT data reduced the mean taxi-out prediction error to nearly zero and the standard deviation to 5 minutes [9].
+2) Compliance with TMI takeoff-time constraintsFor departures with EDCT and APREQ constraints, climb predictability is governed primarily by the degree to which flights comply with target takeoff times.FAA regulations set performance objectives for controllers that specify a 10-minute compliance window for EDCT flights (-5 to +5 minutes of EDCT) and a 3-minute compliance window for APREQ flights (-2 to +1 minutes of APREQ time).For APREQ flights, the window is biased towards early takeoff times since it is easier for controllers to further delay flights if needed once airborne than to advance them.EDCT has a larger compliance window because it is typically used to manage demand and capacity imbalances further downstream, most often at destination airports subject to FAA Ground-Delay Programs.Compliance was examined for 29 airports in the NAS with more than 10,000 APREQ operations in 2014.It was found that an average of 46.9% of flights complied with their EDCT window, and 54.4% of flights complied with their APREQ window.For CLT, the percentage of flights departing within their EDCT and APREQ windows was 56.8% and 42.9%, respectively.The corresponding distributions of compliance errors are shown in the histograms in Fig. 10.For the small number of flights subjected to both EDCT and APREQ restrictions (a total of 517 in 2014), the percentage of APREQ compliant flights was largely unchanged, whereas EDCT compliance dropped to 52%.This is consistent with SME feedback indicating that for flights with both types of constraints APREQ compliance is given higher priority.(ANDYS, BUCKL, DEBIE, JACAL, LILLS, MERIL, and ZAVER).Standard deviations across these combinationsexpressed as a percentage of mean flight time -ranged from 4.4% to 11.3%.Greater variation relative to mean flight time was seen in combinations where flights departed in the opposite direction to their filed departure fix, e.g., flights departing 18L headed for the northern fix JACAL.This is consistent with the earlier finding that controllers often shortcut routes for flights with opposite runway-fix pairings.Such shortcutting performed on an ad-hoc basis would result in greater terminal flight time variance.
+2) Conformace with MIT spacing constraintsFurther insight into the predictability of current operations in terminal airspace can be gained by examining conformance to MIT spacing requirements.For this analysis, differences between actual in-trail spacing at terminal fixes and those stipulated by MIT constraints were examined for departures from April through December 2014.The distribution of these spacing differences for all terminal-fix MIT constraints is shown in Fig. 11.It was found that only 33% of flights conformed to their MIT requirements within +/-5 nmi and that 22% of flights crossed into en-route airspace with less than their target spacing.Flights crossing with less spacing than required is considered a shortfall, since MITs are imposed to limit the number of aircraft entering downstream airspace.ATD-2 aims to address such a shortfall through tactical scheduling that takes MIT restrictions into account.In the majority of cases, however, flights crossed into en-route airspace with excess spacing.This may or may not indicate a shortfall, depending on whether the departure demand from the airport was sufficient to saturate at the maximum throughput implied by the MIT value.This issue is explored further in the throughput analysis that follows.
+VII. RESULTS: THROUGHPUT ANALYSIS
+A. Runway ThroughputWhile ATD-2 is not expected to increase arrival runway throughput, departure runway throughput can potentially be increased through tactical surface scheduling and sequencing that maximizes the use of available runway capacity.To examine opportunities for increasing throughput at CLT, analysis was performed using 2014 track data from SODAA and Aerobahn to search for unused runway slots where aircraft could have theoretically departed.Only periods where departure demand exceeded the actual departure rate were used in the analysis.Departure opportunities were identified by looking for gaps in runway occupancy between combinations of departing, landing, and runway-crossing flight pairs.Analysis was performed on each runway, accounting for wakevortex separation and runway configuration.Also taken into account was the converging runway procedure that constrained departures on runway 18C when runway 23 was in use for arrivals.Fig. 12 shows the resulting increase in takeoff opportunities (departure slots) possible through optimized scheduling and sequencing on the surface.Results show additional takeoff opportunities between existing departing, landing, and runway-crossing flight pairs as a function of the three most common runway configurations: 1) north-flow using the three parallel runways, south-flow using the three parallel runways, and south-flow using all four runways (including the diagonal runway).In south-flow configurations, the greatest number of extra takeoff opportunities was found between departing and crossing flights.In north-flow, extra takeoff opportunities were greatest between departing and landing flights.In total, analysis suggests that 9.4% of excess takeoff demand could potentially be absorbed through optimized scheduling and sequencing, corresponding to a total departure-throughput increase of 1.4%.
+B. Airspace ThroughputOpportunities to increase throughput between terminal and en-route airspace were examined by searching for unused capacity when MIT restrictions enforced at the terminal boundary were in effect.Such restrictions are often put in place to manage the flow of departures during peak traffic periods or when downstream capacity is limited due to weather.In 2014, there were a total of 588 unique MIT restrictions affecting CLT departures.The departure fix most affected was MERIL, which is used predominantly by flights bound for capacityconstrained airspace and airports in the northeast corridor.MIT restrictions for MERIL are enforced at the terminal fix LILIC.In searching for unused capacity, it was important to ensure that uncontrolled departure demand would have resulted in a meaningful portion of flights crossing into en-route airspace with less than the desired spacing, i.e., that the original departure demand was sufficient to overload the fix.To investigate this, times at which flights were predicted to arrive at terminal fixes were computed based on their published gate departures times, nominal taxi-out times, and unimpeded climb times (based on the 10th percentile of observed climb time to the fix from the given departure runway).It was found that LILIC had the largest percentage of flights (45%) that would have crossed with less than the required MIT spacing without control over demand.MIT requirements at LILIC ranged from 10 nmi to 30 nmi.Traffic loading at LILIC when MIT restrictions were in place was examined over the month of April 2015.Fig. 13 shows actual throughput as a percentage of the capacity based on MIT restrictions.It can be seen that the saturation level at LILIC generally increases as the MIT increases, a finding consistent with a NAS-wide study of MIT restrictions found in [12].This is somewhat intuitive, since uncontrolled traffic demand relative to fix capacity increases with increasing MIT, potentially making it easier for controllers to more fully saturate fixes.For the more common in-trail spacing restrictions of 15 and 20 MIT, LILIC was less than 70% saturated on average for each time of day.Moreover, for the most common restriction of 15 MIT, LILIC was less than 45% saturated.Such findings suggest opportunities for ATD-2 to increase throughput by scheduling departures to meet MIT restrictions with minimal excess spacing when sufficient demand exists to saturate fixes under MIT constraints.In the further term, ATD-2 aims to facilitate less conservative airspace constraints by improving departure demand predictions, thus leading to further potential increases in capacity and throughput.
+VIII. CONCLUSIONSOperations at Charlotte-Douglas International Airport were examined to identify benefit opportunities for departure scheduling automation that accommodates both surface and airspace constraints.The automation, planned for initial deployment at CLT in 2017, relies on trajectory-based taxi and climb predictions that incorporate updated airline departure intent data.Analysis identified shortfalls in recent operations relevant to efficiency, predictability, and throughput objectives.Although opportunities for improving efficiency at CLT were found mostly on the airport surface rather than in the airspace directly, the largest taxi-out delays were experienced by flights subject to traffic-flow management initiatives.Across all departure operations in 2014, the average taxi-out delay was 7.2 minutes.Average taxi-out delay for flights subject to TMIs, however, ranged between 7 and 15 minutes, with the largest delays experienced by flights subject to multiple TMIs.Delays were associated with sizable taxi queues with frequent and prolonged stopping on the airport surface.Delays were associated with departure demand that often exceeded runway capacity.Annual excess fuel consumption resulting from both departure and arrival taxi delays was estimated at 36,950 metric tons.Automation offers to reduce taxi delays with scheduling that balances demand and capacity while helping controllers to comply with takeoff restrictions.Opportunities to improve predictability were identified by comparing current airline taxi-time predications with those based on machinelearning methods.Opportunities to further improve predictability were revealed by examining TMI compliance at runways and departure fixes.Finally, analysis suggests opportunities to increase throughput with coordinated departure scheduling that maximizes use of available airport and airspace capacity.Together, these analyses provide quantitative insight for bounding ATD-2 benefit expectations and selecting key performance metrics for upcoming field evaluations.Fig. 1 .1Fig. 1.ATD-2 end-state concept environment
+Fig. 2 .2Fig. 2. CLT airport plan view
+Fig. 3 .3Fig. 3. Distribution of taxi-out time in ramp area and AMA for all departures in 2014
+Fig. 4 .4Fig. 4. Distribution of taxi-out delay distribution for all departures in 2014
+Fig. 5 .5Fig. 5. Departure demand vesus actual operations, averaged over 2014 as a function of local time b) Stopping and Queuing:Stopping on the airport surface was examined using ASDE-X surveillance data and filtering algorithms available through SODAA.For this analysis, stopping was defined by an aircraft's speed falling to zero for multiple sequential points in its trajectory time-history, followed by a sustained, non-zero velocity segment.Using this method, it was found that aircraft stopped an average of 4.5 times between gates and runways with an average stop duration of 4.1 minutes, including stopping at the spot and at designated holding areas.
+Fig. 7 .7Fig. 7. Effect of TMIs on taxi-out delay for all departures in 2014
+Fig. 8 .8Fig. 8. Terminal airspace fixes and tracks, March 31, 2015
+Fig. 9 .9Fig. 9. Distribution of excess path distance flown in terminal airspace for all departures on RNAV routes from May 2014 through April 2015
+Fig. 10 .10Fig. 10.Distribution of compliance with takeoff-ttme restrictions for all departures in 2014 B. Airspace Predicability 1) Variance of terminal climb time By helping controllers comply with airspace restrictions while flights are still on the ground, ATD-2 aims to reduce the need for maneuvering during climb, thus reducing the variance of flight time in terminal and en-route transition airspace.Variation of flight time to the CLT TRACON boundary was investigated for departures filing RNAV SIDs over a 12-month period from May 2014 to April 2015.Flight-time variance was examined for the most common combinations of departure runways (18C, 18L, 36C, and 36R) and departure fixes
+Fig. 11 .11Fig. 11.Distribution of compliance with MIT constraints at terminal boundary for all departures from April through December, 2014
+Fig. 12 .12Fig. 12. Potential increase in takeoff opportunities between aircraft in 2014
+Fig. 13 .13Fig. 13.Saturation of LILIC departure fix when subject to MIT constraints for all departures in April 2015
+TABLE I .IEXCESS TAXI-OUT FUEL AND EMISSIONS PER FLIGHTFuelCO2HxCxCONOx(kg)(kg)(g)(g)(g)Mean83.3256.6199.81999.1362.5Median57.1175.988.31412.0240.1Std. Dev. Fig. 6. Distribution of departure queue size experienced at pushback for all 96.3 296.6 434.1 2336.3 432.6 departures in 2014
+		
+		
+
+			
+ACKNOWLEDGMENTThe authors thank American Airlines for providing the Aerobahn data used to support this analysis.
+			
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+
+	
+		Performance Evaluation of SARDA: An Individual Aircraft-based Advisory Concept for Surface Management
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+			American Institute of Aeronautics and Astronautics (AIAA)
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+
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+	
+		Call for Papers
+		
+			MHayashi
+		
+		
+			THoang
+		
+		
+			YJung
+		
+		
+			MMalik
+		
+		
+			HLee
+		
+		10.1027/2192-0923/a000067
+	
+	
+		Aviation Psychology and Applied Human Factors
+		Aviation Psychology and Applied Human Factors
+		2192-0923
+		2192-0931
+		
+			4
+			2
+			
+			June 23-26
+			Hogrefe Publishing Group
+			Lisbon, Portugal
+		
+	
+	M. Hayashi, T. Hoang, Y. Jung, M. Malik, H. Lee, et al., "Evaluation of Pushback Decision-Support Tool Concept for Charlotte Douglas International Airport Ramp Operations," 11th USA/Europe Air Traffic Management R&D Seminar (ATM2015), Lisbon, Portugal, June 23-26,
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+			SEngelland
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+			ACapps
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+		
+			KDay
+		
+		
+			MKistler
+		
+		
+			FGaither
+		
+		NASA/TM-2013- 216533
+		Precision Departure Release Capability (PDRC) Final Report
+		
+			June 2013
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+	S. Engelland, A. Capps, K. Day, M. Kistler, F. Gaither, et al., "Precision Departure Release Capability (PDRC) Final Report," NASA/TM-2013- 216533, June 2013.
+
+
+
+	
+		Evaluation of the Controller-Managed Spacing Tools, Flight-deck Interval Management and Terminal Area Metering Capabilities for the ATM Technology Demonstration #1
+		
+			JThipphavong
+		
+		
+			JJung
+		
+		
+			HSwenson
+		
+		
+			KWitzberger
+		
+		
+			LMartin
+		
+	
+	
+		10th USA/Europe ATM R&D Seminar (ATM2013)
+		Chicago, Illinois
+		
+			June 2013
+			
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+	J. Thipphavong, J. Jung, H. Swenson, K. Witzberger, L. Martin, et al., "Evaluation of the Controller-Managed Spacing Tools, Flight-deck Interval Management and Terminal Area Metering Capabilities for the ATM Technology Demonstration #1", 10th USA/Europe ATM R&D Seminar (ATM2013), Chicago, Illinois, 10-13 June 2013.
+
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+		Identifying Key Issues and Potential Solutions for Integrated Arrival, Departure, Surface Operations by Surveying Stakeholder Preferences
+		
+			BimalLAponso
+		
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+			RichardACoppenbarger
+		
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+			YoonCJung
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+			CorneliusJO'connor
+		
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+			GaryWLohr
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+			LeightonKQuon
+		
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+			ShawnEngelland
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+		10.2514/6.2015-2590
+	
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+		15th AIAA Aviation Technology, Integration, and Operations Conference
+		Dallas, Texas
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+			American Institute of Aeronautics and Astronautics
+			June 22-26, 2015
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+	B. Aponso, R. Coppenbarger, Y. Jung, N. O'Connor, G. Lohr, G., et al., "Identifying Key Issues and Potential Solutions for Integrated Arrival, Departure, Surface Operations by Surveying Stakeholder Preferences," AIAA 2015-2590, 15th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Dallas, Texas, June 22-26, 2015.
+
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+		Health hazard evaluation report: HETA-81-136-867, FAA Credit Union, JFK Airport, Jamaica, New York.
+		10.26616/nioshheta81136867
+	
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+		Terminal Flight Data Manager (TFDM) Operational Evaluation Report (OER) Site Report for Charlotte-Douglas (CLT) International Airport
+		
+			U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Institute for Occupational Safety and Health
+			June 2015
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+	FAA Airport Surface Efficiency Group (AJR-1200)
+	FAA Airport Surface Efficiency Group (AJR-1200), "Terminal Flight Data Manager (TFDM) Operational Evaluation Report (OER) Site Report for Charlotte-Douglas (CLT) International Airport," June 2015.
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+		Collaborative Decision Making in Air Traffic Management
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+		Berlin Heidelberg
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+	"Destination Charlotte." City of Charlotte. Retrieved May 31, 2016. http://charmeck.org/city/charlotte/Airport/Pages/DestinationCLT.aspx.
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+		Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques
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+			HanbongLee
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+			WaqarMalik
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+			YoonCJung
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+		10.2514/6.2016-3910
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+		16th AIAA Aviation Technology, Integration, and Operations Conference
+		Washington DC
+		
+			American Institute of Aeronautics and Astronautics
+			June 13-17, 2016
+		
+	
+	H. Lee, W. Malik, and Y. C. Jung, "Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques," AIAA-2016-3910, 16th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, , Washington DC, June 13-17, 2016.
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+		Air and Space Law
+		AILA
+		0927-3379
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+			21
+			Issue 2
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+			Kluwer Law International BV
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+	EASA
+	"ICAO Aircraft Engine Emissions Databank." EASA. Retrieved Jan 21, 2016. https://www.easa.europa.eu/document-library/icao-aircraft- engine-emissions-databank#6.
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+		Performance evaluation of individual aircraft based advisory concept for surface management
+		
+			GGupta
+		
+		
+			WMalik
+		
+		
+			LTobias
+		
+		
+			YJung
+		
+		
+			THoang
+		
+		
+			MHayashi
+		
+	
+	
+		10th USA/Europe Air Traffic Management Research and Development Seminars
+		
+			June 2013
+			Chicago, IL
+		
+	
+	G. Gupta, W. Malik, L. Tobias, Y. Jung, T. Hoang, and M. Hayashi, "Performance evaluation of individual aircraft based advisory concept for surface management," 10th USA/Europe Air Traffic Management Research and Development Seminars, Chicago, IL, June 2013.
+
+
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+		Characterization of Nationwide TRACON Departure Operations
+		
+			MatthewSKistler
+		
+		
+			AlanCapps
+		
+		
+			ShawnAEngelland
+		
+		10.2514/6.2014-2019
+	
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+		14th AIAA Aviation Technology, Integration, and Operations Conference
+		Atlanta, Georgia
+		
+			American Institute of Aeronautics and Astronautics
+			2014
+		
+	
+	M. Kistler, A. Capps, and S. Engelland, "Characterization of Nationwide TRACON Departure Operations," 14th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, Atlanta, Georgia, 2014.
+
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+				
+			
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+I. IntroductionA LTHOUGH airborne capabilities such as area navigation and required navigation performance, together with optimized guidance and control through the flight management system (FMS), offer substantial improvements to the efficiency of flight operations, their benefits frequently go unrealized in today's arrival airspace domain.To the frustration of the airspace user, efficiency gained en route using state-of-the-art airborne automation is often squandered during the final stages of flight as the airplane transitions for landing.Here, air-traffic control (ATC) actions often require the airplane to execute suboptimal tactical maneuvers that involve frequent temporary-altitude assignments, speed adjustments, and lateral vectoring to accommodate tightly coupled separation and trafficflow-management constraints.These actions, though designed to manage controller workload and ensure safety in heavy traffic conditions, prevent aircraft from executing an uninterrupted continuous descent approach (CDA) to the runway using low engine power for maximum fuel efficiency and minimum environmental impact.As a result, FMS guidance trajectories, which are optimized over time horizons that transcend ATC boundaries, are seldom executed to completion in today's arrival airspace.Accommodating efficient trajectory-based arrival operations under all traffic conditions is a key objective of the Next Generation Air Transportation System (NextGen) [1].The benefit and feasibility of CDA operations has been a subject of considerable study [2,3].These studies have occasionally involved flight trials, some evolving into limited daily use operations into select airports around the world.Examples include the recent advanced arrivals trials in The Netherlands [4], CDA trials at Louisville [5,6], flight trials in the United Kingdom [7], the 4-D trajectory trials in Sweden, and the initial tailored arrivals trials conducted in Australia [8].Although these studies have involved a myriad of techniques for enabling CDA benefits, their focus has largely been on developing static arrival procedures for near-term deployment.As a result, the application of these CDA initiatives requires periods of low traffic density and/or specialized protocols that potentially limit throughput.A key feature of the flight trials described in this paper, which distinguishes them from related activities in the United States and abroad, is the inclusion of ground-based automation capable of generating dynamic CDA trajectory solutions in the presence of complex airspace constraints.The ability to tailor arrival solutions to accommodate individual aircraft performance, atmospheric conditions, and operational restrictions is critical for enabling CDAs in congested-airspace environments in which potential benefits are greatest.Toward this objective, NASA's En Route Descent Advisor (EDA) was incorporated into the trials to compute advisories for meeting arrival-time constraints artificially imposed at the Terminal Radar Approach Control (TRACON) boundary, designed to emulate operations under heavy traffic conditions.EDA is a research component of the Center TRACON Automation System (CTAS) that works in conjunction with the CTAS Traffic Management Advisor (TMA) to provide controllers with combinations of speed, altitude, and path-stretching advisories that satisfy time-based metering constraints while avoiding separation conflicts [9][10][11].To properly plan and execute a CDA solution, it is important that supporting automation accurately predicts the arrival trajectory.Inaccurate ground-based trajectory predictions will result in poor capacity utilization and increased probability of having to interrupt the descent in response to unanticipated conflicts, thereby quickly erasing CDA benefits.Inaccurate airborne predictions can result in poor overall energy management in the descent, thereby compromising CDA efficiency benefits by increasing the odds that energy will need to be added or subtracted through undesirable throttle or drag-deployment actions.Idle-thrust descents are particularly vulnerable to prediction inaccuracies, due to unavailability of engine throttle as a mechanism for removing excess energy should the need arise.For these reasons, the accuracy and compatibility of ground-based and airborne predictions (facilitated by the exchange of atmospheric state and profile intent data) were a key focus of the flight trials described in this paper.These trials relied upon datalink services and avionics equipage currently available only in oceanic airspace for U.S. air-traffic operations.For this reason, near-term (2008-2012) applications of tailored arrivals will likely be limited to transoceanic operations.The concepts and procedures developed during these trials, however, are extensible to both oceanic and domestic arrival operations under NextGen in 2012 and beyond.The automation systems, clearance composition, and human procedures used in carrying out the EDA-supported flight trials are first described, followed by results illustrating potential fuel and emissions benefits.Given the importance of accurate underlying trajectory predictions in planning and executing tailored arrivals, results are then presented that compare EDA and FMS prediction performance against truth data.
+II. ApproachIn collaboration with the Federal Aviation Administration (FAA) and United Airlines, the oceanic tailored arrivals (OTA) trials were conducted over 40 days with a single United Airlines Boeing 777 flight (UAL76) in commercial service between Honolulu and San Francisco (SFO).The flight was operated along the Central-East-Pacific (CEP) oceanic route structure, composed of a series of fixed parallel tracks from the Hawaiian islands to the California coast [12].In addition to its avionics equipage, UAL76 was chosen largely because of its early morning (0530 hrs local) arrival time at SFO, which avoided congested-airspace conditions, thereby minimizing the likelihood of interference with other traffic.
+A. System ComponentsFigure 1 illustrates the key systems employed in formulating, communicating, and executing the trajectory-based arrival clearances used to support the OTA field trials (the precise content of the data communications between the various system elements is described later in the paper).Clearance delivery was initiated approximately 700 n mile from landing in oceanic airspace controlled by the Oakland Air-Route Traffic Control Center (ARTCC), referred to as ZOA.In oceanic airspace, controllers at ZOA rely upon the FAA's recently deployed Advanced Technologies and Oceanic Procedures (ATOP) system for integrated communication, navigation, and surveillance functions.A prominent capability of ATOP is its ability to support two-way digital messaging between ATC and the flight deck through controller-pilot datalink communications (CPDLC).The OTA route clearance (composed of lateral waypoints together with speed and altitude restrictions) was relayed to the flight deck using a standard CPDLC message format supported by ATOP.The format allowed the OTA instructions, upon pilot approval, to be directly loaded into the B777 FMS through its Future Aircraft Navigation System (FANS) avionics interface.FANS avionics integrate FMS functionality with CPDLC and contractbased automatic dependent surveillance (ADS-C) services in oceanic airspace.Once loaded, the OTA route clearance provided sufficient information for the FANS FMS to compute a 4-D reference trajectory from the aircraft's current position to the runway.This trajectory was then used by the FMS as the basis for its lateral navigation (LNAV) and vertical navigation (VNAV) guidance functions, which provided inputs to the automatic flight control system for determining appropriate aileron, rudder, elevator, and throttle inputs.Once activated, the OTA route clearance allowed the flight to progress with no additional pilot inputs required before configuring the airplane for landing.The nominal guidance law used by the FMS VNAV function was set to path mode, which directed the autopilot to null any positional error between the airplane's current altitude and the FMS reference trajectory's vertical profile.In the event that the airspeed required to control path differed from that suggested by the reference trajectory by more than 10 kt, the VNAV guidance laws would switch from path control mode to speed control mode.To provide a dynamic element to the OTA trajectory-based clearance, a prototype version of NASA's EDA decision-support tool was incorporated into the field trials.EDA was used to compute the maneuver solution needed to target a meter-fix crossing time constraint imposed at the TRACON boundary.Originally designed to assist the domestic en route sector controller in developing conflict-free arrival metering solutions under capacity-constrained conditions, EDA was adapted to ZOA airspace and interfaced with ATOP to receive oceanic surveillance and flight-plan data.These oceanic surveillance data were derived from the airplane's satellitebased positioning system and relayed to ATOP via ADS-C at the maximum available rate of once every 2 min, as specified in the ADS-C contract configured by ZOA.In addition to surveillance and flightplan inputs, EDA required atmospheric data to compute the longrange trajectory predictions needed for its advisories.These input data were derived from the National Oceanic and Atmospheric Administration's (NOAA's) rapid update cycle (RUC) model, which provided 2 h forecasts (updated each hour) of wind speed, wind direction, temperature, and pressure, organized in a Lambertconformal 3-D grid with a lateral resolution of 40 km.For the purpose of the OTA trials, EDA computed only descent calibrated airspeed (CAS) advisories for targeting a scheduled time of arrival (STA) at the TRACON meter fix (waypoint BRINY).EDA cruise-speed and path-stretch advisory capabilities, along with automated conflict resolution functions, were purposely suppressed to avoid complexity in these initial trials.For optimal TRACON throughput under congested conditions, EDA derives its target STAs from TMA.During the time of these trials, however, no capacity constraints existed, and so meter-fix STAs were artificially set to within 4 min of the airplane's original estimated time of arrival (ETA) at BRINY.This original ETA was computed from an EDA trajectory prediction using a nominal assumption of company-preferred descent speed (280 kt) [11].An example of EDA's graphical user interface, showing a descent-CAS advisory for UAL76 of 267 kt, is illustrated in Fig. 2. Advisory information is presented in both the aircraft's flight data block as well as in a separate advisory window that allows controllers to accept or reject EDA Fig. 1 OTA system components (AOC denotes airline operations control).recommendations.In the example shown in Fig. 2, the descent-speed advisory was generated to absorb 2 min of required delay, evident in the ETA and STA timelines shown to the left of the display.
+B. Profile Development and Clearance CompositionA primary challenge for OTA, as with any CDA activity, is the design of the profile constraints that define the VNAV descent profile.These constraints must be carefully chosen to allow the FMS to build a trajectory that can be flown with near-idle thrust, using only elevator inputs and normal drag-device deployment (i.e., flaps and landing gear) for vertical control.This design problem involves energy management in the presence of multiple constraints.The descent trajectory must allow the potential and kinetic energy at top-of-descent (TOD) to be bled off at a sufficient rate to satisfy all ATC and aircraft operational constraints along the descent path, while leaving the aircraft in a suitable energy state and control configuration at the final approach fix.To further constrain the problem to allow the FMS to generate a unique idle-thrust trajectory solution for a given wind forecast, an initial descent-speed profile must be chosen.In the absence of time-based metering requirements, this speed profile is set by the FMS based on a cost index, computed as a ratio of time and fuel related costs for an economical idle-thrust (i.e., ECON) descent [13].For OTA flights in which meter-fix crossing times were imposed, EDA was used to override the cost index in determining the appropriate initial descent-speed profile.This use of EDA and its subsequent impact on TOD, flight-path angle and meter-fix arrival time is described in detail in [9].The OTA route clearance consisted of the entire set of lateral and vertical constraints needed by the FMS for building an idle-thrust guidance trajectory.The OTA route clearance was developed iteratively, relying on extensive flight simulation with United Airlines (UAL) and The Boeing Company line pilots under various wind conditions and descent-speed assumptions.The primary objective was to avoid leaving the airplane low on energy relative to the VNAV path, which would trigger undesired throttle inputs from the autopilot.The second objective was to avoid leaving the airplane high on energy relative to the VNAV path, which would require speed brakes, unusual flap settings, and/or steep-descent segments: all of which can increase pilot workload and passenger discomfort, while compromising desired fuel, emissions, and noise benefits.The OTA route clearance used to support flight scenarios with EDA, including lateral waypoint assignments and associated speed/ altitude crossing constraints, is shown in Fig. 3.The first restriction in the descent occurred at the EDA meter fix BRINY, in which the airplane was required to cross at 240 kt CAS, at an altitude of 11,000 ft.The next restriction occurred at Woodside (designated OSI), in which the airplane was required to cross at 210 kt CAS, at an altitude at or above 7000 ft.The third restriction occurred at MENLO, approximately 12 n mile from SFO, in which the airplane was given no explicit speed restriction but required to cross at an altitude of 4500 ft or higher.Beyond MENLO, the remaining descent trajectory waypoints (CEPIN and AXMUL), crossing restrictions, and runway assignment (28R for these trials) were conveyed by the published approach procedure.Because the details of the approach procedure were already stored in the FMS, only the name "ILS 28R" needed to be sent via datalink along with the OTA route clearance.Upon receipt, the FMS automatically appended the detailed approach procedure to the OTA route clearance, thereby forming the basis of a complete guidance trajectory from oceaniccruise flight to the runway.
+C. ProceduresAs illustrated in Fig. 4, OTA procedures were initiated in ZOAcontrolled oceanic airspace, inside oceanic control sector 4 (OC-4).A key challenge of these flight trials was to design procedures sufficient to allow UAL76 to progress uninterrupted along the intended OTA trajectory while traversing through different regions of airspace control.The challenge of executing the OTA trajectory was to develop procedural techniques for allowing UAL76 to progress uninterrupted from oceanic airspace (OC-4), through domestic en route airspace (ZOA sector 35), into the northern California TRACON (NCT) airspace, and finally to landing at SFO.No specialized training was required for controllers to participate in these trials.Instead, simple instructional bulletins were developed by ZOA managers and distributed and briefed to controllers by area supervisors before operations on each day.Furthermore, these trials were conducted without any changes to sector boundaries, ATC standard operating procedures, or letters of agreement between adjoining facilities.OTA procedures were initiated from the flight deck voluntarily.Flight crews were informed of the OTA trials and expected procedures through a special flight-manual bulletin distributed through UAL crew stations.Once underway, OTA procedures could be terminated at any time at the discretion of either the flight crew or ATC.The flight would then be handled by ATC using normal arrival procedures, requiring the crew to typically disengage FMS LNAV/ VNAV functions.For simplicity, it was decided that once interrupted, no attempt would be made to resume a CDA via OTA uplinks or procedures.At step 1 in Fig. 4, the flight crew requested participation around 90 min before their estimated landing time, approximately 700 n mile from SFO and 450 n mile before entering radar-controlled domestic airspace.This request was made using a free-text CPDLC datalink message that read, "Requesting OTA trials."Upon receiving the crew request, the OC-4 controller configured a new ADS-C contract within ATOP that instructed the aircraft to downlink position, weather, aircraft state, and flight-path intent data at a 2 min rate, represented by step 2 in Fig. 4.Following ADS configuration, the primary OTA clearance, as previously described, was uplinked to the aircraft using a FANSloadable CPDLC message (step 3).The message, conveyed using CPDLC uplink message 83, was as follows: "At COSTS cleared CREAN CINNY BRINY/N0240A110 OSI/N210A070 MENLO/ A045A ILS28R."The test engineer coordinated with the ZOAoceanic supervisor in advance to affirm the assumed landing runway.Before accepting the clearance, the flight crew first ensured that it could be loaded satisfactorily into the FMS to produce a continuous trajectory to the runway (step 4).This FMS trajectory was computed in compliance with the route, speed, and altitude constraints stipulated in the OTA clearance.Upon crew acceptance of the OTA clearance, a "Wilco" message was downlinked to ATOP.Step 5 involved the uplink of wind and temperature data to the flight deck for inclusion in FMS trajectory calculations.These atmospheric data were derived from the same RUC 2 h forecast model used for ground-based EDA trajectory calculations.In addition to the forecast surface temperature at SFO, the data consisted of wind speed/direction at five points along the OTA trajectory corresponding to 1) cruise altitude at the waypoint CINNY, 2) cruise altitude at TOD, 3) 18,000 ft along the descent path, 4) 10,000 ft along the descent path, and 5) threshold crossing at SFO.These data were uplinked to the flight deck to provide FMS trajectory computations with the same atmospheric data available to EDA.This was essential to make valid trajectory-prediction comparisons between ground-based and airborne automation in posttrial analysis.Step 6 involved the uplink of the EDA descent-speed advisory, intended to control arrival time at the waypoint BRINY.The advisory was obtained from a prototype EDA tool running on a laptop computer in the ZOA oceanic control room.Upon extracting the advisory, the test engineer relayed it to the oceanic sector controller managing UAL76.The controller then used ATOP to relay the instruction to the aircraft in a datalink message consisting of the current Mach number and the advised descent CAS.Upon receipt by the flight deck, the descent-speed instructions were manually entered into the FMS VNAV descent page, which resulted in a recalculation of the FMS TOD and trajectory needed to target the BRINY constraint.Once reaching TOD, the FMS commanded the airplane to initiate the descent at an airspeed equal to the current Mach number until capturing the EDA-advised descent CAS.After leaving oceanic airspace and entering the radar-controlled domestic airspace of ZOA sector 35, UAL76 received a voice-based pilot-discretionary descent clearance to 8000 ft (step 7); all clearances were given by voice from this point forward because CPDLC services are not currently available in U.S. domestic airspace.Although coordination had already taken place between ZOA and NCT at the supervisory level, the ZOA sector 35 controller then notified the downstream receiving controller at NCT that UAL76 was "on the OTA."Assuming allowable traffic conditions, the NCT controller, upon accepting the hand-off from sector 35, cleared UAL76 to 4000 ft and issued the appropriate approach clearance and runway assignment (step 8).In general, procedures were designed so that all voice-issued altitude clearances stayed ahead of the altitude restrictions contained in the OTA route clearance being executed through the FMS to avoid interrupting the CDA.
+D. Test Conditions and Data CollectionThe OTA trials were conducted in two phases: phase 1 (17 August-6 September 2006), and phase 2 (13 December-9 January 2007).The divided schedule was due to the airline's choice of when to assign a FANS-equipped B777 to UAL76.Two distinct operational test conditions were employed in the OTA trials, referred to as OTA1 and OTA2.For OTA1 flights, the initial descent speed was not stipulated by ATC; instead, the pilot was free to execute a pilot-discretionary descent using the FMS ECON speed profile computed using the cost index.OTA1 flights were conducted to help identify near-term procedural requirements and assess the immediate benefit of conducting tailored arrivals under light traffic conditions in which EDA automation is not required.OTA2 flights, which included the BRINY metering constraint for interoperability with EDA, were used to support trajectory-prediction comparisons between air and ground automation and congested-airspace benefit assessments.A summary of the OTA flights is shown in Table 1 with a breakdown of successfully completed events.There were 40 flight opportunities over both phases of the trials.Of these, pilots volunteered to participate on 35 occasions.RUC-based winds were successfully uplinked on 27 of these occasions.The total number of uninterrupted CDA operations to the TRACON boundary and runway (independent of successful wind uplinks) were 27 and 20, respectively, of which approximately 80% were OTA2 flights.Upon taking only those flights that had successful wind uplinks together with uninterrupted CDA to the TRACON boundary, and further eliminating those with any unexplained route deviations and/or pilotreported anomalies, 11 flights remained for the detailed trajectory analysis described later in this paper.The quantitative data collected during the OTA trials are shown in Table 2.In addition to quantitative measurements, qualitative data were collected to refine human procedures and identify real-world issues associated with OTA deployment and execution.Qualitative observations were also used to support postflight quantitative analysis by helping to explain any anomalies associated with a particular flight.Qualitative data gathered by project engineers included air-traffic control facility observations, jump-seat observations on the flight deck, and crew interviews at the gate upon arrival at SFO.
+III. Results
+A. Fuel and Emissions Benefits 1. Baseline ConditionsFor baseline fuel and emissions benefits, three flight-path scenarios were constructed to represent today's arrival operations under light, medium, and heavy traffic conditions.These baseline scenarios were derived from observing B777 arrival traffic into SFO off CEP routes, captured for various times of day from flight-track data gathered one week prior and during the OTA phase-1 and phase-2 trials.The lateral and vertical track data from which baseline scenarios were derived are shown in Fig. 5 for various times of day.Particularly for morning and evening flights, when traffic is heaviest, these data show the inefficient lateral vectoring and altitude level-off maneuvers resulting from air-traffic control actions taken to manage separation and throughput constraints.The three baseline trajectories derived from these data are shown in Fig. 6.The light-congestion baseline was used to estimate near-term benefits, because it represents traffic conditions for which OTA procedures could be invoked today without the deployment of EDA.To claim OTA benefits in comparison with the medium and heavy traffic baseline scenarios, it is assumed that EDA is available as a supporting groundside tool for developing conflict-free metering solutions.
+Fuel BenefitsBecause of the unavailability of direct fuel-burn measurements from the aircraft, OTA fuel benefits were estimated Boeing's proprietary BCOP/INFLT database and performance analysis software.Fuel burn associated with each of the three baseline scenarios was estimated in BCOP/INFLT for a range of initial descent-CAS values: 260, 280, 300, and 320 kt.Comparative fuel-burn results were computed for modeled OTA1 and OTA2 trajectory profiles using the same range of initial descent-CAS values used for the baseline scenarios.For the OTA1 profile, the descent CAS was assumed to be constant up to the transition to the built-in FMS speed restriction of 240 kt, occurring whenever the aircraft crossed through 10,000 ft.For OTA2 flights, the descent CAS was held constant before transitioning to the BRINY crossing restriction of 11,000 ft and 240 kt.For each baseline and OTA scenario, Table 3 shows the distance flown and estimated fuel burn between CREAN (located on the oceanic control boundary approximately 240 n mile from landing) and SFO for a B777-200.Over all baseline and OTA scenarios, the effect of initial descent CAS on total fuel burn was small, amounting to less than a 25 lb fuel difference between the optimal descent CAS (280 kt) and the least efficient descent CAS (260 kt).Therefore, to simplify the presentation of results, Table 3 shows the fuel burn averaged across all descent-CAS variations.Under light-congestion traffic conditions, results indicate that OTA1 can provide average fuel savings of 242 lb per flight over current-day baseline operations for B777 flights arriving at SFO along CEP routes.These savings drop slightly to 227 lb for OTA2 as a result of the additional profile constraint introduced by the meter-fix crossing restriction at BRINY.Potential fuel savings under lightcongestion traffic conditions, however, are well represented by the OTA1 results, because EDA would not be required to enable CDA operations.Furthermore, because these savings are not dependent on EDA, they could be realized in the near-term using current oceanic automation systems together with trajectory-based procedures.For medium and heavy traffic-congestion scenarios, average estimated OTA fuel savings increase to 358 and 3219 lb per flight, respectively.The dramatic increase in estimated fuel savings for the heavy traffic comparison was due to the inefficiencies inherent in the baseline scenario for achieving flow management and separation assurance under congested conditions.As seen in Fig. 6, these inefficiencies included an extra 30 n mile path-stretch segment performed in level flight at low altitude (6000 ft).To accurately  estimate OTA fuel savings under heavy traffic conditions, the effect of upstream metering actions needed to approximately match the arrival times of baseline flights into SFO was considered.For these scenarios, delay absorption was required to prevent OTA flights from arriving too early, thereby violating capacity constraints.Although, ideally, some delay could be absorbed on the ground by carefully planning departure times in anticipation of CDA operations in the presence of other traffic, it is more realistic to assume that delay must be absorbed in flight, either as a result of additional EDA advisories in arrival airspace and/or regional applied further upstream.To compute the fuel savings associated with heavy-congestion operations, an approximately 25% fuel-burn penalty was calculated for OTA flights to account for upstream metering actions.It was assumed that this penalty was taken upstream of CREAN in cruise flight in the form of a path-stretching maneuver designed to match baseline arrival times.This penalty Fig. 6 OTA baseline scenarios derived from current-day observations.accounts for fuel savings in the heavy-congestion scenario of Table 3 being less than the difference in fuel burned from CREAN to SFO between baseline and OTA scenarios.No such penalty was needed for OTA flights under light and medium traffic congestion, because required delay absorption could be accomplished with the descent-CAS variation already taken into account.
+Emissions BenefitsEstimates of per-flight OTA emissions reduction in comparison with baseline conditions are shown in Table 4.These results were calculated along with the fuel burn using BCOP/INFLT between CREAN and SFO.Table 4 shows the effect of OTA on the four compounds of primary concern to the environment.These are 1) carbon dioxide (CO 2 ), a greenhouse gas produced as a normal product of organic-fuel combustion; 2) carbon monoxide (CO), a poisonous gas resulting from incomplete combustion; 3) nitrogen compounds, primarily nitric oxide (NO) and nitrogen dioxide (NO 2 ), resulting from high-temperature combustion and commonly associated with ozone and smog; and 4) all species of hydrocarbons (C x H x ), volatile organic compounds resulting from unburned or partially burned fuel passing through the engine [14].As shown in Table 4, results suggest that an idle-thrust, OTA1 descent to the runway can reduce CO 2 emissions by 761 lb per flight in comparison with B777 operations on similar routes conducted during light traffic congestion.In comparison with medium and heavy trafficcongestion baselines, OTA2 has the potential to reduce CO 2 emissions by as much as 1128 and 10,137 lb per flight, respectively.These large greenhouse-gas reductions reflect the approximate 3:1 ratio between fuel burned and CO2 emitted, resulting from the basic combustion chemistry of jet fuel [14].
+B. Trajectory PerformanceData showing the accuracy of FMS arrival-time predictions to the meter fix (BRINY) in comparison with the actual BRINY crossing time, as a function of time-to-go to BRINY, are shown in Fig. 7.These data show the expected continual improvement of airborne predictions as the airplane progresses through oceanic and domestic airspace.Of note are the distinct improvements in prediction accuracy for several flights that can be observed to correspond to the wind and descent-speed uplink events.The latter, of course, was expected whenever the airplane's original VNAV descent speed, based on cost index, differed from the EDA descent-speed clearance that was uplinked and executed.EDA and FMS arrival-time estimates to the meter fix, resulting from trajectory predictions generated 200 n mile=25 min upstream at CREAN, are illustrated by the histogram in Fig. 8.These results pertain to a single prediction at CREAN (i.e., without updates as the flight progresses).These data show mean arrival-time prediction accuracies of 2 s early and 3 s late for FMS and EDA, respectively, with a similar dispersion about the mean ( 20 s) for both.These data suggest that airborne and ground-based automation can predict arrival times over a 25 min horizon with similar accuracy and precision, assuming shared wind and descent-speed-intent information.Because EDA conflict-avoidance functions require accuracy at each point along the trajectory prediction, not just at the meter fix itself, the entire EDA prediction was compared with surveillance truth data.The overall accuracy of EDA trajectory predictions for several look-ahead times is shown in Table 5.These results capture the error in altitude, along-track position, and cross-track position along the entire trajectory prediction to the meter fix for time horizons of 23, 20, and 17 min.These look-ahead times were chosen so that predictions for all flights could be initiated between the oceanic control boundary and TOD to allow use of ARTCC radar surveillance as truth data, captured every 12 s.Inherent latencies associated with the radar data (a constant bias for each flight ranging from 6 to 12 s) were identified and removed in postprocessing by calibrating them with the time-stamped airplane position reports received via ADS-C.Figures 9 and10 show the altitude and alongtrack error for all flights as a function of time for a 23 min horizon.A similar comparison of FMS trajectory-prediction error as a function of time was not possible, because only the FMS time estimates to downstream waypoints were available, not the full trajectory predictions upon which those estimates were based.Results in Table 5 show that EDA's mean altitude prediction error, ranging between 500 and 700 ft, does not vary substantially with time horizon.This is because most altitude error occurs in the descent phase of flight, which is fully contained within each prediction horizon and influenced primarily by speed intent and modeled aircraft performance characteristics, rather than initial conditions in cruise.The negative mean altitude error is due to EDA predicting the airplane to reach the meter-fix crossing altitude earlier than what actually occurred, due to EDA's modeling of the deceleration to the meter-fix crossing speed (240 kt) using a level-flight segment.Although this assumption works well for the current-day TMA operations that EDA was originally designed to support, it is a poor model for CDA operations specifically designed to avoid level-flight segments.The result is a ground-based prediction to the meter fix that lags the actual flight operation.This results in an earlier TOD estimate (evident by the initial spike in altitude error in Fig. 9) and lower overall altitude prediction in comparison with truth.The same phenomenon results in the negative mean along-track prediction errors (ranging between 0:6 and 1:3 n mile) seen in Table 5.Unlike altitude errors, along-track errors averaged over the entire trajectory prediction grow significantly as the time horizon increases from 17 to 23 min.This is due to groundspeed prediction error (resulting primarily from remaining wind uncertainty) occurring over both cruise and descent.This EDA-prediction analysis includes the effect of sharing wind data and descent-speed intent with the airplane in actual operations.To see the importance of shared descent-speed intent (in particular, on air/ground predictions), a simple study was carried out looking at EDA trajectories under descent-speed-intent assumptions ranging from 250 to 320 kt CAS.Over a 25 min prediction horizon, the altitude and along-track differences at any given time can be as large as 6500 ft and 20 n mile as a result of descent-speed-intent uncertainty.
+C. Procedural FindingsSeveral key procedural lessons were learned from the flight trials that have important implications for routine CDA operations, as described next.
+Interairspace CoordinationA key challenge was the design of procedures sufficient to allow UAL76 to progress uninterrupted along the intended OTA trajectory while traversing through different regions of airspace control.Indeed, this represents a general problem associated with accommodating trajectory-based operations (optimized over long time horizons) in today's highly segmented ATC system.Based on feedback from pilots and controllers, it was concluded that the procedures used in these trials for reiterating altitude assignments and crossing restrictions by voice as the airplane progressed from one region of airspace to another were less than ideal.Although these techniques allowed the flight trials to be conducted in conformance with airline and ATC standard operating procedures, they were   deemed too cumbersome for routine operations.More important, there was occasional confusion on the flight deck resulting from a perceived mismatch between restrictions conveyed by voice and those inherent in the previously uplinked OTA clearance.From a pilot perspective, the issuance of step-down altitude clearances via voice served primarily as a cue for dialing down the assigned altitude in the autopilot mode control panel (MCP).Because even with VNAV engaged the autopilot would force the airplane to level off upon reaching the MCP altitude, it was imperative that voice-issued altitude clearances be issued in a timely fashion to prevent interrupting the descent.In posttrail discussions with subject-matter experts, it was concluded that voice procedures could be improved by allowing controllers, upon receiving a hand-off from an upstream facility, to simply instruct the airplane to continue the descent via the uplinked clearance.Although a departure from standard procedures, this approach offers to streamline phraseology while preventing inconsistencies between voice and datalink instructions.
+Wind Data TransferThe importance of accurate wind data to ground-based and airborne predictions was highlighted by the trajectory analysis described in the previous section.Based on these results, it is clear that CDA operations using the FMS for guidance and control require accurate and timely wind forecast data.Although the technique of relaying ATC-derived winds to the aircraft through the company dispatch center worked well for the purpose of the field trials, it was concluded that it would be easier to update winds directly from the company's own forecast data in routine operations.
+Noise-Abatement ProceduresThe enforcement of the 7000-ft-alt minimum over OSI for noiseabatement purposes introduced a difficult constraint on the OTA vertical profile.The 7000-ft-alt minimum over OSI was higher than the ideal altitude over this point for executing an idle-thrust CDA in compliance with speed, altitude, and routing constraints closer to the runway.As a result, pilots often had to apply speed brakes between OSI and MENLO to remove excess energy to stay on the FMS VNAV path.With no altitude constraint on OSI, it was found in flight simulation that a B777 would cross OSI at approximately 5500 ft under normal wind conditions.Because noise-abatement procedures are typically imposed with the assumption that airplanes will be in level powered flight at the specified minimum altitudes, an argument can be made for relaxing these constraints to accommodate idlethrust CDA operations.It is anticipated that the noise reduction  associated with lower engine power will offset the increased noise exposure resulting from the lower altitude profile.Quantitative analysis of noise data gathered during the flight trials will be presented in a follow-up paper, along with recommendations for optimizing the vertical descent path to achieve better energy management without compromising noise abatement.
+IV. ConclusionsThe San Francisco oceanic tailored arrivals field trials demonstrated the ability to conduct highly efficient continuous descent approach operations under real-world conditions with commercial airplanes.By integrating advanced air and ground automation over datalink in oceanic airspace, these trials provide a step toward understanding the feasibility and benefits of conducting trajectory-based arrival operations under NextGen.To progress toward enabling tailored arrivals under congested traffic conditions in which benefits are greatest, NASA's ground-based EDA automation was used to tailor trajectory solutions to accommodate timebased metering constraints.Data gathered during these trials were used to perform an assessment of EDA trajectory-prediction accuracy and precision in comparison with FMS predictions and surveillance truth data.Results show that trajectory-prediction errors can be greatly reduced through the uplink of wind and descent-speedintent data and that similar arrival-time prediction performance can be achieved between air and ground automation.These results are important because accurate and compatible prediction performance between air and ground automation is fundamental to both the planning and execution of trajectory-based arrival operations.An initial benefits assessment based on the real-world data gathered during these trials shows the potential for substantial perflight reductions in fuel burn and environmental emissions with tailored arrivals, especially in comparison with baseline arrival operations conducted under heavy traffic conditions.These results are particularly compelling in the presence of today's high fuel costs and increased environmental awareness.Although EDA and related scheduling automation are important technologies for achieving the benefits associated with uninterrupted CDA operations to the runway under tailored arrivals, further studies are required to determine what additional technologies, particularly in the TRACON domain, might be required to capture the full range of theoretical benefits in actual high-density traffic operations.Fig. 22Fig. 2 EDA plan-view graphical user interface showing descent-speed advisory.
+Fig. 44Fig. 4 OTA procedure steps.
+Fig. 55Fig. 5 Current B777 operations into SFO arriving off CEP routes.
+Fig. 77Fig. 7 Progression of FMS arrival-time predictions to meter fix.
+Fig. 88Fig. 8 Histogram of EDA and FMS arrival-time prediction error to meter fix (200 n mile=25 min horizon).
+Fig. 99Fig. 9 EDA altitude prediction error (23 min time horizon).
+Fig. 1010Fig. 10 EDA along-track prediction error (23 min time horizon).
+Table 11Flight summarySuccessful OTA route clearance uplink Successful wind uplink Successful CDA to TRACON boundary Successful CDA to landingTotal OTA opportunities Pilot concurrenceOTA1 OTA2 Totals OTA1 OTA2 TotalsTest Phase
+Table 22Data collectedSourceData elementsFrequencyADS-CPosition (lat/long, altitude, time stamp)1/(2 min)Weather (wind speed, wind direction, temperature)Earth reference (ground speed, vertical rate, track angle)Air reference (Mach, heading)Projected intent (ETAs at all downstream waypoints)ZOA radar track and flight plan Position1/(12 s)Ground speed, track headingNCT radar track and flight plan Position1/(5 s)Ground speed, track headingEDA outputsTrajectory predictionsEvent-basedSpeed advisoriesNOAA RUC-2Wind speed/direction1/hTemperature, pressureMicrophoneSurface noise level10 HzAircraft uplinksOTA route clearanceEvent-basedEDA speed advisory
+Table 33Fuel burn: OTA vs baseline NO x , lb C x H x , lb CO 2 , lb CO 2 : Baseline-OTA1, lb CO 2 : Baseline-OTA2, lbScenarioDistance flown from CREANFuel burned from CREANFuel: baseline-OTA1, lb Fuel: baseline-OTA2, lbto SFO, n mileto SFO, lbOTAOTA12337485----OTA22337500----BaselineLight2397727242227Medium2447858373358Heavy27311,68032243219Table 4 Emissions: OTA vs baselineEmissions from CREAN to SFOScenario CO, lb OTAOTA117.3133.90.7723,572----OTA217.5134.00.7723,618----BaselineLight19.1135.60.8224,333761715Medium 19.3138.10.8424,74611741128Heavy19.7238.60.9536,78210,15210,137
+Table 55EDA trajectory-prediction accuracy for 23, 20, and 17 min time horizons23 min prediction20 min prediction17 min predictionError categoryMean Std devMinMax Mean Std devMinMax Mean Std devMinMaxAltitude error, ft5027143301 8096646963290 5646936703473 479Along-track error , n mile1:331.454:80 2.300:730.992:82 1.980:610.782:44 1.85Cross-track error, n mile0:120.231:19 0.370:110.211:09 0.15 0.090.211:11 0.24
+			COPPENBARGER, MEAD, AND SWEET
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+AcknowledgmentsThe authors would like to acknowledge the substantial contributions of Federal Aviation Administration (FAA) personnel from ZOA and Northern California Terminal Radar Approach Control in planning and executing these operational field trials.In addition, the support received from FAA headquarters was instrumental in approving and coordinating this activity.Critical systems engineering, data analysis, and human-factors contributions were made by project personnel from The Boeing Company; Sensis Corporation; the San Francisco Noise Abatement Office; Lockheed Martin; NASA Code TH; QSS Group, Inc.; University of California, Santa Cruz; and San Jose State University.Last, but not least, the authors would like to thank to the subject-matter experts and pilot participants of United Airlines that enabled these trials to take place.
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+		Evaluating NGATS Research Priorities at JPDO
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+			DanielGoldner
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+			SherryBorener
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+		10.2514/6.2006-7726
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+		6th AIAA Aviation Technology, Integration and Operations Conference (ATIO)
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 2006
+		
+	
+	Gouldner, D., and Boerner, S., "Evaluating NGATS Research Priorities at JPDO," AIAA Paper 2006-7726, Sept. 2006.
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+
+
+	
+		Co-Operative Air Traffic Management: Concept and Transition
+		
+			ThomasPrevot
+		
+		
+			ToddCallantine
+		
+		
+			PaulLee
+		
+		
+			JoeyMercer
+		
+		
+			VernolBattiste
+		
+		
+			EverettPalmer
+		
+		
+			NancySmith
+		
+		10.2514/6.2005-6045
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 2005
+		
+	
+	Prevot, T., Callentine, T., Lee, P., Mercer, J., Battiste, V., Palmer, E., and Smith, N., "Co-Operative Air Traffic Management: Concept and Transition," AIAA Paper 2005-6045, Aug. 2005.
+
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+
+	
+		Development of an advanced continuous descent concept based on a 737 simulator
+		
+			LRAnderson
+		
+		
+			AWWarren
+		
+		10.1109/dasc.2002.1067907
+	
+	
+		Proceedings. The 21st Digital Avionics Systems Conference
+		The 21st Digital Avionics Systems ConferencePistcataway, NJ
+		
+			IEEE
+			Oct. 2002
+			1
+			
+		
+	
+	Anderson, L. R., and Warren, A. W., "Development of an Advanced Continuous Descent Concept Based on a 737 Simulator," Proceedings of the 21st Digital Avionics System Conference, Vol. 1, Inst. of Electrical and Electronics Engineers, Pistcataway, NJ, Oct. 2002, pp. 1E5-1-1E5- 4.
+
+
+
+	
+		In Service Demonstration of Advanced Arrival Techniques at Schiphol Airport
+		
+			JosephWat
+		
+		
+			JesseFollet
+		
+		
+			RobMead
+		
+		
+			JohnBrown
+		
+		
+			RobertKok
+		
+		
+			FerdinandDijkstra
+		
+		
+			JeroenVermeij
+		
+		10.2514/6.2006-7753
+	
+	
+		6th AIAA Aviation Technology, Integration and Operations Conference (ATIO)
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 2006
+		
+	
+	In Service Demonstration of Advanced Arrival Techniques at Schiphol Airport," AIAA Paper 2006-7753
+	Watt, J., Follet, J., Mead, R., Brown, J., Kok, R., Dijkstra, F., and Vermeij, J., "In Service Demonstration of Advanced Arrival Techniques at Schiphol Airport," AIAA Paper 2006-7753, Sept. 2006.
+
+
+
+	
+		Community Noise Reduction Using Continuous Descent Approach: A Demonstration Flight Test at Louisville
+		
+			KevinElmer
+		
+		
+			JoesephWat
+		
+		
+			BelurShivashankara
+		
+		
+			AnthonyWarren
+		
+		
+			Kwok-OnTong
+		
+		
+			John-PaulClarke
+		
+		10.2514/6.2003-3277
+		AIAA Paper 2003-3277
+	
+	
+		9th AIAA/CEAS Aeroacoustics Conference and Exhibit
+		
+			American Institute of Aeronautics and Astronautics
+			May 2003
+		
+	
+	Elmer, K., Wat, J., Warren, A., Tong, K., and Clark, J., "Community Noise Reduction Using Continuous Descent Approach: A Demon- stration Flight Test at Louisville," AIAA Paper 2003-3277, May 2003.
+
+
+
+	
+		Continuous Descent Approach: Design and Flight Test for Louisville International Airport
+		
+			John-Paul B.Clarke
+		
+		
+			NhutTHo
+		
+		
+			LilingRen
+		
+		
+			JohnABrown
+		
+		
+			KevinRElmer
+		
+		
+			Kwok-OnTong
+		
+		
+			JosephKWat
+		
+		10.2514/1.5572
+	
+	
+		Journal of Aircraft
+		Journal of Aircraft
+		0021-8669
+		1533-3868
+		
+			41
+			5
+			
+			2004
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Clarke, J., Ho, N., Ren, L., Brown, J., Elmer, K., Tong., K., and Wat, J., "Continuous Descent Approach: Design and Flight Test for Louisville International Airport," Journal of Aircraft, Vol. 41, No. 5, 2004, pp. 1054-1066. doi:10.2514/1.5572
+
+
+
+	
+		History, Development and Analysis of Noise Abatement Arrival Procedures for UK Airports
+		
+			TomReynolds
+		
+		
+			LilingRen
+		
+		
+			John-PaulClarke
+		
+		
+			AndrewBurke
+		
+		
+			MarkGreen
+		
+		10.2514/6.2005-7395
+	
+	
+		AIAA 5th ATIO and16th Lighter-Than-Air Sys Tech. and Balloon Systems Conferences
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 2005
+		
+	
+	Reynolds, T., Ren, L., Clarke, J., Burke, A., and Green, M., "History, Development and Analysis of Noise Abatement Arrival Procedures for UK Airports," AIAA Paper 2005-7395, Sept. 2005.
+
+
+
+	
+		Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages
+		
+			CRoberts
+		
+		
+			BCornell
+		
+		
+			RMead
+		
+		10.47886/9781934874561.ch10
+	
+	
+		Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages
+		Canberra, ACT, Australia
+		
+			American Fisheries Society
+			Dec. 2004
+		
+	
+	Roberts, C., Cornell, B., and Mead, R., "Tailored Arrival Joint Project Phase One, Final Report," Airservices Australia, Canberra, ACT, Australia, Dec. 2004.
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+		Design and Development of the En Route Descent Advisor (EDA) for Conflict-Free Arrival Metering
+		
+			RichardCoppenbarger
+		
+		
+			RichardLanier
+		
+		
+			DougSweet
+		
+		
+			SusanDorsky
+		
+		10.2514/6.2004-4875
+	
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+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 2004
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+	Paper 2004-4875
+	Coppenbarger, R., Lanier, R., Sweet, D., and Dorsky, S., "Design and Development of the En-Route Descent Advisor for Conflict Free Arrival Metering," AIAA Paper 2004-4875, Aug. 2004.
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+	
+		Descent Advisor preliminary field test
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+			StevenGreen
+		
+		
+			RobertVivona
+		
+		
+			BeverlySanford
+		
+		10.2514/6.1995-3368
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+	Green, S., "Descent Advisor Preliminary Field Test," AIAA Paper 1995- 3368, Aug. 1995.
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+		A Time-Based Approach to Metering Arrival Traffic to Philadelphia
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+			TFarley
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+			JFoster
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+			KLee
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+	Grabbe, S., and Sridhar, B., "Central East Pacific Flight Routing," AIAA Paper 2006-6773, Aug. 2006.
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+		Selection of an optimal cost index for airline hub operation
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+			AbhijitChakravarty
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+		10.2514/3.20054
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+IntroductionIn order to assist with Air-Traffic Management (ATM) under capacity-constrained conditions, the Center-TRACON Automation System (CTAS) has been developed at NASA Ames Research Center.CTAS includes a collection of software decision-support tools that enhance situational awareness and provide clearance advisories to assist controllers in separating, scheduling, sequencing, and spacing aircraft in en route and terminal airspace.The en route CTAS tools, which have been evaluated in the field, include the Traffic Management Advisor (TMA), the En route Descent Advisor (EDA), and Conflict Prediction and Trial Planner (CPTP).TMA generates schedules and sequences for aircraft transitioning from en route to terminal airspace, subject to airport capacity constraints.EDA assists controllers in developing efficient, conflict-free, descent advisories to deliver aircraft to the Center-TRACON boundary in accordance with TMA schedules 1 .CPTP assists with the identification and resolution of conflicts for all aircraft, whether in climb, cruise, or descent 2 .Together, the CTAS tools are designed to maximize airspace and airport capacity while improving the overall efficiency of flight operations.A central capability of CTAS, which provides the foundation for all CTAS tools, is the ability to accurately predict the future spatial and temporal position of each aircraft in the airspace over a range of look-ahead times.This capability is provided by the Trajectory Synthesizer (TS) module, often referred to as the CTAS "computational engine" 3 .The accuracy of trajectory predictions in en route (Center) airspace impacts ATM conflict predictions and Estimated Times of Arrival (ETAs) to control fixes 4 .Preliminary studies have shown that trajectory uncertainties can significantly effect the schedules and maneuver advisories that CTAS generates to manage traffic flows and resolve separation conflicts 5 .For the airspace user, inaccurate trajectory predictions may result in less-than-optimal maneuver advisories in response to a given traffic management problem.These include missed advisories and false advisories.Missed advisories refer to the lost opportunity of resolving a traffic management problem in a manner most efficient to the airspace user.An example of a missed advisory is the failure to resolve a conflict between two aircraft at the earliest opportunity, requiring the least amount of fuel-burn between them.False advisories refer to the suggestion of an unnecessary maneuver that may cause an aircraft to depart from its most efficient, or userpreferred, trajectory.An example of a false advisory occurs when an aircraft is vectored unnecessarily off its American Institute of Aeronautics and Astronautics filed or preferred flight path in response to a false conflict alert.Although false and missed advisories can result in lost efficiency to user operations, they do not compromise safety.This is because ATM decision support tools involve long-range strategic trajectory predictions that are frequently updated and used by the service provider to continually reassess the traffic situation.Traffic management problems that are not resolved at the earliest, or most desirable, time can be expected to be resolved at a future time, but at the expense of user efficiency and controller workload.In addition to limiting user and ATM efficiency, inaccurate trajectory predictions also waste airspace capacity by forcing the controller to apply larger than required separation buffers in response to position uncertainties.A limiting factor in the accurate prediction of aircraft trajectories is the difficulty in obtaining precise trajectory calibration and intent data for individual flights.Trajectory calibration data refers to aircraft state, aircraft performance, and atmospheric characteristics that influence the external forces acting on the aircraft.Trajectory intent information includes the anticipated route, speed profile, and maneuvering procedure of the aircraft over the trajectory prediction time horizon.The pilot, in concurrence with ATM clearance instructions, ultimately establishes the trajectory intent of a given flight.The En route Data Exchange (EDX) research program at NASA Ames is addressing the exchange of trajectory information between the airspace user and the air traffic control system.The initial objective of EDX is to improve CTAS en route trajectory predictions by obtaining timely calibration and intent data from user systems.In an operational environment, user information can be acquired from either ground-based or airborne sources.Ground-based sources, which can readily provide pre-departure flight-planning data, include Airline Operational Control (AOC) centers and Flight Service Stations.Airborne data sources include the Flight Management System (FMS) and other avionics systems that can provide post-departure data through air-ground data link.In the interest of exploiting currently available technology and minimizing cost, EDX will consider obtaining trajectory data from ground-based resources, prior to focusing on aircraft systems and data link capabilities.This paper describes an initial EDX study to investigate the potential improvement that AOC flight-planning information offers to CTAS climb trajectory predictions.In support of current CTAS en route tools, accurate climb predictions are necessary in order to probe for conflicts and issue direct-route clearances that enhance user efficiency by shortening flight time.The algorithms and input data used in the current climb trajectory prediction process are first described, followed by a description of pertinent data elements that are either available or derivable from AOC flight-planning resources.Results are then presented which show the operational range of AOC trajectory parameters and their impact on the accuracy of CTAS climb predictions.
+TS Algorithms and Existing Input DataThe CTAS TS uses a simplified set of point-mass aircraft equations of motion for generating fourdimensional (4D) trajectories consisting of aircraft position (x-y) and altitude over range of future times.In Center airspace, new predicted trajectories are generated for all aircraft in the airspace following each complete radar sweep (12 seconds).Trajectories are generated by integrating between predefined waypoints, defined by the current aircraft position, filed flight plan route, and airspace description data.The simplified equations of motion used by the TS, described fully in Reference 6, are given bydh / dt = a V T = i V G (1)V G = U W cos ( G -W ) + (2) V T cos ( sin -1 ( U W sin ( G -W ) / V T ) ) dV T / dt = (T -D )/ m -g a -g i V T dU W / dt (3)where h is geometric altitude; m is the aircraft mass; g is acceleration due to gravity; U W is wind speed; V G is ground speed; V T is true airspeed; W and G are the wind direction and ground speed direction, respectively; a and i are the aerodynamic and inertial flight path angles, respectively; T is the aircraft thrust; and D and is the aircraft drag.To simplify the prediction process, the horizontal and vertical components of the trajectory are de-coupled.First, an approximate vertical profile is computed in order to compute true airspeed as a function of path distance.This approximate speed profile is then used to calculate turn radii used in the synthesis of the horizontal trajectory, consisting of a series of straightline segments connected by constant radius turns.This horizontal trajectory is then used as the basis for computing the actual vertical profile.American Institute of Aeronautics and Astronautics For computing undelayed trajectory positions and waypoint ETAs for en route climbs, the TS first checks to see if there is a company-preferred speed profile available from static files.Company-preferred profiles are typically defined for each aircraft type, but are not tailored specifically for individual flights.In absence of any known ATM constraints or clearances, these company speed profiles, together with the filed flightplan, communicate trajectory intent.In general, climb speed profiles are defined in terms of a constant Calibrated Airspeed (CAS) segment and a constant Mach segment.In flying this profile, a pilot/FMS will hold a constant CAS and variable Mach until the specified profile Mach number is reached.At this point the pilot/FMS will transition to a constant Mach/ variable CAS profile to continue the climb.The altitude at which this transition takes place is typically near 27,000 ft for most jets, depending on the chosen CAS/Mach speed profile and atmospheric conditions.A typical CAS/Mach climb profile is illustrated in Figure 1.The inputs to CTAS 4-D trajectory predictions can be characterized in terms of calibration, intent, and constraint data types.Specific data elements and data sources used in this process are summarized in Table 1.
+Calibration DataCalibration data includes current and predicted aircraft and atmospheric properties that effect the accuracy of trajectory predictions.This data, described in general in Reference 7, is crucial for calibrating ground-based trajectory predictions with those performed by an aircraft FMS using the latest airborne information.Calibration   State data, consisting of inertial position, altitude, speed, and heading, is used to initialize the trajectory prediction process for each aircraft.This data, currently obtained from the FAA Center Host computer, is derived from surveillance radar and Mode C altitude returns every 12 seconds.Performance data is used in calculating the aerodynamic, propulsive, and gravitational forces acting on the aircraft.Aerodynamic drag forces are computed from drag coefficients stored in CTAS files for each aircraft type.In general, the total drag coefficient is calculated by summing the composite coefficients attributed to the clean airframe, flaps, speed brakes, and landing gear.Propulsive performance data, also stored in static CTAS files, allows for the calculation of thrust and fuel-flow as a function of airspeed, altitude, and engine control setting.In particular, the propulsive model data allows for the computation of maximum and idle thrust as a function of aircraft and atmospheric state for use in climb and descent trajectory predictions.The gravitational force is based on the estimated weight of the aircraft, stored in a static CTAS file.For climb predictions, CTAS currently uses a nominal estimated takeoff weight, which is identical for all aircraft of a given type.This is clearly a gross approximation since there is no accounting for real-world payload differences.The CTAS TS, however, does employ an adaptive algorithm that is capable of decrementing the nominal weight estimate in response to altitude envelope limitations.Atmospheric information for CTAS en route trajectory predictions is obtained from the National Oceanic and Atmospheric Administration (NOAA).CTAS accesses 1-hour forecasts from the NOAA Rapid Update Cycle model to obtain horizontal wind speed, temperature, and pressure.This data is available in a 3-dimensional (3D) grid defined by a horizontal resolution of 40 km and a vertical resolution of 25 mb pressure altitude.The CTAS TS linearly interpolates between the RUC 3D grid points at each integration time step along the predicted trajectory 8 .
+Intent DataIntent data consists of information associated with the intended flight path of an individual aircraft.This includes the originally filed flight plan amended by any ATM clearance instructions.Filed flight plan data includes the identification of horizontal waypoints that define the intended route of flight, along with the intended cruise speed.Intent data also includes companypreferred speed profiles for climb and descent.In the current CTAS, company-preferred speed profiles are represented by a desired climb/descent CAS.This CAS value is used to compute a companion Mach number for flights with an initial/terminal cruise altitude above 27,000 ft.Together, the CAS/Mach pair defines the nominal speed profile for undelayed climbs and descents.
+Constraint DataConstraints effecting the trajectory prediction process can be placed into three categories: performance, procedural, and traffic-management.Flight path constraints will override flight path intent/preference.Performance-related constraints, defined originally by the user and stored in CTAS files, establish the allowable speed and altitude envelop for each aircraft type.These speed and altitude constraints are used to limit the range of controller advisories generated by CTAS.Procedural constraints may include any combination of speed, altitude, or heading restrictions imposed by ATM regulations or airline company policy.Procedural constraints used by CTAS are stored within CTAS files and are specific to the airspace and airports to which CTAS has been adapted.Finally, traffic-management constraints are those generated internally within CTAS in response to scheduling advisories or conflict avoidance resolutions.For example, in the case of descent metering, TMA may generate a scheduling constraint for a given flight in the form of a meter fix crossing time.The iterative solution used in calculating a conflict-free trajectory that satisfies this scheduling constraint is managed by the EDA tool.EDA invokes the CTAS TS as needed to perform this function.
+AOC Flight Plan DataThe primary limitation of current CTAS airline data is that it represents nominal performance and preference characteristics for all aircraft of a given type, without considering variations associated with specific flight operations or aircraft sub-types.Flight-specific predeparture data, used by CTAS, is currently limited to flight plans available from the FAA Host computer.The ATM flight plan is limited to a broad description of aircraft type, expected route waypoints, and anticipated cruise altitude and airspeed.Detailed operational flight plans, available from AOC centers, can provide a rich source of calibration and intent data for improving ATM trajectory predictions, especially for en route climbs American Institute of Aeronautics and Astronautics (above 10,000 ft).This data includes such items as aircraft weight, thrust and drag performance factors, and speed profile intent.In addition to improving the accuracy of ground-based predictions, the receipt of AOC flight-planning data can allow the air traffic system to be more responsive to airline operational preferences.Pre-departure intent data provided by the AOC represents the preferred speed and routing for an individual flight, in absence of any unknown ATM, weather, or airspace restrictions.Airline trajectory preferences may be tailored to the fuel performance of an individual flight, or to the overall schedule efficiency of the airline as a whole.With better knowledge of the trajectory preferences associated with individual flights, ATM automation can more effectively accommodate airline operational considerations into traffic management advisories.AOC calibration and intent data, useful for improving climb prediction performance, is presented in Table 2.These data items are known to be either directly available from existing AOC operational flight plans or thought to be derivable from the AOC flight-planning process 9 .The usefulness and availability of each parameter to CTAS is described as follows.
+Airframe and Engine TypeThe explicit airframe and engine type specification are known by the AOC and incorporated in their flight planning.This information could be sent to CTAS to identify specific aircraft sub-types (e.g.B737-400 vs. B737-800) and engine fits (i.e.identical airframes fitted with different engine types).This information could then be used by CTAS to select more specific drag and propulsive models for use in the TS process.
+Estimated Takeoff WeightThe estimated aircraft gross weight at takeoff is easily obtained from standard AOC operational flight plans.Climb trajectory synthesis is extremely sensitive to errors in takeoff weight, especially at higher altitudes near top-of-climb (TOC).Indeed, the absolute altitude ceiling for a given airframe/engine configuration will be determined solely by the aircraft gross weight.Without knowledge of takeoff weight on a per-flight basis, ATM decision-support tools are forced to apply an average takeoff weight to all aircraft of a given type.This is clearly a crude approximation due to the uncertainties associated with fuel, passenger, and cargo weights.In addition, the weight of the empty airframe may change over time due to equipment installation/removal.For scheduled, non-chartered flights, the passenger and cargo weights are not typically well known by the AOC within the flight-planning time horizon prior to departure.The fuel weight, however, is usually well known by the AOC pre-departure, although last-minute adjustments can be made at the pilot's discretion.Total planned fuel weight is a function of the anticipated duration of the flight as well as other factors that influence the amount of extra fuel carried for holding, alternative routing, and ferry transport.Ferried fuel is that to be used for future flight legs.Airlines often ferry fuel from locations where fuel prices are cheapest.
+Thrust and Drag Calibration FactorsCertain AOC centers may take advantage of additional airframe-specific factors for computing thrust and drag performance.Thrust calibration factors, beyond the engine type specification previously discussed, can include an indication of how an airframe-specific engine is performing.In particular, knowledge of any degradation in thrust output or fuel consumption may be obtainable from AOC databases 9 .Similarly, drag performance factors may include any known changes in the drag characteristics associated with an individual airframe.It is likely that AOC centers will have knowledge of these characteristics from their analysis of in-flight performance data 10 .
+Climb Speed ProfileClimb speed profile, as described in the previous section, is fundamental to ATM climb trajectory predictions in en route airspace.In the current CTAS model, this parameter does not account for operational variations among individual flights.Although the pilot has the final authority in determining which CAS/Mach speed profile is flown, the AOC could provide a recommendation based upon operational considerations.For example, the importance of making up for lost time due a schedule slip will influence the decision to choose a profile that maximizes climb rate as opposed to maximizing fuel efficiency.For FMS-equipped flights, this recommendation is commonly issued by the AOC in the form of a cost index for climb power management.
+Climb Throttle Setting and Acceleration ProcedureClimb throttle setting is a variable that determines aircraft thrust and is set at the discretion of the pilot in compliance with company procedures.For example, many airlines recommend reduced takeoff and climb thrust settings, when feasible, in order to prolong engine life and reduce maintenance costs.Other climb procedure data, potentially available from the AOC, includes the preferred procedure for accelerating from the TRACON speed, below 10,000 ft, to the initial en route climb speed, above 10,000 ft.The pilot may choose to perform this acceleration in level flight or during the climb, depending on operational objectives.Although secondary in importance to speed profile, these additional intent parameters, if well established by the AOC, could be sent to ATM to further improve climb prediction accuracy.
+ResultsThe following results indicate the potential improvement that AOC flight-planning data has on CTAS climb trajectory predictions.In particular, the actual or anticipated impact of AOC takeoff weight, speed profile, and thrust calibration data on CTAS trajectory synthesis is examined.
+WeightBased on data collected from two major airlines, the observed operational range in takeoff weight among a variety of common aircraft types is shown in Table 3.This data was collected from AOC flight plans during the months of March and April 1999 for operations departing from Dallas/Ft.Worth Airport and Denver International Airport.Takeoff weight estimates for approximately 8,000 operations were obtained.The results in Table 3 show maximum variations of up to 50% of mean takeoff weight for certain aircraft types.The potential impact of the observed weight deviations in Table 3 on CTAS climb performance was calculated using a stand-alone version of the CTAS TS.A single climb prediction, with a 40 minute look-ahead time, was performed as each aircraft passed through an altitude of 10,000 ft.The expected range of real-world climb performance due to weight variation was compared against the current nominal CTAS prediction.The nominal CTAS prediction was computed using a static weight estimate, thought to be representative of the general aircraft type.Figure 2 shows the climb performance of a B757 for the observed maximum and minimum AOC weight estimates.The altitude profile, altitude error, and path distance error time-histories in Figure 2 are show in American Institute of Aeronautics and Astronautics comparison with the nominal CTAS trajectory, using the generalized weight estimate.Table 4 summarizes the effect of observed takeoff weight variation on climb performance among the aircraft types for which data was obtained from the AOC centers.In Table 4, the metrics chosen to represent climb performance in comparison with nominal CTAS trajectories are: 1) maximum altitude error over the prediction time horizon, 2) maximum longitudinal path distance error over the prediction time horizon, 3) time required to reach TOC, and 4) longitudinal path distance required to reach TOC.The purpose of Table 4 is to show "worst case" scenarios, not typical CTAS errors, in comparing trajectory predictions with and without AOC data exchange.For this purpose, the extreme minimum and maximum weight observations from the AOC data were used.In generating Table 4, terminal altitudes were chosen as representative operational cruise altitudes for each aircraft type.In the event that the cruise altitude was beyond the operational ceiling for the heaviest aircraft using the current CTAS performance model, the target altitude was lowered accordingly.It should be noted that in the case of the B737, B747, and A319 aircraft types, the CTAS nominal weight estimates did not fall within the range of operational weight estimates obtained from AOC data over the recording period.This indicates the existence of obvious modeling inaccuracies that could be corrected with the data from this experiment.The results in Table 4 show significant potential errors in CTAS climb predictions due to observed variations in takeoff weight.Potential peak altitude errors of nearly 10,000 ft are shown for the B747 and B767 aircraft.Peak longitudinal errors in the climb of 15 nmi.or more are shown for the B747, DC10, and MD80 aircraft.Table 4 also indicates a dramatic potential variation in time and distance to TOC for these aircraft types -up to 35 minutes and 230 nmi.In order to show improvement in climb trajectory prediction accuracy with the inclusion of AOC weight estimates, predicted climb profiles were compared against actual radar track data.This was accomplished by extracting radar track data from CTAS archives and matching it with flights for which AOC takeoff weight data was obtained.In order to make the trajectory predictions valid, wind and temperature data files, relevant to the flights of interest, were also retrieved from CTAS archives.Finally, care was taken to ensure that flights selected for this analysis did not get their   3 shows an example of corroborated climb trajectory enhancement with AOC weight data, for specific flights of a B757 and MD80.Although most of the TS predictions showed improvements similar to those in Figure 3, a few predictions proved to be less accurate with the inclusion of AOC weight estimates.This is likely due to inaccuracies in current CTAS engine models.This points to an important requirement of matching accurate weight estimates with more precise aircraft thrust performance modeling.
+Speed ProfileIn order to show the impact of speed-profile variation on CTAS trajectory synthesis, climb profiles are shown in Figure 4 for a ±10% variation in CAS/Mach.This offnominal value was chosen only to show TS sensitivity to speed profile error.A more precise measurement of actual speed-profile variation for real-world operations remains a subject for further study.The altitude and path distance error profiles in Figure 4 shows peak errors of 2,700 ft and 25 nmi, over the duration of the climb.This shows that longitudinal error is highly sensitive and grows with time due to CAS/Mach variation.This analysis points to the importance of obtaining flight-specific
+Climb ThrustThe potential influence of engine fit on ATM climb prediction is shown in Figure 5, based upon an AOCreported average climb thrust variation of approximately 12% for two different engine types for the B727.The results in Figure 5 were generated for identical weights and climb speed profiles.The altitude and path distance error profiles show peak errors of 5,700 ft and 6 nmi, respectively, for a 12% variation in climb thrust
+ConclusionThe CTAS climb trajectory prediction process has been described along with current input data for establishing trajectory calibration and pilot intent.Current input data has been shown to be broadly defined for generic aircraft types and nominal airline preference, without taking into account the operational considerations of specific flights or performance variations among individual aircraft of a given type.Flight-planning data from AOC centers offers substantial improvement in en route climb prediction accuracy, promising capacity and efficiency benefits for the airspace user.In particular, AOC-provided takeoff weight, speed profile, and engine type specification can significantly reduce climb trajectory uncertainty.Although the results of this analysis indicate dramatic potential benefits of including AOC performance and intent data in the ATM trajectory prediction process, aircraft performance models must be of sufficient fidelity in order to appropriately benefit from airline information.FigureFigure 1: General CAS/Mach Climb Profile
+data includes current aircraft state and performance information along with atmospheric conditions along the flight path.
+TimeFigure 2 :2Figure 2: Potential Impact of Observed Weight Variation on B757 Climb Trajectory Synthesis
+Figure 3 :3Figure 3: Example of Improvement in CTAS Climb Prediction with AOC Weight Estimate for B757 and MD80 a) B757 b) MD80
+Table 1 .1Current Climb TS Data Inputs and SourcesAccelerate toinitial cruiseClimb atMachconstant MachClimb atconstant CASCruise at initialMachClimb and accelerate toclimb CAS1: General CAS/Mach Climb Profile
+Table 2 .2Potential AOC Data for ATM ClimbTrajectory EnhancementDataDataClassificationElementCalibration• Specific airframe type• Specific engine type• Estimated takeoff weight• Engine thrust factors• Aircraft drag factorsIntent• Intended/preferred climbspeed profile (CAS/Mach)• Intended/preferred climbacceleration procedure• Intended/preferred takeoffthrottle setting
+Table 4 .4Potential Effect of Weight Variation on ClimbTrajectory Predictionclimb profiles interrupted in any way by unexpected ATM clearance instructions.Figure
+		
+		
+
+			
+AcknowledgementsThe work by Rey Salcido, of Ratheon STX Co., in providing the software engineering and data analysis support for this work is greatly appreciated.Thanks is also given to Steve Green and Gerd Kanning, of NASA Ames Research Center, for their contributions to this effort.
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+I. IntroductionController decisions within an Air Traffic Control (ATC) system, regardless of its complexity, are ultimately dependent upon the availability and accuracy of airborne traffic information.In today's system, the current and predicted traffic situation, as viewed by ATC, is derived from surveillance radar returns together with Instrument Flight Rules (IFR) flight plans.Radar-derived information includes horizontal track data (position and ground speed) along with transponded aircraft altitude reports.Flight plans are typically filed with ATC well in advance of a flight's departure, and are not normally updated by the airspace user once a flight is in progress.It is has been recognized for some time that the breadth and quality of data available to ATC for flight status and planning can be vastly improved by connecting ATC with data systems maintained by the airspace user, both on the ground and in the air 1 .Several ATC modernization efforts in both the U.S and Europe are exploring the benefits and feasibility of delivering data to ATC directly from airborne systems over air-to-ground data link 2 , 3 .Although these activities are concerned with a wide range of groundbased applications and data link architectures, they share a common need to access a subset of the parameters readily available from most modern aircraft equipped with a Flight Management System (FMS).Potential applications of aircraft parameters to airtraffic management fall into four general categories: 1) controller awareness, 2) enhanced surveillance, 3) monitoring and diagnostics, and 4) decision-support automation.Applications of controller awareness involve displaying a common set of aircraft data to both the controller and flight crew (e.g., airspeed and heading) for use in verifying clearance delivery and conformance.Applications of enhanced surveillance involve supplementing current-day radar data with aircraft-reported position and velocity, together with additional parameters used to indicate the onset and magnitude of maneuvers.Applications of monitoring/diagnostics involve the potential use of aircraft data to improve awareness and shorten response time with regards to situations relating to safety and security.These applications could include the use of data link as a means of providing a nondestructible source of data for post-incident analysis (e.g., remote "black box").The fourth category of ATC applications, and the subject of this research, involves the use of aircraft parameters for improving the performance of controller decision-support tools.These tools are characterized as Real-Time Data Link of Aircraft Parameters to the Center-TRACON Automation System (CTAS) software-based systems that assist controllers with separation assurance and traffic flow management tasks.Systems of these types, which typically employ strategic planning horizons ranging from 5 min to 30 min, are highly dependent upon accurate fourdimensional (4-D) trajectory predictions of individual flights.An example of a system of this type is the Center-TRACON Automation System (CTAS) that has been developed by NASA Ames Research Center and fielded, in part, under the FAA Free Flight Phase 1 initiative 4 .CTAS requires accurate trajectory predictions in order to generate controller advisories that will make efficient use of available capacity, while minimizing deviations from the speed and routing preferences expressed by airspace users.For example, more accurate long-range trajectory predictions can enable controllers to resolve separation conflicts earlier in time with smaller, more fuel-efficient maneuver advisories.Furthermore, the rate of missed alerts and false alarms associated with these advisories can be reduced 5 .This paper describes the system characteristics and preliminary findings from a field test involving the automatic data link of aircraft parameters to CTAS for the purpose of improving strategic trajectory prediction accuracy.This activity was conducted between January and August of 2001 under a NASA project referred to as En Route Data Exchange (EDX).EDX involved the real-time transfer of data to CTAS from operational, revenue aircraft.With cooperation from United Airlines, data was collected from over 1,000 flights of EDX-enabled Boeing 777 (B-777) aircraft operating within the Denver Air-Route Traffic Control Center (ARTCC).The paper begins by discussing the relevance of aircraft parameters to CTAS trajectory predictions, followed by a description of the system architecture and specific data parameters involved in the EDX evaluation.Results are then presented that illustrate the impact of real-time data link on CTAS trajectory prediction accuracy.
+II. Overview of CTAS Trajectory PredictionThe CTAS Trajectory Synthesizer (TS) uses a simplified set of point-mass aircraft equations of motion for generating 4-D trajectories consisting of aircraft position and altitude as a function of lookahead time.In Center airspace, new predicted trajectories are generated by TS approximately every 12 seconds.The simplified equations of motion, numerically integrated by TS, are described fully in Ref. 6. Within CTAS, a separate process known as the Route Analyzer (RA) provides the routes upon which predictions are based.The RA provides the TS with a set of waypoint locations that represent the expected course of flight in en route airspace.These waypoints are defined by flight plan information, normally provided by the airspace user prior to departure, in conjunction with detailed airspace data stored within CTAS.To simplify the prediction process, the horizontal and vertical components of the trajectory are de-coupled.First, an approximate vertical profile is constructed in order to calculate airspeed as a function of path distance.Using this approximate airspeed profile, the horizontal trajectory is computed as a series of straight-line path segments connected by constant radius turns.This horizontal trajectory is then used as the basis for computing the actual vertical trajectory.Measures are taken within the TS/RA to ensure that the final 4-D prediction is in conformance with all relevant constraints on the trajectory, imposed by aircraft performance, pilot/ATC procedures, and airspace capacity limitations.The basic input data required by the TS/RA process is shown in Fig. 1.These data fall into three general categories: 1) aircraft state, 2) atmospheric state, and 3) flight intent.For the purpose of trajectory prediction, aircraft state data are defined by those parameters relating to the kinetic and dynamic properties of the aircraft.Kinetic parameters -used by the TS to initialize the trajectory prediction process -consist of inertial position, altitude, speed, and heading.Within baseline CTAS -i.e., the system without data link  Dynamic state parameters are those that define the thrust, drag, weight, and lift forces acting on the aircraft.Thrust and drag forces are computed from aircraft-specific performance models maintained within CTAS.In baseline CTAS, aircraft weight is derived from stored, static values representative of the general aircraft type (e.g., Boeing 777-200).This is a clearly an approximation, however, since it does not account for actual, flight-specific, fuel and payload weight variations 2 .Atmospheric state data employed by the TS includes horizontal wind speed, wind direction, static pressure, and static temperature.These data -used to obtain accurate airspeed estimates along the predicted trajectory -are provided by the National Oceanic and Atmospheric Administration (NOAA) Rapid Update Cycle 2 (RUC2) model.The CTAS TS obtains atmospheric state at each time step along the predicted trajectory by linearly interpolating between 3dimensional grid points within the RUC2 database.The RUC2 database is updated with 1-hour forecasts and stores data using a horizontal grid resolution of 40 km, and a vertical grid resolution of 25 mb pressure altitude 7 .Flight intent inputs to the TS/RA consist of data pertaining to the intended actions of the flight crew in controlling the future path of the aircraft, in compliance with any known airspace or ATC constraints.An important component of flight intent data is the expected routing and speed profile over future flight segments.For baseline CTAS, routing information is obtained from the FAA Center Host computer, which updates the originally filed IFR flight plan with any ATC flight plan amendments entered by the controller.The intended speed profile during the cruise portion of the flight is based on either the filed or detected cruise airspeed, depending on whether the aircraft is in steady-state cruise at the time the prediction is initiated.For climbs and descents, the vertical speed profile of the flight is based upon a CTAS estimate of the most likely climb/descent procedure to be executed by the pilot in light of ATC constraints, such as crossing restrictions and known clearance instructions.Vertical speed profiles are modeled as constant airspeed segments, defined by either a constant Calibrated Airspeed (CAS) or constant Mach number during the climb or descent.For jets, this constant airspeed assumption together with a known thrust setting is sufficient for the TS to define the vertical profile.For turboprop aircraft, the vertical profile is typically defined by a constant airspeed together with constant vertical rate (or flight path angle) assumption.In absence of any specific climb or descent CTAS advisories, such as those provided by the En Route Descent Advisor (EDA) tool, the vertical airspeed profile used by baseline CTAS is obtained from stored company preference information 8 .This company preference is represented by static values of climb and descent CAS, obtained from published procedures designed to maximize fuel efficiency for a given aircraft type.At high altitudes (typically above Flight Level 280), CTAS models the vertical profile with a constant Mach number instead of constant CAS.This is consistent with pilot and ATC procedures that recognize that typical aircraft performance is limited more by maximum Mach number than maximum CAS at higher altitudes 9 .
+III. EDX System Architecture Airborne SystemThe system used to support EDX is shown schematically in Fig. 2. Parameters, described fully in section IV, were down-linked from UAL B-777 aircraft operating with Denver ARTCC airspace.The B-777 was chosen due to its non-intrusive, integrated FMS-ACARS capability.The aircraft comes equipped with a Honeywell Airplane Information Management System (AIMS) architecture that ties together numerous avionics systems, including the FMS, Airplane Condition Monitoring System (ACMS), and VHF data link.Data are shared between these systems over the AIMS back-plane bus.Furthermore, data can be readily extracted from the ACMS through a configurable/loadable software module known as an Airline Modifiable Interface (AMI) file.Because the ACMS/AMI is partitioned off from safety-critical components, modifications needed to support EDX were made to the AMI without requiring additional FAA certification.This was a key consideration in identifying airframe/avionics that minimized the lead time for the data collection activity.The EDX AMI contained instructions for extracting the appropriate data parameters from avionics systems and automatically triggering their down-link.In addition, the AMI was designed so that parameters controlling down-link performance could be modified via up-link messages.For example, data link trigger conditions based on geographic boundaries and altitude constraints could be modified by up-link commands sent from the EDX lab at NASA Ames.Up-link commands could be sent to enable/disable EDX data link and specify the data link rate.These controls enabled the EDX researchers to maximize data collection within the designated airspace while minimizing the communication load on the ACARS system.A total of forty-eight B-777 aircraft were equipped with the EDX AMI.This equipage was performed by UAL as part of a routine avionics software upgrade cycle.
+Data Link SystemThe data link system used by EDX was the Aircraft Communications Addressing and Reporting System (ACARS) 10 .ACARS, owned and operated by ARINC Inc., is the most prevalent data link system employed in the U.S at this time.ACARS consists of VHF transceivers connected to a nationwide network.All terminal nodes on the network, including both ground stations and aircraft, have a unique address to which messages are routed.In support of EDX, an ACARS ground station was established at NASA Ames with access to the network over a dedicated line.For downlink, the ground station collected aircraft messages for transfer to CTAS.For up-link, the ground station allowed for messages to be composed by the EDX team and sent to aircraft destinations, designated by their ACARS network address.Although ACARS was not designed to support mission-critical ATC applications, it did provide an excellent platform for conducting the EDX data collection activity.The purpose of EDX was not to critique current data link infrastructure, but rather to validate requirements for next-generation data link systems and services.
+CTASOn the ground side, CTAS was run in a passive/shadow mode (i.e., without controllers) from the EDX lab at NASA Ames.Aircraft state, atmospheric state, and flight intent data were delivered to CTAS at a nominal rate of once per minute.CTAS was adapted to incorporate EDX aircraft parameters directly into the TS/RA process.The EDX evaluation involved substantial modifications to CTAS in order to introduce aircraft parameters into the TS and RA processes.In support of EDX demonstrations and future analyses, CTAS was configured to run the following en-route decisionsupport tools while incorporating aircraft parameters
+ACARS Ground Station
+EDX CTAS Baseline CTAS Output Data
+EDX Processing
+B-777 FMS
+EDX Data
+EDX Post-Processing
+Play back loopHost + RUC2 Data Fig. 2 EDX System Architecture into TS/RA: Traffic Management Advisor (TMA), En Route Descent Advisor (EDA), and Direct-To (D2) 4 .As shown in Fig. 2, the impact of aircraft data on CTAS performance was assessed by comparing baseline CTAS with EDX CTAS (i.e., the version with data link augmentation).EDX CTAS was also designed to support the processing of aircraft data post flight, using a play back interface.This interface supported post flight EDX research by allowing aircraft parameters to be time-blended with archived radar track, flight plan, and weather data.
+IV. EDX Data ContentEDX aircraft down linked the parameters shown in Table 1 and 2 at a nominal rate of once per minute.During occasional periods where few active EDX aircraft were present in the airspace, data link was increased up to a rate of once every 12 seconds in order to mirror the update rate of data from the FAA Center Host computer.The entire set of 40 parameters was packaged together into a single ACARS digital message totaling 192 bytes in length.As shown by Table 1 and 2, parameters were characterized as either primary or secondary.Primary parameters were those used to support the analysis presented later in this paper; secondary parameters were collected to support future research activities.
+V. Results
+Data Collection and Delay CharacteristicsOver an eight-month period, data were collected for over 1,000 operations within Denver ARTCC airspace, consisting of 288 departures, 372 over-flights, and 341 arrivals.Data link messages containing the EDX parameters were transmitted automatically for aircraft operating within 250 nmi of Denver International Airport.Because each EDX message was stamped with Coordinated Universal Time (UTC) aboard the aircraft, it was possible to measure the total delay associated with data transmission.For 60 randomly selected flights, involving an equal number of departures, overflights, and arrivals, the average message delay was found to be 9 sec with a standard deviation of 12 sec.In support of the analyses presented in this paper, a perfect data link was emulated by removing message delay in post processing using the play back capability previously described.The was done since transit delay can potentially cause highly-dynamic parameters such as position, speed and heading to become invalid for stringent real-time applications.It should be noted, however, that the transit delays observed with the ACARS system were much lower than expected, and might have been lowered further by providing a higher priority to message delivery (EDX messages were purposely given a low priority so as not to interfere with routine airline operational messaging).
+Comparison of Trajectory Prediction Input DataThe primary EDX parameters, shown in Table 1, were compared against their corresponding baseline-CTAS values.This was done in order to examine errorsdefined as the difference between existing ATC trajectory-prediction input data and corresponding aircraft-derived values (considered as truth).For example, Fig. 3 shows the mean ground speed error detected over each departure operation.Table 3 provides a summary of these types of results for all primary parameters over every flight for which data was available.The results in Table 3 point to significant errors in the trajectory prediction data used today by ATC automation without the benefit of data link.
+Flight Intent ErrorThe calculation of speed and routing intent errors in Table 3 was more complex than that for other trajectory prediction input parameters.For departures, speed intent error was represented by the difference between EDX and baseline-CTAS values for CAS and Mach speed targets, used to define climb profiles as previously described.For arrivals, only CAS targets were compared since CTAS does not store target Mach numbers explicitly for trajectory prediction, but instead assumes that the aircraft will descend at the cruise Mach number until reaching an altitude where the target CAS is captured.Lateral route intent error, shown in the last row of Table 3, was defined as the difference in the planned horizontal route constructed with and without the incorporation of EDX waypoint information originating from the aircraft FMS.As illustrated in Fig. 4, horizontal route intent was defined, at any given time, by connecting a path through all known downstream waypoints.Additional complexity was introduced by the intrinsically dynamic nature of route intent; route intent employed by baseline CTAS was affected by flight path amendments entered by the controller, while EDX route intent was affected by pilot inputs into the FMS.For this reason, the results in Table 3 were found by examining the route intent error present each time a trajectory prediction was made.At each time interval, the intent error was found by marching the aircraft along the intended routes -defined separately by baseline-CTAS and EDX waypoints -and measuring the distance variation between them.As indicated in Fig. 4, the Lateral Route Intent Error (LRIE) at time t was defined as the mean distance error between baseline and EDX predicted routes at that instant.The mean LRIE was then computed over the entire flight segment for which EDX data was available.Finally the   The potential magnitude of lateral intent error is illustrated in Fig. 5, which shows a histogram of maximum LRIE for all flights, organized by flight phase.Fig. 5 provides statistical insight into how much ATC and FMS route intent can differ at any instance in time.The results in Fig. 5 show that approximately 40% of all flights experienced mean route intent differences over the prediction window of between 4 nmi and 8 nmi at some instance.
+Trajectory Prediction Accuracy AnalysisBased upon the observed variation of trajectory prediction input data, analysis was carried out to examine the accuracy of resulting CTAS trajectory predictions.For select flights, twenty-minute predictions, both with and without EDX data, were compared against truth -as established by the final track of the aircraft.A total of 50 flights with large observed errors between baseline-CTAS and EDX input data were processed.Twenty departures were processed based on weight error; 10 departures were processed based on speed intent error; and 20 overflights were processed based on route intent error.In addition, the cumulative impact of weight and speed intent error was evaluated for the ten departures examined for speed intent alone.For each flight, single 20-minute trajectory predictions were calculated with baseline-CTAS and EDX input parameters.In order to isolate the effect of the parameter in question, all other input parameters were set equal to baseline-CTAS values.A comparison of the accuracy of EDX and baseline-CTAS trajectory predictions for the 50 flights is summarized in Table 4.The results in Table 4 show that EDX weight data alone improved the accuracy of predicted altitude by an average of nearly 30%.The incorporation of EDX speed intent improved the accuracy of predicted path distance by an average of 20%.Furthermore, the use of both EDX weight and speed intent improved altitude and path distance predictions by an average of 53% and 24%, respectively.Perhaps most significant of all, the results in Table 4 show an improvement in lateral path prediction accuracy of 80% with the use of EDX route intent data, provided in the form of FMS waypoints.As an illustrative example, Fig. 6 shows the improvement in altitude profile prediction accuracy with the use of EDX data.Along with the baseline-CTAS prediction and actual track (truth), Fig. 6 shows separate predictions based on the incorporation of 1) EDX weight, 2) EDX speed intent, and 3) EDX weight + speed intent.Fig. 7 shows the corresponding path distance error measured for the same set of EDX and baseline predictions shown in Fig. 6.It can be seen in Fig. 7 that path-distance error was largely unaffected by aircraft weight.This observation is backed up by the equations of motion (Ref.6) which show that weight primarily influences the altitude dynamics, not path distance.Path distance error, however, was impacted by EDX speed intent, as expected.For this example flight (Flight #1), the maximum altitude error over a 20-minute prediction window was reduced by 73% with the incorporation of EDX weight and speed intent data.Similarly, the maximum path distance error was reduced by 60% with the incorporation of speed intent.It should be noted that the magnitude of the error reduction in both altitude and path are well beyond the separation standards of low altitude en route airspace (1,000 ft and 5 nmi in altitude and path, respectively).This leads to the conclusion that EDX data could significantly influence ATC automation advisories and controller decisions relating to conflict avoidance and traffic flow management.Fig. 8 shows an example of the improvement in lateral routing intent possible with the receipt of aircraft FMS waypoint information.In this example, the FMS data indicated that a direct route was to be flown from fix 1 to fix 3, thereby bypassing a dog-leg introduced by fix 2 in the filed flight plan.As shown in Fig. 8, truth data, gathered post-flight, was used to validate FMS route intent.Fig. 9 shows the total horizontal error corresponding to the predictions in Fig. 8. Fig. 9 indicates a reduction in maximum horizontal prediction error of over 95% with the use of EDX aircraft data, for this particular 20-minute prediction example.
+VI. ConclusionsThe results of this study suggest substantial benefits associated with the delivery of aircraft parameters to ATC over real-time data link.In particular, the EDX project has explored the use of aircraft data for improving ATC trajectory prediction accuracy in en route and transition airspace.Accurate trajectory predictions are crucial for maximizing the performance, benefits, and controller acceptance of ATC decision-support tools such as CTAS.Based on the collection of real-world operational data, the results of this study showed 1) sizable errors associated with existing ATC data sources, and 2) significant improvement in CTAS trajectory prediction accuracy with the incorporation of aircraft data.Finally, as an important engineering achievement, this study proved that a wealth of aircraft data could be extracted with minimal avionics intrusion, and transferred to ATC over existing ACARS with minimal transit delay.The results of this work are intended to support ongoing efforts aimed at developing data requirements and establishing benefits for next-generation data link systems and services.Future EDX work at NASA Ames is intended to explore concepts that further integrate CTAS with flight deck information systems, for the benefit of controllers and airspace users alike.
+BiographiesFig. 11Fig. 1 Inputs to CTAS Trajectory Prediction Process
+Fig. 33Fig. 3 Signed Error in Mean Ground Speed
+Fig. 44Fig. 4 Definition of Lateral Route Intent Error (LRIE)
+Fig. 6 Fig. 7 Fig. 8 Fig. 96789Fig. 6 Effect of EDX Data on Predicted Altitude Profile Accuracy
+Table 22Secondary EDX Parameters
+Table 33Absolute Error in Trajectory PredictionInput Parameters Across all Flights overall mean LRIE, as presented in Table3, was found by further averaging LRIE over all flights.
+5 Histogram of Maximum LRIE Across all Flight PhasesParameter(s)Mean Altitude Error (ft)Mean Lateral Error (nmi)BaselineEDXBaselineEDXWeight1,34698012.010.5Speed Intent1,3601,16012.310.0Weight +Speed Intent1,36064412.39.3Lateral RouteIntent--2.420.98
+Table 44Summary of the Impact of EDX Parameters on CTAS Trajectory Prediction Accuracy
+Richard A. Coppenbarger Rich Coppenbarger, has been employed with NASA Ames Research Center at Moffett Field, California since 1989.During his tenure at Ames, Mr. Coppenbarger acquired a Masters degree in Aerospace Engineering from Stanford University.Mr. Coppenbarger has been involved with ATC automation research for the past three years.During this time, he has focused on data link research and en route decision-support-tool development under the AATT/CTAS project.His prior research activities at Ames included microburst wind shear accident investigation, and helicopter obstacle-avoidance guidance and control.Gerd Kanning Gerd Kanning has been employed at the NASA Ames Research Center for 38 years.He received his Masters Degree in Electrical Engineering from the University of Santa Clara.He has worked on a variety of projects from mathematical modeling of gravity-stabilized satellites to modeling of helicopter rotor dynamics.He developed parameter identification techniques for V/STOL aircraft and applied Kalman filter theory to the automatic landing of V/STOL aircraft.He helped develop the automation system for landing of aircraft using the Global Positioning Satellite network.His most recent work has been in the development of a data link between aircraft and ground for the Center-TRACON Automation System (CTAS).Salcido Rey Salcido earned a B.S. in chemical engineering from Stanford University and has worked in software development since 1995.Mr. Salcido joined Raytheon ITSS in 1998 and has since been involved with software development and analysis in support of the EDX research project.Rey
+			th USA/Europe ATM R&D seminar, Santa Fe, NM, Dec.[3][4][5][6][7] 2001
+		
+		
+
+			
+VII. AcknowledgementThe authors would like to acknowledge the substantial contribution of United Airlines in carrying out this research activity.In addition, the efforts and support of Honeywell, Seagull Technology, the FAA Airborne Data Link Project Office, and the NASA Advanced Air Transportation Technology (AATT) Project Office were indispensable to the completion of this work.Special thanks goes out to Randy Kelly (United Airlines) and Steve Quarry (Honeywell) for lending their superb guidance and technical expertise to this effort.
+VIII. References
+			
+
+			
+
+				
+
+
+	
+		Enabling user preferences through data exchange
+		
+			StevenGreen
+		
+		
+			TsuyoshiGoka
+		
+		
+			DavidWilliams
+		
+		
+			StevenGreen
+		
+		
+			TsuyoshiGoka
+		
+		
+			DavidWilliams
+		
+		10.2514/6.1997-3682
+	
+	
+		Guidance, Navigation, and Control Conference
+		New Orleans, LA
+		
+			American Institute of Aeronautics and Astronautics
+			August 1996
+		
+	
+	Green, S.M., Goka, T., Williams, D.H., "Enabling User Preferences Through Data Exchange," Proceedings of the AIAA Conference on Guidance, Navigation, and Control, New Orleans, LA, August 1996.
+
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+	
+		En route climb trajectory prediction enhancement using airline flight-planning information
+		
+			RichardACoppenbarger
+		
+		10.2514/6.1999-4147
+	
+	
+		Guidance, Navigation, and Control Conference and Exhibit
+		Portland, OR
+		
+			American Institute of Aeronautics and Astronautics
+			August 1999
+		
+	
+	Coppenbarger, R. A., "Climb Trajectory Prediction Enhancement Using Airline Flight- Planning Information," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Portland, OR, August 1999.
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+		Fast Calculation of Single Aircraft Optimal Descent Trajectory
+		
+			ExperimentalEurocontrol
+		
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+			Center
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+		No. 18/98
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+			September 1998
+			American Institute of Aeronautics and Astronautics (AIAA)
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+	EUROCONTROL Experimental Center, "Study of the Acquisition of Data from Aircraft Operators to Aid Trajectory Prediction Calculation," EEC Note No. 18/98, September 1998.
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+		Challenges of air traffic management research - Analysis, simulation, and field test
+		
+			DallasDenery
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+			HeinzErzberger
+		
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+			ThomasDavis
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+			StevenGreen
+		
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+			BDMcnally
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+			DallasDenery
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+			HeinzErzberger
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+			ThomasDavis
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+			StevenGreen
+		
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+			BDMcnally
+		
+		10.2514/6.1997-3832
+	
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+		Guidance, Navigation, and Control Conference
+		New Orleans, LA
+		
+			American Institute of Aeronautics and Astronautics
+			August 1997
+		
+	
+	Denery, D. G., H. Erzberger, T. J. Davis, S. M. Green, B. D. McNally, "Challenges of Air Traffic Management Research: Analysis, Simulation, and Field Test," Proceedings of the AIAA Guidance, Navigation, and Control Conference, New Orleans, LA, August 1997.
+
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+
+	
+		Modeling ATM interruption benefits
+		
+			TaraWeidner
+		
+		
+			TGDavidson
+		
+		
+			RichCoppenbarger
+		
+		
+			SteveGreen
+		
+		10.2514/6.1999-4296
+	
+	
+		Guidance, Navigation, and Control Conference and Exhibit
+		Portland, OR
+		
+			American Institute of Aeronautics and Astronautics
+			August 1999
+		
+	
+	Weidner, T., Davidson, T.G., Coppenbarger, R. A., Green, S., "Modeling ATM Interruption Benefits," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Portland, OR, August 1999.
+
+
+
+	
+		En-route descent trajectory synthesis for air traffic control automation
+		
+			RASlattery
+		
+		
+			YZhao
+		
+		10.1109/acc.1995.532248
+	
+	
+		Proceedings of 1995 American Control Conference - ACC'95
+		1995 American Control Conference - ACC'95Seattle, WA
+		
+			American Autom Control Council
+			June 1995
+		
+	
+	Slattery, R. A., Zhao, Y., "En route Descent Trajectory Synthesis for Air Traffic Control Automation," Proceedings of the American Control Conference, Seattle, WA, June 1995.
+
+
+
+	
+		Fast Calculation of Single Aircraft Optimal Descent Trajectory
+		
+			ExperimentalEurocontrol
+		
+		
+			Center
+		
+		10.2514/6.2022-3838.vid
+		No. 18/98
+		
+			September 1998
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	EEC Note
+	EUROCONTROL Experimental Center, "Study of the Acquisition of Data from Aircraft Operators to Aid Trajectory Prediction Calculation," EEC Note No. 18/98, September 1998.
+
+
+
+	
+		En route Descent Advisor concept for arrival metering
+		
+			StevenGreen
+		
+		
+			RobertVivona
+		
+		10.2514/6.2001-4114
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Montreal, CA
+		
+			American Institute of Aeronautics and Astronautics
+			August 2001
+		
+	
+	S.M. Green, R.A. Vivona, "En Route Descent Advisor Concept for Arrival Metering," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Montreal, CA, August 2001.
+
+
+
+	
+		Wind Prediction Accuracy for Air Traffic Management Decision Support Tools
+		
+			RECole
+		
+		
+			SGreen
+		
+		
+			MJardin
+		
+		
+			BESchwartz
+		
+		
+			SGBenjamin
+		
+	
+	
+		Europe Air Traffic Management R&D Seminar
+		
+			June 2000
+			Napoli, Italy
+		
+	
+	3 rd USA/
+	R.E. Cole, S. Green, M. Jardin, B.E. Schwartz, and S.G. Benjamin, "Wind Prediction Accuracy for Air Traffic Management Decision Support Tools," 3 rd USA/Europe Air Traffic Management R&D Seminar, Napoli, Italy, June 2000.
+
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+		Dissemination of Terminal Weather Products to the Flight Deck via Data Link
+		
+			SDCambell
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+		Proceedings of the fifth Annual Conference on Aviation Weather Systems
+		the fifth Annual Conference on Aviation Weather SystemsVienna, VA
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+			1993
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+	Cambell, S.D., and Martin, R.C., "Dissemination of Terminal Weather Products to the Flight Deck via Data Link," Proceedings of the fifth Annual Conference on Aviation Weather Systems, Vienna, VA, 1993
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+I. IntroductionHE benefits of Time-Based Metering (TBM) for achieving arrival capacity objectives have been well established in recent years.This is exemplified by the success of the Traffic Management Advisor (TMA), 1 developed as a component of the Center-TRACON Automation System (CTAS).TMA computes the optimal arrival schedule and sequence for aircraft in transition from en route to terminal airspace, and has been deployed at six Air-Route-Traffic-Control Centers (ARTCCs) in the United States under the FAA's Free Flight Phase 1 program.This deployment builds upon extensive prototype testing at the Ft.Worth ARTCC during the late 1990s.Recently, a significant effort has been undertaken to adapt TMA capabilities to the northeast corridor of the U.S, where arrival flows into the terminal area are managed from multiple feeder ARTCC facilities.This capability is referred to as T Multi-Center TMA (or MC-TMA). 2 TMA is designed for use by both Traffic Management Coordinators (TMCs) and sector controllers.TMCs use TMA information, presented in the form of timelines and load graphs, to strategically predict and plan arrival traffic flows.Sector controllers receive TMA arrival time information in the form of metering lists, presented directly on their plan-view radar display during TBM operations.Although TMA provides controllers with arrival time targets for efficient metering, it does not provide the actual maneuver solutions required to achieve the prescribed, metering-conformant, traffic flows.In today's environment, controllers must rely entirely on their skill and judgment to bring aircraft into conformance with metering constraints while simultaneously guarding against separation conflicts.This is a difficult four-dimensional traffic management problem that is complicated by the convergent nature of arrival traffic.For this reason, a large number of corrective clearance instructions are often required to achieve safe and effective TBM, resulting in high levels of workload for the air-traffic controller.Under very heavy traffic conditions, the theoretical capacity benefits of TMA may even be compromised by the high workload required to "fill" each arrival slot in the TBM schedule.In addition, fuel efficiency for the airspace user is compromised by the frequent and aggressive maneuvering that commonly results from metering actions that are planned in a hurried, tactical manner due to a lack of supporting automation.For example, TBM actions in today's environment often require aircraft to execute inefficient maneuvers, such as step-down altitude descents, excessive lateral vectoring, and airborne holding.To address these problems and enable more effective and efficient TBM operations, a new CTAS tool, referred to as the En Route Descent Advisor (EDA), is being developed at NASA Ames Research Center.EDA is intended for the ARTCC sector controller working at the Radar position (i.e."R side").EDA provides efficient maneuver advisories for bringing aircraft into conformance with their TMA-computed Scheduled Time-of-Arrival (STA) constraint at the meter fix, while simultaneously avoiding separation conflicts along the descent trajectory.Under typical TBM operations, solving this constrained "meet-time" problem requires efficiently delaying an aircraft in flight, using a combination of strategic speed, altitude, and heading maneuvers. 3onceptually, EDA is deep rooted within the CTAS project at NASA Ames Research Center.Initial work on automated arrival metering began in the late 1980s. 4,5This work led to the development of a pre-cursor to EDA known as the Descent Advisor (DA).A number of important simulation studies were conducted with DA in the early 1990s, followed by a series of field evaluations at the Denver ARTCC. 6,7These early activities with DA focused on validating many of the core trajectory prediction and meet-time algorithms now incorporated into EDA.The current EDA effort, however, improves upon the original DA prototype in several critical areas: 1) additional, more powerful, meet-time modes involving speed and path-stretching have been introduced; 2) a realistic controller interface, absent in DA, has been implemented that allows controllers to interact with EDA advisories prior to acceptance; and 3) the current EDA prototype has been fully integrated into the modern CTAS software architecture, which includes an interface to TMA for receiving STA targets for metering.The purpose of this paper is to describe the fundamental design and operation of EDA, associated with its recent development as a research prototype within CTAS.The underlying trajectory prediction process that supports EDA is first summarized.The core meet-time and conflict resolution algorithms are then described, along with the fundamentals of the user-interface for enabling controllers to receive, evaluate, and accept advisories.A closed-loop simulation system for evaluating EDA in the laboratory is then discussed, along with a number of important findings discerned from preliminary controller-in-the-loop simulation experiments.
+II. General Solution MethodologyThe overall process for computing conflict-free, meet-time solutions within EDA is illustrated in Fig. 1.This entire process is executed once over each 12-second computational cycle, defined to correspond with the receipt of updated Center radar track and flight-plan data from the FAA Host computer.Under a heavy arrival "rush", EDA might be required to perform this process for as many as 20 inbound aircraft at a time, within a given sector of en route airspace.As shown in Fig. 1, EDA first attempts to compute a solution that satisfies just the arrival time constraint at the meter fix, prior to any direct conflict resolution action.This is done in order to take advantage of the spacing between arrivals that occurs once aircraft are placed onto trajectories that meet their TMA-computed STAs at the meter fix.The temporal separation that TMA provides at the meter fix frequently prevents conflicts between arrivals that would otherwise develop along inbound trajectories.Once the basic meet-time process is completed, conflict detection is performed for each aircraft along its predicted trajectory to the meter fix, in order to identify any remaining separation conflicts.In the event that conflicts remain, a separate conflict resolution process is invoked that attempts to resolve them on an aircraft-byaircraft basis.The following sections describe the basic sub-processes identified in Fig. 1, involved in the computation a complete EDA solution and subsequent advisory.
+III. Trajectory Prediction
+A. OverviewFundamental to EDA is the ability to accurately compute the predicted location (x, y, z) of an aircraft as a function of time (t), from its current position through to the arrival meter fix located at the TRACON boundary.This four-dimensional trajectory prediction capability is used not only to detect conflicts and compute the Estimated Time-of Arrival (ETA) at the meter fix, but it's also the key computational engine for iteratively computing EDA's conflict-free, meet-time solutions.Trajectory prediction is performed by the CTAS Trajectory Synthesizer (TS) process 8 .For the purpose of EDA, the TS prediction is strategic in nature, involving look-ahead time horizons of up to 40 minutes, depending on the proximity of the aircraft to the arrival meter fix.The prediction is computed by integrating the basic point-mass aircraft equations of motion along the intended route of flight.As illustrated in Fig. 2, inputs to this process include: 1) accurate aerodynamic and propulsion models for each aircraft type, 2) current position and velocity state based on the latest radar track data, 3) intended route and speed profile obtained from filed flight plans and nominal airline preferences, 4) en route winds and atmospheric information obtained from sophisticated weather models, and 5) all known speed and altitude constraints that apply to the flight en route to the meter fix.The TS re-computes this prediction once every 12-seconds, upon the receipt of new radar track data from the FAA Center Host computer.As described in detail in Ref. 6, the TS must generate a prediction that satisfies crossing conditions at the meter fix.These crossing conditions stipulate the altitude and maximum allowable airspeed for TRACON entry.Although meter-fix crossing conditions vary slightly between facilities, FAA regulations typically require aircraft to cross the fix at a barometric altitude of 10,000 ft above-sea-level, with a Calibrated Airspeed (CAS) of 250 kt or less.To simplify the computational process, the TS decouples the lateral and vertical trajectory dynamics.An approximate lateral trajectory is first computed that turns "inside" the waypoints identified in the current flight plan; (the last of which is the meter fix itself).A vertical trajectory is then generated along this path based on the thrust, airspeed, and flight-path-angle parameters intended for each flight segment.For steady-state flight, knowing how any two of these parameters is varied as a function of time, while satisfying crossing conditions, is sufficient for the TS to define the vertical descent trajectory and associated Top-of-Descent (TOD) point.
+B. Speed TransitionsFor the jet transports that EDA is currently designed to accommodate, descents are assumed to be conducted at an idle-thrust power setting.** As described previously, the descent trajectory then becomes a sole function of the intended calibrated airspeed to be maintained during the descent to the meter fix (CAS D ).To compute an initial trajectory prediction prior to any action by EDA, the TS builds a trajectory by assuming a nominal, constant, descent airspeed (CAS D,nom ) that is stored within CTAS as a function of aircraft type and airline company preference 9 Although the descent trajectory is defined by CAS D , the correct procedure for transitioning to CAS D from cruise conditions is dependent on the final cruise altitude, h C , and whether an airspeed acceleration or deceleration is required.The reason for this is that airspeed accelerations that occur at high altitude (typically above 27,000 ft.) are potentially constrained by the maximum operational Mach number (M max ), in order to prevent undesirable sonic effects on the airframe/engine.Values for M max are stored within CTAS as a function of aircraft type.This M max constraint is illustrated in Fig. 3, which shows lines of constant Mach and CAS on a plot of altitude vs. true airspeed (TAS), under standard atmosphere conditions.As shown in Fig. 3, the operational speed envelope during a descent is constrained on the slow end by the minimum descent CAS (CAS D,min ) and by either maximum descent CAS (CAS D,max ) or M max on the fast end, depending on altitude.** The idle thrust descent represents the preferred, minimum-fuel, procedure for jet transports.In building a descent trajectory, the TS must decide how to model the speed transition from cruise to descent.In the case of an airspeed acceleration where the descent CAS (CAS D ) is greater than the cruise CAS (CAS C ), the TS builds a trajectory with a final cruise segment conducted at the cruise Mach number, followed by a descent at that Mach number to an altitude where CAS D is captured, followed by a final descent to the meter-fix altitude at constant CAS D. .This type of trajectory, represented by Case 1 in Fig. 3, is generated in order to safeguard against exceeding operational Mach limits during acceleration maneuvers at high altitude.In the case of an airspeed deceleration where CAS D is less than CAS C , the TS builds a trajectory that involves a level-flight deceleration to CAS D at idle thrust, followed by a descent to the meter-fix altitude at constant CAS D .This type of trajectory is exemplified by Case 2 in Fig. 3. Regardless of trajectory type, any final acceleration/deceleration required to satisfy the meter-fix crossing restrictions is assumed to occur in level flight at the meter-fix crossing altitude.It's important to note that the method by which speed transitions are modeled by the TS is consistent with how similar transitions are modeled and flown by a modern aircraft Flight Management System (FMS).As described later in this paper, this congruency between EDA and FMS trajectory modeling is leveraged in order to simplify the required advisory instructions between the controller and flight-deck.
+C. Active vs. Provisional TrajectoriesTo facilitate further discussion of the EDA automation and user interface, the two basic types of trajectory predictions -active and provisional -will first be defined.An active trajectory is defined as the best prediction of where an aircraft will be over the time horizon of interest, using everything that is currently known and accepted regarding that flight.Flight-intent information used to compute the active trajectory is based on the filed flight plan, stored speed preferences, and controller inputs that have already been entered into the FAA Host computer and/or CTAS.Updates to an aircraft's flight intent are based upon a controller's communicated clearance instructions regarding speed, altitude, and routing.In contrast, a provisional trajectory is one that has been generated by EDA for the purpose of solving a given meet-time and/or conflict resolution problem; i.e., it represents a trial-plan trajectory generated by the EDA automation, from which advisories are extracted.The active trajectory is replaced by the provisional trajectory providing that the controller accepts the corresponding advisories, as described later in this paper.
+IV. Meet-Time Automation
+A. OverviewA meet-time problem exists whenever an aircraft's current predicted ETA at the meter fix is out of conformance with its corresponding STA computed by TMA for efficient throughput.In order to prevent EDA from issuing premature advisories based on a meter-fix schedule that hasn't yet stabilized, only STAs that are not subject to further change by the TMA scheduler are sent to EDA for advisory computation; such STAs are referred to as "frozen".The associated freeze horizon can be specified within TMA as either a time or distance threshold.For jet arrival traffic it is typically set to either 20 minutes or 130 n.mi from the targeted meter fix.† †The general meet-time problem is defined by: 1) the current position and velocity state of the aircraft, 2) the STA target at the meter fix, and 3) crossing conditions at the meter fix, required for TRACON entry.The meter fix targeted by TMA and EDA is the entry point into terminal airspace, located at the intersection of the Standard Terminal Arrival Route (STAR) and the TRACON boundary.The current EDA prototype attempts to solve this meet-time problem using speed and heading as maneuver Degrees-of-Freedom (DOFs).In order to conserve fuel and minimize operational complexity in the airspace, EDA attempts to first resolve a given meet-time problem with speed changes alone.In the event that the required delay is too large to be absorbed with just speed, lateral routing changes that stretch the path to the meter fix are prescribed.
+B. Speed ModesSpeed is typically used to absorb delays of up to 4 minutes, depending on the available airspace over which the speed change will be effective.The solution is based on identifying a unique cruise speed, descent speed, or combination of the two that will result in a provisional trajectory with an ETA at the meter fix that conforms to the required STA.In all cases, EDA attempts to identify a specific airspeed profile that should be maintained over the remaining cruise and/or descent flight segments.The solution strategy depends on the one-to-one mapping of selected airspeed profile to meter-fix arrival time, described previously.EDA can be pre-configured by the controller to develop solutions based on three distinct speed modes, referred to as Descent-Only, Cruise-Only, and Cruise-Equals-Descent.These modes are described separately as follows.
+Descent Only ModeIn this mode, EDA employs descent speed alone as the maneuver DOF for achieving the desired arrival time, and is typically effective for absorbing delays of up to 3 minutes in duration, depending on initial conditions the aircraft's operational speed envelope.In this mode, the aircraft is assumed to complete the cruise portion of its flight while maintaining its current cruise airspeed, and then transition to the advised descent airspeed, CAS D,adv , for the descent to the meter fix.The available solution space for achieving arrival times with CAS D,adv in Descent-Only mode is therefore defined as lying between CAS D,Max and CAS D,Min .This solution space is shown in Fig. 4, along with an illustration of a Descent-Only vertical profile.As discussed previously, the implied procedure associated with the transition regions in Fig. 4 is dependent on the relationship between initial cruise conditions, advised descent speed, and crossing restrictions at the meter fix.‡ ‡ The correct speed transition procedure is selected automatically by the TS when building a trajectory for a given descent speed.For this reason, the iteration process within EDA takes place solely in the variable CAS D,adv .Similarly, the output to the controller consists solely of this advised descent airspeed.Since the TOD is dependent on CAS D, adv , it does not need to be given as a component of the EDA advisory as long as it is assumed that aircraft are operating with a FMS that is capable of computing a TOD based on descent speed that is consistent with that computed by EDA.This assumption, which depends on modeling similarities between CTAS and the FMS, not only simplifies the EDA advisory given to controllers, but also simplifies the resulting clearance instructions delivered to the flight deck.
+Cruise-Only ModeIn this mode, EDA employs cruise speed alone as the maneuver DOF for achieving the desired arrival time, and is typically effective at absorbing delays of up to 2 minutes in duration, depending on initial conditions, speed envelope, and available airspace within the sector in which EDA is deployed.Here, the aircraft is assumed to transition to an EDA-advised cruise airspeed, CAS C,adv , for completing the remaining cruise portion of the flight, prior to TOD.The CAS to be targeted during the descent, however, is considered unchanged from the nominal descent CAS determined by company/pilot preference, i.e., CAS D,nom .The relevant solution space for the Cruise-Only mode is shown in Fig. 5, along with a representative vertical trajectory profile.As shown in Fig. 5, the solution space is bounded on the slow end by CAS C,min , and on the fast end by either CAS C,max or the CAS equivalent to M max ‡ ‡ Note that airspeed accelerations from cruise conditions to descent conditions will still result in later arrival times as long as CAS C,i < CAS D, adv < CAS D, nom .at cruise altitude, whichever is lowest.Since the correct speed transition procedures and fast CAS limit are selected automatically by the TS when building a trajectory for a given cruise speed, the iteration process within EDA takes place solely in the variable CAS C,adv .It should be noted that the ability of this mode to absorb delay is dependent on the distance remaining in cruise prior to TOD, at the time the advisory is computed.The output provided by EDA in this mode consists solely of CAS C,adv .Alternatively, this can be expressed as an equivalent Mach number at higher altitudes, if desired by the controller.
+Cruise-Equals-Descent ModeIn this mode, EDA computes a combination of cruise and descent airspeed advisories, CAS C, adv and CAS D, adv , which will satisfy the arrival time constraint at the meter fix.Depending on available airspace, initial conditions, and the aircraft's operational speed envelope, this mode is effective at absorbing delays of up to 4 minutes in duration.In order to minimize the number of speed change maneuvers required by the flight crew, the Cruise-Equals-Descent mode attempts to identify solutions where CAS C,adv and CAS D,adv can be made identical.This allows a single airspeed target to be maintained from cruise all the way to the bottom-of-descent at the meter fix altitude.An illustration of a vertical trajectory associated with the Cruise-Equals Descent mode is shown in Fig. 6.The complete solution space for the Cruise-Equals-Descent mode is shown in Fig. 7, which shows the relationship between CAS C,adv and CAS D,adv for solving meet-time problems requiring both earlier and later arrival times.As represented in Fig. 7 by Case A and Case B, there are two variations of the CAS C,adv vs. CAS D,adv solution space, depending on whether the initial cruise speed, CAS C,i , is lower or higher that the nominal descent speed, CAS D,nom .The reason why CAS C,adv and CAS D,adv are not strictly equal over the entire solution space is to accommodate two important operational considerations: 1) the pilot-preferred procedure for absorbing small delays is to vary either cruise speed alone or descent speed alone; 10 and 2) the range of operationally feasible descent speeds is typically larger than the range operationally feasible cruise speeds, for a given aircraft type.The former consideration is addressed by the vertical and horizontal regions at the center of the solution space in Fig. 7 where cruise and descent speed advisories are varied independently of one another for solving small meettime problems.The decision of whether to use cruise or descent speed to solve a minor meet-time problem is determined by whether the solution space is governed by Case A or Case B, which is dependent on initial aircraft conditions as described above.In this manner, the Cruise-Equals-Descent mode exhibits behavior similar to that of the Descent-Only or Cruise-Only mode for minor meet-time problems.To accommodate the second operational consideration mentioned above, it can be seen in Fig. 7 that CAS D,adv is varied over a wider range than CAS C,adv .This results in large meet-time problems (i.e., those requiring earlier or later arrival times at the extreme limits of what can be achieved with the Cruise-Equals-Descent mode) being solved by advisories where CAS D,adv and CAS C,adv are not equal.To facilitate iteration, the solution space in Fig. 7 is collapsed into a single variable upon which arrival time at the meter fix is a monotonic function.This is accomplished with the simple transformation: CAS CD,adv = CAS D,adv + CAS C,adv .This transformation, together with the fact that the correct speed transition procedures are selected automatically by the TS in building trajectories, allows the iteration to proceed in the single, dummy, variable CAS CD,adv .Once a solution is found, CAS CD,adv is decomposed into its component cruise and descent speed advisories, CAS C,adv and CAS D,adv , which are provided as output to the controller.This decomposition is accomplished by first computing the arrival times associated with the break-points in the CAS CD,adv solution space that correspond to changes in the relationship between CAS C,adv and CAS D,adv , as previously described.
+C. Path-Stretch ModeThe EDA Path-Stretch mode was introduced to solve meet-time problems that cannot be effectively resolved with the speed-based maneuvers described above.In this mode, EDA advises a simple "dog-leg" maneuver that stretches the lateral flight path en route to the meter fix.In the current design, the Path-Stretch mode is only made available once the delay absorption capability of the pre-selected speed mode has been exhausted.For example, if the controller has chosen Cruise-Equals-Descent as the default meet-time mode, Path-Stretch is activated only at the slow end of the solution space in Fig. 7; i.e., where the speed profile is represented by CAS C,adv = CAS C,min , and CAS D, adv = CAS D,min .Depending on the underlying speed mode, initial aircraft conditions, and the amount of operationally-available airspace, Path-Stretch is typically applied to meet-time problems requiring 4 to 10 minutes of delay absorption.In the current EDA prototype, Path-Stretch solutions are presented as an option to the controller once the underlying speed mode has been exhausted, as described in the User Interface section of this paper.The general Path-Stretch solution space and sample trajectory are illustrated in Fig. 8.The solution involves establishing the aircraft on an outbound heading that is off-set from the current route of flight by a fixed angle, and identifying the point at which the aircraft should be turned back towards the original flight plan to absorb the required delay at the meter fix.In the current design, the waypoint at which the stretched path rejoins the original flight plan is defined as the meter fix itself.Since the controller pre-selects the outbound heading off-set (as described later in this paper) the arrival time at the meter fix becomes a sole function of the time-of-flight along the stretched trajectory, prior to turn-back.As shown in Fig. 8, the solution space is bounded by trajectories corresponding to the minimum and maximum turn-back time, t TB,min , and t TB,max .The value for t TB,min is currently set to a constant value that allows the aircraft to be stabilized along the outbound heading prior to initiating the turnback maneuver.The value for t TB,max is calculated dynamically as a function of speed and aircraft bank angle in order to prevent an excessive turn-back maneuver from occurring close to the meter fix.The solution trajectory, calculated between these minimum and maximum limits, is translated into an advisory output that conveys 1) the wind-corrected magnetic heading to be flown along the outbound leg of the stretched route, 2) the turn-back point (specified as either time or distance), and 3) the return heading (or waypoint name) associated with rejoining the original flight plan route.
+D. General Meet-Time Computation MethodRegardless of the meet-time mode, EDA employs a common approach for computing solutions.EDA begins by calculating three trajectories of interest to the problem.The first is the nominal trajectory (NOM), which is based on pilot/company intent preferences, as previously described.The second is referred to as the FAST trajectory, which represents the trajectory with the earliest achievable ETA using the iteration DOF pertaining to the meet-time mode in use.The third is referred to as the SLOW trajectory, which represents the trajectory with the latest achievable American Institute of Aeronautics and Astronautics ETA using the iteration DOF.The FAST and SLOW trajectories are used establish the boundaries of the iteration space within which feasible meet-time solutions are sought.Upon first checking NOM to determine if it satisfies the meet-time requirement, the FAST and SLOW trajectories are then tested.If one of these trajectories is found to satisfy the required STA, then it is returned as the appropriate solution.In addition, if the ETA associated with the FAST trajectory occurs later than the target STA, then the FAST trajectory is returned as the closest achievable solution.Similarly, if the ETA associated with the SLOW trajectory occurs earlier than the target STA, then the SLOW trajectory is returned as the closest achievable solution.In the more common case where the STA falls between the FAST and SLOW ETA limits, then a series of linear interpolations are performed until a solution is found.For each DOF interpolation, the TS process is called to compute the corresponding ETA at the meter fix, which is tested against the desired STA.In general, this linear interpolation process converges upon a solution within three iterations.
+V. Conflict DetectionUpon computing a provisional meet-time solution for each metered arrival, EDA performs a Conflict Detection (CD) process on each aircraft in the airspace.This process incorporates the standard CTAS conflict probe used by other Center automation tools, such as Direct-To. 11A conflict is defined by the predicted violation of an aircraft's Protected Air Zone (PAZ) by another aircraft over the CD time horizon, nominally set to 20 minutes for strategic conflict detection.The CD process first checks for any active conflicts, defined as a predicted PAZ violation along any aircraft's active trajectory prediction over the CD time horizon.For active conflicts, the minimum size of the PAZ used by CD is defined by the FAA minimum separation requirements in Center airspace: 5 n.mi horizontal separation or 2,000 ft vertical separation, for aircraft flying above 18,000 ft (5 n.mi horizontal separation or 1,000 ft vertical separation, for flight below 18,000 ft).§ § In the event that active conflicts are detected, data such as time-to-conflict and minimum predicted separation are presented to the controller for each conflict pair.For EDA, active conflict detection is performed in order to identify cases when the EDA solution results in both meet-time conformance and strategic conflict resolution.As described later, these cases can be pointed out to the controller as an aid to prioritizing advisory extraction and communication.Once active conflicts have been detected, CD is repeated again in order to predict conflicts between each aircraft's provisional (meet-time only) trajectory and other aircraft active trajectories.The Conflict Resolution (CR) process is then invoked to resolve these provisional conflicts.In order to enable CR to generate conservative resolutions that account for conflict detection and maneuver execution uncertainty, the PAZ for conflicts between provisional and active trajectories is typically set larger than FAA minimum separation requirements.
+VI. Conflict Resolution Automation
+A. OverviewTo provide strategic separation assurance, EDA performs an automated Conflict Resolution (CR) process once a provisional meet-time solution has been found.Although aircraft subject to metering are sometimes spaced to avoid conflicts well upstream of the meter fix, exceptions often occur.One typical example of this is the "blow by" situation, which results when a faster aircraft overtakes a slower aircraft to meet an STA at the meter fix that is earlier than that scheduled for the slower aircraft.Although the CR algorithm described here was originally designed to resolve only arrival vs. arrival conflicts, 12 it has recently been extended to resolve arbitrary conflicts between metered arrivals and other aircraft in the airspace.If a conflict is detected, however, the resolution maneuver is applied exclusively to the EDA arrival aircraft by adjusting its provisional meet-time solution.The current algorithm is limited to resolving conflicts with speed and altitude changes.Future research, however, will investigate the use of path adjustment as an additional method of conflict resolution.The CR algorithm resolves conflicts by making speed and altitude adjustments about the provisional meet-time solution already developed.The general idea is to vary speed or altitude in the flight phase where the conflict occurs, and then make a compensating change in the remaining phase in order to change the trajectory geometry while preserving the meet-time solution.Two typical options are: 1) increase CAS C,adv while decreasing CAS D,adv or, 2) increase CAS D,adv while decreasing CAS C,adv .In the event that speed changes alone are insufficient to satisfy both meet-time and separation constraints, then altitude is introduced as a secondary DOF.An example of applying this resolution strategy to cruise and descent conflicts involving converging aircraft illustrated in Fig. 9 and Fig. 10, respectively.
+B. Resolution AlgorithmPrior to resolution, each aircraft in conflict is arranged in a list together with the aircraft that it's first predicted to come into conflict with.The speed and routing used to initialize CR is that associated with the underlying meet-time provisional trajectory.In the event that no DOF adjustments occurred during the meet-time process, then the initial speed and routing used for CR is identical to that upon which the current active trajectory is based.The basic CR algorithm is shown in Fig. 11.Since the algorithm is dependent on adjustments to both cruise and descent DOFs, it is only valid in cases where the aircraft has not yet reached its initial TOD.The search for a conflict-free, meet-time solution begins by incrementing CAS D,adv (by 5 kt).The TS then builds a new trajectory based on the adjusted descent speed, which is again tested for meet-time conformance at the meter fix.In the event that the trajectory no longer satisfies the meet-time constraint, as would typically be the case as a result of the faster descent speed, CAS C,adv is decremented in 5 kt intervals down to CAS Cmin , until a meet-time solution is found.The trajectory is then re-tested for separation conflicts against all other active trajectories in the airspace.If no conflicts  American Institute of Aeronautics and Astronautics are detected, then the trajectory and associated speed advisories are returned by the CR process.In the event that conflicts remain, the algorithm resorts again to iterating on CAS .For each CAS D,adv iteration, the algorithm alternately increments and decrements descent speed (in 5 kt intervals) until the min/max operational descent CAS limits are reached.For each descent speed adjustment, the algorithm repeats the process of adjusting cruise speed for meet-time conformance, and checking for conflicts until a complete solution is found.If cruise speed limits are reached prior to finding a meet-time solution during the iteration process, then cruise altitude is invoked as an additional DOF *** In this case, a lower cruise altitude, represented by h C,adv , is tested to help identify a trajectory solution that satisfies both arrival time and separation constraints.For a given airspeed profile, lowering the altitude at which the aircraft flies over the remaining cruise segment has the effect of slowing groundspeed and delaying arrival time.Following the adjustment in h C,adv , the provisional cruise and descent airspeed DOFs, CAS C,adv and CAS D,adv , are reset to the initial values described previously.The iteration process then begins again with speed adjustments.In the event that the entire process continues to the point where h C,adv is decremented beyond the low-altitude limit (currently set to the meter-fix crossing altitude), the CR algorithm concludes that no satisfactory solution exists, given available DOFs.
+VII. User Interface
+A. OverviewThe EDA user interface design is based upon the premise of minimizing controller workload, while simultaneously providing sufficient control and flexibility over advisory content and timing.Past TBM field tests with CTAS have demonstrated the importance of automating advisory formulation and display to the greatest extent possible in order to mitigate workload in the arrival domain. 13The EDA user interface therefore attempts to deliver a high degree of automation while still placing the controller at the center of decision-making process.This is accomplished by allowing controllers to view and evaluate advisories in a provisional manner before accepting them and delivering them as clearances to the flight-deck.Also, a controller can, for any reason, choose to ignore or cancel an EDA advisory and resort back to manual methods for solving the arrival problem.
+B. ConfigurationPrior to operation, controllers can configure EDA to provide advisories using any of the default meet-time speed modes previously described, i.e., Descent-Only, Cruise-Only, or Cruise-Equals-Descent.This is accomplished through the configuration panel shown in Fig. 12.Similarly, through this panel, the controller can choose to turn automated CR on or off.Other options, selected through this panel, pertain to the format in which advisories are displayed, and related trigger conditions.Conditions that trigger advisories to appear on the controller's Plan-view Graphical User Interface (PGUI) are based on the tolerance between an aircraft's current ETA -computed from the most recent active trajectory prediction -and it's required STA at the meter fix.The meet-time tolerance for initial EDA advisory annunciation is typically set to 30 seconds.
+C. EDA PGUIA snapshot of the CTAS research PGUI, adapted for EDA, is shown in Fig. 13.The time line on the left of the display presents the metering schedule information.TMA-derived STAs are indicated by the position of the Aircraft Identifiers (ACIDS) on the right side of the timeline, while current ETAs are indicated by the corresponding ACIDS on the left.Whenever an aircraft is in conformance with the TMA schedule, its ACIDs to the left and right of the time line will be in alignment, indicating that its meter-fix ETA matches its TMA-computed STA.Similarly, any difference on the time line between ETA and STA ACIDS represents the amount of time that needs to be absorbed (or made up) through a subsequent EDA advisory.Fig. 13 shows a Ft.Worth Center traffic scenario involving arrivals from the northeast being metered to the fix KARLA, en route to the Dallas/Ft.Worth airport.In this case, the controller has dwelled on the aircraft designated as EGF 764, which highlights a total required delay of 4.5 minutes for meet-time conformance at KARLA.The availability of a corresponding EDA meet-time advisory is indicated by the presence of the EDA portal, designated in yellow by the letters "EDA" in the last line of the aircraft's Flight Data Block (FDB).Following the standard format for Center automation, the first three lines of the FDB convey ACID, destination airport, altitude, aircraft type, and ground speed information.The 4 th line of the FDB is reserved for any active conflicts that might be detected.The 5 th line of the FDB is reserved exclusively for EDA advisory information.
+D. Advisory DisplayAs shown in Fig. 14, once the controller is ready to receive an EDA advisory, he/she clicks on the EDA portal, which results in the portal being replaced by explicit advisory information.In this example, the default speed mode has been set to Cruise-Equals-Descent, and the initial advisory that results for EGF 764 is given as: "C/250 D/250 OK", which implies a 250 kt CAS advisory for both cruise and descent segments.The "OK" designation signifies that the provisional trajectory associated with this advisory is conflict free.† † † Clicking on the portal also results in the display of a provisional TOD associated with speed advisory, along with provisional ETA information on the † † † If an original active separation conflict is resolved by the provisional meet-time solution, "OK*" is presented in this field.If the provisional meet-time solution is predicted to cause a conflict with another aircraft's active trajectory, then "C" is presented in this field in orange.time-line.This provisional (i.e., advised) information is displayed in yellow, as opposed to active trajectory information that is displayed in white.Due to the magnitude of the delay required, Fig. 14 shows that only a partial solution is achievable with the default speed mode.This is evident on the time-line by observing that the provisional ETA results in less than 3 minutes of total delay absorption.The fact that Cruise-Equals-Descent has been saturated is further evident by the yellow bar at the left of the timeline, which shows the range of arrival times achievable with this mode.Note; the ETA limits of this time-range bar are those associated with the FAST and SLOW trajectories, described previously.As shown in the lower-right of Fig. 14, clicking the portal also results in the opening of the EDA Advisory Window.The advisory displayed in the FDB is repeated in this window along with any relevant conflict information.The window also contains options for accepting, canceling, or modifying the current advisory solution.In the current example, the controller is presented with the option of adding a Path-Stretch advisory component to resolve the remaining meet-time problem.Here, the controller can choose between turn-out angle options of 30˚, 45˚, and 60˚, and whether the path-stretch maneuver should occur to the left or right side of the nominal route.These options were developed in order to enable controllers to customize a conflict-free path-stretch solution that absorbs the required delay without violating airspace boundary constraints.In the current example, the controller decides to introduce a left-side path stretch maneuver with an initial turnout heading deviation of 30˚.This results in the display of a new provisional path and ETA reflecting this maneuver option, as shown in Fig. 15.From the time-line, it can be seen that adding Path-Stretch solves the remaining meettime problem.The entire advisory associated with the complete Cruise Equals Descent plus Path Stretch solution is now given as "P/187 C/250 P/61/KARLA D/250".‡ ‡ ‡ In addition to the cruise and descent speed components Following the acceptance and communication of these initial instructions, the remaining components already discussed, EDA advises stretching the route by flying an initial turn-out heading of 187˚, followed by a turnback to KARLA that is initiated at 61 n.mi from the reference fix.Note that the portion of the advisory that requires immediate action by the pilot (i.e., "P/187 C/250") is displayed in front of that requiring future execution (i.e., "P/61/KARLA D/250").Upon receipt by the flight crew, it is expected that clearance instructions requiring future execution would be entered into the FMS.Once the entire advisory has been accepted, all provisional information on the PGUI disappears; the Advisory Window closes; and the portal changes from yellow to green, indicating that the meet-time problem has been solved.In addition, the TS updates the active trajectory prediction for the aircraft based upon intended actions associated with the accepted advisory.This results in the partial conformance of the active ETA on the timeline with the target STA.However, because active trajectory predictions are based on current cruise speed and heading, the ETA will only drift into full conformance with the STA once the aircraft complies with the initial advisory instructions.If for any reason the aircraft is again predicted to not meet its arrival STA, the EDA portal will reappear in yellow, indicating that a corrective EDA solution is available.To allow the controller to track issued advisories and monitor conformance, the most recent advisory can be recalled in 5 th line of the FDB, whenever the controller dwells on the EDA portal.In addition, an Advisory History list is available upon request in order to allow controllers to view all recent EDA advisories issued to aircraft.
+VIII. Closed-loop Testing
+A. Simulation SystemThe current EDA development process is characterized by incremental design and development followed by extensive human-in-the-loop testing by end-users (i.e., full-performance-level air-traffic controllers).The ability to conduct realistic simulation experiments that enable the control loop between EDA and the aircraft to be closed is essential to this process.To support this, the simulation system depicted in Fig. 16 has been implemented.In this system, TMA is operated by establishing an airport acceptance rate that translates into STA assignments at the meter fix.The controller, through the EDA PGUI, receives EDA advisories and communicates clearances to pseudo pilots through an audio link.Pseudo pilots receive these instructions, and input them into an FMS/autopilot emulator referred to as the Multi Aircraft Control System (MACS). 14MACS then sends the appropriate control instructions to an aircraft target generator known as the Pseudo Aircraft System (PAS). 15Data is then sent back to EDA from PAS in order to simulate the track and flight-plan information that would normally be received from the FAA Host computer.In order to facilitate engineering tests without requiring pseudo pilots, a two-way interface has been implemented that allows EDA maneuver instructions to be automatically up-linked and entered into MACS, following controller acceptance from the PGUI.
+B. Preliminary FindingsWith the emphasis on concept definition and system development, most findings associated with recent experiments have been qualitative in nature.In general, controller subjects have commented on the enormous potential for EDA to reduce workload and increase efficiency during TBM operations.This results from the ability of EDA to generate a single, comprehensive, conflict-free, meet-time solution 20 to 40 minutes upstream of the meter fix.Simulation results, in the presense of flight execution error, indicate that aircraft can be routinely delivered to the meter fix within 20 seconds of their required STA, based on a single EDA clearance instruction given 25 minutes upstream of the meter fix.This equates to approximately a 70% improvement over operations American Institute of Aeronautics and Astronautics using manual methods to target TMA arrival times.(Manual TBM techniques used in today's operation are generally associated with a meter-fix delivery accuracy of ±90-seconds, even with the use of multiple corrective clearances.) 16lthough current operations do not support the delivery of strategic clearances that require maneuvers to be executed at future points in the flight, controllers believe that this can be accomplished with minimal changes to existing procedures.Similarly, controllers are optimistic that inter-sector coordination requirements associated with strategic EDA clearances can be successfully addressed with minor procedural changes.Although future air-ground data-link technology would certainly simplify the communication of EDA clearances, controllers feel strongly that EDA can be made to work in today's voice-based environment.To this end, the phraseology associated with EDA clearance delivery has been significantly streamlined during recent simulation experiments with controllers.
+IX. ConclusionA comprehensive description of the design, operation, and testing of EDA has been provided.The supporting trajectory prediction process has been explained in detail, along with the core meet-time and conflict resolution functions.In addition, the displays and mechanisms that allow controllers to receive, inspect, and accept EDA advisories have been explained through example.A system for conducting human-in-the-loop simulation experiments in order to obtain valuable, end-user design feedback has been described.Based on this system, preliminary experiments with controllers suggest that EDA has tremendous potential for improving the accuracy, efficiency, and workload associated with time-based metering operations.Figure 2 .2Figure 2. Trajectory Prediction Input/Output
+Figure 4 .4Figure 4. Descent-Only Solution Space and Illustrative Vertical Trajectory
+Figure 5 .5Figure 5. Cruise-Only Solution Space and Illustrative Vertical Trajectory
+Figure 6 .Figure 7 .67Figure 6.Illustrative Trajectory for the Cruise-Equals-Descent Mode
+Figure 8 .8Figure 8. Path-Stretch Solution Space and Illustrative Lateral Trajectory
+Figure 99Figure 9. Cruise Conflict Example
+Figure 10 .10Figure 10.Descent Conflict Example
+Figure 12 .12Figure 12.EDA Configuration Panel
+Figure 13 .13Figure 13.Scenario Showing Meet-Time Problem and Availability of EDA Advisory
+Figure 14 .14Figure 14.Partial Solution with Cruise-Equals-Descent Advisory
+Figure 15 .15Figure 15.Complete Solution with Cruise-Equals-Descent Plus Path-Stretch Advisory
+Figure 16.Closed-Loop Simulation System
+000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 240 320Mach = 0.480.520.560.600.640.680.720.760.800.840.88Altitude, ft.C A S D = 1 6 0 k t 1 8 0 2 0 0 2 2 0 5,1 4 0 2 4 0 2 6 02 8 03 0 03 2 03 4 03 6 03 8 04 0 00280360400440480520560True Airspeed, kt.
+Fast Mach limit (M max ) Fast CAS limit (CAS D,max ) Slow CAS limit (CAS D,min ) Case 1 Case 2Mach = 0.480.520.560.600.640.680.720.760.800.840.88Altitude, ft.5,0280360400440480520560True Airspeed, kt.
+000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 240 3201 4 0= 1 6 0 k tC A S D1 8 02 0 02 2 02 4 0 2 6 02 8 03 0 03 2 03 4 03 6 03 8 04 0 0
+Fast Mach limit (M max ) Fast CAS limit (CAS D,max ) Slow CAS limit (CAS D,min ) Case 1 Case 2 Case 1 Case 2 Case 1 Case 2 Figure 3. Typical Descent Speed Envelope for a Jet-Transport Aircraft with Examples of CAS Acceleration and Deceleration Maneuvers
+			† † The magnitude of the TMA freeze horizon is subject to air-traffic facility preference.Although larger freeze horizons allow a wider range of EDA solutions, it results in a higher probability of the need to reschedule due to system uncertianties, such as aircraft that "pop up" upon departure from airports within the freeze horizon.
+			§ § Reduced Vertical Minimum Separation (RVSM), anticipated in January 2005, will require only 1,000 ft vertical separation throughout the Center.
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+AcknowledgmentsThe authors would like to acknowledge the following individuals for their crucial contribution to the design and development of the current EDA prototype: Dr. Husni Idris, Dr. Leo Javits, and Dr. David Chesler of Titan Corporation; Rey Salcido of the University of California, Santa Cruz; and Jeff Gateley and Liang Cheng of Seagull Technology.
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+			RobertVivona
+		
+		10.2514/6.2001-4114
+	
+	
+		AIAA Guidance, Navigation, and Control Conference and Exhibit
+		Montreal, Canada
+		
+			American Institute of Aeronautics and Astronautics
+			2001
+		
+	
+	Green, S. M., and Vivona, R. A., "En route Descent Advisor Concept for Arrival Metering," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Montreal, Canada, 2001.
+
+
+
+	
+		Design of Automated System for Management of Arrival Traffic
+		
+			HErzberger
+		
+		
+			WNedell
+		
+		102201
+		
+			1989
+		
+	
+	NASA Technical Memorandum
+	Erzberger, H., and Nedell, W., "Design of Automated System for Management of Arrival Traffic," NASA Technical Memorandum 102201, 1989.
+
+
+
+	
+		Piloted simulation of a ground-based time-control concept for air traffic control
+		
+			ThomasDavis
+		
+		
+			StevenGreen
+		
+		10.2514/6.1989-3625
+	
+	
+		Guidance, Navigation and Control Conference
+		
+			American Institute of Aeronautics and Astronautics
+			1989
+		
+	
+	Davis, T.J., and S.M. Green, "Piloted Simulation of a Ground-Based Time Control Concept for Air Traffic Control," NASA Technical Memorandum 101086, 1989.
+
+
+
+	
+		Descent Advisor preliminary field test
+		
+			StevenGreen
+		
+		
+			RobertVivona
+		
+		
+			BeverlySanford
+		
+		10.2514/6.1995-3368
+	
+	
+		Guidance, Navigation, and Control Conference
+		Baltimore, MD
+		
+			American Institute of Aeronautics and Astronautics
+			1995
+		
+	
+	Green, S., "Descent Advisor Preliminary Field Test," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Baltimore, MD, 1995.
+
+
+
+	
+		Field evaluation of Descent Advisor trajectory prediction accuracy
+		
+			StevenGreen
+		
+		
+			RobertVivona
+		
+		10.2514/6.1996-3764
+	
+	
+		Guidance, Navigation, and Control Conference
+		San Diego
+		
+			American Institute of Aeronautics and Astronautics
+			1996
+		
+	
+	Green, S., "Field Evaluation of Descent Advisor Trajectory Prediction Accuracy," Proceedings of the AIAA Guidance, Navigation, and Control Conference, San Diego, 1996.
+
+
+
+	
+		En-route descent trajectory synthesis for air traffic control automation
+		
+			RASlattery
+		
+		
+			YZhao
+		
+		10.1109/acc.1995.532248
+	
+	
+		Proceedings of 1995 American Control Conference - ACC'95
+		1995 American Control Conference - ACC'95Seattle, WA
+		
+			American Autom Control Council
+			1995
+		
+	
+	Slattery, R.A., and Zhao, Y., "En Route Descent Trajectory Synthesis for Air Traffic Control Automation," Proceedings of the American Control Conference, Seattle, WA, 1995.
+
+
+
+	
+		Real-Time Data Link of Aircraft Parameters to the Center-TRACON Automation System (CTAS)
+		
+			RACoppenbarger
+		
+		
+			GKanning
+		
+		
+			RSalcido
+		
+	
+	
+		Proceedings of the 4th USA/Europe ATM R&D Seminar
+		the 4th USA/Europe ATM R&D SeminarSanta Fe, NM
+		
+			2001
+		
+	
+	Coppenbarger, R. A., Kanning, G., and Salcido, R., "Real-Time Data Link of Aircraft Parameters to the Center-TRACON Automation System (CTAS)", Proceedings of the 4th USA/Europe ATM R&D Seminar, Santa Fe, NM, 2001.
+
+
+
+	
+		Sliding mode regulator design
+		
+			LJavits
+		
+		10.1049/pbce066e_ch2
+	
+	
+		Variable Structure Systems: from principles to implementation
+		Billerica, MA
+		
+			Institution of Engineering and Technology
+			
+			
+		
+	
+	unpublished
+	Javits, L., "Design Document for Implementation of Cruise-Equals-Descent Meet-Time mode in RAPS,", Titan Systems Corporation, Billerica, MA, 2004 (unpublished).
+
+
+
+	
+		Conflict Detection and Resolution In the Presence of Prediction Error
+		
+			HErzberger
+		
+		
+			RAPaielli
+		
+		
+			DRIsaacson
+		
+		
+			MMEshow
+		
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+	
+		Proceedings of the 1st USA/Europe Air Traffic Management R&D Seminar
+		the 1st USA/Europe Air Traffic Management R&D SeminarSaclay, France
+		
+			1997
+			12
+		
+	
+	Erzberger, H., Paielli, R. A., Isaacson, D. R., and Eshow, M.M., "Conflict Detection and Resolution In the Presence of Prediction Error," Proceedings of the 1st USA/Europe Air Traffic Management R&D Seminar, Saclay, France, 1997. 12
+
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+	
+		Terminal area trajectory synthesis for air traffic control automation
+		
+			RASlattery
+		
+		
+			SMGreen
+		
+		10.1109/acc.1995.520941
+	
+	
+		Proceedings of 1995 American Control Conference - ACC'95
+		1995 American Control Conference - ACC'95
+		
+			American Autom Control Council
+			1994
+		
+	
+	Slattery, R.A., and S.M. Green, Conflict-Free Trajectory Planning for Air Traffic Control Automation, NASA TM-108790, 1994.
+
+
+
+	
+		Field test evaluation of the CTAS conflict prediction and trial planning capability
+		
+			BDMcnally
+		
+		
+			RalphBach
+		
+		
+			WilliamChan
+		
+		10.2514/6.1998-4480
+	
+	
+		Guidance, Navigation, and Control Conference and Exhibit
+		Boston, MA
+		
+			American Institute of Aeronautics and Astronautics
+			1998
+		
+	
+	McNally, B. D., Bach, R.E., Chan, W., "Field Test Evaluation of the CTAS Conflict Prediction and Trial Planning Capability," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Boston, MA, 1998.
+
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+	
+		A Multi-Fidelity Simulation Environment for Human-In-The-Loop Studies of Distributed Air Ground Traffic Management
+		
+			ThomasPrevot
+		
+		
+			EverettPalmer
+		
+		
+			NancySmith
+		
+		
+			ToddCallantine
+		
+		10.2514/6.2002-4679
+		AIAA-2002-4679
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Reston, VA
+		
+			American Institute of Aeronautics and Astronautics
+			2002
+		
+	
+	Prevot, T., Palmer, E., Smith, N., and Callantine, T., "A Multi-Fidelity Simulation Environment for Human-in-the-Loop Studies of Distributed Air Ground Traffic Management", AIAA-2002-4679, Reston, VA, 2002.
+
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+	
+		Pseudo Aircraft Systems - A multi-aircraft simulation system for airtraffic control research
+		
+			ReidWeske
+		
+		
+			GeorgeDanek
+		
+		10.2514/6.1993-3585
+	
+	
+		Flight Simulation and Technologies
+		Monterey, CA
+		
+			American Institute of Aeronautics and Astronautics
+			1993
+		
+	
+	Weske, R.A., and Danek, G. L., "Pseudo Aircraft Systems: A Multi-Aircraft Simulation System for Air Traffic Control Research," Proceedings of the AIAA Flight Simulation Technologies Conference, Monterey, CA, 1993
+
+
+				
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+	
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+		
+I. IntroductionLTHOUGH airborne capabilities such as Area Navigation (RNAV) and Required Navigation Performance (RNP), together with optimized guidance and control through the Flight Management System (FMS), offer substantial improvements to the efficiency of flight operations, their benefits frequently go unrealized in today's arrival airspace domain.To the frustration of the airspace user, efficiency gained en route using state-of-the-art airborne automation is often squandered during the final stages of flight as the airplane transitions for landing.Here, Air-Traffic Control (ATC) actions often require the airplane to execute sub-optimal, tactical maneuvers that involve frequent temporary-altitude assignments, speed adjustments and lateral vectoring to accommodate tightly coupled separation and traffic-flow-management constraints.These actions, though designed to manage controller workload and ensure safety in heavy traffic conditions, prevent aircraft from executing an uninterrupted Continuous Descent Approach (CDA) to the runway using low engine power for maximum fuel efficiency and minimum environmental impact.As a result, FMS guidance trajectories, which are optimized over time horizons that transcend ATC boundaries, are seldom executed to completion in today's arrival airspace.Accommodating efficient, trajectory-based arrival operations under all traffic conditions is a key objective of the Next Generation Air Transportation System (NextGen). 1 The benefit and feasibility of CDA operations has been a subject of considerable study. 2,3These studies have occasionally involved flight trials, some evolving into limited daily use operations into select airports around the world.Examples include the recent Advanced Arrivals trials in the Netherlands, 4 CDA trials at Louisville, 5,6 flight trials in the U.K. 7 and Sweden, and the initial Tailored Arrivals trials conducted in Australia. 8Although these studies have involved a myriad of techniques for enabling CDA benefits, their focus has largely been on developing static arrival procedures for near-term deployment.As a result, the application of these CDA initiatives requires either periods of low traffic density or specialized protocols that limit throughput.A key feature of the flight trials described in this paper, which distinguishes them from related activities in the U.S. and abroad, is the inclusion of ground-based automation capable of generating dynamic CDA trajectory solutions in the presence of complex airspace constraints.The ability to tailor arrival solutions to accommodate individual aircraft performance, atmospheric conditions, and operational restrictions is critical for enabling CDAs in congested airspace environments where potential benefits are greatest.Towards this objective, NASA's En Route Descent Advisor (EDA) was incorporated into the trials to compute advisories for meeting arrival-time constraints artificially imposed at the TRACON boundary, designed to emulate operations under heavy traffic conditions.[11] The automation systems, clearance composition, and human procedures used in carrying out the EDA-supported OTA trials are first described, followed by results describing potential fuel and emissions benefits.Due to the importance of accurate ground-based and airborne trajectory predictions in planning and executing Tailored Arrivals, results are then presented that compare EDA and FMS trajectory-prediction performance.Additional results obtained from these trials pertaining to human procedures, noise exposure, and wind-data comparisons will be documented in detail at a later date.
+II. ApproachIn collaboration with the FAA and United Airlines, The Oceanic Tailored Arrivals (OTA) trials were conducted over 40 days with a single United Airlines Boeing 777 flight (UAL76) in commercial service between Honolulu and San Francisco (SFO).The flight was operated along the Central-East-Pacific (CEP) oceanic route structure, comprised of a series of fixed, parallel tracks from the Hawaiian Islands to the California coast. 12In addition to its avionics equipage, UAL76 was chosen largely because of its early morning (5:30 AM local) arrival time at SFO, which avoided congested airspace conditions, thereby minimizing the likelihood of interference with other traffic.
+A. System ComponentsFigure 1 illustrates the key systems employed in formulating, communicating, and executing the trajectory-based arrival clearances used to support the OTA field trials (the precise content of the data communications between the various system elements is described later in the paper).Clearance delivery was initiated approximately 700 nmi from landing in oceanic airspace controlled by the Oakland Air-Route Traffic Control Center (ARTCC), referred to as ZOA.In oceanic airspace, controllers at ZOA rely upon the FAA's recently deployed Advanced Technologies and Oceanic Procedures (ATOP) system for integrated CNS functions.A prominent capability of ATOP is its ability to support two-way digital messaging between ATC and the flight deck through Controller-Pilot Data-link Communications (CPDLC).The OTA route clearance -comprised of lateral waypoints together with speed and altitude restrictions -was relayed to the flight deck using a standard CPDLC message format supported by ATOP.The format allowed the OTA instructions, upon pilot approval, to be directly loaded into the B777 FMS through its Future Aircraft Navigation System (FANS) avionics interface.FANS avionics integrate FMS functionality with CPDLC and contract-based Automatic Dependent Surveillance (ADS-C) services in oceanic airspace.Once loaded, the OTA route clearance provided sufficient information for the FANS FMS to compute a 4-D reference trajectory from the aircraft's current position to the runway.This trajectory was then used by the FMS as the basis for its Lateral Navigation (LNAV) and Vertical Navigation (VNAV) guidance functions, which provided inputs to the automatic flight control system for determining appropriate aileron, rudder, elevator and throttle inputs.Once activated, the OTA route clearance allowed the flight to progress with no additional pilot inputs required prior to configuring the airplane for landing.The nominal guidance law used by the FMS VNAV function was set to PATH mode, which directed the autopilot to null any positional error between the airplane's current altitude and the FMS reference trajectory's vertical profile.In the event that the airspeed required to control path differed from that suggested by the reference trajectory by more than ±10 kt, the VNAV guidance laws would switch from PATH to SPEED control mode.To provide a dynamic element to the OTA trajectory-based clearance, a prototype version of NASA's EDA decision-support tool was incorporated into the field trials.EDA was used to compute the maneuver solution needed to target a meter-fix crossing time constraint imposed at the Terminal Radar Approach Control (TRACON) boundary.Originally designed to assist the domestic en route sector controller in developing conflict-free arrival metering solutions under capacity constrained conditions, EDA was adapted to ZOA airspace and interfaced with ATOP to receive oceanic surveillance and flight-plan data.This oceanic surveillance data was derived from the airplane's satellite-based positioning system, and relayed to ATOP via ADS-C at the maximum-available rate of once every 2 minutes, as specified in the ADS-C contract configured by ZOA.In addition to surveillance and flight plan inputs, EDA required atmospheric data to compute the long-range trajectory predictions needed for its advisories.These input data were derived from the National Oceanic and Atmospheric Administration's Rapid Update Cycle (RUC) model, which provided 2-hour forecasts (updated each hour) of wind speed, wind direction, temperature and pressure, organized in a Lambert-Conformal 3-D grid with a lateral resolution of 40 km.For the purpose of the OTA trials, EDA computed only descent Calibrated Airspeed (CAS) advisories for targeting a Scheduled Time of Arrival (STA) at the TRACON meter fix (waypoint BRINY).EDA cruise-speed and path-stretch advisory capabilities, along with automated conflict resolution functions, were purposely suppressed to avoid complexity and unnecessary risk in these initial trials.For optimal TRACON throughput under congested conditions, EDA derives its target STAs from TMA.During the time of these trials, however, no capacity constraints existed, so meter-fix STAs were artificially set to within ±4 minutes of the airplane's original Estimated Time of Arrival (ETA) at BRINY.This original ETA was computed from an EDA trajectory prediction using a nominal assumption of company-preferred descent speed (280 kt). 11An example of EDA's graphical user interface, showing a descent CAS advisory for UAL76 of 267 kt, is illustrated in Figure 2. Advisory information is presented in both the aircraft's flight data block as well as in a separate advisory window that allows controllers to accept or reject EDA recommendations.In the example shown in Figure 2, the descent-speed advisory was generated to absorb 2 minutes of required delay, evident in the ETA and STA timelines shown to the left of the display.
+B. Profile Development and Clearance CompositionA primary challenge for OTA, as with any CDA activity, is the design of the profile constraints that define the VNAV descent profile.These constraints must be carefully chosen to allow the FMS to build a trajectory that can be flown with near-idle thrust, using only elevator inputs and normal drag-device deployment, i.e., flaps and landing gear, for vertical control.This design problem essentially reduces to one of energy management under multiple constraints.The descent trajectory must allow the potential and kinetic energy at Top-of-Descent (TOD) to be bled off at a sufficient rate to satisfy all ATC and aircraft operational constraints along the descent path, while leaving the aircraft in a suitable energy state and control configuration at the final approach fix.To further constrain the problem to allow the FMS to generate a unique idle-thrust trajectory solution for a given wind forecast, an initial descentspeed profile must be chosen.In the absence of time-based metering requirements, this speed profile is set by the FMS based on a Cost Index, computed as a ratio of time and fuel related costs for an economical idle-thrust (i.e., ECON) descent. 13For OTA flights where meter-fix crossing times were imposed, EDA was used to override Cost Index in determining the appropriate initial descent-speed profile.This use of EDA and its subsequent impact on TOD, flight-path angle, and meter-fix arrival time is described in detail in Ref. 9.The OTA route clearance consisted of the entire set of lateral and vertical constraints needed by the FMS for building an idle-thrust guidance trajectory.The OTA route clearance was developed iteratively, relying on extensive flight simulation with UAL and Boeing line pilots under various wind conditions and descent-speed assumptions.The primary objective was to avoid leaving the airplane low on energy relative to the VNAV path, which would trigger undesired throttle inputs from the autopilot.The second objective was to avoid leaving the airplane high on energy relative to the VNAV path, which would require speed brakes, unusual flap settings, and/or steep descent segments -all of which can increase pilot workload and passenger discomfort, while compromising desired fuel, emissions, and noise benefits.The OTA route clearance used to support flight scenarios with EDA, including lateral waypoints assignments and associated speed/altitude crossing constraints, is shown in Figure 3.The first restriction in the descent occurred at the EDA meter fix BRINY, where the airplane was required to cross at 240 kt CAS, at an altitude of 11,000 ft.The next restriction occurred at Woodside (designated OSI), where the airplane was required to cross at 210 kt CAS, at an altitude at or above 7,000 ft.The third restriction occurred at MENLO, approximately 12 nmi from SFO, where the airplane was given no explicit speed restriction but required to cross at an altitude at or above 4,500 ft.Beyond MENLO, the remaining descent trajectory waypoints (CEPIN and AXMUL), crossing restrictions, and runway assignment (28R for these trials) were conveyed by the published approach procedure.Because the details of the approach procedure were already stored in the FMS, only the name "ILS 28R" needed to be sent via datalink along with the OTA route clearance.Upon receipt, the FMS automatically appended the detailed approach procedure to the OTA route clearance, thereby forming the basis of a complete guidance trajectory from oceanic-cruise flight to the runway.
+C. ProceduresAs illustrated in Figure 4, OTA procedures were initiated in ZOA-controlled oceanic airspace, inside Oceanic Control sector 4 (OC-4).A key challenge of these flight trials was to design procedures sufficient to allow UAL76 to progress uninterrupted along the intended OTA trajectory while traversing through different regions of airspace control.Indeed, this problem represents a key obstacle to accommodating the trajectory-based operations being advocated under NextGen in today's procedurally segmented ATC system.Today, managing flights between ATC sectors and facilities is handled through a multitude of coordination fixes, preferred routes, and airspace boundaries, together with associated speed/altitude crossing restrictions.Although these constraints help produce predictable traffic flows and divide the ATC problem into manageable, well-defined regions of control, they often occur at the expense of flight efficiency.The challenge of executing the OTA trajectory was to develop procedural techniques for allowing UAL76 to progress uninterrupted from oceanic airspace (OC-4), through domestic en route airspace (ZOA sector 35), into the Northern California TRACON (NCT) airspace, and finally to landing at SFO. OTA procedures were initiated from the flight deck voluntarily.Flight crews were informed of the OTA trials and expected procedures through a special flight-manual bulletin distributed through UAL crew stations.Once underway, OTA procedures could be terminated at any time at the discretion of either the flight crew or ATC.The flight would then be handled by ATC using normal arrival procedures, requiring the crew to typically disengage FMS LNAV/VNAV functions.For simplicity, it was decided that once interrupted no attempt would be made to resume a CDA via OTA uplinks or procedures.
+Figure 3. OTA Route Clearance Showing Profile ConstraintsAt Step 1 in Figure 4, the flight crew requested participation around 90 min prior to their estimated landing time, approximately 700 nmi from SFO and 450 nmi prior to entering radar-controlled, domestic airspace.This request was made using a free-text CPDLC datalink message that read "Requesting OTA trials."Upon receiving the crew request, the OC-4 controller configured a new ADS-C contract within ATOP that instructed the aircraft to downlink position, weather, aircraft state, and flight-path intent data at a rate of once every 2 minutes, represented by Step 2 in Figure 4.Following ADS configuration, the basic OTA clearance, as previously described, was up-linked to the aircraft using a FANS-loadable CPDLC message (Step 3).The message, conveyed using CPDLC Uplink Message (UM) 83, was as follows: "At COSTS cleared CREAN CINNY BRINY/N0240A110 OSI/N210A070 MENLO/A045A ILS28R."The test engineer coordinated with the ZOA-oceanic supervisor in advance to affirm the assumed landing runway.Prior to accepting the clearance, the flight crew first ensured that it could be loaded satisfactorily into the FMS to produce a continuous trajectory to the runway (Step 4).This FMS trajectory was computed in compliance with the route, speed, and altitude constraints stipulated in the OTA clearance.Upon crew acceptance of the OTA clearance, a "Wilco" message was downlinked to ATOP.Step 5 involved the uplink of wind and temperature data to the flight deck for inclusion in FMS trajectory calculations.These atmospheric data were derived from the same RUC 2-hour forecast model used for ground-based EDA trajectory calculations.In addition to the forecast surface temperature at SFO, the data consisted of wind speed/direction at five points along the OTA trajectory corresponding to 1) cruise altitude at the waypoint CINNY, 2) cruise altitude at TOD, 3) 18,000 ft along the descent path, 4) 10,000 ft along the descent path, and 5) threshold crossing at SFO.These data were uplinked to the flight deck to provide FMS trajectory computations with the same atmospheric data available to EDA.This was essential to make valid trajectory-prediction comparisons between ground-based and airborne automation in post-trial analysis.Step 6 involved the uplink of the EDA descent-speed advisory, intended to control arrival time at the waypoint BRINY.The advisory was obtained from a prototype EDA tool running on a laptop computer in the ZOA-Oceanic control room.Upon extracting the advisory, the test engineer relayed it to the oceanic sector controller managing UAL76.The controller then used ATOP to relay the instruction to the aircraft in a datalink message consisting of current Mach number and the advised descent CAS.Upon receipt by the flight deck, the descent speed instructions were manually entered into the FMS VNAV descent page, which resulted in a recalculation of the FMS TOD and trajectory needed to target the BRINY constraint.Once reaching TOD, the FMS commanded the airplane to initiate the descent at an airspeed equal to the current Mach number until capturing the EDA-advised descent CAS.After leaving oceanic airspace and entering the radar-controlled domestic airspace of ZOA Sector 35, UAL76 received a voice-based pilot-discretionary descent clearance to 8,000 ft (Step 7); all clearances were given by voice from this point forward since CPDLC services are not currently available in U.S-domestic airspace.Although coordination had already taken place between ZOA and NCT at the supervisory level, the ZOA Sector 35 controller then notified the downstream, receiving controller at NCT that UAL76 was "on the OTA."Assuming allowable traffic conditions, the NCT controller, upon accepting the hand-off from Sector 35, cleared UAL76 to 4,000 ft and issued the appropriate approach clearance and runway assignment (Step 8).In general, procedures were designed so that all voice-issued altitude clearances stayed ahead of the altitude restrictions contained in the OTA route clearance being executed through the FMS to avoid interrupting the CDA.It is recognized that these above procedures, although acceptable for limited flight trials, are likely to be too cumbersome for routine trajectory-based operations.The piecemeal voice instructions needed to communicate standard altitude clearances and crossing restrictions, while staying ahead of the OTA route clearance in the FMS, could likely be replaced by a simple "descend via the OTA" instruction.Although requiring formal changes to ATC and airline Standard Operating Procedures, this approach is currently being pursued in support of near-term deployable Tailored Arrivals.§
+D. Test Conditions and Data CollectionThe OTA trials were conducted in two phases: Phase 1 (August 17 -September 6, 2006), and Phase 2 (December 13 -January 9, 2007).The divided schedule was due to the airline's choice of when to assign a FANS-equipped B777 to UAL76.Two distinct operational test conditions were employed in the OTA trials, referred to as OTA1 and OTA2.For OTA1 flights, the initial descent speed was not stipulated by ATC; instead the pilot was free to execute a pilot-discretionary descent using the FMS ECON speed profile computed using Cost Index.OTA1 flights were conducted to help identify near-term procedural requirements and assess the immediate benefit of conducting Tailored Arrivals under light traffic conditions where EDA automation is not required.OTA2 flights, which included the BRINY metering constraint for interoperability with EDA, were used to support trajectory-prediction comparisons between air and ground automation and congested-airspace benefit assessments.A summary of the OTA flights is shown in Table 1 with a breakdown of successfully completed events.There were a total of 40 flight opportunities over both phases of the trials.Of these, pilots volunteered to participate on 35 occasions.RUC-based winds were successfully up-linked on 27 of these occasions.The total number of uninterrupted CDA operations to the TRACON boundary and runway (independent of successful wind uplinks) were 27 and 20, respectively, of which approximately 80% were OTA2 flights.Upon taking only those flights that had successful wind uplinks together with uninterrupted CDA to the TRACON boundary, and further eliminating those with any unexplained route deviations and/or pilot-reported anomalies, 11 flights remained for the detailed trajectory analysis described later in this paper.The quantitative data collected during the OTA trials are shown in Table 2.In addition to quantitative measurements, qualitative data were collected to refine human procedures and identify real-world issues associated with OTA deployment and execution.Qualitative observations were also used to support post-flight quantitative analysis by helping to explain any anomalies associated with a particular flight.Qualitative data gathered by project engineers included air-traffic control facility observations, jump-seat observations on the flight deck, and crew interviews at the gate upon arrival at SFO. § Although not addressed here, numerous air/ground procedural findings were made pertaining to the near-term implementation of Tailored Arrivals in the current FAA system under accommodating traffic conditions.
+III. Results
+A. Fuel and Emissions Benefits
+Baseline ConditionsTo baseline fuel and emissions benefits, three flight-path scenarios were constructed to represent today's arrival operations under light, medium and heavy traffic conditions.These baseline scenarios were derived from observing B777 arrival traffic into SFO off CEP routes, captured for various times of day from flight-track data gathered one week prior and during the OTA Phase 1 and Phase 2 trials.The lateral and vertical track data from which baseline scenarios were derived is shown in Figure 5, color-coded for various times of day.Particularly morning and evening flights when traffic is heaviest, these data show the inefficient lateral vectoring and altitude level-off maneuvers resulting from air-traffic control actions taken to manage separation and throughput constraints.The three baseline trajectories derived from these data are shown in Figure 6.The light-congestion baseline was used to estimate near-term benefits, since it represents traffic conditions for which OTA procedures could be invoked today without the deployment of EDA.To claim OTA benefits in comparison to the medium and heavy traffic baseline scenarios, it is assumed that EDA is available as a supporting groundside tool for developing conflict-free metering solutions.
+Table 1. Flight Summary Table 2. Data Collected
+Fuel BenefitsDue to the unavailability of direct fuel-burn measurements from the aircraft, OTA fuel benefits were estimated post flight using Boeing's proprietary BCOP/INFLT database and performance analysis software.Fuel burn associated with each of the three baseline scenarios was estimated in BCOP/INFLT for a range of initial descent CAS values: 260, 280, 300 and 320 kt.Comparative fuel burn results were computed for modeled OTA1 and OTA2 trajectory profiles using the same range of initial descent CAS values used for the baseline scenarios.For the OTA1 profile, the descent CAS was assumed constant up to the transition to the built-in FMS speed restriction of 240 kt, occurring wherever the aircraft crossed through 10,000 ft.For OTA2 flights, the descent CAS was held constant prior to transitioning to the BRINY crossing restriction of 11,000 ft and 240 kt.For each baseline and OTA scenario, Table 3 shows the distance flown and estimated fuel burn between CREAN (located on the Oceanic Control Boundary approximately 240 nmi from landing) and SFO for a B777-200.Over all baseline and OTA scenarios, the effect of initial descent CAS on total fuel burn was small, amounting to less than a 25 lb fuel difference between the optimal descent CAS (280 kt) and the least efficient descent CAS (260 kt).Therefore, to simplify the presentation of results, Table 3 shows the fuel burn averaged across all descent-CAS variations.slightly to 227 lbs for OTA2 as a result of the additional profile constraint introduced by the meter-fix crossing restriction at BRINY.Potential fuel savings under light-congestion traffic conditions, however, are well represented by the OTA1 results, since EDA would not be required to enable CDA operations.Furthermore, since these savings are not dependent on EDA, they could be realized in the near-term using current oceanic automation systems together with trajectory-based procedures.For medium and heavy traffic congestion scenarios, average estimated OTA fuel savings increase to 358 lbs and 3,219 lbs per flight, respectively.The dramatic increase in estimated fuel savings for the heavy traffic comparison was due to the inefficiencies inherent in the baseline scenario for achieving flow management and separation assurance under congested conditions.As seen in Figure 6, these inefficiencies included an extra 30 nmi path-stretch segment performed in level flight at low altitude (6,000 ft).To accurately estimate OTA fuel savings under heavy traffic conditions, the effect of upstream metering actions needed to approximately match the arrival times of baseline flights into SFO was considered.For these scenarios, delay absorption was required to prevent OTA flights from arriving too early, thereby violating capacity constraints.Although, ideally, some delay could be absorbed on the ground by carefully planning departure times in anticipation of CDA operations in the presence of other traffic, it is more realistic to assume that delay must be absorbed in flight, either as a result of additional EDA advisories in arrival airspace and/or regional flow-management directives applied further upstream.To compute the fuel savings associated with heavy-congestion operations, an approximately 25% fuel-burn penalty was calculated for OTA flights to account for upstream metering actions.It was assumed that this penalty was taken upstream of CREAN in cruise flight in the form of a path-stretching maneuver designed to match baseline arrival times.This penalty accounts for fuel savings in the heavy congestion scenario of Table 3 being less than the difference in fuel burned from CREAN to SFO between baseline and OTA scenarios.No such penalty was needed for OTA flights under light and medium traffic congestion, since required delay absorption could be accomplished with the descent-CAS variation already taken into account.
+Emissions BenefitsEstimates of per-flight OTA emissions reduction in comparison with baseline conditions are shown in Table 4.These results were calculated along with the fuel burn using BCOP/INFLT between CREAN and SFO.Table 4 shows the effect of OTA on the four compounds of primary concern to the environment.These are 1) carbon dioxide (CO 2 ) -a greenhouse gas produced as a normal product of organic-fuel combustion; 2) carbon monoxide -a poisonous gas resulting from incomplete combustion; 3) nitrogen compounds -primarily nitric oxide (NO) and nitrogen dioxide (NO 2 ) resulting from high temperature combustion and commonly associated with ozone and smog; and 4) all species of hydrocarbons (C x H x ) -volatile organic compounds resulting from unburned or partially burned fuel passing through the engine. 14As shown in Table 4, results suggest that an idle-thrust, OTA1 descent to the runway can reduce CO 2 emissions by 761 lbs per flight in comparison with B777 operations on similar routes conducted during light traffic congestion.In comparison with medium and heavy traffic-congestion baselines, OTA2 has the potential to reduce CO 2 emissions by as much as 1,128 lbs and 10,137 lbs per flight, respectively.These Table 3. Fuel Burn: OTA vs. Baseline large greenhouse gas reductions reflect the approximate 3:1 ratio between fuel burned and CO 2 emitted, resulting from the basic combustion chemistry of jet fuel. 14
+B. Trajectory PerformanceData showing the accuracy of FMS arrival-time predictions to the meter fix (BRINY) in comparison with the actual BRINY crossing time, as a function of time-to-go to BRINY, are shown in Figure 7.These data show the expected, continual improvement of airborne predictions as the airplane progresses through oceanic and domestic airspace.Of note are the distinct improvements in prediction accuracy for several flights that can be observed to correspond to the wind and descent-speed uplink events.The latter, course, was expected whenever the airplane's original VNAV descent speed, based on Cost Index, differed from the EDA descent speed clearance that was uplinked and executed.8.These results pertain to a single prediction at CREAN, i.e., without updates as the flight progresses.These data show mean arrival-time prediction accuracies of 2 seconds early and 3 seconds late for FMS and EDA, respectively, with a similar dispersion about the mean (σ ≈ 20 sec) for both.These data suggest that airborne and ground-based automation can predict arrival times over a 25 min horizon with similar accuracy and precision, assuming shared wind and descent-speed-intent information.Because EDA conflict avoidance functions require accuracy at each point along the trajectory prediction, not just at the meter fix itself, the entire EDA prediction was compared to surveillance truth data.The overall accuracy of EDA trajectory predictions for several look-ahead times is shown in Table 6.These results capture the error in altitude, along-track position, and cross-track position along the entire trajectory prediction to the meter fix for time horizons of 23 min, 20 min, and 17 min.These look-ahead times were chosen so that predictions for all flights could be initiated between the Oceanic Control Boundary and TOD to allow use of ARTCC radar surveillance as truth data, captured every 12 seconds.Inherent latencies associated with the radar data (a constant bias for each flight ranging from 6 to 12 seconds) were identified and removed in post processing by calibrating them with the timestamped airplane position reports received via ADS-C.Figures 10 and11 show the altitude and along-track error for all flights as a function of time for a 23-minute prediction time horizon.A similar comparison of FMS trajectory prediction error as a function of time was not possible, since only the FMS time estimates to downstream waypoints were available, not the full trajectory predictions upon which those estimates were based.6 show that EDA's mean altitude prediction error, ranging between -500 and -700 ft, does not vary substantially with time horizon.This is because most altitude error occurs in the descent phase of flight, which is fully contained within each prediction horizon and influenced primarily by speed intent and modeled aircraft performance characteristics rather than initial conditions in cruise.The negative mean altitude error is due to EDA predicting the airplane to reach the meter-fix crossing altitude earlier than what actually occurred due to EDA's modeling of the deceleration to the meter-fix crossing speed (240 kt) using a level flight segment.Although this assumption works well for the current-day TMA operations that EDA was originally designed to support, it is a poor model for CDA operations specifically designed to avoid level flight segments.The result is a ground-based prediction to the meter fix that lags the actual flight operation.This results in an earlier TOD estimate (evident by the initial spike in altitude error in Figure 10) and lower overall altitude prediction in comparison to truth.The same phenomenon results in the negative mean along-track prediction errors (ranging between -0.6 and -1.3 nmi) seen in Table 6.Unlike altitude errors, along-track errors averaged over the entire trajectory prediction grow significantly as the time horizon increases from 17 to 23 minutes.This is due to groundspeed prediction error -resulting primarily from remaining wind uncertainty -occurring over both cruise and descent.This EDA-prediction analysis includes the effect of sharing wind data and descent-speed intent with the airplane in actual operations.To see the importance of shared descent-speed intent, in particular, on air/ground predictions, a simple study was carried out looking at EDA trajectories under descent-speed-intent assumptions ranging from 250 kt to 320 kt CAS.Over a 25-minute prediction horizon, the altitude and along-track differences at any given time can be as large as 6,500 ft and 20 nmi as a result of descent-speed-intent uncertainty.
+Results in Table
+IV. ConclusionsThe San Francisco Oceanic Tailored Arrivals field trials demonstrated the ability to conduct highly efficient Continuous Descent Approach operations under real-world conditions with commercial airplanes.By integrating advanced air and ground automation over datalink in oceanic airspace, these trials provide a step towards understanding the feasibility and benefits of conducting trajectory-based arrival operations under NextGen.To progress towards enabling Tailored Arrivals under congested traffic conditions where benefits are greatest, NASA's ground-based EDA automation was used to tailor trajectory solutions to accommodate time-based metering constraints.Data gathered during these trials was used to perform an assessment of EDA trajectory prediction accuracy and precision in comparison with FMS predictions and surveillance truth data.Results show that trajectory prediction errors can be greatly reduced through the uplink of wind and descent-speed-intent data, and that similar arrival-time prediction performance can be achieved between air and ground automation.These results are important because accurate and compatible prediction performance between air and ground automation is fundamental to both the planning and execution of trajectory-based arrival operations.Finally, an initial benefits assessment based on the real-world data gathered during these trials shows substantial per-flight reductions in fuel burn and environmental emissions afforded by Tailored Arrivals, especially in comparison with baseline arrival operations conducted under heavy traffic conditions.These results are particularly compelling in the presence of today's record high fuel costs and increased environmental awareness.Figure 1 .Figure 2 .12Figure 1.OTA System Components
+Figure 4 .4Figure 4. OTA Procedure Steps
+Figure 5 .Figure 6 .56Figure 5. Current B777 Operations into SFO Arriving off CEP routes
+Table 4 .Figure 7 .47Figure 7. Progression of FMS Arrival-Time Predictions to Meter Fix
+Figure 8 .8Figure 8. Histogram of EDA and FMS Arrival-Time Prediction Error to Meter Fix (200 nmi/ 25 min horizon)
+Table 6 .Figure 10 .Figure 11 .61011Figure 10.EDA Altitude Prediction Error (23-min time horizon)
+
+
+
+
+		
+		
+
+			
+AcknowledgmentsThe authors would like to acknowledge the substantial contributions of FAA personnel from Oakland Center and Northern California TRACON in planning and executing these operational field trials.In addition, the support received from FAA headquarters (ATO-E) was instrumental in approving and coordinating this activity.Critical systems engineering, data analysis, and human-factors contributions were made by project personnel from Boeing, Sensis Corporation, the San Francisco Noise Abatement Office, Lockheed Martin, NASA Code TH, QSS Group Inc., University of California Santa Cruz, and San Jose State University.Last but not least, the authors would like to thank to the subject-matter experts and pilot participants of United Airlines that enabled these trials to take place.
+			
+
+			
+
+				
+
+
+	
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+			DanielGoldner
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+	Elmer, K., Wat, J., Warren, A., Tong, K., et al., "Community Noise Reduction Using Continuous Descent Approach: A Demonstration Flight Test at Louisville," Proceedings of the 9 th AIAA Aeroacoustics Conference, Hiltin Head, SC, May 2003
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+		Continuous Descent Approach: Design and Flight Test for Louisville International Airport
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+		Selection of an optimal cost index for airline hub operation
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+			AbhijitChakravarty
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+I. IntroductionConcepts and technologies to handle arrival, departure, and surface operations have been under development by NASA, the Federal Aviation Administration (FAA), and industry to improve the flow of traffic into and out of the nation's busiest airports.Whereas trajectory-based concepts and technologies have been developed for specific phases of flight, their integration across surface and airspace domains to more fully optimize traffic flow remains a considerable challenge. 1o address this challenge, NASA has committed to developing the Airspace Technology Demonstration-2 (ATD-2) concept which provides an Integrated Arrival, Departure, and Surface (IADS) traffic management system that extends traffic sequencing all the way from the gate to the overhead stream and back again for multi-airport, metroplex environments.The ATD-2 concept builds on and integrates previous NASA research such as the Terminal Sequence and Spacing (TSAS), 2 the Precision Departure Release Capability (PDRC), 3 and the Spot and Runway Departure Advisor (SARDA) 4,5 which were focused on individual airspace domains.These concepts have been integrated into a single traffic management tool and tested in Human-In-The-Loop simulations 6 and eventually deployed to the Charlotte Douglas International Airport (CLT) for field evaluation.A key component of the ATD-2 system focuses on surface scheduling and aims to generate schedules that allow aircraft to taxi, climb, and insert within the overhead stream with minimal interruptions. 1 During time periods when departure demand at the runway overwhelms the available capacity, ATD-2 generates flight-specific gate hold advisories to absorb delay at the gate prior to engine start to reduce fuel burn and gas emissions. 7In order to generate schedules that manage the desired departure runway queue time and to efficiently merge controlled flights with Traffic Management Initiative (TMI) constraints into the overhead stream, it is necessary for the ATD-2 scheduler to balance the demand at the runway with the available capacity, while simultaneously predicting accurate takeoff times.The purpose of this paper is to measure and assess the ability of the scheduler in the tactical time frame to balance the demand and capacity while generating accurate takeoff time predictions.The data that we analyze are from the operational ATD-2 IADS system during the Phase 1 demonstration at the CLT airport.We use the data to evaluate the ability of the scheduler to accurately balance the demand and capacity by comparing the rate of the scheduled operations to the rate of the realized operations during peak traffic demand.We also compare the accuracy of the predicted takeoff times to the actual takeoff times as a function of the lookahead time.We identify a surprising relationship between the rate of the operations and the accuracy of the schedules which highlights the complexities present in scheduling surface operations.This paper is organized as follows.Section II provides background information on the ATD-2 IADS system operating at the CLT airport.In Section III we provide a description of the ATD-2 tactical surface scheduler and describe the logic that is used for scheduling the departure operations.Section IV provides an analysis of the demand capacity balancing and Section V analyzes the accuracy of the predicted takeoff time compared to the actual takeoff time as a function of the lookahead time.In Section VI we describe the main challenges that were identified when scheduling in the tactical time frame in the live operational environment and Section VII provides concluding remarks.
+II. Background on ATD-2 IADS system at CLT AirportTraffic demand at CLT is characterized by definite peaks and valleys which define a traffic bank. 6There are clear distinctions between departure and arrival banks throughout the day.Within a bank, the departure and arrival banks can overlap and the combined departure and arrival bank takes approximately two hours.Bank 2 typically has the heaviest traffic and occurs between 9am EST and 11am EST.Starting on November 29th, 2017 the IADS tactical surface scheduler has been available to air traffic controllers and the ramp operators to assist in surface metering for bank 2 operations.This has provided a unique opportunity to assess the performance of the tactical scheduler in a live operational environment where the controllers could actively use the tactical scheduler advisories.This paper analyzes data that were collected between 2018-03-01 through 2018-04-30 during bank 2 operations and focuses on the immediate tactical time frame 30 minutes prior to takeoff to the actual off event.For this analysis, we focused on North flow operations with a dedicated arrival only runway 36L, a predominantly departure only runway 36C that accommodates some arrival operations, and a dual use runway 36R that accommodates both arrivals and departures, see Fig. 1.North flow operations were selected for analysis because we wanted to eliminate some of the uncertainty and complexity that is present in South flow operations due to the close proximity of the ramp area and the departure runway which results in a greater number of aircraft occupying and interacting within the ramp area during peak traffic demand.Analyzing only the North flow operations allows us to focus on the uncertainty of the runway operations and eliminate some of the uncertainties and challenges present in ramp operations.
+III. Tactical Surface Scheduler DesignOne of the components of the ATD-2 IADS system is the tactical surface scheduler 8 (tactical scheduler) which assigns scheduled times for each aircraft at various control points.A core component of the tactical scheduler is a surface model which drives inputs to the scheduler.The tactical scheduler uses these inputs and applies rules to determine the order of consideration which dictates the order in which flights are assigned scheduled runway usage times.Based on the order of consideration, de-conflicted schedules and flight specific advisories are generated.In the following paragraphs, we briefly describe the different components of the tactical scheduler.A more detailed description of the IADS system and the tactical scheduler can be found within the ATD-2 Technology Description Document 8 and the ATD-2 Phase 1 Concept of Use. 9
+A. Surface ModelThe tactical scheduler interacts with a surface model 8 which tracks, updates, and disseminates information on key surface events.Actual surface event data (e.g., Actual OUT information) is used in conjunction with derived data and model processing logic to produce a single cohesive view of airport operations.The core surface modeler functions include computing the three-dimensional (3D) (x,y,t) surface trajectory from the gate to the runway for departures, and from the runway to the gate for arrivals, based on the expected airport/runway configuration and gate configuration.The surface modeler uses surveillance data, when available, to detect the actual surface trajectory and update the estimates.The surface modeler uses coded taxi routes defined by the adaption using the airport resource information to select the available routes or default to shortest path when the coded taxi routes are not available in the adaptation.For a departure aircraft, the model generates an Unimpeded Off-Block Time (UOBT), Unimpeded Taxi Time (UTT), and Unimpeded-Takeoff Time (UTOT) estimate.The off-block time refers to the time the aircraft initiates the pushback from the gate.The model is provided with an Earliest Off-Block Time (EOBT) prediction provided by the airlines.The UOBT is the maximum of the EOBT and current time and represents the best estimate of the time the aircraft will initiate the pushback process.For the UOBT we use the maximum between the EOBT and current time because if the EOBT estimate is in the past, then the current time is the earliest the flight would be available to initiate the pushback process.The UTT is derived from nominal taxi speeds and the expected taxi route and is used to generate the UTOT defined as the UOBT + UTT.
+B. Scheduler Inputs and Order of ConsiderationThe surface model provides EOBT, UOBT, UTOT and other detailed flight-specific modeled input.The tactical scheduler uses the EOBT and UTOT for departures and the Unimpeded Landing Time (ULT) for arrivals as the basis for developing the schedule.For departures, the EOBT and UTOT are used to assign aircraft to three main scheduling groups: Uncertain, Planning and Taxi that define the order of consideration which dictates the order aircraft are inserted into the schedule.The order of consideration is guided by a heuristic that flights with higher certainty should have higher precedence in scheduling.Flights in the Taxi group are assigned runway usage times before flights in the Planning group which are still at the gate.These flights are assigned runway usage times before flights in the Uncertain group.Flights at the gate with an EOBT within the ten minute planning horizon are assigned to the Planning group and the order of consideration within the group is defined by a Ration By Schedule 8 (RBS) approach.For departures in the Uncertain group or taxi group, the order of consideration within the group is governed by a First Come First Served (FCFS) logic based on the UTOT.For an arrival aircraft, NASA's research Time Based Flow Management 8 (rTBFM) generates a wheels-on time at the runway which is passed to the surface model as the ULT.The tactical scheduler receives this wheels-on time as input and does not adjust the arrival schedules.The order of consideration for arrivals runway use scheduling is based on the ULT and is governed by a FCFS logic.
+C. Scheduling Target Takeoff Times and the Delay Propagation FormulaArrivals have the highest order of consideration and are assigned runway usage times known as the Targeted Landing Time (TLT) before the departures.The departures are then assigned runway usage times, which are referred to as the Target Takeoff Times (TTOTs), in order based on the order of consideration and are scheduled at the earliest feasible times such that the TTOTs satisfy all known constraints, including aircraft type (i.e., taxi speed, wake vortex separation), dual-use runways, converging runway operations, any TMIs, and conflicts at the runway thresholds.For departures, the tactical scheduler provides the de-conflicted TTOTs which are used to generate TOBTs and Target Movement Area entry Times (TMATs) to provide specific event times for pushback, movement area entry, and wheels up to the users of the system.Surface metering on a per flight basis is accomplished by assigning a de-conflicted TTOT for each flight and then back calculating a TOBT such that the difference TTOT -TOBT is bounded.This bound is achieved by the delay propagation formula given byT OBT = max[U OBT, T T OT -U T T -TargetQueueLength] (1)where the TargetQueueLength is a parameter defined in time units set by the users that influences the maximum amount of excess taxi time the aircraft will experience.The smaller (larger) the TargetQueueLength translates into less (more) excess taxi time and more (less) gate hold times.
+IV. Balancing Departure Runway Demand and CapacityWhen the departure demand for a runway exceeds the available capacity, the scheduler suggests holding aircraft at the gate to reduce the amount of time aircraft spend off the gate with engines running.In order to properly manage the available runway capacity, it is important for the scheduler to schedule the departure operations at a rate that is consistent with the available capacity.During peak demand, if the scheduled rate is greater than (less than) the capacity of the runway the scheduler will release more (less) aircraft than the runway can accommodate.The rate at which the scheduler schedules the departure operations is not explicitly defined by an Airport Departure Rate (ADR).Instead, each departure is dependent on other departure and arrival operations and a minimum-time separation constraint is applied.The minimum-time separation constraints between any two operations are defined by the FAA wake vortex separation 10 constraints.Scheduling each aircraft at the earliest time such that the separation constraints are satisfied will result in a unique scheduled rate for the given traffic demand.The purpose of this Section is to evaluate if the runway capacity defined by the wake vortex separation constraints provides an accurate estimate of the actual runway capacity.The wake vortex separation constraints schedule the runway at the theoretical maximum capacity and do not provide for runway crossings or missed departure opportunities.Here we measure the difference between the theoretical capacity and the actual runway capacity.
+A. Single Day OperationsTo analyze the rate at which the scheduler is scheduling operations and the rate at which the runway is operating, we count the number of departure and arrival operations in a fixed time period, see Figures 2 and3.These figures illustrate data related to the scheduled number of operations and the realized number of operations within a 15-minute interval for runway 36C and runway 36R, respectively.The data shown in these figures are from the operation data on 2018-04-24 during bank 2 operations that range between 9:00 -11:00am local time at CLT. Runway 36C is primarily a dedicated departure runway that accommodates arrival operations occasionally, whereas runway 36R is a dual use runway that accommodates both arrival and departure operations.In Figures 2 and3 the x-axis represents the start of a 15-minute time bin and the y-axis represents the number of operations within the time bin.Starting at 9:00am, we sample the scheduler at five-minute intervals and count the number of operations that are scheduled between the sampled time and a 15-minute interval into the future.We plot the scheduled number of departure (arrival) operations with a blue (grey) dashed line.At each five minute interval that we sample the scheduler, we also count the number of operations that are realized between the sampled time and a 15-minute interval into the future.We plot the realized number of departure (arrival) operations with a blue (grey) solid line.At each time point that we sample the scheduler, we know how many departure and arrival operations were scheduled and realized between the sampled time and a 15-minute interval into the future.The differences between the scheduled and realized number of departure (arrival) operations are illustrated by the blue (grey) bar chart.For example in Fig. 3, when we sampled the scheduler at 09:45am, we had scheduled eight departure operations and six arrival operations between 09:45am and 10:00am.During this time period, we realized seven departure operations and seven arrival operations.Therefore, in this 15-minute time interval we had scheduled one more departure operation and one less arrival operation than were realized, which are illustrated by the blue and grey bar chart.
+B. Aggregation of Many Days OperationsFigures 2 and 3 provide measurements and insights into a single days operations.On any given day, the difference between the scheduled number of operations and the realized number of operations can occur due to a wide variety of reasons and may not be indicative of a general bias towards scheduling at a higher or lower rate than the available runway capacity.To understand if the scheduler has a general bias towards scheduling at a higher or lower rate than the available runway capacity, we sample schedules at consistent time points during all North flow days between 2018-03-01 through 2018-04-30 and measure the error between the scheduled and realized number of operations for both runways 36C and 36R, see Fig. 4 and Fig. 5. Starting at 9:00am local, we sample the scheduler at five-minute intervals and plot the mean error between the scheduled and realized number of departure (arrival) operations in blue (grey) solid line.The +/-one standard deviation are illustrated with blue (grey) bands around the mean value for the departure (arrival) operations.As can be seen in Figures 4 and5, the average error between the scheduled and realized number of arrivals for both runways tends to oscillate near zero.Since the scheduler is not adjusting arrival schedules, this indicates that the rate at which rTBFM is scheduling the arrival operations does not contain any obvious bias.For the arrivals, we believe that small deviations in the final approach can introduce the uncertainty that is observed in the error between the scheduled and realized number of arrival operations within the 15-minute interval.For departures, we observe that on average the scheduler schedules more departure operations in a 15minute interval than the actual (realized) runway use.This bias towards scheduling more departures than the actual runway use is observed on both runway 36C, which is mainly a departure only runway, and runway 36R, which is a dual use runway.We believe that the cause of the over scheduling is driven by different factors for each runway.For the dedicated departure only runway 36C illustrated in Fig. 4, we observe that the error between the scheduled number of departures and realized number of departures builds throughout the bank and reaches a peak somewhere around 10:00am -10:10am local.The timing of the growth and peak of the error coincides with the growth and peak of the arrival demand landing on the dedicated arrival only runway 36L shown in Fig 1 .As the arrivals land, they must cross the active departure runway 36C in order to get to their gates.Since the tactical scheduler simply separates departure operations by the minimum-time separation defined by the wake vortex constraints, we do not plan for or schedule runway crossings.We believe that the bias we see in Fig. 4 of scheduling more departures than actually use the runway is caused by the runway crossings.For the dual use runway 36R illustrated in Fig. 5, we observe that the error between the scheduled number  of departures and realized number of departures peaks early in the bank and then oscillates at a consistent level just below one.During peak demand, arrivals are typically delivered to this runway in such a way that between any two arrival aircraft there is enough space to depart a single departure.For a variety of reasons, such as compression between two arrivals, the departure that is supposed to takeoff between two arrivals does not always utilize the available slot.This results in a departure that was scheduled to depart taking off at a later point in time.Since we can not make up for this missed opportunity, it seems reasonable to observe the slight bias of over scheduling on the dual use runway.
+V. Target Takeoff Time AccuracyIn addition to surface metering, the scheduler is responsible for generating accurate Target Takeoff Time (TTOT) estimates, which can be used for a variety of reasons including estimating an Earliest Feasible Takeoff Time (EFTT) used to negotiate a Controlled Takeoff Time (CTOT) for aircraft subject to TMI constraints.Evaluating the error in the TTOT prediction can inform the buffers that should be included in an EFTT prediction.The purpose of this section is to measure the TTOT accuracy as a function of the lookahead time.
+A. Accuracy of TTOT PredictionFigure 6 contains histograms of the accuracy of the TTOT on runway 36C measured as the difference between the Actual Takeoff Time (ATOT) and the TTOT in minutes.A negative value indicates that the ATOT was earlier than the TTOT whereas a positive value indicates the ATOT was later than the TTOT. Figure 6a -Fig.6f represent the ATOT -TTOT for runways 36C at lookahead times 30, 25, 20, 15, 10, and 5 minutes prior to the ATOT. Figure 7 illustrates the mean value of ATOT -TTOT as a function of the lookahead time for runways 36C and 36R in blue and green, respectively.The +/-one standard deviation are shown in the blue and green bands around the mean value for runways 36C and 36R.As can be seen in Fig. 7, the TTOT predictions for runway 36C are most accurate at shorter lookahead times, and the mean error in the TTOT prediction is relatively small and unbiased between 30-minutes prior to ATOT and 5-minutes prior to ATOT.At a 30-minute lookahead, the mean ATOT -TTOT is 0.61 minutes compared to a mean of 0.13 minutes at 15-minute lookahead and a mean of 0.34 minutes at a 5-minute lookahead.Whereas the mean TTOT error does not change much 30-minutes prior to ATOT to 5-minutes prior to ATOT, the standard deviation of the error does improve noticeably as the lookahead time decreases.At a 30-minute lookahead, the standard deviation of ATOT -TTOT is 7.84 minutes compared to a standard deviation of 4.93 minutes at 15-minute lookahead and a standard deviation of 1.60 minutes at a 5-minute lookahead.For the dual use runway 36R shown in Fig. 7, the TTOT predictions seem to have a slightly increased bias as the lookahead time increases.At a 30-minute lookahead, the mean ATOT -TTOT is -1.4 minutes compared to a mean of -0.4 minutes at 15-minute lookahead and a mean of 0.2 minutes at a 5-minute lookahead.For the dual use runway 36R, the standard deviation of the error improves as the lookahead time decreases but is slightly worse than the dedicated departure only runway.At a 30-minute lookahead, the standard deviation of ATOT -TTOT is 8.62 minutes compared to a standard deviation of 5.41 minutes at 15-minute lookahead and a standard deviation of 1.73 minutes at a 5-minute lookahead.
+B. Relationship Between Runway Rate and TTOT AccuracyWhen we consider the bias that was observed in the runway rate illustrated in Figures 4 and5 we were expecting to recover that bias in the TTOT accuracy shown in Fig. 7. Given that the runway is accommodating less operations in a 15-minute interval than we are scheduling, it would be reasonable to expect at a 15-minute lookahead time the TTOT predictions would be earlier than the ATOT.Surprisingly, Figure 7 does not show this relationship as the mean ATOT -TTOT at a 15-minute lookahead are 0.13 minutes and -0.4 minutes for runway 36C and 36R, respectively.Somehow at a 15minute lookahead, the TTOT predictions show less bias than we were anticipating.This was an unexpected result that was uncovered during the exploratory data analysis.One factor that is influencing the relationship between the runway rate and the TTOT accuracy is shown in Fig. 8.For each aircraft, we sample the scheduler 15 minutes prior to ATOT and count the number of departures that are scheduled to takeoff before the aircraft, and define this as the scheduled queue.We then   compare this to the number of aircraft that actually takeoff within the 15 minutes prior to ATOT, and define this as the realized queue.The difference between the scheduled queue and the realized queue is plotted as a histogram in Fig. 8 for runways 36C and 36R.A positive value indicates at a 15-minute lookahead prior to ATOT more aircraft were scheduled to takeoff in front of a given aircraft than actually took off, whereas a negative value indicates more aircraft actually took off in front of a given aircraft than were scheduled.This phenomenon helps explain the relationship between the bias in the runway rate and the TTOT accuracy.The runway rate illustrated in Figures 4 and5 indicated that we are over scheduling.However, the predicted queue shown in Fig. 8 illustrates that for any given aircraft, not all the aircraft scheduled to takeoff in front of the aircraft materialize.These two factors seem to counteract each other and result in TTOT predictions that are less biased than we would expect looking at either one of these factors individually.The relationship between the runway rate, predicted queue and TTOT accuracy highlight the challenges that are present on the airport surface.Accurately predicting TTOTs require accurately predicting the departure sequence and accurately predicting the rate at which the departures and arrivals will use the runway.Uncertainty in these predictions can materialize as inaccuracy in the TTOT prediction and can emerge as bias in either direction when analyzing the ATOT -TTOT metric.
+VI. Challenges in the Tactical Time FrameOne of the main challenges that we encountered scheduling in the live operational environment is the uncertainty in the underlying trajectory predictions.Uncertainty in EOBT, taxi route, taxi speed, controller actions, ULT and other factors results in very dynamic UTOT predictions.For aircraft that are off the gate, the order of consideration is dictated by a FCFS ordering of the UTOT.The dynamic nature of the UTOT predictions results in a constantly updating FCFS ordering of the departure UTOTs and ultimately the ordering of the TTOTs evolve with the updated predictions.Another challenge we encountered is the uncertainty related to the EOBT predictions and when a departure will be available to push back from the gate.During the time periods of peak demand, aircraft are scheduled assuming that other aircraft will be available to depart at their EOBTs.If the departure demand does not materialize at the predicted EOBTs, this can result in the scheduler holding back aircraft that are ready to pushback and as a result the scheduler does not feed the runway at a consistent rate.This uncertainty is magnified as the target queue length decreases and the scheduler holds more demand back at the gate.In addition to the uncertainty in the trajectory predictions and EOBT, challenges are present on the dual use runway and with the dedicated departure runway as mentioned in Section IV.On the dual use runway 36R, the arrivals can compress, making it infeasible for a departure to takeoff in a slot that was predicted to be feasible.On the departure only runway 36C, the arrival crossings are not modeled and result in the runway operating at a slightly slower rate than would be predicted by the minimum-time wake vortex separation constraints.
+VII. Summary and DiscussionIn this paper, we provided a data driven analysis to assess the performance of a tactical surface scheduler in the time frame ranging from 30 minutes prior to takeoff through actual off.The tactical scheduler's two main functions include surface metering and scheduling controlled flights that are subject to traffic management initiatives.To achieve these tasks, it is critical for the scheduler to balance the departure demand with the available runway capacity while simultaneously generating accurate takeoff time predictions.To measure the performance of the scheduler in balancing the demand with the available capacity we analyzed the rate at which the scheduler is scheduling operations compared to the rate at which the runway is operating at.For both runways that we analyzed, we observed that using the minimum-time wake vortex separation constraints to implicitly define the runway capacity resulted in the scheduler scheduling departure operations at a slightly higher rate than the runway was operating at.For the dual use runway, we believe the difference between the scheduled rate and the realized rate is driven by the departures occasionally missing an opportunity to takeoff in an available slot.On the dedicated departure only runway, the arrivals that land and need to cross the active departure runway results in the runway operating at a slightly lower rate than we are scheduling.To assess the accuracy of the scheduler predictions we compared the TTOT to the ATOT as a function of the lookahead time prior to actual off.We observed on both runways that as the lookahead time to ATOT decreases the standard deviation of the ATOT -TTOT error improves.For the dedicated departure only runway, the mean error showed little bias between 30 minutes prior to takeoff to ATOT.For the dual use runway, the mean error at 30 minutes prior to takeoff showed a slight bias to predicting TTOT that were later than the ATOT, but this bias tended toward zero as the lookahead time decreased.An unanticipated relationship was found between the runway rate and the TTOT accuracy.Knowing that we were scheduling at a higher rate than the runway was operating, we expected to recover this bias in the TTOT accuracy by measuring aircraft taking off late compared to their TTOT predictions.Instead, we discovered that the TTOT predictions contained less bias than we anticipated.One factor that influenced this relationship is that for each departure at a 15-minute lookahead prior to ATOT, we scheduled more departures to takeoff prior to a given departure than actually materialized.This seems to have offset the bias of scheduling at a higher rate and also illustrates the challenges in generating accurate TTOT predictions as they are dependent on predicting both the departure sequence and the runway rate.Future research will investigate new techniques in which we can reduce the inaccuracies observed in the runway rate by incorporating the runway crossings into the logic.We also plan to research the uncertainty in the EOBT and departure trajectory predictions as this uncertainty feeds into inaccurately predicting the departure sequence.Figure 1 .1Figure 1.Layout of runways at CLT airport with three North flow runways 36L, 36C, and 36R.
+Figure 2 .2Figure 2. Data illustrating the rate for runway 36C which is primarily a departure only runway.The x-axis represents the start of a 15-minute time bin and the y-axis represents the number of operations.
+Figure 3 .3Figure 3. Data illustrating the rate for runway 36R which is a dual use runway accommodating departures and arrivals.The x-axis represents the start of a 15-minute time bin and the y-axis represents the number of operations.
+Figure 4 .4Figure 4. Data illustrating the mean error measured as scheduled -realized operations for runway 36C which is primarily a departure only runway.The x-axis represents the start of a 15-minute time bin and the y-axis represents the error in the number of operations.Starting at 9:00am local time, we sample the scheduler over a two month period at consistent five minute intervals and compute the average error between the scheduled and realized operations within the 15 minute interval into the future.
+Figure 5 .5Figure 5. Data illustrating the mean error measured as scheduled -realized operations for runway 36R which is a dual use runway accommodating departures and arrivals.The x-axis represents the start of a 15-minute time bin and the y-axis represents the error in the number of operations.Starting at 9:00am local time, we sample the scheduler over a two month period at consistent five minute intervals and compute the average error between the scheduled and realized operations within the 15 minute interval into the future.
+Figure 6 .6Figure 6.Histograms of the schedule accuracy defined as ATOT-TTOT (Minutes) as a function of the lookahead time to ATOT for runway 36C.
+Figure 7 .7Figure 7. Accuracy of ATOT -TTOT (Minutes) illustrated as the mean value and +/-one standard deviation bands as a function of the lookahead time to ATOT for runways 36C and 36R.
+Figure 8 .8Figure 8. Histogram showing the difference between number of aircraft scheduled to takeoff in front of a given departure at a 15-minute lookahead (predicted queue size) compared to the number of aircraft that actually takeoff in front of a departure in the same 15-minute interval (actual queue size).A positive value indicates that more aircraft were scheduled in the predicted queue than materialized in the actual queue.
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+	
+	Wake Turbulence Recategorization
+	Jung, Y., Engelland, S., Capps, A., Coppenbarger, R., Hooey, B., Sharma, S., Stevens, L., and Verma, S., "Airspace Technology Demonstration 2 (ATD-2) Phase 1 Concept of Use (ConUse)," 2018. 10 "Wake Turbulence Recategorization," https://www.faa.gov/documentLibrary/media/Order/Final_Wake_Recat_Order. pdf, Accessed: 2018-04-25.
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+I. INTRODUCTIONConcepts and technologies to manage arrival, departure, and surface operations have been under development by NASA [1]- [5], the Federal Aviation Administration (FAA) [6], and industry to improve the flow of traffic into and out of the nation's busiest airports.NASA is conducting the Airspace Technology Demonstration-2 (ATD-2) to evaluate an Integrated Arrival, Departure, and Surface (IADS) traffic management system that demonstrates these technologies [7], [8].The IADS system was deployed to Charlotte Douglas International Airport (CLT) for a three-year field evaluation that is divided into three distinct Phases lasting one year each.During Phase 1 the IADS system successfully demonstrated three key capabilities [9]: 1) data exchange and integration, 2) departure scheduling and electronic negotiation of release times of controlled flights for overhead stream insertion, and 3) tactical departure surface metering [10].In Phase 2 of the field evaluation the scheduling capabilities were improved [11] to enable strategic Surface Metering Programs (SMP).The IADS system at CLT with Phase 1 and 2 capabilities is a precursor of the FAA Terminal Flight Data Manager (TFDM), which is slated to be rolled out across the United States' busiest airports starting in 2020, after the conclusion of NASA's field evaluation.The Phase 3 field evaluation extends the coordinated scheduling of arrivals, departures, and surface traffic from a single airport at CLT [11] to a Metroplex environment in North Texas containing Dallas Love International Airport (DAL), Dallas Fort Worth International Airport (DFW), and other small satellite airports in the D10 Terminal Radar Approach CONtrol (TRACON).The challenges in the D10 Metroplex are fundamentally different than the challenges addressed by the Phase 1 and Phase 2 IADS capabilities in CLT.In CLT surface congestion and constraints from controlled flights are the main challenges, whereas in the D10 Metroplex the main constraint is the departure fix capacity as multiple major airports compete for the same limited resources.This problem can be magnified when inclement weather impacts D10 and reduces the capacity at the terminal fixes which can propagate delay to the surface of each airport in the D10 Metroplex.When inclement weather constrains the capacity at a given fix in the D10 Metroplex, there are often alternative fixes that are not impacted by the weather that have available capacity.When this situation occurs, a flight that is originally routed through the constrained fix can reroute through the alternative fix with little to no delay.The tradeoff for the flight to reroute to the alternative fix is often a longer route in terms of air miles and requires additional fuel.By comparing the additional mileage of the alternative route to the delay savings, the airline operators can make an informed decision about when it is advantageous to fly the alternative route.The IADS Phase 3 system in the D10 Metroplex aids the decision to reroute aircraft over an alternative fix by assessing the delay savings on each alternative route defined by each flight operator's Trajectory Option Set (TOS).The TOS is a set of alternative routes the flight is willing to fly and each route has an associated Relative Trajectory Cost (RTC).The delay savings for each route in the TOS is compared to its RTC to determine when the delay savings on an alternative route rises above the RTC threshold value.In addition to computing the delay savings for individual flights, the IADS Phase 3 system also calculates the overall savings at the system level resulting from a reroute of a single flight.The savings at the system level is important for the flight operators as they are able to see how rerouting a single flight can benefit their fleet.In this paper, we describe the IADS Phase 3 scheduling algorithm which provides the coordinated schedules between each airport surface and the terminal boundary in the D10 Metroplex.This is a challenging scheduling problem because constraints imposed by fix closures and Miles-In-Trail need to be properly accounted for at both the terminal boundary and each airport surface.In addition to describing the scheduling algorithm, we define the metrics used to inform the flight operators about opportunities to reroute aircraft and metrics used to assess the performance of the system including potential benefits.Using the operational data, we illustrate the benefits of the Phase 3 IADS scheduling concept in the D10 Metroplex.
+II. BACKGROUND INFORMATION ON D10 AND TOS
+A. D10 Airspace and Demand at the Terminal BoundaryThe D10 TRACON is centered on Dallas/Fort Worth International airport (DFW) and extends outward approximately forty miles.It contains two major airports, DFW and Dallas Love Field (DAL), which are separated by approximately ten miles, see KDFW and KDAL in Fig. 1.Several busy general aviation airports, a regional cargo hub, and a Naval Air Station Joint Reserve Base are also located within the D10 TRACON, contributing to operational complexity [12].Along the boundary of the D10 terminal boundary there are 16 departure fixes that are shared among all the airports.Although each airport contributes to demand at the departure fixes, the majority of flights originate from DFW and DAL.The departure fixes are arranged in groups of four along the North, East, South, and West departure gates, see Fig. 1.Traffic through the departure fixes and gates is not evenly dis- Fig. 2 shows a summary of the demand by airport and contains all departures from DFW and DAL between 2019-07-01 through 2019-09-30.During this time period there were 95,791 and 27,657 departure operations from DFW and DAL, respectively.The East gate was utilized most, with 44,231 operations (36% of overall demand).Each departure with a route filed through the East gate competes for the fix with all other airports.This competition for a shared resource at the terminal boundary can increase delays.Due to the imbalanced demand among the departure gates and fixes there is often unused capacity at the terminal boundary.For example, the South gate was utilized less than half as often as the East gate.During periods where more departures are competing for the East gate than can be accommodated, flight operators can often route flights through the South or North gate where they will not be subject to the demand/capacity imbalance and will experience less delay.This reroute comes at a cost, however, as the route through the South or North gate typically requires the flight to fly farther along a less direct route.
+B. Capacity and Restrictions at the Terminal BoundaryCapacity at the terminal boundary is defined by minimum separation constraints and Traffic Management Initiative (TMI) restrictions that are enforced at the departure fix.TMIs at the terminal boundary are typically triggered by weather events or downstream flow constraints that propagate back to the TRACON environment [12], and ultimately the departure airports.In response to weather events around or near the terminal boundary the Traffic Management Unit within Air Traffic Control (ATC) will close departure fixes which result in the departure gate being partially or completely blocked, see Fig. 3(a).The Fig. illustrates a situation where three of the four East departure fixes have been closed and traffic through these fixes is rerouted to the single remaining fix along the East gate.This compression of the departure fixes reduces the capacity at the terminal boundary and delays can be amplified when additional MIT restrictions are enforced at the departure fix.In the D10 TRACON, weather events often lead to multiple, dynamic TMI restrictions being issued by ATC.Fig. 4(a) shows the count of fix closure and MIT restrictions during the time period 2019-07-01 through 2019-09-30.Some days during this time period had 30 or more unique fix closures throughout the day.For a given day, we see fewer MIT restrictions than fix closures because ATC often responds to weather events by closing multiple fixes and putting a single MIT restriction on the compressed flow, see Fig. 3(a).
+C. Trajectory Option Set (TOS)When TMI restrictions reduce the capacity at the terminal boundary there are often opportunities to route around the restrictions and reduce the delay.Consider Fig. 3(b) which shows the situation where the East gate is limited to a single fix with a MIT restriction, while the North gate and South gate have all four fixes available.Since the traffic volumes through the North and South gate are relatively light, see Fig. 2, and green routes are not impacted by a TMI restriction, a flight could reroute through the North or South gate with little to no delay.A flight operator defines the Trajectory Option Set (TOS) which is the set of feasible routes for a given flight.The filed route is typically the most direct route and is preferred by the flight operators under nominal operations.The cost of each route, often a function of the additional mileage needed to fly the route, is provided by the flight operators in the form of a Relative Trajectory Cost (RTC).The RTC is a way for the flight operators to express their willingness to fly a more costly route when the delay savings exceeds the RTC threshold.To inform flight operators about reroute opportunities the predicted delay on the filed route and each TOS alternative route is needed.This difference in delay between the filed route and the alternative route is compared to the RTC threshold to determine if and when a reroute is warranted.The delay savings on the alternative route minus the RTC represents the net savings to the rerouted flight.
+D. Aggregate System-Wide Benefits from TOS RerouteThere are often additional system-wide benefits from a single flight reroute in addition to the net savings to the individual flight that is rerouted.When the terminal boundary is operating as a significant constraint to the flow of traffic, in contrast to situations where the runway is acting as the main constraint, there exists benefits to the set of flights that are not rerouted and subject to the terminal constraints.The system-wide aggregate benefits materialize if MIT restricted flights are able to move one slot earlier owing to the rerouted flight giving up its slot.If the MIT restrictions at the terminal boundary are operating as the the main constraint on the system, then there is often available capacity at the runway to accommodate the rerouted flight without delaying other flights.The system-wide aggregate delay savings will be discussed in more detail in Section IV-C.The remainder of this paper describes our approach to estimate delay among the different routes and the metrics created to inform flight operators about TOS reroute opportunities.
+III. SCHEDULERThe Terminal Scheduler generates the delay estimate on the filed route and the TOS alternative routes.The Terminal Scheduler is composed of an Orchestrator, Trajectory Modeler, Departure Fix Scheduler, and Airport Scheduler for each airport in D10, see Fig. 5.The Fig. illustrates Loop k, which is a single iteration of the Terminal Scheduler that is run every 10 seconds and generates an Estimated Take Off Time (ETOT) for every flight on all filed and TOS alternative routes.The ETOT is generated by an iterative process between the Departure Fix Scheduler and the Airport Schedulers and accounts for all terminal and surface constraints.Within each Loop k, the Orchestrator is initialized with the set of known flights and then executes three sub-routines, referred to as Loop k1, Loop k2, and Loop k3.
+A. Loop k1After initialization we execute Loop k1 which loops through each route including filed and TOS routes and calls the Trajectory Modeler.For a departure aircraft, the Trajectory Modeler generates an Unimpeded Off-Block Time (UOBT), Unimpeded Taxi Time (UTT), Unimpeded Take Off Time (UTOT), Unimpeded Flight Time (UFT), and Unimpeded Fix Crossing Time (UFXT) estimate.The off-block time refers to the time the aircraft initiates the pushback from the gate.The model is provided with an Earliest Off-Block Time (EOBT) prediction from the airline operators.The UOBT represents the best estimate of the time the aircraft will initiate the pushback process.It is defined as the maximum of the EOBT and the current time because if the EOBT estimate is in the past, then the current time is the earliest the flight would be available to initiate the pushback process.The UTT and UFT are derived from nominal taxi / flight speeds and the expected taxi / flight route and are used to generate the UTOT defined as the UOBT + UTT and the UFXT defined as UTOT + UFT.One of the core functions of the Trajectory Modeler is computing the four-dimensional (4D) (x,y,z,t) surface + terminal trajectory from the gate to the runway to the departure fix based on the expected airport/runway configuration, gate/runway assignment, and fix/runway mapping.The Trajectory Modeler uses surface surveillance data, when available, to detect the actual surface trajectory and update the trajectory prediction all the way to the fix.In the Phase 3 system DFW is the only airport using surface surveillance in D10.The Trajectory Modeler uses coded taxi routes defined by the adaptation using the airport resource information to select the available routes or default to the shortest path when the coded taxi routes are not available in the adaptation.
+B. Loop k2The purpose of Loop k2 is to build a schedule at the terminal boundary and then try to enforce the terminal constraints within the local Airport Schedulers.If the airport surface constraints violate the terminal constraints we communicate this back to the Departure Fix Scheduler by redefining the UFXT as the surface constrained Target Take Off Time (TTOT) plus transit time to the terminal boundary.The Departure Fix Scheduler then builds a new schedule knowing about the surface constraints and we again check the feasibility at the airport surface.Through this iterative process the scheduler converges to a schedule that satisfies all known terminal and surface constraints.After the trajectories are generated in Loop k1, we execute Loop k2 which is the core scheduling loop.The output of Loop k2 is an ETOT on the filed route for each departure flight.We begin by sorting flights by UFXT and send this sorted list to the Departure Fix Scheduler.The Departure Fix Scheduler applies a simple First Come First Served (FCFS) heuristic to schedule flights according to minimum separation constraints and MIT restrictions.In the absence of MIT restrictions, the Departure Fix Scheduler enforces a minimum separation of 5NM for all flights through the East gate, but does not apply minimum separation constraints for flights through the North, South, and West gates as we assume ATC can accommodate the flow.
+Terminal SchedulerLoop k Flights that are subject to MIT restrictions or that travel through the East gate and thus subject to minimum separation constraints must be able to depart their origin airport at the required time to comply to the scheduled Target Fix Crossing Time (TFXT).These flights are assigned a Terminal Controlled Off Time (TCOT) defined as TCOT = TFXT -UFT.The output of the Departure Fix Scheduler is a TCOT for the set of flights restricted at the terminal boundary.These TCOTs will be passed to the Airport Schedulers as input.After scheduling flights at the terminal boundary the scheduler checks to see if the assigned TCOTs violate any known surface constraints.To check if the terminal schedule is feasible, the Orchestrator passes the TCOT constraints to the Airport Schedulers which each build a surface schedule that tries to honor the TCOT constraints.The methodology of how we apply TCOT constraints will be described in Section III-D.The output of the Airport Scheduler is a TTOT for each flight.For the Airport Scheduler, arrivals are inserted into the schedule and assigned a Target LanDing Time (TLDT) before departures.The departures are then assigned TTOTs in order based on a selection criteria defined by the UTOT and Scheduling Group.The scheduler is modular to allow for different selection criteria to be implemented.Once a departure is selected to be inserted into the schedule, the departure is assigned a feasible TTOT such that the TTOT satisfies constraints including aircraft type (i.e., taxi speed, wake vortex separation), dual-use runways, converging runway operations, any TMIs, and conflicts at the runway thresholds [11].After building the surface schedule at both airports, the scheduler checks to see if the schedule has settled.The schedule is defined to settle when the output of the Departure Fix Scheduler (TCOT) matches the output of the Airport Schedulers (TTOT).If the schedule did not settle, the UFXT is redefined as UFXT = TTOT + UFT and Loop k2 continues.If the schedule has settled, the scheduler breaks out of Loop k2 and returns an ETOT on the filed route for each flight.
+C. Loop k3After the ETOTs on the filed routes are generated, we execute Loop k3 which computes the ETOT on the TOS alternative routes.For each TOS alternative route, Loop k3 takes the underlying trajectories from the Trajectory Modeler used in Loop k2 and changes a single trajectory.For each TOS alternative trajectory we substitute the alternative trajectory for the original filed trajectory and this is passed as input to the core scheduling Loop k2.We execute the scheduling loop and return an ETOT on the TOS alternative route for the flight in question.A set of ETOTs for all other flights given the reroute are also returned.As explained in Section II-D, a single reroute can impact the ETOT of other flights in the schedule.The set of ETOTs given the reroute will be used in Section IV-C to measure the systemwide aggregate benefits for a given TOS alternative option.
+D. Applying TCOT Constraints in the Airport SchedulerThe Terminal Scheduler requires the Airport Scheduler to enforce TCOT constraints when possible and communicate surface constraints when not possible.If we used a simple Order Of Consideration (OOC) algorithm where the restricted TCOT flights are inserted into the scheduler before any nonrestricted flight then we could fail to communicate the surface constraints to the Departure Fix Scheduler, see Fig. 6.In Fig. 6(a) we show a vertical timeline with 12:00 at the bottom and times later than 12:00 above.We show a situation with 5 blue non-restricted flights that are in a physical queue and a red restricted flight that arrived at the back of queue.We imagine that the red restricted flight has a TCOT from the terminal scheduler at 12:02.If the Airport Scheduler uses a simple order of consideration and first assigns the red flight to the 12:02 slot and then schedules the blue flights around we could have the TTOT timeline shown.Fig. 6(a) shows that even though the red restricted flight is physically in the back of the queue and can not take off at 12:02, the Airport Scheduler has assigned the 12:02 slot to the red flight and failed to communicate the surface constraints.Instead, the Airport Scheduler implements a heuristic that the TCOT constraint can delay a flight but can not advance the flight in the UTOT surface sequence.The result is the schedule shown in Fig. 6(b).The TTOT from the schedule on the right can then be used to defined UFXT = TTOT+UFT and thus the surface constraints are communicated back to the Departure Fix Scheduler.If the TCOT constraints result in a restricted flight being delayed the Airport Scheduler will delay the restricted flight while allowing non-restricted flights to drop down the timeline and fill the gap between the restricted flights, see Fig. 6 c).The restricted flights are colored red and non-restricted flights colored blue.In the UTOT sequence there are three restricted flights in a row: XYZ400, XYZ500, and XYZ600.To comply to the MIT restrictions these flights will be separated leaving one slot between any two restricted flights.This allows the non-restricted flights XYZ700 and XYZ800 to fill the gap.Delaying the restricted flights while allowing the nonrestricted flights to drop in between we create a mismatch between the UTOT sequence on the left hand side of the timeline and the TTOT sequence on the right hand side of the timeline.We find this mismatch acceptable as we make the assumption that ATC ground or local controllers will delay the restricted flights to comply to the MIT restrictions while simultaneously filling the slots between the restricted flights to ensure there is no wasted capacity at the runway.As flights get closer to taking off we expect the UTOT sequence to align with the TTOT sequence.For example, XYZ100 through XYZ400 are the first four flights on the timeline and ATC ground controllers have delivered a feasible sequence that complies to the MIT restrictions.The flights higher up on the timeline XYZ500 through XYZ800 are the flights where the UTOT sequence does not match the TTOT sequence.Our expectation is that through the actions of the ATC ground or local controller, as these flights get closer to taking off the UTOT sequence would change to align with the TTOT sequence.
+IV. METRICS A. Estimated Take Off Time AccuracyThe system relies on accurate ETOT predictions to evaluate the delay savings between the filed route and TOS alternative routes.To provide flight operators and ATC a tool to judge the accuracy of the system we measure and display the ETOT Fig. 7. ETOT accuracy as a function of lookahead time prior to ATOT accuracy as a function of lookahead time prior to the Actual Take Off Time (ATOT).As an example, Fig. 7 contains all departure flights from DFW and DAL between 2020-05-01 through 2020-05-30.A filter is applied to only include predictions for flights scheduled on the runway the flight actually used.We also apply a filter to eliminate any observations that are beyond the median value ±3.5× IQR where IQR is the difference between the 25 th and 75 th quantile.The horizontal axis of Fig. 7 represents the lookahead time from 60 minutes prior to ATOT to 5 minutes prior to ATOT.The vertical axis is the accuracy of the ETOT prediction measured as ATOT -ETOT in minutes.A positive / negative value means the aircraft took off later / earlier than the prediction.The median is illustrated with a solid line and the IQR is shown as a dark shade around the line.A light shade around the median and IQR illustrates the 2.5 and 97.5 quantiles.The difference in ETOT accuracy at DFW and DAL is likely due to many factors including the complexity and number of operations and also the availability of surface surveillance at DFW but not DAL.The surface surveillance enables the airport model within the Terminal Scheduler to consistently update UTOT predictions which help improve the ETOT accuracy as the flight gets closer to take off.The ETOT accuracy provides useful information to the flight operators when considering a TOS reroute.The predicted delay savings should be considered in the context of accuracy of the ETOT predictions.If the delay savings is much larger / smaller than the underlying uncertainty in the ETOT prediction then we should have more / less conviction that the delay savings will materialize.To help facilitate this comparison we developed a new metric that measures the probability that the delay savings will exceed the RTC.The following Section IV-B describes our approach to measure this value.
+B. Probability that Delay Savings Exceeds RTCThe ETOT predictions on the filed and TOS alternative route are used in combination with the accuracy measurements shown in Fig. 7 to construct the probability that the delay savings will exceed the RTC.In Scenario 1, at 12:00 local time a DFW flight has ETOT F = 12:45 and ETOT T = 12:20 where ETOT F and ETOT T are the ETOT on the filed and TOS alternative route, respectively.This implies a lookahead value of 45 minutes on the filed route and 20 minutes on the TOS alternative route.To approximate the uncertainty in the prediction for the filed and TOS alternative route we fit a Normal distribution to the residuals defined at the 45 and 20 minute lookahead, respectively.Using the Normal distributions we construct two new variables:AT OT F = ETOT F + N (µ 45 , σ 45 ) (1) AT OT T = ETOT T + N (µ 20 , σ 20 )(2)where the F subscript denotes the filed route, the T subscript denotes the TOS alternative, and µ X and σ X represent the mean and standard deviation of the ATOT -ETOT residuals at X minute lookahead (derived from the data shown in Fig. 7).Variables AT OT F and AT OT T are defined as a constant plus a Normal, and are thus Normal themselves.For Scenario 1 if we plug in the values ETOT F = 12:45 and ETOT T = 12:20 to (1) and (2) we can visualize AT OT F and AT OT T , see Fig. 8.In the top subgraph we plot AT OT F with a grey line and AT OT T is plotted with a green line.The distributions represent where we think the flight would take off on the filed and TOS alternative route given the ETOT predictions and the underlying accuracy.We define the delay savings distribution DS from AT OT F and AT OT T as follows:DS = AT OT T -AT OT F (3)where a negative / positive value represents the TOS alternative route experiencing less / more delay compared to the filed route.The DS is simple to construct as it is the difference between two Normal distributions.For Scenario 1 we plot DS with a dashed green line in the bottom subgraph of Fig. 8. DS is centered near -25, the difference between ETOT F = 12:45 and ETOT T = 12:20.The DS distribution mean is not exactly at -25, however, as the mean values of AT OT F and AT OT T account for the bias in the residuals.The DS distribution from ( 3) is converted into a Cumulative Distribution Function (CDF) to calculate the probability that the delay savings is greater than the RTC value.The CDF for DS is shown with a green dotted line in the bottom subgraph of Fig. 8. Once the CDF is constructed we can estimate the probability that the delay savings DS exceeds any given RTC value as follows:pr(DS ≤ RTC) = CDF(RTC)(4)where CDF(RTC) is the CDF evaluated at the value.For example, if the TOS alternative route in Scenario 1 was set as RTC = -10 the pr(DS ≤ -10) = 0.908, i.e. there is a 90.8% chance that the delay savings on the TOS alternative route would exceed the RTC.We can compare the results of Scenario 1 shown in Fig. Fig. 9 shows that if the difference between ETOT F and ETOT T is small compared to the underlying uncertainty in the ETOT predictions, then the AT OT F and AT OT T distributions overlap and the DS distribution is centered near zero with density for positive values.Evaluating (4) for Scenario 2, we calculate pr(DS ≤ -10) = 0.314, i.e. there is a 31.4% chance that the delay savings on the TOS alternative route would exceed the RTC.
+C. System-Wide Aggregate Delay SavingsFor each TOS alternative trajectory, in Loop k3 we calculate an ETOT T for the rerouted flight and ETOT R for the entire schedule given the reroute.We define the system-wide aggregate delay savings ADS as:ADS = ETOT T -ETOT F + F ETOT R -ETOT F (5)which is the delay savings to the rerouted flight plus a sum over the set of flights F of the ETOT difference between the ETOT R given the reroute and the schedule on the filed route ETOT F .When a single flight is rerouted and the reroute results The set of flights F that we include in the ADS summation can be defined to provide different flavors of the metric.We can include all flights in D10, flights only from DFW, flights only from DAL, or any other constraints.The different versions of ADS could be valuable to different decision makers.ATC might be interested in looking at ADS summed over the D10 TRACON to understand the impact of a single reroute to the flow through the terminal whereas flight operators might be more interested in the set of flights F from a specific airport or even a specific flight operator to understand the impact of the reroute decision on their fleet.An additional constraint enforced on the set of flights F is that the flight must provide an EOBT and the UTOT must be within 60 minutes of current time.This constrains the calculation to only include flights with high quality trajectories driven by the EOBT predictions and within a reasonable lookahead time.An illustration of the ADS metric across D10, DAL, and DFW is shown in the top subgraph of when the majority of the flow through the system is subject to MIT restrictions.For example, consider a situation where 100% of the flights are subject to MIT restrictions.The runway schedule would then show that between any two flights there is an empty slot at the runway.If a restricted flight is rerouted, the rerouted flight could occupy one of these empty slots at the runway without impacting any other flights.This would result in the rerouted flight receiving a benefit and in addition all flights on the restricted route that were originally behind the rerouted flight would move up one slot.The reroute of the single flight results in an ADS above and beyond the benefit to the individual flight.If the runway is the main constraint on the system, then an ADS above and beyond the savings to the individual flight might not materialize because all the available slots at the runway would already be assigned.This typically happens when there is a mix of restricted and non-restricted flights where a non-restricted flight is able to occupy every available slot between any two restricted flights.In this situation, if a restricted flight is rerouted we will observe a benefit to the other restricted flights, as previously described, where each restricted flight moves up one slot.The rerouted flight, however, will no longer be able to occupy a vacant slot at the runway because all slots are occupied.The rerouted flight will jump ahead of some non-restricted flights and occupy a slot that was previously assigned to a different flight.The result is that at the system level the set of restricted flights will see a delay reduction, however, the set of non-restricted flights will see an increase in delay as the rerouted flight will end up delaying some non-restricted flights.The bottom subgraph of Fig. 10 shows an example of the ADS when the runway is the main constraint to the system.If we compare the bottom subgraph of Fig. 10 to the top subgraph we see that the bottom subgraph contains more flights and more delay than the top subgraph.If the ADS was simply a function of demand we would expect the ADS to be much higher for the bottom subgraph.Instead, we measure an ADS across D10, DAL, and DFW as 6.2, 4.2, and 2.0 minutes, respectively.The low value of ADS is driven by a situation where the runway is the main constraint.This can be observed in the histogram of delay values showing a bi-modal structure where the left mode is formed from the non-restricted flights and the right mode formed by the restricted flights.There is enough non-restricted demand at the runway that all slots are assigned the non-restricted flights are experiencing delay.This is in contrast to the top subgraph where the non-restricted flights have zero delay and the restricted flights are the only flights experiencing delay in the system.
+V. CONCLUSIONIn this paper, we described NASA ATD-2 Phase 3 scheduling in a Metroplex environment incorporating TOS options provided by flight operators.We began by describing the D10 TRACON and summarizing the demand and the restrictions that are the main driver of delay during inclement weather events.We described the TOS concept and explained the strategy of using TOS alternative trajectories to route around fix closures and MIT restrictions and explained the benefits to the rerouted flight and the aggregate system-wide benefits.Next, we described at a high level the Phase 3 Terminal Scheduler which is composed of an Orchestrator, Departure Fix Scheduler, Trajectory Modeler, and Airport Scheduler for each airport in D10.We showed how the different components of the Terminal Scheduler are working within three subroutines and explained how the Departure Fix Scheduler and Airport Scheduler are working together within the core scheduling loop.We then described how the core scheduling loop is leveraged for each TOS alternative route to calculate the individual and aggregated delay savings that would result from the reroute.We provided high level details describing the heuristics we follow when applying TCOT constraints.Lastly, we defined the metrics that were developed to inform flight operators and ATC about the performance of the system and reroute opportunities.We showed the ETOT accuracy as a function of lookahead time prior to ATOT and recommended that the delay savings estimate should be considered in context with the accuracy of the system.We described our methodology to build a distribution of delay savings that incorporates both the delay savings prediction and the accuracy of the system and allows us to calculate the probability that the delay savings will exceed the RTC.We defined the system-wide aggregate delay savings metric and showed how the system-wide savings can be different when the terminal boundary or runway is the main constraint to the system.Future work will focus on analyzing the results from the Phase 3 Field Evaluation.The Phase 3 Field Evaluation was intended for the Summer of 2020 but is now extended through the Summer of 2021 due to the impact of COVID-19.In addition to analyzing the results from the Field Evaluation, future work will also focus on extending the Phase 3 concept to other Metroplexes across the NAS.Of particular interest is the New York TRACON (N90) which has three major airports sharing capacity at the terminal boundary and interacting with each other.We believe that the lessons learned during the Phase 3 Field Evaluation in D10 will put us in a good position to tackle future challenges in the NAS.Fig. 1 .1Fig. 1.D10 airspace showing how airports share the 16 departure fixes along the terminal boundary.
+Fig. 2 .2Fig. 2. Top: Demand for departure gate.Bottom: Demand for departure fix
+Fig. 3 .3Fig. 3. a) D10 airspace with weather impacting the East gate.b) Available TOS routes not impacted by weather constraints.
+Fig. 4 .4Fig. 4. a) Daily count of restrictions.b) Percentage of flights subject to MIT restriction.c) Percentage of flights subject to fix closure.
+Fig. 4 (4Fig. 4(b) shows the percentage of departure flights from DFW and DAL that are subject to a MIT restriction at the OFF event.Similarly, Fig. 4(c) shows the percentage of departure flights from DFW and DAL that are subject to a fix closure at their Scheduled Off Block Time (SOBT).Since fix closures and MIT restrictions are often enforced together the percentage of flights that are subject to the different types of restrictions is similar.
+Fig. 5 .5Fig. 5. Terminal Scheduler diagram showing how Orchestrator, Trajectory Modeler, Departure Fix Scheduler, and Airport Scheduler work together.
+Fig. 6 .6Fig. 6. (a): Schedule generated with OOC heuristic.(b): Schedule generated using heuristic that TCOT can delay flight but not advance flight in UTOT sequence.(c): Schedule showing that non-restricted flights can fill the gap between restricted flights.
+Fig. 8 .8Fig. 8. Top: Scenario 1 AT OT F and AT OT T represent when we think the flight will take off on the filed and TOS alternative route, respectively.Bottom: Scenario 1 delay savings DS and associated CDF
+8 to Scenario 2 where at 12:00 local time a DFW flight has ETOT F = 12:45 and ETOT T = 12:40.Scenario 2 differs from Scenario 1 only in the value of the ETOT T on the alternative route.Fig. 9 contains the same information as Fig. 8 and can be compared to understand how the AT OT T and DS distributions are impacted by the change in ETOT T .
+Fig. 9 .9Fig. 9. Top: Scenario 2 AT OT F and AT OT T represent where we think the flight will take off on the filed and TOS alternative route, respectively.Bottom: Scenario 2 delay savings DS and associated CDF
+Fig. 10 .Fig. 10 .1010Fig. 10.Top: Example of system-wide aggregate delay savings when the terminal boundary is the main constraint.Bottom: Example of system-wide aggregate delay savings when the runway is the main constraint.
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+		Evaluation of the Terminal Sequencing and Spacing system for Performance-Based Navigation arrivals
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+			JaneThipphavong
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+			JaewooJung
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+			HarrySwenson
+		
+		
+			LynneMartin
+		
+		
+			MelodyLin
+		
+		
+			JimmyNguyen
+		
+		10.1109/dasc.2013.6712503
+	
+	
+		2013 IEEE/AIAA 32nd Digital Avionics Systems Conference (DASC)
+		
+			IEEE
+			2015
+		
+		
+			11th USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	Thipphavong, J., Jung, J., Swenson, H. N., Witzberger, K. E., Lin, M. I., Nguyen, J., Martin, L., Downs, M. B., and Smith, T. A., "Evaluation of the controller-managed spacing tools, flight-deck interval manage- ment, and terminal area metering capabilities for the ATM Technology Demonstration 1," 11th USA/Europe Air Traffic Management Research and Development Seminar, 2015.
+
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+	
+		
+			SAEngelland
+		
+		
+			RCapps
+		
+		
+			KBDay
+		
+		
+			MSKistler
+		
+		
+			FGaither
+		
+		
+			GJuro
+		
+		Precision Departure Release Capability (PDRC) Final Report
+		
+			2013
+		
+	
+	Engelland, S. A., Capps, R., Day, K. B., Kistler, M. S., Gaither, F., and Juro, G., "Precision Departure Release Capability (PDRC) Final Report," 2013.
+
+
+
+	
+		Performance Evaluation of SARDA: An Individual Aircraft-based Advisory Concept for Surface Management
+		
+			YoonJung
+		
+		
+			TyHoang
+		
+		
+			MiwaHayashi
+		
+		
+			WaqarMalik
+		
+		
+			LeonardTobias
+		
+		
+			GautamGupta
+		
+		10.2514/atcq.22.3.195
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
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+			22
+			3
+			
+			2014
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Jung, Y., Malik, W., Tobias, L., Gupta, G., Hoang, T., and Hayashi, M., "Performance evaluation of SARDA: an individual aircraft-based advi- sory concept for surface management," Air Traffic Control Quarterly, Vol. 22, No. 3, 2014, pp. 195-221.
+
+
+
+	
+		Evaluation of pushback decision-support tool concept for Charlotte Douglas International Airport ramp operations
+		
+			MHayashi
+		
+		
+			THoang
+		
+		
+			YCJung
+		
+		
+			WMalik
+		
+		
+			HLee
+		
+		
+			VLDulchinos
+		
+		
+			2015
+		
+		
+			11th USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	Hayashi, M., Hoang, T., Jung, Y. C., Malik, W., Lee, H., and Dulchinos, V. L., "Evaluation of pushback decision-support tool concept for Char- lotte Douglas International Airport ramp operations," 11th USA/Europe Air Traffic Management Research and Development Seminar, 2015.
+
+
+
+	
+		Design Characteristics of a Terminal Departure Scheduler
+		
+			AlanCapps
+		
+		
+			MathewSKistler
+		
+		
+			ShawnAEngelland
+		
+		10.2514/6.2014-2020
+	
+	
+		14th AIAA Aviation Technology, Integration, and Operations Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2014
+			2020
+		
+	
+	Capps, A., Kistler, M. S., and Engelland, S. A., "Design characteristics of a terminal departure scheduler," 14th AIAA Aviation Technology, Integration, and Operations Conference, 2014, p. 2020.
+
+
+
+	
+		An Integrated Collaborative Decision Making and Tactical Advisory Concept for Airport Surface Operations Management
+		
+			GautamGupta
+		
+		
+			WaqarMalik
+		
+		
+			YoonJung
+		
+		10.2514/6.2012-5651
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2014
+		
+	
+	FAA Air Traffic Organization Surface Operations Office
+	FAA Air Traffic Organization Surface Operations Office, "U.S. airport Surface Collaborative Decision Making (CDM) Concept of Operations (ConOps) in the near-term: application of the surface concept at United States airports," 2014.
+
+
+
+	
+		Airspace Technology Demonstration 2 (ATD-2) phase 1 Concept of Use (ConUse)
+		
+			YJung
+		
+		
+			SEngelland
+		
+		
+			ACapps
+		
+		
+			RCoppenbarger
+		
+		
+			BHooey
+		
+		
+			SSharma
+		
+		
+			LStevens
+		
+		
+			SVerma
+		
+		
+			2018
+		
+	
+	Jung, Y., Engelland, S., Capps, A., Coppenbarger, R., Hooey, B., Sharma, S., Stevens, L., and Verma, S., "Airspace Technology Demonstration 2 (ATD-2) phase 1 Concept of Use (ConUse)," 2018.
+
+
+
+	
+		
+		
+			AGing
+		
+		
+			SEngelland
+		
+		
+			ACapps
+		
+		
+			MEshow
+		
+		
+			YJung
+		
+		
+			SSharma
+		
+		
+			ETalebi
+		
+		
+			MDowns
+		
+		
+			CFreedman
+		
+		
+			TNgo
+		
+		
+			HSielski
+		
+		
+			EWang
+		
+		
+			JBurke
+		
+		
+			SGorman
+		
+		
+			BPhipps
+		
+		
+			MorganRuszkowski
+		
+		
+			L
+		
+	
+	
+		Airspace Technology Demonstration
+		
+			2
+			2
+			2018
+		
+	
+	Technology Description Document (TDD
+	Ging, A., Engelland, S., Capps, A., Eshow, M., Jung, Y., Sharma, S., Talebi, E., Downs, M., Freedman, C., Ngo, T., Sielski, H., Wang, E., Burke, J., Gorman, S., Phipps, B., and Morgan Ruszkowski, L., "Airspace Technology Demonstration 2 (ATD-2) Technology Descrip- tion Document (TDD)," 2018.
+
+
+
+	
+		Field evaluation of the baseline integrated arrival, departure, surface capabilities at Charlotte Douglas Interntional Airport
+		
+			YJung
+		
+		
+			WCoupe
+		
+		
+			ACapps
+		
+		
+			SEngelland
+		
+		
+			SSharma
+		
+	
+	
+		Thirteenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2019)
+		
+			2019
+		
+	
+	Jung, Y., Coupe, W., Capps, A., Engelland, S., and Sharma, S., "Field evaluation of the baseline integrated arrival, departure, sur- face capabilities at Charlotte Douglas Interntional Airport," Thirteenth USA/Europe Air Traffic Management Research and Development Semi- nar (ATM2019), 2019.
+
+
+
+	
+		A Data Driven Analysis of a Tactical Surface Scheduler
+		
+			JeremyCoupe
+		
+		
+			LeonardBagasol
+		
+		
+			LiangChen
+		
+		
+			HanbongLee
+		
+		
+			YoonCJung
+		
+		10.2514/6.2018-3666
+	
+	
+		2018 Aviation Technology, Integration, and Operations Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2018
+		
+	
+	Coupe, W. J., Bagasol, L., Chen, L., Lee, H., and Jung, Y., "A data driven analysis of a tactical surface scheduler," AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 2018.
+
+
+
+	
+		ATD-2 Phase 3 Scheduling in a Metroplex Environment Incorporating Trajectory Option Sets
+		
+			WilliamJCoupe
+		
+		
+			YoonJung
+		
+		
+			LiangChen
+		
+		
+			IsaacRobeson
+		
+		10.1109/dasc50938.2020.9256509
+	
+	
+		2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC)
+		
+			IEEE
+			2019
+		
+		
+			Thirteenth USA/Europe Air Traffic Management Research and Development Seminar
+		
+	
+	Coupe, W. J., Jung, Y., Lee, H., Chen, L., and Robeson, I., "Schedul- ing improvements following the Phase 1 field evaluation of the ATD-2 integrated arrival, departure, and surface concept," Thirteenth USA/Europe Air Traffic Management Research and Development Semi- nar (ATM2019).
+
+
+
+	
+		Characterization of Nationwide TRACON Departure Operations
+		
+			MatthewSKistler
+		
+		
+			AlanCapps
+		
+		
+			ShawnAEngelland
+		
+		10.2514/6.2014-2019
+	
+	
+		14th AIAA Aviation Technology, Integration, and Operations Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2014
+			2019
+		
+	
+	Kistler, M. S., Capps, A., and Engelland, S. A., "Characterization of Nationwide TRACON Departure Operations," 14th AIAA Aviation Technology, Integration, and Operations Conference, 2014, p. 2019.
+
+
+				
+			
+		
+	
diff --git a/file169.txt b/file169.txt
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+	
+	
+		
+I. INTRODUCTIONConcepts and technologies to manage arrival, departure, and surface operations have been under development by NASA, the Federal Aviation Administration (FAA), and industry to improve the flow of traffic into and out of the nation's busiest airports.To demonstrate these technologies NASA is conducting the Airspace Technology Demonstration-2 (ATD-2) to evaluate an Integrated Arrival, Departure, and Surface (IADS) traffic management system [1], [2].The IADS concept extends traffic sequencing for the entire life-cycle of a flight from departure gate to arrival gate within multi-airport, metroplex environments.The IADS concept builds on and integrates previous NASA research such as the Terminal Sequencing and Spacing (TSAS) [3], the Precision Departure Release Capability (PDRC) [4], and the Spot and Runway Departure Advisor (SARDA) [5], [6] which each focused on individual airspace domains.The IADS concept was initially developed based on the Surface Collaborative Decision Making (S-CDM) ConOps [7] and refined over time.The IADS system was deployed to the Charlotte Douglas International Airport (CLT) for a three year field evaluation beginning in September 2017.During the field evaluation the IADS system has succesfully demonstrated three key capabilities: 1) data exchange and integration, 2) departure surface metering, and 3) departure scheduling and electronic negotiation of release time of controlled flights for overhead stream insertion [8].The IADS system in CLT is a precursor of the FAA Terminal Flight Data Manager (TFDM) which will begin to be rolled out across the United States' busiest airports, starting in 2020, after the conclusion of NASA's field evaluation [9].A core idea of the IADS concept is to generate a coordinated schedule which enables a smooth flow of traffic from gate to runway for departures and from runway to gate for arrivals [10].To ensure a smooth flow an arrival flight needs an available gate or else the arrival flight will be considered to be in a gate conflict.To resolve the gate conflict ramp controllers typically hold the arrival in either the Airport Movement Area (AMA) or the ramp area.As the number of gate conflicts increases the complexity and workload for ramp controllers increase [11].In the future TFDM system the ATC Traffic Management Coordinator (TMC) will be required to set parameters for surface metering which could impact the number of gate conflicts.To help inform the TMC about the impact of the selected parameters TFDM requirements include a prediction of the number of gate conflicts in upcoming Surface Metering Programs (SMP) [12].The TMC can use this prediction to help calibrate the parameters that govern the amount of excess taxi time that is passed from the AMA back to the gate.Too often, the development and implementation of new capabilities within the traditional FAA systems has involved cost and or schedule overruns that detract from the anticipated operational value, or involve times to acquire or develop that are not sufficiently responsive to the needs of a changing environment [13].For non safety critical applications, there is opportunity for third party service providers to offer this type of data-driven prediction in near real-time.Both the IADS system and the future TFDM system are powered by real-time FAA System Wide Information Management (SWIM) [14] data feeds.The real-time data published across the SWIM cloud enables a software layer on the other side of SWIM to ingest these feeds and provide near real-time decision support outside of the traditional FAA systems.In this paper we explore the gate conflict prediction problem as a concrete example for a data-driven service that could help drive efficiencies.The purpose of this paper is to explain the iterative process of model building, model validation, and evaluation used to assess the efficacy of our approach.We aim to quantify our predictive accuracy and identify paths for improvement.Through the iterative approach we expect for our models and methods to evolve as the data informs us.The remainder of the paper is organized as follows.In Section II we provide background information on gate conflict research.Section III describes the different data feeds the IADS system pulls from SWIM and the NASA-developed Fuser technology that is foundational to building a cohesive view of a single flight.In Section IV we implement various regression models and estimate the accuracy for out of sample data.In Section V we describe directions for future research.Lastly, Section VI provides concluding remarks.
+II. BACKGROUND ON GATE CONFLICTSPrevious research has found the number of gate conflicts is influenced by the airline schedules.There is a relationship in the scheduling practice involving bank operations and the time interval between banks that influences the requirement for gates [15].Moreover, the fluctuation of the actual demand from the scheduled demand creates additional potential for gate conflicts.To mitigate the impact of gate conflicts, researchers have investigated different forms of the gate assignment problem [16].Various objective functions have been considered, including a robust gate assignment which aims to minimize the number of gate-reassigned aircraft [17], [18] and a gate assignment that aims to minimize the variance of idle times at the gates [19].Others considered the gate assignment problem to minimize physical conflicts in the ramp area and reduce interaction between the arrivals and departures [20].Whereas previous research focused on strategies to mitigate gate conflicts, they did not work towards predicting time periods or specific banks when gate conflicts would be expected to be elevated.In this paper we present a model to predict both the count of gate conflicts and the rate of gate conflicts per arrival flight within a bank of operations.This work is enabled by newly available data that is generated by the IADS system.High fidelity data elements such as the Earliest Off Block Time (EOBT) in combination with FAA SWIM feeds are leveraged for gate conflict predictions.Identifying and labeling gate conflicts are non trivial tasks.Since two aircraft can't occupy the gate at the same time, the Actual Off Block Time (AOBT) and Actual In Block Time (AIBT) for the departure and arrival, respectively, will not provide insights to whether or not a gate conflict occurred.Labeling of gate conflicts by ramp controllers would be the most accurate approach.However, this approach is not realistic as capturing such a data element would increase ramp controllers' workload to unacceptable levels.To address this challenge the IADS system leverages a combination of actual and predicted events to label an arrival as a gate conflict.At the time point that the arrival touches down at CLT, the IADS system generates an Unimpeded In Block Time (UIBT) which represents the time the system expects the arrival to get to the assigned gate.At the time point that the arrival touches down, the system also has a prediction of when the departure will push back from the same gate in the form of the Unimpeded Off Block Time (UOBT) outside of surface metering or a Target Off Block Time (TOBT) during surface metering.If the UIBT ≤ UOBT + buff push (TOBT + buff push during surface metering) then the arrival flight is labeled as a gate conflict.The parameter buff push defines the expected time the departure will occupy the gate during the pushback process.In addition to departures, aircraft not currently associated with a flight are persisted at the gate.The location of these persisted aircraft are manually updated in the system by the ramp managers and controllers when they are towed to and from the gates.If an arrival is destined for a gate with a persisted aircraft, the arrival will be labelled as a gate conflict.Other definitions of a gate conflict could be used which account for the intensity of the gate conflict by requiring UIBT ≤ UOBT + buff push -∆ to label an arrival a gate conflict.In this definition ∆ is a parameter defining the minimum duration of the gate conflict and the value could be determined by Subject Matter Experts (SMEs).For example, if SMEs agree that a gate conflict less than five minutes should not be labeled as a gate conflict, then we can define ∆ = 5.Even with leveraging the actual and predicted events the identified gate conflicts might not materialize.Similarly, arrivals that are identified to not have a gate conflict could still experience a gate conflict if the departure occupying the gate pushes back later than expected.Although these challenges are present in the data our methodology to identify gate conflicts is reasonable given the current system.
+III. DATA SOURCES AND PREPROCESSING A. Data SourcesThe ATD-2 IADS system is powered by real-time SWIM data feeds including Traffic Flow Management System (TFMS), SWIM Terminal Data Distribution System (STDDS), SWIM Flight Data Publication Service (SFDPS), Time Based Flow Management (TBFM), Terminal Flight Data Manager (TFDM), and Terminal Automation Information Service (TAIS) [21].These SWIM data feeds are complemented by other data sources such as ramp surveillance and gate information provided by the airline operators.The SWIM feeds contain valuable data but can provide inconsistent information on the same flight that is difficult for consumers to understand.Without deep knowledge of the underlying 3T (TFMS, TBFM, and TFDM) systems, plus FAA air traffic systems En Route Automation Modernization (ERAM) and Standard Terminal Automation Replacement System (STARS), the consumption logic may not lead toward the benefit the community desires [22].To address this potential mismatch, ATD-2 invested in developing the logic that could address SWIM flight data processing and mediation complexities.Much of this work is embodied in the Fuser service which is illustrated in Fig. 1.The Fuser framework mediates between the disparate sources of data, pulling in the right data, at the right time.The Fuser leverages heuristics and analysis on which data source is best to use for a specific need and provides access to the information in common well defined data model.The fused data sources are used as input by the ATD IADS system and the output is written to a database.In addition to consuming SWIM data the IADS system publishes the TFDM Terminal Publication (TTP) feed back to SWIM and can be consumed within the SWIM Research and Development (R&D) environment.The ATD-2 TTP feed on the SWIM R&D matches the specifications of the future TTP feed to foster industry innovation in preparation for TFDM.The data written to the database is valuable but often too verbose to be used effectively for analysis.To address this problem, ATD-2 developed the Flight Summary report to serve analysts and user needs [23].The Flight Summary helps standardize the ATD-2 approach to handling such conditions as human inputs, business logic, measurement convention, complexities of data mediation, order of processing messages, and changes from earlier versions of ATD-2 software.The Flight Summary is generated every morning for postoperations analysis about the previous day operations and contains each unique flight as a row containing over 500 columns of unique data elements and predictions captured at some discrete points in time.A subset of the flights included in the Flight Summary files are distributed to the airline operators for their internal analysis.
+B. Data Preprocessing for Machine LearningIn this paper we use data from the Flight Summary files between 2018-01-01 and 2019-09-30 and focus on departures and arrivals operating within an identified scheduled or actual bank.A bank of traffic is a concentrated period of demand and can be identified for the departures, arrivals, or the total operations.Airlines typically define banks by the original scheduled demand and static boundaries in time that define the stop and start of the bank.In contrast, the IADS system is capable of defining banks by both the scheduled demand and actual demand and the boundaries that define the start and end time of the bank are dynamic and adapt with the profile of scheduled or actual demand.To identify the banks and the associated start and stop times we use the DBSCAN [24] clustering algorithm to label banks based on the density of gate or runway operations.Using this approach we can cluster banks on the density of scheduled or actual demand and can also define and identify banks using a gate-centric or runway-centric view of operations.We find the clustering approach to be more informative than using a bank definition with static start and stop times.As the actual demand fluctuates from the scheduled demand, the bank has the potential to shift earlier or later than the static bank definition and the clustering approach is able to capture this shift and adapt our bank start and stop times to better represent what happened.The first step in the workflow is data preprocessing.We begin by selecting a subset of the available data elements and metrics available in Flight Summary to use as features in our gate conflict prediction model.The selected features are shown in the data dictionary provided in the appendix.We filter out any bank containing a feature that deviates more than 3.5 times the InterQuartile Range (IQR) away from the median.Before filtering we had 3197 distinct banks and after the filtering process we are left with 1956 unique banks representing 61% of the original data.After filtering we normalize the data such that each metric is transformed to have mean equal to zero and unit variance.The filter we implemented is relatively conservative given that it only excludes banks that contain a metric deviating more than 3.5 times the IQR.It was surprising to see the filter eliminated 39% of the overall data.This is indicative of the overall uncertainty and noise in the data.The prediction accuracy and results shared in Section IV-E should be taken in context with the understanding that the underlying data is noisy and that the quality of the signal extracted could be impacted by the uncertainty.
+IV. GATE CONFLICT REGRESSIONIn this section we investigate two different flavors of the gate conflict regression problems.In the first problem we define the target as the count of gate conflicts within a distinct bank.In the second problem we define the target as the fraction of gate conflicts per arrival to normalize our predictions against the overall arrival demand.For each target we consider the 37 features described in the data dictionary.For each target we consider a Support Vector Machine (SVM) regression and a Gradient Boost (GB) regression.To validate the entire workflow we implement an iterative cycle of feature selection, hyperparameter tuning, and model validation shown in Fig. 2.After iterating through this cycle ten times the validation metrics are evaluated by a Subject Matter Expert (SME) and potential improvements are identified and investigated.Through this repeated process we aim to develop a model capable of near real-time application.To implement the validation we use the scikit-learn [25] library for Python.For feature selection, hyperparameter tuning, and measuring prediction accuracy we use Recursive Feature Elimination Cross Validation (RFECV) [26], Grid Search Cross Validation (GridSearchCV) [27], and negative mean squared error (mean squared error) [28], respectively.
+A. Model ValidationThe validation process is described in Python psuedo-code in Algorithm 1.The algorithm is illustrated using the SVM regressor but the same process was implemented for the GB regressor.To better understand the confidence we have in our estimated validation metrics, we implement multiple loops through the feature selection, hyperparameter tuning, and validation.For each loop we save the data to analyze the distribution and estimate the mean with associated 95% confidence interval for the mean.We iterate through ten loops where we first randomly split the data 70/30 into a TRAIN and TEST set.For the TRAIN set we call RFECV to select a subset of the features to use.After the features are identified, we iterate through five loops where we randomly shuffle the data and then call GridSearchCV and record the results.The combination of hyperparameters showing the best results are then used to fit the model to the TEST set and the performance is saved for evaluation in Section IV-E.
+B. Feature SelectionFor the SVM regression it is important to identify the subset of features that provide good performance for the given target.To identify the optimal set of features is computationally expensive so we implement a heuristic approach based on RFECV.Whereas the features identified by RFECV might not be optimal, in practice they seem to be acceptable from a prediction accuracy perspective.To identify features using RFECV, first the estimator is trained on the initial set of features and k-fold cross validation is implemented to calculate the negative Mean Squared Error (nMSE) for the given set of features.The importance of each feature is obtained from the fitted coefficients and the least important feature is pruned from the current set of features.That procedure is recursively repeated on the pruned set.The set of features that generated the largest nMSE is selected and used in the remainder of outer loop i, described in Algorithm 1.The results of this process are shown in Fig. 3 and the identified number of features is illustrated with an orange star.Throughout the iteration process through the outer loop i described in Algorithm 1, we noticed that the features returned by RFECV seemed sensitive to the random selection of the original TRAIN data set.Whereas the general shape and value of the curves describing the nMSE as a function of the number of features was stable, the exact maximum of that curve was vulnerable to fluctuations due to the saturation in prediction accuracy as features increase.In future work, our implementation of RFECV might be improved upon in terms of stability by selecting features within some threshold of the maximum.
+C. Hyperparameter fittingThe performance of the SVM and GB regressors can be improved by properly selecting the hyperparameters of the model.For the SVM, the hyperparameters we consider are the kernel type and the parameters C and and we search over the grid defined by kernel ∈ {linear, rbf, poly} × To perform the grid search over hyperparameters we use GridSearchCV function.GridSearchCV iterates over each combination of hyperparameters, performs 3-fold cross validation and returns the nMSE averaged over the 3 folds.When we implement the 3-fold cross validation on our TRAIN data set containing 1369 rows, we split the data up into 3 discrete subsets of 456 observations.We select one of the three subsets as the test set and use the other 2 subsets as the training set.With only 456 observations in the test set, we observe that the prediction performance can vary.To better understand the prediction performance of the hyperparameters in the context of the relatively small sample size, we randomly shuffle the data before we pass it to GridSearchCV and repeat this process 5 times.Each of the 5 random shuffles of the data will implement the 3-fold cross validation process on a unique discretization of the data and will generate an average nMSE across the 3 folds.The distribution of the average nMSE across the 5 random shuffles is shown in Fig. 4. The performances of SVM with linear, rbf, and poly kernel are illustrated in yellow, green, and purple respectively and the performance of GB is illustrated in blue.As can be seen in Fig. 4, the performance between the different methods varies.These results are for visualization purposes only as the final validation will be done using the TEST dataset.For the target count gate conflict the SVM with linear kernel showed the best performance and for the target gate conflict per arrival the GB showed the best performance.For each of these methods we plot the distribution of the average nMSE across the five random samples for the top three hyperparameter combinations with the best combination plotted with a darker color.For Subsections IV-D and IV-E the SVM and GB methods use the best hyperparameter combination illustrated with darker color in Fig. 4.
+D. Feature ImportanceFor the SVM with linear kernel and the GB regressor we can use the coef and feature importances attributes of the model to evaluate the relative importance of the features.For the SVM the relative importance of the features is estimated with the square of the coefficients [29].The mean value and standard deviation for the SVM squared coefficient and the GB feature importance aggregated from the ten iterations through the outer loop i in Algorithm 1 are shown in Figs. 5 and6 for the target count gate conflicts and the target gate conflicts per arrival, respectively.For the target count gate conflicts the top four features for SVM ranked from most to least important were 6, 20, 25, and 2, whereas the top four features for GB ranked from most to least important were 35, 6, 20, and 22. Features 6 and 20 show up for both SVM and GB and are the count of total arrivals and the mean arrival schedule delay predicted at landing.The arrival schedule delay predicted at landing metric is captured when the arrival lands, and measures how early or late the arrival is with respect to its Scheduled In Block Time.The most important feature for GB was the count of arrivals with Undelayed In Time at Landing earlier than the end of the actual departure bank, defined by the time the last departure in the bank pushes back.For the target gate conflicts per arrival, the top four features for SVM were 20, 32, 3, and 35 and the top four features for GB were 35, 20, 22, and 34.Again we find features 20 and 35 important, which are related to how early or late the arrival is at landing with respect to its Scheduled In Block Time and the count of arrivals with Undelayed In Time at Landing earlier than the end of the actual departure bank.Overall it seems that both the SVM and the GB regressors tend to identify the features related to metrics capable of detecting early arrivals and metrics capable of detecting the interaction between the arrival and departure banks.The early arrivals make sense as a mechanism to increase gate conflicts as the airline schedules only provide small buffers between when a departure occupies the gate and the subsequent arrival is scheduled to arrive.Other features of interest include features 10-14 describing the controlled flights and surface metered flights.None of these features were identified as important as the ones previously discussed.We notice that for the target gate conflict per arrival, the SVM identified the count of surface metered flights as having some importance, but identified the total gate hold as having no importance.This could be indicative that the underlying congestion and demand which has triggered metering is leading to gate conflicts and not the actual gate holding associated with metering leading to gate conflicts.We note that only a fraction of banks between 2018-01-01 and 2019-09-30 were surface metered, which might obscure the relationship if one were to exist.Given the importance of gate conflicts in the context of surface metering this is an important relationship to keep in mind and to continue to investigate in future research.
+E. Validation ResultsThe results of the validation described in Algorithm 1 in the form of the distribution for the nMSE and the explained variance R 2 are shown in Figs.7 and8, respectively.A summary of the results in the form of the estimated 95% confidence interval for the mean of the nMSE and R 2 are shown for the two targets count gate conflicts and gate conflict per arrival in Tables I and II, respectively.As can be seen in the figures and tables, the nMSE and R 2 metrics are similar between the SVM and GB regressors.Since we normalized the target with mean zero and unit variance we can easily interpret the results of the nMSE in relation to a prediction rule that always uses the average value.A nMSE of less than -1.0 represents a rule with worse performance than always predicting the average, and a nMSE greater than -1.0 represents a rule with better performance.For the target count gate conflict we estimate the nMSE to be -0.62 and -0.62 for the SVM and GB, respectively.Similarly, for the target gate conflict per arrival we estimate the nMSE to be -0.71 and -0.69, respectively.Comparing the sum of the squared error to the variance in the data we can calculate the explained variance R 2 .The R 2 results show that the fraction of the variance in the target that is explained by the SVM and GB regressors are 0.37 and Overall, we interpret these results as a detectable but weak signal.Without examples of other gate conflict prediction models and their prediction accuracy, it is hard to put the results in context.Given the complexity and uncertainty on the airport surface, different phases of surface operations such as ramp taxi have proven difficult to accurately predict [30].Moving forward, the validation results of the SVM and GB regressors will be used as a data point to provide context in the evaluation of improved models.
+V. FUTURE WORKThe general direction of future work is to continue to pursue concrete examples of data-driven services that could help drive efficiencies in the NAS.At this early stage in the research, the identification of good use cases is as important as the development of the models.We hope through the identification of use cases we can draw the attention of the aviation and data science communities towards the opportunity to develop this software layer of services on the other side of SWIM.The immediate direction for the gate conflict prediction research is to explore techniques to improve the prediction performance.The results shown in this paper were focused on features derived from descriptive metrics of the bank.A different approach might focus on predicting individual gate conflicts at the flight level and aggregating the results across the bank.An approach based on an individual flight level gate conflict prediction could leverage the high fidelity schedule and surveillance systems to drive more accurate predictions.Further analysis is needed to understand the relationship between the different features and their impact on gate conflicts.In this analysis, we identified the relationship between the arrival bank and departure bank as important when predicting gate conflicts.This relationship will be investigated further including exploring new metrics which could capture and quantify this relationship in new or better ways.We also note that the SVM showed a potential relationship between gate conflicts and flights that are controlled or surface metered.If we find that there are specific situations where surface metering is having a negative impact for gate conflicts we can explore the idea to alert the Air Traffic Control (ATC) Traffic Management Coordinator (TMC) that some particular banks should not be surface metered.Once the model has been improved to the desired level of accuracy, there is a need to transition the model from post operations analysis (post-ops) to a real-time environment.We expect this transition from post-ops to real-time to be nontrivial.The data elements and metrics that we find valuable in post-ops might not be readily available in real-time.For example, the relationship between the departure and arrival banks which we identified as important features require the identification of the start and end of each bank.In post-ops, the start and end of the bank can be consistently determined by our clustering approach but real-time identification of the start and end of a bank would be based on predicted times, as opposed to actual times, which could impact the performance of the model.
+VI. CONCLUSIONIn this paper we introduced the gate conflict prediction problem as a concrete example of a data-driven service that could be implemented in near real-time within a software layer on the other side of SWIM.The gate conflict prediction problem is relevant to the future TFDM concept as the system is required to provide a prediction of the number of gate conflicts in the upcoming SMP to help the ATC TMC calibrate parameters that govern the amount of excess taxi time that is passed back from the departure queue to the gate.For nonsafety critical applications, these types of third-party decision support services could help drive efficiencies throughout the National Airspace System.As a first step in this direction, we implemented and analyzed the performance of regression models applied to the gate conflict prediction problem.The features we used were descriptive metrics aggregated at a bank level.For the target metrics, we considered count gate conflicts and gate conflicts per arrival.The models were developed in post-ops analysis to assess our ability to detect a signal and to explore the relationship between the target variables and the features.The analysis showed a weak but detectable signal between gate conflicts and the features based on the descriptive metrics of the bank.The most important features were determined to be features related to metrics capable of detecting early arrivals and metrics capable of detecting the interaction between the arrival and departure banks.An important avenue of future research will be to explore different features aggregated at the bank level, or the individual flight level, and assess their predictive accuracy.Overall, the current level of prediction accuracy is not high enough for real-time decision support, but it is hard to fully evaluate the efficacy of our approach in the absence of other gate conflict prediction models.Moving forward, as we continue to develop and evolve our gate conflict prediction model these results will provide a valuable data point to anchor our expectations.Fig. 1 .1Fig. 1.Data architecture and data flow between SWIM, ATD-2, and airline.
+Fig. 2 .2Fig. 2. ATD-2 predictive analytics workflow.
+Fig. 3 .3Fig. 3. Support vector regression feature selection.
+Fig. 4 .4Fig. 4. Hyperparameter results.
+Fig. 5 .5Fig. 5. Feature importance for Target = count gate conflict.
+Fig. 6 .6Fig. 6.Feature importance for Target = gate conflict per arrival.
+Fig. 7 .7Fig. 7. Validation results for nMSE.
+Fig. 8 .8Fig. 8. Validation results for R 2
+		
+		
+			
+APPENDIX			
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+			2019-10-01
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+		Yellowbrick: Visualizing the Scikit-Learn Model Selection Process
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+		Gene selection for cancer classification using support vector machines
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+			StephenBarnhill
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+		Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport
+		
+			HanbongLee
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+			JeremyCoupe
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+		
+			YoonCJung
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+		10.2514/6.2019-2933
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+		AIAA Aviation 2019 Forum
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+	Lee, H., Coupe, J., and Jung, Y. C., "Prediction of Pushback Times and Ramp Taxi Times for Departures at Charlotte Airport," AIAA Aviation 2019 Forum, 2019, p. 2933.
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+I. INTRODUCTIONConcepts and technologies to manage arrival, departure, and surface operations have been under development by NASA, the Federal Aviation Administration (FAA), and industry to improve the flow of traffic into and out of the nation's busiest airports.Whereas trajectory-based concepts and technologies have been developed for specific phases of flight, their integration across surface and airspace domains to increase efficiency of the traffic flows remains a considerable challenge [1].To address this challenge, NASA is conducting the Airspace Technology Demonstration-2 (ATD-2) to evaluate an Integrated Arrival, Departure, and Surface (IADS) traffic management system [2], [3].The IADS concept extends traffic sequencing for the entire life-cycle of a flight from departure gate to arrival gate within multi-airport, metroplex environments.The IADS concept builds on and integrates previous NASA research such as the Terminal Sequencing and Spacing (TSAS) [4], the Precision Departure Release Capability (PDRC) [5], and the Spot and Runway Departure Advisor (SARDA) [6], [7] which each focused on individual airspace domains.The IADS concept was initially developed based on the Surface Collaborative Decision Making (S-CDM) ConOps [8] and refined over time.The IADS concept and system was then tested in Human-In-The-Loop (HITL) simulations [9].The IADS system was deployed to Charlotte Douglas International Airport (CLT) for a three-year field evaluation.The Phase 1 field evaluation began in September 2017 and ended September 2018.During this time the IADS system was evaluated for three key capabilities 1) data exchange and integration, 2) tactical surface metering, and 3) departure scheduling and electronic negotiation of release time of controlled flights for overhead stream insertion.The IADS scheduler provides the tactical surface metering and departure scheduling capabilities on top of the foundation of data exchange and integration.The purpose of this paper is to describe how data from the Phase 1 field evaluation helped identify scheduler improvements and guided the implementation of refinements.The improvements in the IADS scheduler are incorporated into the IADS Phase 2 scheduler enabling strategic Surface Metering Programs (SMPs) and will be evaluated during the field demonstration.This paper is organized as follows.Section II begins with background information on surface management concepts.Section III provides a high level summary of the surface modeler and scheduler, which are two core components of the IADS system.Section IV describes the arrival scheduling methodology shows the accuracy of the arrival predictions.Section V and Section VI describe how the departure scheduling methodology and the tactical metering trigger logic evolved throughout the Phase 1 field evaluation.Section VII shows the compliance with the assigned Target Off-Block Times (TOBT) and Target Movement Area entry Time (TMAT) and discusses different approaches that can help improve TMAT compliance.Section VIII contains concluding remarks.
+II. BACKGROUDBoth EUROCONTROL and the FAA have developed a collaborative decision making framework to manage airport surface operations where each concept aims to improve the efficiency of airport operations by reducing congestion on the airport surface, improving the traffic flow efficiency, and reducing uncertainties during airport operations [10].The EUROCONTROL Airport Collaborative Decision Making (A-CDM) [11] concept has been implemented across 17 airports with benefits including but not limited to taxi-out time savings, increased peak departure rates at the runway, and improved take-off time predictability [12].The A-CDM concept is flexible to allow for different scheduling approaches to be implemented at different airports.One such scheduling approach developed by the German Aerospace Center (DLR) is the Departure Management System (DMAN), including the Controller Assistance for Departure Optimization (CADEO), which provides scheduling and pre-departure sequencing by calculating off-block times to reduce runway queue and surface congestion [13].The FAA developed the Surface Collaborative Decision Making (S-CDM) [8] concept in 2012 building upon prior surface management research including the Surface Management System (SMS) [14] and Collaborative Departure Queue Management (CDQM) [15] which were field tested at Memphis International Airport.In 2015 FAA and NASA committed to the Airspace Technology Demonstration-2 to evaluate the IADS system which was developed from the S-CDM concept.During the Phase 1 field evaluation the IADS concept demonstrated benefits including but not limited to taxi time savings from tactical surface metering [16].In 2020 after completion of the IADS field evaluation the FAA will begin to install the Terminal Flight Data Manager (TFDM) with surface metering capabilities, which was built from the S-CDM ConOps, across 27 of the Nation's busiest airports [17].The tactical surface metering benefits observed in the ATD-2 Phase 1 field evaluation are enabled by the IADS scheduler [3], [18].The IADS scheduler provides the coordinated runway schedule which accounts for both arrivals and departures while honoring all known constraints including aircraft type (i.e., taxi speed, wake vortex separation), dual-use runways, converging runway operations, any Traffic Management Initiatives (TMIs), and conflicts at the runway thresholds.During time periods when demand for a runway exceeds the available capacity, the IADS scheduler triggers tactical surface metering on and departure demand is controlled by honoring gate specific pushback advisories to reduce surface congestion and taxi times.The remainder of this paper is focused on the IADS scheduler functionality that enables both the runway scheduling and surface metering.
+III. IADS SURFACE MODELER AND SCHEDULERThe logic of the IADS scheduler is described in Fig. 1  and implementation can be found in supporting documents [3], [18].The scheduler interacts with a surface modeler [3] which tracks, updates, and disseminates information on key surface events.Actual surface event data (e.g., Actual OUT information) is used in conjunction with derived data and model processing logic to produce a single cohesive view of airport operations.At a rate of once every ten seconds, the surface modeler leverages this view of the surface operations to generate predictions of the Unimpeded TakeOff Time (UTOT) for departures.For arrivals, the surface modeler mediates between different data sources to generate the most accurate Predicted Landing Time (PLT).In addition to the UTOT and PLT, the model assigns each aircraft to a Scheduling Group which is one of the data elements used to select the next aircraft to schedule.Using UTOT, PLT, and Scheduling Groups the scheduler implements two main processing steps.The scheduler first selects the next aircraft that will be inserted into the schedule and then inserts the aircraft at the earliest feasible time such that all wake vortex constraints are satisfied.The feasibility of the scheduled time is defined as at or after the UTOT or PLT for departures and arrivals, respectively.
+A. Surface Modeler Trajectory GenerationOne of the core functions of the surface modeler is computing the three-dimensional (3D) (x,y,t) surface trajectory from the gate to the runway for departures, and from the runway to the gate for arrivals, based on the expected airport/runway configuration and gate/runway assignment.The surface modeler uses surveillance data, when available, to detect the actual surface trajectory and update the trajectory prediction.The surface modeler uses coded taxi routes defined by the adaptation using the airport resource information to select the available routes or default to the shortest path when the coded taxi routes are not available in the adaptation.For a departure aircraft, the model generates its Unimpeded Off-Block Time (UOBT), Unimpeded Taxi Time (UTT), and UTOT estimate.The off-block time refers to the time the aircraft initiates the pushback from the gate.The model is provided with an Earliest Off-Block Time (EOBT) prediction from the airlines.The UOBT is defined as max[EOBT,current time] and represents the best estimate of the time the aircraft will initiate the pushback process.For the UOBT we use max[EOBT,current time] because if the EOBT estimate is in the past, then the current time is the earliest the flight would be available to initiate the pushback process.The UTT is derived from nominal taxi speeds and the expected taxi route and is used to generate the UTOT defined as the UOBT + UTT.
+B. Surface Modeler Scheduling Group AssignmentAssigning aircraft to Scheduling Groups is a core function of the surface modeler.The Scheduling Groups are used within the Select Next Aircraft to Schedule logic block which dictates the order aircraft are inserted into the schedule.The Scheduling Groups and selection of the next aircraft to insert in the schedule are guided by a heuristic that flights with higher certainty in their UTOT predictions should have higher precedence in scheduling.The main Scheduling Groups for departure aircraft ordered from highest certainty to lowest certainty include Active, Ready, Planning, and Uncertain.For departures, assignment to the different groups is dependent upon the state of the flight and the EOBT.Any departure that has already pushed back is assigned to the Active group.Aircraft that have called ramp controllers for pushback and are put on hold are assigned to the Ready group.Assignment to the Planning group and Uncertain group is based on the flight's EOBT and has evolved throughout the Phase 1 field evaluation.Additional details about the Planning and Uncertain group assignment will be discussed in Section V-A.In addition to the assignment of departures to the different groups, the role of the different groups in the selection of the next aircraft to schedule has evolved throughout the Phase 1 field evaluation and will be described in Section V-B.
+C. IADS SchedulerArrivals are inserted into the schedule and assigned a Targeted LanDing Time (TLDT) before departures.The departures are then assigned runway usage times, which are referred to as the Target TakeOff Times (TTOTs), in order based on a selection criteria defined by the UTOT and Scheduling Group.The scheduler is modular to allow for different selection criteria to be implemented.Once a departure is selected to be inserted into the schedule, the departure is assigned a feasible TTOT such that the TTOT satisfies all known constraints, including aircraft type (i.e., taxi speed, wake vortex separation), dual-use runways, converging runway operations, any TMIs, and conflicts at the runway thresholds.The rate at which the scheduler schedules the departure operations is not explicitly defined by an Airport Departure Rate or Runway Departure Rate.Instead, each departure is dependent on other departure and arrival operations and a minimumtime separation constraint is enforced.The minimum-time separation constraints between any two operations are defined by the FAA wake vortex separation [19] constraints.Scheduling each aircraft at the earliest time such that the separation constraints are satisfied will result in a unique scheduled rate for the given traffic demand.For departures, surface metering is accomplished by generating the de-conflicted TTOTs which are used to calculate TOBTs and TMATs to provide specific advisories for pushback, movement area entry, and wheels up to the users of the system.The key idea for surface metering on a per flight basis is that the taxi time calculated by the difference TTOT -TOBT is bounded.This bound is achieved by the delay propagation formula given byT OBT = max[U OBT, T T OT -U T T -Target] (1)where the UTT is provided by the model and fit from historical data and the Target is a parameter defined in time units set by the users that influences the maximum amount of excess taxi time the aircraft will experience.The smaller the Target translates into less excess taxi time and larger gate hold times.After the TOBT is assigned the TMAT is computed asT M AT = T OBT + U RT T (2)where the URTT is the Unimpeded Ramp Transit Time from gate to taxiway spot and is given by the surface modeler.
+IV. ARRIVAL SCHEDULING
+A. Arrival Predicted Landing Time Data SourcesThe IADS system builds the picture of arrival demand by leveraging data from three external systems -the FAAs Traffic Flow Management System (TFMS), the FAAs Time Based Flow Management (TBFM) system, and NASAs research version of TBFM.All three systems provide Estimated Time of Arrival (ETA) predictions of when a flight will land at CLT independent of other arrivals.TFMS predicts an ETA for flights up to 24 hours in advance of departure based on airline schedule data.As airlines file flight plans and update their predicted pushback times with EOBT, TFMS updates its ETA predictions based on the latest data including TMIs such as a Ground Delay Program (GDP) or Airspace Flow Program (AFP).Once the flight is airborne, both TFMS and TBFM will update the ETA prediction based on the latest track data.Because TFMS has ETAs farther in advance and updates the ETA based on EOBT and TMI data in addition to the flight plan, the IADS system favors the TFMS ETAs over the TBFM ETAs.In addition to the TBFM ETA predictions, the TBFM system schedules the arrivals into CLT and generates Scheduled Time of Arrivals (STAs) leveraging internal TBFM high fidelity modeling of flight dynamics, weather, and arrival terminal route adaptation.The TBFM STAs also account for runway capacity constraints by enforcing wake vortex constraints.The FAA's operational TBFM will freeze the STAs of flights as they cross a set of freeze horizons.These freeze horizons are set to provide Air Route Traffic Control Center (ARTCC) controllers with a planned sequence that does not change.However, the planned sequences may not materialize due to operational constraints and changes in the terminal area.As a result, the frozen STA's from the FAA's TBFM system may not reflect the current situation.The NASA research TBFM system, on the other hand, does not freeze STAs and continues to update the STA all the way to the runway.The NASA research TBFM system also has updated adaptation data to improve the STA predictions.For this reason, the IADS system favors the STAs from the NASA research TBFM system over the FAA's operation TBFM system.In the following text, the TBFM STA refers to the NASA research TBFM system.The TBFM STAs are better at predicting actual ON than the TFMS ETA.Fig. 2 shows the accuracy of the TBFM STA and TFMS ETA measured as the difference actual ON -PLT as a function of lookahead prior to actual ON.The median error is shown with a solid line and the InterQuartile Range (IQR) is illustrated with a shaded region in blue and green for the TBFM STA and TFMS ETA, respectively.As can be seen in the figure, the median error for the TBFM STA is below the median error for the TFMS ETA within 75 minutes of actual ON.For predictions more than 75 minutes prior to actual ON the median error for the TBFM STA is greater than the median error of the TFMS ETA, however, the IQR for the TBFM STA is much tighter than the IQR for the TFMS ETA.The benefit of using the TBFM STA is the increased accuracy but the limitation is the availability of the data.The TBFM STA is not available until the flight is airborne.Fig. 3 shows the percentage of flights with TBFM STA (have both TBFM STA and TFMS ETA) and TFMS ETA (TFMS ETA only) as a function of time prior to actual ON.The horizontal axis represents the lookahead prior to actual ON, and the vertical axis represents the percentage of arrival flights which had a TBFM STA in blue or a TFMS ETA only in green.As can be seen in the figure, only 30% of flights 90 minutes prior to actual ON have a TBFM STA.
+B. Arrival Scheduling MethodologyTo address the accuracy and availability of the PLT the IADS system uses both the TFMS ETA and TBFM STA arrival data to build a cohesive view of arrival demand.The ETAs provide a view of demand prior to the flight departing it's upline airport, and the STAs provide a more accurate picture of demand once the flight is airborne.To leverage the increased accuracy of the TBFM data the PLT passed to the scheduler is defined as the TBFM STA whenever available, else the TFMS ETA.Given that flights actively operating in the NAS will have TBFM STAs, almost all arrivals predicted to arrive at CLT within the 30 minute tactical time frame will have a TBFM STA.Any arrivals still on the ground with only a TFMS ETA will typically have a flight time of greater than 30 minutes.By combining the TBFM STA data with the TFMS ETA data we are able to leverage the most accurate predictions in the tactical time frame while maintaining the most up to date view of traffic demand beyond the tactical time frame.To reduce the impact of inaccurate TFMS ETA arrival predictions on the departure capacity, the IADS scheduler identifies the arrivals that have a TFMS ETA without a TBFM STA and pushes these arrivals out past current time plus 30 minutes.This occurs for about 4.6% of flights and results in the IADS scheduler knowing about the overall arrival demand.However, the arrival demand in the immediate tactical time frame is only populated by airborne flights that are tracked by TBFM.For arrivals inside the 30 minute tactical time frame we assign a Target Landing Time (TLDT) equal to the STA as the TBFM STAs are already separated from each other.For arrivals outside the 30 minute time frame, the TLDT is obtained by the scheduler applying wake vortex separation between arrivals in a First Come First Served (FCFS) order with flights ordered by STA if available and otherwise ETA.This logic allows the arrival demand to account for arrivals that have not yet departed from close-in airports while also ensuring a feasible sequence of arrivals.
+V. DEPARTURE SCHEDULING FOR SURFACE METERING PROGRAM
+A. Assignment of Departures to Scheduling GroupsThe logic used to assign departures to the Scheduling Groups evolved throughout the Phase 1 field evaluation.Originally the status of EOBT with respect to current time was used to assign departure aircraft at the gate to two Scheduling Groups: Uncertain and Planning.All departures started in the Uncertain group and transitioned to the Planning group when their EOBT was within the planning horizon defined as current time plus ten minutes.This approach prioritizes aircraft with an EOBT within ten minutes of current time and ensures that these aircraft are scheduled into the available runway capacity before any aircraft whose EOBT is outside of the planning horizon.We implemented this to align with the heuristic that flights with greater certainty should take precedence in scheduling and to incentivize flight operators to provide high quality EOBTs.Although this reduces the delay for aircraft within the ten minute planning horizon, aircraft outside of the planning horizon get assigned unrealistic amounts of delay.Consider The significant difference in TTOT for aircraft in Planning vs. Uncertain makes it challenging to accurately predict the delay for the Uncertain aircraft outside of the ten minute planning horizon.This is undesirable because predictions of when surface metering will trigger are dependent upon the delay calculations of these aircraft outside the ten minute planning horizon.For tactical surface metering this is not a major problem, however, in the Phase 2 field evaluation the IADS scheduler will shift focus to strategic SMPs which rely on these predictions to inform users about the start times and the average and maximum gate hold times of future SMPs.To address the unrealistic delay assigned to aircraft outside of the ten minute planning horizon we redefined the criteria used to assign aircraft to the Planning and Uncertain groups.The updated logic assigns any aircraft with an EOBT to the Planning group.The Uncertain group is reserved for aircraft that do not provide an EOBT or do not call ready within 13 minutes of their EOBT.Because aircraft are no longer transitioning from the Uncertain group to the Planning group ten minutes prior to EOBT, the TTOTs that are assigned to aircraft at the gate with EOBT outside of the ten minute planning horizon better reflect the true delay that aircraft will experience.Since aircraft no longer transition between Uncertain and Planning, the aircraft no longer experience the jump in TTOT shown in Fig. 4.This should help increase the accuracy of SMP predictions.
+B. Role of the Scheduling Groups and Order of ConsiderationThe role of the Scheduling Groups and order of consideration changed during the field evaluation.Originally, the Scheduling Groups and UTOT of unscheduled aircraft were sorted to generate the order of consideration which defined the sequence that aircraft would be inserted into the schedule.To build the order of consideration, departures were first sorted by Scheduling Group, and then within each group, departures were sorted by UTOT for Active and Uncertain and sorted by Scheduled Off-Block Time (SOBT) + UTT for Planning.The SOBT is provided by the airline operators and is not the IADS schedule.The hierarchical structure of the order of consideration allowed the scheduler to prioritize flights for which we had higher confidence in the accuracy of the UTOT prediction.The problem, however, was that this approach created mismatches between the sequence of the UTOTs and the sequence of the TTOTs.If there is a mismatch between the sequences of UTOTs and TTOTs, then the TTOT of an aircraft transitioning between groups can jump due to the hierarchical structure of the order of consideration.Consider Fig. 5a which shows a timeline with Active departures colored in blue and Planning departures colored in red.The left hand side of the timeline is the UTOT for each departure, and the right hand side is the TTOT which would be generated under the hierarchical order of consideration.The vertical line represents the timeline and the bottom is current time.As you go up the timeline you go further into the future.Because all Active aircraft are inserted into the timeline before the Planning departures, XYZ987 is scheduled behind all the Active departures even though XYZ987 can arrive at the runway before Active departures XYZ023, XYZ067, and XYZ423 according to the predicted UTOTs.Fig. 5b shows the same timeline after XYZ987 transitions from Planning to Active.As can be seen in the figure, once the aircraft transitions and the order of consideration sorts XYZ987 with other Active aircraft according to the UTOT, the TTOT for XYZ987 jumps down the timeline.This type of jumping in the schedules causes challenges for accuracy and stability.The mismatch between UTOT sequence and TTOT sequence is also present when trying to apply the SOBT + UTT order of consideration in the Planning group.In the next Section, we will describe the problem encountered sorting by SOBT + UTT and we will introduce new approach we developed which builds schedules with consistent UTOT sequences and TTOT sequences.
+C. First Scheduled First Served for Surface Metering ProgramDuring surface metering, the S-CDM ConOps [8] recommends that runway usage times be allocated according to Ration By Schedule (RBS), an extension to First Scheduled First Served (FSFS).During the Phase 1 field evaluation we implemented FSFS by sorting departures in the Planning group by SOBT + UTT to build the order of consideration.This sorting order can create mismatches between the UTOT and TTOT sequences as described in Section V-B.More specifically, the mismatch between UTOT and TTOT for Planning departures is a problem when the delay is below the Target.When a set of departures are being inserted into the schedule according to the order of consideration sorted by SOBT + UTT, the TTOT sequence that is generated by this order of consideration might not match the UTOT sequence if the SOBT = UOBT.Because the delay is below the Target and aircraft are not being gate held, we expect aircraft to push back at UOBT and the UTOT sequence given by UOBT + UTT might not align with the TTOT sequence given by SOBT + UTT order.When the delay is above the Target, however, we can define the controlled UTOT which is defined as UT OT = TOBT + UTT.The difference between the UTOT and UT OT is the gate hold assigned for surface metering and is equal to the difference between the UOBT and the TOBT.When the delay is above the Target and all aircraft in the Planning group are experiencing gate hold, then the UT OT sequence will exactly match the TTOT sequence generated from the SOBT + UTT ordering (UT OT = TTOT-Target).If aircraft push back at TOBT then at the point in time of pushback the UOBT = TOBT and thus UTOT = UT OT which ensures we deliver aircraft to the runway according to SOBT + UTT ordering.The gate hold beyond the UOBT is what gives us the ability to control the sequence we deliver aircraft to the runway.To address the mismatch between UTOT sequence and TTOT sequence due to the Scheduling Groups and the SOBT + UTT order of consideration, we designed new logic which is applied in the Select Next Aircraft to Schedule logic block seen in Fig. 1.The key idea of this logic depends on detecting when delay is above the Target.If delay is below the Target we have no control and we assign the TTOT sequence according to a FCFS principle since aircraft will push back at UOBT and be delivered to the runway in the FCFS order.When delay goes above the Target we can identify the set of aircraft which will be assigned gate hold, and thus the set of aircraft we have control over, and assign the TTOT sequence according to the SOBT + UTT order of consideration.The new logic applied in the Select Next Aircraft to Schedule logic block is shown in Fig. 6.We start with a sorted list of aircraft based on their UT OT + a buffer.The size of the buffer is determined by the Scheduling Groups and allows us to prioritize one group over another.Using the first aircraft in this list we identify the TTOT the aircraft would be assigned if selected to be scheduled and define this as the T T OT .We don't insert this aircraft into the schedule yet, however, because if there are multiple aircraft whose delay would be above the Target if scheduled at the given T T OT then we should allocate this T T OT to the aircraft with the earliest SOBT + UTT.Given the T T OT , we identify all flights where T T OT -UT OT ≥ Target which represents the set of flights whose delay is above the Target threshold if scheduled at the T T OT .If no aircraft satisfy this criteria, the delay is below the Target and we have no control.Thus we schedule the aircraft with the earliest UT OT according the the FCFS principle.If there exists aircraft that satisfy the criteria, then we have control over these aircraft and select the aircraft with the earliest SOBT + UTT.Then the scheduler inserts this aircraft into the schedule at the T T OT .By checking if any aircraft satisfy the criteria T T OT -UT OT ≥ Target we can determine when we have control and are able to assign runway times based on the FSFS principle.When we don't have control we maintain a FCFS principle which helps increase stability and predictability of the schedule as the TTOT sequence will match our predicted UTOT sequence.
+D. Decoupling TTOT and TOBT for PredictionThe TTOTs assigned according to the logic described in Section V-C assume that we will meter if delay rises above the Target.Whereas this is true for the tactical scheduler, in Phase 2 of the field evaluation we will assess the performance of a strategic scheduler which predicts SMPs in the future and allows users to either accept or reject a proposed SMP.During time periods where an SMP is proposed, but not accepted yet by the user, we need to build a schedule that assumes we will meter to provide realistic predictions of the SMP including SMP start and average gate hold times while also generating TTOT predictions and a timeline for the users that assume metering will not be used because the SMP is not affirmed yet.To address this case, the final TTOTs which we display on the timeline are decoupled from the TOBTs that are assigned for metering during an SMP.To achieve this decoupling, we introduced a second prediction pass of the scheduler that can decouple the TTOTs from the TOBTs.The first pass of the scheduler assigns the metering times and the TOBTs and the second pass of the scheduler applies a FCFS order of consideration to the UT OT for aircraft within an affirmed SMP, else UTOT for aircraft not within an affirmed SMP.For the second prediction pass we apply the FCFS scheduling logic to the UT OT , defined by the TOBT calculated in the first pass of the scheduler, because the UT OT will automatically adjust once the SMP is affirmed to represent the controlled sequence which we want to achieve at the runway.Using this logic, we can generate predictions of delay in future SMPs in the first pass while simultaneously generating a timeline with TTOTs that automatically adjust to reflect if an SMP is affirmed or not.
+VI. TRIGGERING METERING WHEN DEMAND EXCEEDS CAPACITYThe transition from non-metering to metering at the correct point in time is important.Transitioning to metering too early poses a risk that the queue has not fully built up and the system recommends gate holds when the surface congestion does not justify metering.This can result in a slow start to traffic and the overall demand being shifted where aircraft take off at a later time in comparison to non-metered traffic.In contrast, transitioning to metering too late poses a risk that the demand taxiing towards the runway overwhelms the available runway capacity and the efficiency of surface metering is greatly reduced.In this Section we show the results from the original trigger mechanism used for tactical surface metering, describe the updates that we made to the trigger logic, and illustrate how the new logic improved the transition between non-metering and metering.
+A. Original Trigger for MeteringAt the beginning of the Phase 1 field evaluation, the scheduler relied on predictions of the demand to trigger the transition from non-metering to metering.The prediction of when the metering would trigger was based on an estimated excess taxi time (delay) for each flight.To trigger metering we required that one flight be at the gate with EOBT within 10 minutes of current time and predicted excess taxi time at or above the Target.In addition, we also required that a second aircraft assigned to the same runway be at the gate with EOBT within 10 minutes of current time and predicted excess taxi time at or above the upper threshold.When these conditions were met simultaneously, metering turned on.We found that solely relying on the predictions caused metering to turn on too early, before the traffic level justified gate holding.Early reports from the field indicated that the bank had a slow start and ramp controllers reported that the the system was recommending gate holds when there was little to no delay in the physical queue (active aircraft off the gate).This was later confirmed through data analysis.Consider Fig. 7 which illustrates the excess taxi time and gate hold for each flight operating on runway 18C in bank 2 on 2017-12-05 during the first week of metering at CLT.The vertical axis is the excess taxi time and each grey bar represents a single aircraft's excess taxi time measured as actual taxi time minus UTT.The red bar stacked on top of the grey bar represents the amount of gate hold the aircraft experienced due to surface metering.The blue horizontal line is the Target excess taxi time that controllers used on the given day.The horizontal axis represents the sequence of Actual TakeOff Times (ATOT) such that the first bar on the left is the first aircraft that took off in the bank and the last bar on the right is the last aircraft that took off in the bank.Aircraft that took off early in the bank were experiencing gate hold (red bar) even though their excess taxi time (grey bar) was well below the Target excess taxi time (blue line).Whereas these flights were not assigned a Target Off-Block Time (TOBT) that was beyond their EOBT, they were assigned a TOBT equal to their EOBT but happened to call in earlier than their EOBT.This EOBT error resulted in aircraft at the beginning of the bank being gate held against their EOBT even though the active queue had not built up enough to justify gate holds.
+B. Updated Trigger for MeteringTo address the problem of triggering metering on too early we added a requirement to the trigger logic.In addition to the requirement that the delay for aircraft at the gate must be at or above some threshold, we required that there must be an active aircraft that is off the gate with predicted excess taxi time at or above the Target excess taxi time.This allows for the active queue to naturally build up to the Target excess taxi time in the presence of the EOBT error that caused us to erroneously gate hold previously.Fig. 8 shows the same graph containing the excess taxi time and gate hold on a per flight basis for runway 18L on 2018-01-21 after we had implemented the new trigger logic.As can be seen in the figure, the first flight that was held at the gate (red bar) came after some aircraft's excess taxi times had reached the Target excess taxi time.This is the desired behavior that allows the excess taxi time to naturally build up to the Target, and once at the Target, any additional excess taxi time is transferred to the gate.By adding the active excess taxi time logic to the metering trigger we are able to properly build the queue up, effectively control the queue size, and transfer additional excess taxi time above the Target to the gate.Fig. 7 shows that by triggering too early and creating a slow start to the bank, ramp controllers over compensated by not gate holding aircraft towards the end of the bank and there are a significant amount of aircraft with excess taxi time above the Target that were not gate held.In contrast to Fig. 7, Fig. 8 shows that improving the trigger logic and better controlling the queue size, very few aircraft experience excess taxi above the queue and the red bar representing gate hold is efficiently transferring additional delay above the Target to the gate.
+VII. HONORING TOBT AND TMAT ADVISORIESAfter metering has been triggered, the performance of surface metering relies on ramp controllers honoring the TOBT and TMAT advisories given by Expressions (1) and (2), respectively.In this Section we present the results of the compliance with TOBT and TMAT observed in the Phase 1 field evaluation.We also show the empirical relationship between the TMAT compliance and the TOBT compliance and show what the optimal TMAT compliance could have been if operators were allowed to swap TMATs between their own flights.
+A. TOBT ComplianceRamp controllers were advised that when possible, the TOBT advisory should be honored within ± 2 minutes.Fig. 9 shows the TOBT compliance for 4,778 bank 2 metered flights operating between 2018-01-01 through 2018-09-30.The horizontal axis is the difference between the Actual Off-Block Time (AOBT) and the TOBT measured in minutes.The vertical axis is the frequency of flights with the given AOBT -TOBT value.As can be seen in the figure, the TOBT compliance defined by ± 2 minutes was 45.9%.Flights that were not compliant to TOBT were likely to push back earlier than the advised push back time which can be seen from the peak of the distribution centered around -2 minutes and the heavy left tail.Ramp controllers bring up a variety of reasons why aircraft might be released earlier than their TOBTs including gate conflicts, flights delayed well beyond SOBT, and other operational constraints.
+B. TMAT ComplianceThe ATD-2 concept focuses on TOBT compliance which is in contrast to the TFDM surface metering concept which focuses on TMAT compliance defined as ± 5 minutes.Whereas ATD-2 does not ask ramp controllers to comply with the TMAT times, we have assessed TMAT compliance.Fig. 10 shows the TMAT compliance in blue for the set of 4,778 aircraft contained in Fig. 9.The horizontal axis is the difference between the Actual Movement Area entry Time (AMAT) and the TMAT measured in minutes.The vertical axis is the frequency of aircraft with the given AMAT -TMAT value.In addition to the blue histogram showing the TMAT compliance for all aircraft assigned both a TOBT and TMAT, the orange histogram shows the TMAT compliance for the aircraft that were compliant with the TOBT (within the ± 2 minute TOBT compliance illustrated by the two vertical black lines in Fig. 9).As can be seen in Fig. 10, the chance of complying with the TMAT increases given the compliance with the TOBT advisory.The TMAT compliance increases to 80.6% for aircraft compliant with the TOBT compared to 65.9% compliance for any aircraft assigned both a TOBT and TMAT.If we consider the shape of the orange histogram compared to the shape of the blue histogram, we see that compliance to the TOBT advisory significantly reduces the density on the left tail of the distribution while maintaining very similar density on the right tail of the distribution.By shifting the density of the left tail into the ± 5 minute TMAT compliance window while maintaining the right tail, the TOBT compliance increases the overall TMAT compliance.
+C. TMAT Swapping for Optimal ComplianceTOBTs and TMATs are assigned to control the flow of demand towards the runway.When the scheduler assigns TOBTs and TMATs to meter the flow, the scheduler is indifferent to which specific aircraft gets delivered to the Active Movement Area (AMA) at the assigned TMAT time.This creates an opportunity for operators to swap TMAT times to improve TMAT compliance, while simultaneously maintaining the metered flow of traffic towards the AMA.We view this as a win-win for the operators who improve TMAT compliance and for the predictability of the system where an aircraft is delivered to the AMA within the expected compliance window.In order to measure the opportunity to improve the TMAT compliance we solved an optimization problem which assigns TMAT times in such a way that we maximize the TMAT compliance.For the optimal TMAT compliance, a TMAT swap is constrained to only be feasible for aircraft from the same flight operator, assigned a TMAT from the same SMP, and departing off the same runway.Fig. 11 illustrates the actual TMAT compliance in blue and the optimal TMAT compliance in green for the set of 4,778 flights shown in blue in Fig. 9 and Fig. 10.As can be seen in the figure, the opportunity to increase TMAT compliance and deliver aircraft to the AMA when the system expects increases to 83.6% percent with TMAT swapping compared to 65.9% without TMAT swapping.In practice, achieving the optimal TMAT compliance might be challenging in real-time.The optimal TMAT compliance is computed in post-analysis where we have perfect information about every aircraft's TMAT and AMAT.In real-time, an operator would know the set of TMATs that have been assigned, but without knowing the AMATs the swapping would rely on a predicted AMAT instead of an actual AMAT.Due to these limitations, the optimal compliance seen in Fig. 11 should be viewed as an upper bound of what is possible in real-time.
+VIII. CONCLUSIONIn this paper, we described the IADS scheduler functionality that enables both the runway scheduling and surface metering.We used operational data to identify scheduler improvements and guide the implementation of refinements.The improvements described in this paper have been incorporated into the IADS Phase 2 scheduler enabling strategic SMPs.Future research will evaluate the performance of the IADS scheduler during a SMP.The functionality that we will be testing in the Phase 2 field evaluation include SMP predictions, freezing of TOBT and TMAT in advance, and updated logic for inserting controlled flights into the overhead stream.Fig. 2 .2Fig. 2. Median of error illustrated with solid line and IQR illustrated with shaded region.
+Fig. 3 .3Fig. 3. Percentage of arrival flights with TBFM STA (blue) and TFMS ETA (green) as a function of lookahead time prior to actual ON.
+Fig. 4 .4Fig. 4. The difference in the TTOT as aircraft transition from the Uncertain group to the Planning group measured as TTOT(Planning) -TTOT(Uncertain).
+Fig. 44which illustrates the difference in the TTOT as the aircraft transitions from the Uncertain group to the Planning group.This figure contains all bank 2 departures between 2018-06-01 through 2018-06-30 totaling 2,346 flights.As can be seen in the figure, when aircraft transition from Uncertain to Planning the mean difference in TTOT after the transition is 2.1 minutes earlier.
+Fig. 5 .5Fig. 5. a) Example of a schedule composed of Active (blue) and Planning (red) flights.Flight XYZ987 was scheduled with a later TTOT than flight XYZ067 even though XYZ987's UTOT is earlier than XYZ067's UTOT.b) Example of a schedule after XYZ987 transitions from Planning to Active which causes the TTOT to jump down the timeline.
+Fig. 6 .6Fig. 6.Updated logic used in the Select Next Flight to Schedule logic block which is shown in Fig. 1.
+Fig. 7 .7Fig. 7. Excess taxi time (grey) and gate hold (red) illustrated as a function of takeoff sequence.The horizontal blue line is the Target excess taxi time selected by the users.Flights in the circled region were assigned gate hold even though the excess taxi time was well below the Target.
+Fig. 8 .8Fig. 8. Excess taxi time (grey) and gate hold (red) illustrated as a function of takeoff sequence.The horizontal blue line is the Target excess queue time selected by the users.Flights are only assigned gate hold after the excess taxi time has built above the Target.
+Fig. 9 .9Fig. 9. Compliance for all aircraft assigned a TOBT during surface metering illustrated in blue.TOBT compliance is measured as AOBT -TOBT and controllers were trained with a TOBT compliance window of ± 2 minutes illustrated by the vertical dashed black lines.
+Fig. 10 .10Fig. 10.Compliance for all aircraft assigned a TMAT during surface metering illustrated in blue.The compliance for aircraft assigned a TMAT that were compliant to the TOBT advisory are shown in orange.
+Fig. 11 .11Fig.11.Compliance for all aircraft assigned a TMAT during surface metering illustrated in blue.The optimal compliance which allows for aircraft to swap TMAT times is shown in green.
+at a high level.A more detailed description of the scheduler designModeler OutputScheduler OutputUnimpededTargetTakeOff Time PredictedSelect Next Aircraft to ScheduleSchedule at RunwayMoreFalseTakeOff Time TargetLanding TimeLanDing TimeScheduling GroupsWake VortexTrueFig. 1. High level design of the IADS scheduler.
+		
+		
+			
+AUTHOR BIOGRAPHIESDr. William Jeremy Coupe is an aerospace engineer at NASA Ames Research Center.He received his BS degree in mathematics from the University of San Francisco and both MS degree in applied mathematics and statistics and Ph.D. degree in computer engineering from the University of California, Santa Cruz.Dr. Hanbong Lee is an aerospace engineer at NASA Ames Research Center.He received his BS degree in mechanical and aerospace engineering from Seoul National University, Korea, and both MS and Ph.D. degrees in aeronautics and astronautics from Massachusetts Institute of Technology.Dr. Yoon Jung is an aerospace engineer at NASA Ames Research Center, specialized in surface traffic management research.He received his BS and MS degrees in mechanical engineering from Seoul National University, Korea, and Ph.D. degree in mechanical engineering from the University of California, Davis.Mr. Liang Chen is a software engineer at Moffett Technologies Inc.He received his BEng degree in Electronics and Information Engineering from Imperial College London, UK.Mr. Isaac Robeson is an analyst at Mosaic ATM.He received his BS and MS degrees in Aerospace Engineering from the Georgia Institute of Technology.			
+			
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+				
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+		Cognitive Workload and Visual Attention Analyses of the Air Traffic Control Tower Flight Data Manager (TFDM) Prototype Demonstration
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+	Coupe, W. J., Bagasol, L., Chen, L., Lee, H., and Jung, Y., "A data driven analysis of a tactical surface scheduler," AIAA Aviation Technology, Integration, and Operations (ATIO) Conference, 2018.
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+		Mitigating Wake Turbulence Risk During Final Approach Via Plate Lines
+		10.2514/6.2020-2835.vid
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+		
+			2019-02-08
+			American Institute of Aeronautics and Astronautics (AIAA)
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+	
+	"Wake turbulence recategorization," https://www.faa.gov/ documentLibrary/media/Order/Final Wake Recat Order.pdf, Accessed: 2019-02-08.
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+I. IntroductionAirport runways and taxiways have been identified as a bottleneck of the national airspace system (NAS), and the major inhibiting factor for serving an increasing air traffic demand.Since many airports operate at or close to their maximum capacity, 1 an optimization of runway and taxiway operations is necessary.However, once their operations are improved by an optimal taxiway schedule, its execution depends on ramp-area aircraft maneuvers, 2 which is the focus of this paper.Unlike aircraft maneuvers on taxiways, ramp-area maneuvers are frequently not confined to well-defined aircraft trajectories.The shape and timing of the trajectories are subject to uncertainties resulting from pilots' decisions as well as other factors involved in ramp-area operations, which can impede an optimal taxiway schedule plan.To address this problem, we model the trajectories as stochastic processes.However, ramp-area trajectory data that can be used to build maneuver models are not readily available mainly due to the lack of surveillance data in the ramp-area.Investments in collecting such data are unlikely unless the usefulness of the data in increasing airport efficiency is illustrated.The main goal of this paper is not only to illustrate this, but to point to an inexpensive way to collect the data.In this paper, we collect the critical data on how a human operator drives aircraft in a ramp area using an inexpensive scaled-down wheeled robot experiment.The use of robots is essential for collecting realistic data because of kinematic imperfections associated with a tolerance in assembly and interaction between the ground and the wheels.This type of uncertainty is reasonable to anticipate with any wheeled vehicle including aircraft.We use E-puck robots 3 and model the movement of aircraft from the gates to the spots in the Terminal C ramp-area of Dallas-Fort Worth International Airport (DFW).The spots are physical areas on the airport surface where the transfer of control from the ramp controller to the air traffic control tower take place.We assume that a taxiway scheduling plan will be computed by the spot release planner 4 providing us the spot time, which is the concept implemented and tested in NASA's decision support tools for the air traffic controllers (Spot And Runway Departure Advisor -SARDA 2,5 ).Therefore, the goal of the analysis is to understand general limitations and trade-offs in the ramp-area scheduling, to provide conflict free movement both in the ramp-area and on taxiway.Previous work on optimizing airport surface operations has focused on modeling an airport as a graph or a connected network, allowing aircraft to traverse between the nodes along straight edges.The authors used genetic algorithms, 6,7 mixed integer linear programs (MILPs), or hybrids of these. 8,9 he MILP approach has been used in [10][11][12][13][14][15] and recently also included one form of uncertainties. 16A more detailed characterization of uncertainties in aircraft taxiing has been considered in, 17 but it only addressed maneuvers in the active movement area (taxiways and runways), and due to unavailability of data it did not tackle the problem in the ramp-area.We are unaware of any work in relation to modeling and planning of maneuvers in the ramp area.To the best of our knowledge this paper is the first attempt to address the integration of a state-of-the-art implementation of an optimal taxiway scheduler, such as SARDA, and ramp area aircraft maneuvers.By using the robot system experiments, we overcome the limitations imposed by the lack of data on ramp operations.Section II provides a description of the robot experiment that we use to collect our data, while Section III describes our model of aircraft maneuvers.It is followed by Section IV describing our computational analysis based on the model.Results of the computational analysis are summarized by the so-called separation diagram and Section V illustrates the use of ramp area separation graph within SARDA schedulers.Section VI shows that by tailoring aircraft pushback intervals we can decrease the chance of having ramp-area conflicts and illustrates constraints that are used in achieving that decrease.
+II. Robot Experiment for Airport Ramp Area Data CollectionThe essence of our experiment is in using E-puck robots to obtain estimates of uncertainties of the ramp-area aircraft maneuvers, in particular, the variability of real-life trajectories of pilot driven aircraft.In our experiment, we focus on a single terminal in which a pilot navigates an E-puck simulated Boeing 747 (B747-400) from a designated gate to a taxiway spot.In this paper, we use a robot experiment with a scaled-down layout of the Terminal C ramp-area of DFW Airport, see Fig. 1.The surface of this scaled-down ramp-area occupies about one half of our 6 ft by 6 ft robotic arena 18 surface.At that scale, the length of a B747 19 is 1.2 times the diameter of the E-puck robot tracked using a ceiling camera.We selected a B747 because the volume it occupies within the ramp-area is larger than the volume of an average aircraft, as well as because the use of a B747 assumes the turning kinematics and constraints of a large body aircraft, while a smaller aircraft can perform the same maneuvers in a smaller operating space.Therefore, our analysis is conservative with respect to the aircraft size.The slight discrepancy in the dimensions and the major difference in the kinematics between a B747 and an E-puck robot are incorporated in our Tcl/Tk graphical user interface (GUI) for E-puck control, so that an exact match of maneuvers between E-puck and B747 during the pushback and taxi is achieved.v a v a a a b v b φ r a r ⊥ra θ φ v b b v a = v b cos θ v b sin φ = ω a r f 2r r f ω a i 0 j 0 O 1 . 3 7 i n 1 . 5 5 i nAircraft kinematics can be modeled as a tricycle 20 with two fixed axis rear wheels and a single front wheel with the controlled steering angle φ , see Fig. 2.During pushback, the aircraft motion is powered through the front wheel.Consequently, the pushback variables we control through our Tcl/Tk GUI are the velocity of the front wheel v b and the angle φ .Starting from the tricycle model of the aircraft, we can writev b r v = v a r a + (ω a r f ) r ⊥ r a(1)where r v is the unit vector pointing in the direction of the front wheel located at b, r a is the unit vector pointing in the direction from a to b, r ⊥ r a is the unit vector that is perpendicular to r a , and ω a is the angular velocity around the point a.From this, we derive two relations between our control variables and the value of the velocities in the points a and b, and the angular velocity around the point av b cosφ = v a , ω a = v b sinφ r f(2)On the other hand, the control variables for the E-puck motion are velocities of the left (v L ) and the right (v R ) wheels, and their relations to the velocity of the point a on the E-puck (v E a ) and the corresponding angular velocity (ω E a ) arev L = v E a -ω E a r, v R = v E a + ω E a r(3)By setting v E a = v a and ω E a = ω a , we can derivev L = v b cosφ (1 - r r f tan φ ), v R = v b cosφ (1 + r r f tan φ )(4)which provides the match between the aircraft and E-puck kinematics, i.e., for our control variables v b and φ , we can compute v L and v R so that the E-puck moves in the same way as the aircraft.At a first glance one may think the pushback and taxi would follow the same model; however, there is a slight difference.While the pushback is powered through a velocity applied at the front wheel v b , we can consider that the taxi is powered through a forward velocity applied at the center (point a) of the rear wheels v a .Therefore, the control variables we consider are v a and φ .Following similar reasoning as in the pushback case, we findv L = v a (1 - r r f tan φ ), v R = v a (1 + r r f tan φ )(5)which are relations providing that for a given forward velocity v a and the front wheel angle φ the E-puck moves the same way as the aircraft, i.e., their kinematics are matched.We can note that the matching of the kinematics in the pushback and taxi modes at any scale depends on the ratio r/r f , and, in the case of a B747, we have r/r f = 0.88.Movements of aircraft in the ramp-area are not constrained to well-defined trajectories as illustrated by Fig 3. Upon receiving the pushback clearance, a tug (operated by ground crew) pushes back the aircraft from the gate.At the end of the pushback procedure, the aircraft stops and the tug disengages.This stop period lasts for some time during which the pilot goes through a checklist and then starts the aircraft engine(s).The pilot then taxies the aircraft towards a designated taxiway spot, see Fig. 3.During the maneuvers, the transitions over the motion phases, as well as the path lengths during the pushback and taxi are determined by human operators and are stochastic in nature.In our experiment, the "pilot" drives the E-puck, which moves as if it were an aircraft, using the Tcl/Tk GUI sliders for the steering angle and velocity in the pushback and taxi modes.This provides realistic aircraft motion data.However, we do not know a probability distribution of the time that a real aircraft spend in the stop between the pushback and the forward taxi and this uncertain time has to be inferred from the data.To account for that uncertainty in our experiments, we ask the "pilot" to solve a simple mathematical problem, for example finding eigenvalues of a random 2 × 2 matrix.Once the "pilot" has solved the problem, he is free to taxi forward towards the taxiway spot.In order to capture and analyze the data from our experiment, we construct a motion capturing system that works through the use of a general webcam that captures an .AVI video to be processed in MATLAB.We identify 3 points associated with our E-puck and use them to track the position and heading angle of our E-puck.Each individual trajectory is captured providing us with spatio temporal information that includes the uncertainties dues to human operators.
+III. Stochastic Model of Aircraft TrajectoriesBased on the data captured from our experiment, we model the trajectories of the aircraft pushing back from their gate and taxiing to the spot.We use the model to computationally generate as many trajectories as we need without any further time-consuming robot experiments.This is an important step since our aircraft's conflict-analysis relies on these trajectory samples.The model describes the trajectories of the aircraft i center (point a, see Fig. 2) which starts at the gate, i.e., at the position (x i 0 , y i 0 ), and a random heading angle θ i drawn from the normal distribution N (θ i 0 , (0.18) 2 ) with the mean value θ i 0 and the variance (0.18) 2 , see Table 1.The trajectories are described based on a hybrid automaton with 5 discrete states as shown in Fig. 4 and continuous dynamics as described below with x i , y i and θ i coordinates defined with respect to the global coordinate frame ( i 0 , j 0 ) , see Fig. 2. Each trajectory is initialized with q = 0 (Gate) corresponding to the aircraft stopped at the gate.The kinematics in this state, as well as in the states q = 2 (Stop) and q = 4 (Spot), is defined bydx i = 0, dy i = 0, dθ i = 0,(6)In the discrete state q = 1 (Pushback), the kinematics is assumed to be deterministic because of small variations in experimentally collected trajectories during that phase of aircraft maneuver, i.e.,dx i = -v cos(θ i )dt, dy i = -v sin(θ i )dt, dθ i = - v R i dt (7)where the velocity is assumed to be constant v = 3.5m/s and values R i are provided in Table 1 for every i = 1, 2, 3, 4.Finally, the kinematics in the state q = 3 isdx i = v cos(θ i )dt, dy = v sin(θ i )dt, dθ = σ i θ dW θ i(8)where dW θ i are increments of mutually independent unit intensity Wiener processes and σ i θ are their scaling factors, i=1, 2, 3, 4.All the above mentioned parameters are estimated based on the experimental data and we use the same data to estimate the time distributions of an aircraft in the discrete states q = 1 (Pushback), q = 2 (Stop) and q = 3 (Taxi).These times are modeled by the gamma distributions shown in Fig. 5.We use these distributions and the model in Fig. 4 with kinematics ( 6)-( 8) to generate aircraft trajectories.However, to account for the fact that aircraft maneuver towards the spot, we accept a generated trajectory only if at some terminal time t = T , the trajectory belongs to the spot defined as 129 < x i (T ) < 149, -537 < y i (T ) < -517 and |θ i (T )π/2| < 0.326.a Figure 6 (left) shows a fraction of our experimental data with an E-puck under a red-dotted lid that can be easily tracked in a sequence of video frames.The right panel of the same figure shows our model-generated aircraft trajectories.Note that the model is defined to account for all possible ways aircraft can go from a gate to the spot, therefore the model generated trajectory distributions wider than these may interfere with a tighter fit from experimental data.Having a model that accounts for more possible trajectories makes our analysis more robust against sources of uncertainty of aircraft maneuver trajectories.
+IV. Aircraft Trajectory Conflict AnalysisAlthough the movement of the aircraft in the ramp-area is not constrained to well-defined trajectories and is subject to uncertainties, it is expected that the aircraft reach the spot at specific times provided by the taxiway scheduler; this a In our experiments, the spot is a 20m × 20m area centered at point (139m, -527m), measured from the point O (see Fig. 2) with coordinate (0m, 0m), and with positive directions north for the y coordinate and east for the x coordinate.We do not explicitly constrain how the aircraft approach the spot, but only require that the heading angle of aircraft within the spot at the terminal time T is in the interval [-π 2 -0.326,π 2 +0.326].Table 1.Model parameters for aircraft i trajectory: (x i 0 , y i 0 ) -initial aircraft position, θ i 0 -initial heading angle , R i -radius of the aircraft trajectory during the pushback, σ i θ -scaling factor of the heading angle variations, [t i S0 ,t i F0 ] -interval in which aircraft i has to push back to be at the spot at t = 0.i  ensures that the aircraft enter the taxiway on-time and without stopping at the spot.For the three aircraft presented in Fig. 3, let us introduce four labels i = 1, 2, 3, 4 equivalent to labels A, BR, BL and C that uniquely identify the aircraft and the push back maneuver type.The scheduler provides the spot times t i sch , but since labels 2 and 3, i.e., BR and BL, correspond to the same aircraft positioned at the gate B that can perform maneuver to the right, or to the left we have t 2 sch = t 3 sch .Once we are able to generate aircraft trajectories from every gate to the spot, we can calculate the time intervals [t i S0 ,t i F0 ] when aircraft i has to pushback to reach the spot at t i sch = 0.These values are provided in the last two columns of Table 1.In general, we assume that the terminal time for each trajectory t i sch is provided by a taxiway scheduler and the corresponding pushback time intervals [t i S , t i F ] for achieving t i sch can be computed as t i S = t i S0 + t i sch and t i F = t i F0 + t i sch .If the difference between two aircraft i and j spot times, i.e., t j sch -t i sch , is small, then the taxiway scheduler requires the two aircraft to be at the spot within a small time interval.This will likely result in the conflict at the spot.However, the trajectory overlaps in Fig. 6 (right) suggest that a conflict may also happen along the paths from gates to the spot.then the aircraft at the gate A can be scheduled to the spot 123s, however if BL was performed then the gate A aircraft can be scheduled to the spot 53s after the gate B aircraft.(x i 0 , y i 0 )(m) θ i 0 ( o ) R i (m) σ i θ t i S0 t i F0[s] [s] [s] [s] [s] [s] [s] [s] [s] [s] [s] [s]
+V. Scheduling Based on the Separation-Time Graph and Integration with SARDAThe graph in Fig. 8(a) captures the results from our aircraft maneuver conflict analysis in section IV.It is a directed graph, G = (V, E), where the edges e i j represent the minimum separation-time that aircraft j can reach the spot after aircraft i to ensure no conflict along the path from the gates to the spot.The graph G provides a conservative timeseparation at the spot that guarantees conflict-free trajectories.In this section we provide a way to use the graph G in the SARDA scheduler, such that the spot times obtained from the SARDA scheduler guarantee conflict free trajectories in the ramp-areas.Since the spot times calculated in this method already considers the required separations at the spot, it leads to trivial solutions for the gate push-back times.This method is attractive because it provides a simple extension to the Mixed Integer Linear Program (MILP) based runway scheduler for single runway and provides a solution to the surface management problem (conflict-free trajectories for ramp and active movement areas).
+A. Spot And Runway Departure Advisor (SARDA)In this subsection, we provide a brief overview of SARDA's algorithm.SARDA uses the Spot Release Planner (SRP) 4,21 algorithm to provide advisories to the ATCT (Air Traffic Control Tower) controllers.The main idea is to provide spot release advisories to the ground controller (GC) in order to achieve a small queue at the runway and achieve an overall reduction in movement area taxi times.The GC releases the aircraft from the spot at the advised spot-release time and is responsible for maintaining required separations on the taxiway.The calculation of the optimal spot release involves a two stage algorithm 4 .In the first stage, an optimal runway schedule for the set of aircraft (take-off times for departures and crossing times for arrivals) is generated.For each aircraft, its weight class, and earliest available time at the runway are the main inputs to the SRP algorithm.The earliest available times at the runway are calculated by assuming unimpeded movement of aircraft on the taxiway.The optimization problem of this first stage, formulated as a mixed integer linear program, is given below.min Γ := max i∈A t i (9)z i j + z ji = 1 ∀i, j ∈ A (10) z i j (t j -t i -∆ r i j ) ≥ 0 ∀i, j ∈ A (11)t i ≥ a i ∀i ∈ A (12) z i j ∈ {0, 1} ∀i, j ∈ A, i = j (13)t i ∈ R + ∀i ∈ A (14)where A is the set of flights, z i j is a binary variable for relative sequencing of aircraft i and j at the runway, t i is a continuous variable for the runway usage time for aircraft i.The parameter a i represents the earliest available time at the runway for aircraft i and ∆ r i j is the minimum required separation-time that aircraft j can use the runway after aircraft i.Runway use can denote a departure take-off, an arrival landing, or an aircraft crossing the active runway.The second stage of the SRP determines optimal times to release aircraft from assigned spots to meet departure schedules calculated in the first stage and can be calculated using,t is = t i -τ i , ∀i ∈ departures (15)where τ i is the unimpeded taxi time for the i th aircraft.At DFW, once aircraft leave the spots they have specific set of taxi routes to the departure runway.The SARDA simulations 5 considered only East side operations at DFW in south flow configuration with one departure runway (17R) and two arrival runways (17C and 17L).Arrivals landing on 17C or 17L cross 17R at one of the five runway crossing points.The wake-vortex separation between two departures are given in Table 1.Moreover, arrivals can cross runway 17R forty seconds after a departure and they take twenty-one seconds to clear the runway.If two arrivals cross the runway consecutively, the minimum temporal separation between them is five seconds if they are at different crossings, or twenty seconds if they are at the same crossing.Given the minimum separation-time requirements, ∆ r i j for all given pairs of aircraft that use runway 17R can be determined.G L = (V L , E L ) and G R = (V R , E R ), with V L ={A, BL,C} and V R = {A, BR,C}, corresponding to the decision of right/left pushback of aircraft B that we envision to be communicated to the aircraft B prior its pushback from the gate.Without loss of generality, we will use G s = (V s , E s ) to interchangeably stand for either G L or G R , where e s i j ∈ E s provides the separation-times at the spot for the given pushback (left/right) decision.DFW airport has a set of structured routes for departures from the spots to the runway.Since aircraft can take different routes to the runway, let us define the parameter δ i j to represent the projected separation at the runway to ensure the required separation at the spot.Given the estimated taxi-time tt i and tt j of aircraft i and j respectively, δ i j can be defined as: δ i j = e s i j + (tt jtt i ) Let ∆ i j = max(∆ r i j , δ i j ) be the new required separation between aircraft i and j at the runway.By considering the maximum of the wake-vortex separation and the projected runway separations, we constrain the problem to satisfy the ramp-area constraints in addition to the required runway separations.The modified MILP below provides a solution to the surface management problem (with ramp-area management) in one step.Note that this is achieved using the same number of decision variables as SARDA, and should be computationally comparable.min Γ := max i∈A t i(16)z i j + z ji = 1 ∀i, j ∈ A (17) z i j (t j -t i -∆ i j ) ≥ 0 ∀i, j ∈ A (18) t i ≥ a i ∀i ∈ A (19) z i j ∈ {0, 1} ∀i, j ∈ A, i = j(20)t i ∈ R + ∀i ∈ A(21)The analysis we present here clearly illustrates several things.It allows a preliminary integration of ramp-area constraints directly into SARDA without taking care of specific trajectories in the ramp-area.Using the modified ∆ i j does not add any additional variables to the SRP formulation, and is not expected to increase the computation time of the algorithm.By considering separation times with zero probability of conflict we may allow the runway to go under-utilized.If we define the separation time at the spot based on an accepted non-zero level of risk, i.e., based on the graph presented in Fig. 8(b), the throughput can be increased.Any further increase of the throughput has to rely on a more detailed analysis of conflicts between aircraft in the ramp-area, and is discussed in the next section.
+VI. Using Conflicting Time Combination Cluster Boundaries for Optimal Conflict ResolutionThe analysis in the previous section is based on conflict ratios inferred from samples of the stochastic maneuver trajectories.It is conservative because it is based only on differences in the scheduled time at the spot without taking into account the exact push-back time of each aircraft.In this section, we present results of the computational analysis from Section IV, but this time taking into account specific pushback times.For an illustration, let us focus on the combination (A, BR), which has a bi-modal conflict ratio distribution, and consider the situation t 2 scht 1 sch = -70 when aircraft BR reaches the spot 70 seconds before aircraft A. Assuming that t 1 sch = 0, we know that the pushback time interval for aircraft A is [t 1 S0 ,t 1 F0 ]=[-162 -102] ( see Table 1) and that the time interval for aircraft B is shifted for -70, i.e., [t 2 S t 2 F ] = [-217 -180].When we computed the conflict ratio, we used these pushback time intervals.However, in this case, instead of just counting conflicts, we are going to describe all combinations of pushback times that lead to conflicts.
+-70nc-  In Fig. 9, panel "-70nc" (no-conflict and conflict combinations), any time the combination of pushback times results in a conflict, we plot a red dot; otherwise, we plot a blue dot.Although we can distinguish regions containing red, or blue dots, we note that these regions are not well separated by plotting the red and blue dots separately in panels "-70n"(no-coflict combinations) and "-70", respectively.Since we only care about conflict resolution, we focus on modeling the regions in which we find red dots.The panels from "-70" to "40" show the conflict pushback combinations labeled with the difference in time when A and BR aircraft reach the spot.The shape of conflict zones, i.e., clusters of pushback combinations resulting in maneuver conflicts, is not simple and we can have more than one cluster of pushback combinations in a single diagram, see, for example, Fig. 9 panel "-40".This is likely due to the conflicts along the maneuver paths.For the difference of scheduled time -10 seconds, we know that the conflict happens around the spot and the conflict zone can be bounded by a horizontal line.The shape of the zone says that both aircraft can reach the scheduled time, but the pushback interval for aircraft A has to be narrowed.Otherwise, aircraft A can be too early and in conflict with aircraft B. At the time difference 0 seconds, we have 100% of conflicts as expected and, for the difference of scheduled time of 10 seconds, the conflict zone is limited by a vertical line.In the latter case, aircraft A reaches the spot 10 seconds before aircraft BR.Therefore, the pushback interval of BR has to be narrowed providing that BR is late enough to avoid the conflict with aircraft A.Next, we examine the detailed conflict zone for t 1 scht 2 sch = -55 given in Fig. 10a.The figure shows that if -55 seconds is to be achieved, then a possible option is to take pushback time combination below the line connecting (-202, -149) and (-165, -116).These pushback times are given by the following inequality  10b shows that any extension of the pushback time interval for A leads to a shorter pushback time interval for aircraft BR.Consequently, it makes sense to make a trade-off of the time intervals, so that their minimal length is maximal.In this case, it means that their lengths should be equal.Their limits are given by tand, consequently, t 1 Ft 1 S = t 2 Ft 2 s = 24.3s.However, note that this length of the pushback time intervals is a valid choice only for the time difference at the spot of -55 and cannot be applied in general.Figure 10d shows that if we use the same pushback time interval lengths in the case of the spot time difference of -70, we enter the conflict zone.The example in Fig. 10e shows that the region of pushback time combinations leading to conflicts can be more complex, so to keep a linear expression for the pushback time conflict region boundaries, we have to accept a certain nonzero conflict ratio.In the case of the boundary presented in Fig. 10e, the level of conflict is 1%, which is likely to be acceptable.From the analysis of this section, we conclude that: (1) the conflict zones can be composed of more than one cluster of combinations leading to the conflict; (2) these zones can be bounded by linear constraints if we accept a certain level of the conflict, therefore, potentially the constraint can be easily integrated within the SARDA ; finally, (3) the time intervals providing an accepted level of conflicts are specific to the exact value of the aircraft time difference at the spot.This suggests that the control of surface traffic should be organized in such a way that the taxiway scheduler computes t i sch and the ramp-area maneuver optimization uses the differences among t i sch to compute pushback time intervals.
+VII. ConclusionsIn this work, we used the scaled-down robot experiment to provide us the critical trajectory data in the ramp-area, which are not available due to the lack of surveillance data in airport ramp-areas.Based on our robot experiment data and stochastic model for the aircraft trajectories, we can for the first time inspect aircraft pushbask time combinations leading to trajectory conflicts, which are also the result of specific aircraft maneuvers and ramp-area geometry.We analyzed time constraints to avoid conflicts and developed a time-separation graph at the spot that provided conflict-free ramp-area trajectories.Then, we provided a scheme to use the time-separation graph data within SARDA.While this shows the usefulness of our result, the scheme is conservative, i.e., by providing a conflict-free motion of aircraft, it may lead to the underuse of taxiway.Therefore, we provided a detailed analysis of pushback times leading to conflicts.The analysis shows that sets of conflict-free gate pushback time combination intervals can be formulated based on linear constraints.This means that less conservative time-separation constraints taking into account the ramp-area can be integrated within SARDA.Now that we understand the type of constraints in the ramp-area, our future work will focus on quantitative methods for generating constraints that we can use in pushback time interval computations, as well as on optimization methods for computing pushback time intervals and integration with SARDA.We also consider important validating our robot experiment data driven analysis against the data from ramp-area operations, for example by comparing the distribution of times that take aircraft from the gate to the spot.In our future work, we would like to consider not only departing, but also arriving aircraft and multiple spots.To that end, we shall work on an upgrade of our experiment to include multiple robots, in which some of the robots will move autonomously to simulate arriving aircraft.Finally, it is worth mentioning that in this work, we used a robot of the size that with respect to the laboratory experiment ramp-area layout dimensions matches the size of a B-747.Since it is a large aircraft, our analysis is conservative with respect to the aircraft size.Collecting data for a smaller aircraft will require either the use of a robot of a smaller size, or a larger dimension of the laboratory ramp-area layout.While there are no obvious obstacles in collecting and using such data from robot experiments, it is likely that the necessity for the data is airport specific.In a big picture of airport operation optimization, our work indicates the importance of the customization of tools such as SARDA to specific ramp-area layouts.Therefore, our plan is to perform the same experiments and analysis for ramp-areas of other US airports.Figure 1 .1Figure 1.Left panel: Layout of Terminal C at DFW (left); Right panel: scaled-down laboratory model of Terminal C at DFW showing an E-puck robot under a red-dotted lid with three distinctive points helping its tracking, scale is 1 foot = 198 meters (photo from D. Milutinović's Robotics and Control Laboratory, UC Santa Cruz).
+Figure 2 .2Figure 2. Kinematics characteristics: Boeing 747 (left); E-puck robot (right); v a -point a (center) velocity; ω a -angular velocity around the point a (center); v b -front wheel velocity; φ -front wheel angle; θ -the heading angle; r f -distance between the point a (center) and the front wheel.
+Figure 3 .3Figure 3. Ramp-area scheduling problem: the aircraft are parked at gates A, B and C, and scheduled to be at the taxiway spot at a given time.The number and letter labels of trajectories provided in brackets are equivalent and used as follows: 1(A) and 4(C) are the trajectories of the aircraft parked at gates A and C, respectively.2(BR) and 3(BL) are the trajectories of the aircraft parked at gate B, which can push back to the right, or to the left, respectively.
+Figure 4 .4Figure 4. Discrete states of the hybrid automaton model of a ramp-area aircraft trajectory.The states are: 0 -Gate, aircraft is parked at a gate; 1 -Pushback, aircraft pushes back; 2 -Stop, stop after the pushback, 3 -Taxi, taxi towards the taxiway spot, and 4 -Spot, aircraft is at the taxiway spot.
+Figure 5 .5Figure 5.Time distributions of an aircraft in the discrete states: Pushback (green), Stop (red) and Taxi (blue); the time scale presented in seconds
+Figure 6 .6Figure 6.Left panel: a fraction of experimentally recorded trajectories in a layout of the scaled-down ramp-area; Right panel: the model-based generated trajectory samples.
+Figure 8 .8Figure 8. Graphs of time separations (in seconds) at the spot: (a) separations providing 0% of conflict; (b) separations providing 10% of conlfict
+Figure 9 .9Figure9.Structure of the conflict between A and BR: Each panel shows the combinations of pushback times for A (vertical axis) and BR (horizontal axis) under the assumption that BR reaches the spot after the time indicated above the panel.The negative values indicate that BR reaches the spot before aircraft A. The panel "-70nc" shows both conflicting and nonconflicting combinations, while "-70n" shows only nonconflicting ones.The corresponding conflicting combinations are presented in the panel "-70".Note that the panel "40" is empty, which means that there are no conflicts.
+Figure 10 .10Figure 10.Linear constraints and pushback times for A(vertical axis) and BR (horizontal axis) in the panels (a)-(d) and BR(vertical axis) and C(horizontal axis) in the panel (d): (a) linear constraints bounding the conflicting combinations of pushback times; (b) the full rectangle representing the regions of pushback times providing no conflicts; (c) the full rectangle representing the regions of pushback times providing that the length of time intervals for both aircraft is equal (d) the region of pushback times of the same size as in the panel (c) and its relation to the conflict zone for the time difference at the spot -70; (e) the conflict zone with a possible linear constraint, which does not allow conflict free pushback times.The level of the accepted conflict ratio is 10 -2 .
+
+Table 2 .2Wake vortex separation (in seconds) for departure aircraft.Large Heavy B-75xLarge6110991Heavy619091B-75x6110991B. Incorporating uncertain ramp trajectories in SARDA schedulerWe can decompose the spot separation-time graph G into two subgraphs
+where in any case t 2 ∈ [-202, -165].Therefore, a possible combination of pushback time intervals is [t 1 S = -162,t 1 F = -149] and [t 2 S = -202,t 2 F = -165] resulting in the minimal time interval of 13 seconds for aircraft A and the maximal time interval of 37 seconds for aircraft BR.Figuret 1 <-116 + 149 -165 + 202(t 2 + 202) -149 =33 37(-202 -t 2 ) -149(22)
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 23, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4884Copyright © 2013 by Dejan Milutinovic.Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 23, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4884Copyright © 2013 by Dejan Milutinovic.Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 23, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4884Copyright © 2013 by Dejan Milutinovic.Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 23, 2013 | http://arc.aiaa.org| DOI:
+			10.2514/6.2013-4884Copyright © 2013 by Dejan Milutinovic.Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
+		
+		
+			
+Conflict ratioConflict ratio   Frequencies of maneuver conflicts against separation times t j scht i sch , i < j at the spot for all aircraft combinations, i.e., gate-spot trajectories taking into account that the aircraft from gate B can perform left (BL) or right (BR) maneuver.Panel BR, A (bottom-left) is a mirror image of distribution A, BR(up-left) and illustrates that distributions for combinations i > j are mirror images of distributions for i < jTo get an insight into a possible conflict resolution strategy, we perform the following computational analysis.Computational analysis: We first take aircraft i = 1, i.e., the aircraft at gate A, and assign it the spot time t i sch = 0, therefore its trajectories start at the interval [t i S0 ,t i F0 ].Then, we take another aircraft, j = 2(BR), which starts at gate B and performs the right turn, and assign it t j sch with a certain negative, or positive value.Therefore, the BR trajectories start at the interval [t j S , t j F ]. We sample trajectories for these two aircraft, measure their proximity and compute the conflict ratio, i.e., the frequency of trajectory pairs that lose separation.The result for i = 1 and j = 2 against t j sch -t i sch is plotted in the (A, BR) panel of Fig. 7. Clearly, we can repeat the same analysis for i = 2(BR) and j = 1(A).In this case, we receive the result plotted in (BR, A) panel, which is as expected a mirror image of the previous result.This illustrates that we have to analyze only combinations of i and j such that i < j, i, j = 1, 2, 3, 4.Moreover, we do not analyze combination i = 2(BR), j = 3(BL) because both of these two labels are associated with the aircraft from gate B and the labels distinguish the type of its maneuver.All results of our analysis are presented in Fig. 7.Note that t j scht i sch < 0 means that aircraft i reaches the spot after aircraft j.Similarly, t j scht i sch > 0 means that aircraft i reaches the spot before aircraft j.Therefore, it is not surprising that around t j scht i sch = 0 we have a frequency of conflicts close to 1.We see that some of the distributions are unimodal (A, BL) and (A, C), bimodal (A, BR) and (BR, C), and finally, the distribution (BL, C) seems to be somewhere between these two types of distributions.If the conflict of aircraft maneuvers happens only around the spot, we will have a sharp unimodal distribution.Any other mode of distribution is a consequence of conflicts between aircraft maneuvers along the paths from gates to the spot.The computed diagrams are very useful and can be used to predict the likelihood of conflicts between aircraft for a given separation time at the spot t j sch -t i sch , or find the separation times providing aircraft maneuvers without conflicts.For example, we can conclude that if t 2 scht 1 sch > 37, then there are no conflicts between aircraft 1(A) and 2(BR).The conflict between them does not exist also if t 2 scht 1 sch < -123.The negative value means that aircraft 2(BR) reaches the spot at least 123 seconds before aircraft 1(A).All time separations at the spot providing that aircraft maneuvers are not in conflict can be inferred from Fig. 7 and are summarized by the graph in Fig. 8(a).The graph in Fig. 8(b) is less conservative and shows time separations leading to 10% of conflicts between aircraft menauvers.Each arrow on these graphs points from the aircraft that reaches the spot towards the aircraft that reaches the spot after it.The required separation time is presented above or below each arrow.For example, the graph in Fig. 8(a) shows that to avoid the conflict, aircraft at the gate C executing the only possible maneuver C has to be scheduled to the spot 110s before the aircraft at the gate B performing the right turn maneuver (BR), or 106s before is the gate B aircraft perform the left turn maneuver (BL).If the BR is performed			
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+I. IntroductionAir traffic demand continues to grow and forces operations of the National Air Space (NAS) to keep pace with increased capacity.A key concept in air traffic flow management is balancing demand and capacity to maintain safety while minimizing delays.Accuracy of the wheels-off time estimate affects the quality of traffic demand estimates.Inaccurate traffic demand estimates can result in either inadequate or unneeded flow restrictions [1].A large source of error in predicting the demand results from the prediction errors of the taxi times [2], which also plays a role in the prediction of the wheels-off time.Wheels-off time uncertainty is caused by uncertainty in gate departure time, ramp area transit time, and taxi-out time.The ramp area transit time is the time between push back from the gate and arrival at the taxiway spot, which is the ramp area exit point.The purpose of this paper is to better understand time distributions of different processes within the ramp area transit time.This type of analysis is important because the uncertainty within the distributions should be accounted for in order to execute safe and efficient ramp area operations.Mean value estimates for the different processes can be used to schedule aircraft, however, many aircraft will deviate from the mean.This can force aircraft to slow down or stop along the route to avoid a loss of separation.In contrast, we propose to use the full distribution for the different processes within the ramp area.This provides for schedules where aircraft can travel along their route unimpeded in the presence of other aircraft and trajectory uncertainties [3,4].A recent study [5] estimated as much as 18% of fuel consumption during taxi operations was due to stop-and-go activity.The study also concluded that under the assumption of 15 knots or greater speed for all unimpeded aircraft, there is the potential to reduce overall fuel consumption on the surface by at least 21%.In this paper we analyze ramp area transit time data that was collected at the Charlotte Douglas (CLT) airport over a three day period.Using the collected data, we also infer conflict distributions representing combinations of push back times, for aircraft at different gates, that lead to a conflict in their sampled trajectory data.Understanding the conflict distribution is important because it plays a critical role in computing aircraft push back time windows that ensure conflict free trajectories [6] within the ramp area.Unlike maneuvers on taxiways and runways, aircraft maneuvers within the ramp area are typically not confined to well-defined trajectories.The shape and timing of the trajectories are influenced by the pilot and are stochastic in nature.Most previous research focused on taxi-out times and did not analyze ramp area transit time.The taxi-out times were modeled using a Erlang and log-normal distribution [7][8][9][10][11] and the goodness-of-fit was also assessed [12,13].In our previous work [3,4] based on a scaled-down robot experiment, we collected aircraft trajectory data related to the ramp area transit time.We used data from a robot experiment because trajectory data are not readily available mainly due to the lack of surveillance equipment in the ramp area.Moreover, investments in collecting ramp area trajectory data are unlikely unless the usefulness of the data in increasing airport efficiency is illustrated.We then modeled ramp area trajectories as a stochastic process with three discrete states: push back, stop, and taxi.The time spent in each discrete state was modeled as a gamma distribution and fitted to the robot experiment data.In this paper, we explore not only the gamma distribution, but also a log-normal distribution to the collected airport operational data for the push back, stop and taxi processes.Since the Erlang distribution is a special case of the gamma distribution we do not consider it here.The goodness-of-fit of the distribution is assessed using hypothesis testing with multiple statistical tests.We then sample a large number of ramp area trajectories using the fitted parametric distributions as input to a stochastic model of ramp area trajectories.The sampled trajectories are used to estimate conflict distributions defined by the time separation of two aircraft at the taxiway spot.These two-dimensional conflict distributions are analyzed and the goodness-of-fit of a multivariate Gaussian, Gaussian Copula, and t-Copula are considered.This paper is organized as follows.In Section II, we describe the experiment that was performed at the CLT airport and report the raw data that was collected.Then in Section III we assess the goodness-of-fit of the log-normal and gamma distributions to the collected data.In Section IV we use the fitted distributions to sample ramp area trajectories and conflict distributions.Next, in Section V we analyze the goodness-of-fit of a multivariate Gaussian, Gaussian Copula, and t-Copula.Lastly, in Section VI we provide an overview of the work that was done and concluding remarks.
+II. Data Collection Methodology and Raw Collected DataIn this paper, we analyze data that was collected within the CLT south sector ramp area between August 23-25, 2015.The layout of the CLT airport is shown in Fig. 1a and a zoomed in view of the south sector ramp area is illustrated in Fig. 1b.The ramp tower is colored in red and provides the south sector ramp controllers with a view down the center alley.The experimental data was collected by an observer located in the ramp tower.The observer had access to the controller radio frequency and was able to monitor communication between pilots and the ramp controller.This allowed for the observer to distinguish among trajectories that are allowed to proceed unimpeded and trajectories that are held by controllers for various reasons.The goal of the experiment was to collect unimpeded trajectory data between the gate and various spots within the ramp area.Although both departure and arrival trajectory data were collected, in this paper we focus on the departure data.For a single departure trajectory, six pieces of data were collected.1) We collected the gate number from which the aircraft trajectory begins.2) We recorded the time that the aircraft push back is initiated.3) We recorded the time that the stop phase of the trajectory is initiated.4) We recorded the time that the taxi phase of the trajectory is initiated.5) We recorded the time that the trajectory arrives at the spot.6) We recorded the spot number that the trajectory arrived at.The four spots are indicated in Fig. 1b.After the data was collected over the three day period, the data was processed which provides the time that each trajectory spends in discrete states push back, stop, and taxi.Figure 2 shows an example of the processed data that was collected.In the figure, each sub figure illustrates the histogram of data for different gates as well as different days.Within each subfigure there are 3 histograms which illustrate the histogram for the push back maneuver, the stop maneuver and the taxi maneuver from top to bottom respectively.Data from all gates is shown in the first column, data from the middle gates B6-B12 and C7-C13 is shown in the second column and data from the back gates B2-B4 and C3-C5 is shown in the second column.Data that was collected over all three days is shown in the first row, data collected on the first day is shown in the second row, data collected on the second day is shown in the third row and data collected on the third day is shown in the fourth row.In Fig. 3 we analyze the collected data from all gates over all days colored in red and in Fig. 4 we analyze the collected data from the middle gates over all the days colored in magenta.
+III. Statistical Testing of Collected 1-D Time DistributionsIn this Section and Section V we consider the problem of comparing samples from two probability distributions f and g defined on the domain X by proposing the null hypothesis H 0 : f (x) = g(x), for every x ∈ X .Statistical tests are implemented to test against the alternative hypothesis H 1 : f (x) = g(x), for some x ∈ X .We use the statistical tests to compare the collected data to known parametric distributions.If the collected data and the parametric distributions are a good fit within a 95% confidence interval then we will accept the null hypothesis, else we reject the null hypothesis.
+Kolmogorov-Smirnov 1-Dimensional Test:The Kolmogorov-Smirnov(KS) [14,15] test is a 1-dimensional distribution free test.We analyze the empirical distribution CDF F n against the CDF of the parametric distribution G.The KS statistic for empirical distribution F n with n iid observationsD n = sup x |F n (x) -G(x)|(1)By the Glivenko-Cantelli theorem [16], if the sample F n (x) comes from the distribution G(x), then D n converges to 0 almost surely in the limit when n goes to infinity.
+Kernel Method 2 Sample Test:The kernel method [17] is a non-parametric 2 sample test.The test statistic is the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS).The statistical test is implemented based on a large deviation bound for the test statistic.Given observations X := {x 1 , x 2 , ..., x m } and Y := {y 1 , y 2 , ..., y n } drawn independently and identically distributed (i.i.d.) from f and g respectively, let F be a class of functions f : X → R and let f, g, X, Y be defined as above.We define the maximum mean discrepancy (MMD) and its empirical estimate asMMD[F, f, g] := sup f ∈F E x∼f [ f (x)] -E y∼g [ f (y)] (2) MMD[F, X, Y ] := sup f ∈F 1 m Σ m i=1 f (x i ) - 1 n Σ n i=1 f (y i )(3)Let F be a unit ball in a universal RKHS H defined on the compact metric space X with associated kernel function k(•, •). Then MMD[F, f, g] = 0 if and only if f = g. Since E[ f (x)] =< µ(f ), f >, we may write MMD[F, f, g] = sup || fH ||≤1 < µ[f ] -µ[g], f >= ||µ[f ] -µ[g]|| H (4) Using µ[X ] := 1 m Σ m i=1 φ(x) and k(x, x ) =< φ(x), φ(x ) >, an empirical estimate of MMD becomes MMD[F, X, Y ] = 1 m 2 Σ m i,j=1 k(x i , x j ) - 2 mn Σ m,n i,j=1 k(x i , y j ) + 1 n 2 Σ n i,j=1 k(y i , y j )(5)Intuitively we expect MMD[F, X, Y ] to be small if f = g, and the quantity to be large if the distributions are far apart.The statistical test can be carried out using a large deviation bound with the acceptance regionMMD[F, X, Y ] < 2 K m 1 + log α -1(6)where α is the desired significance level of the test.Quantile-Quantile (Q-Q) plot:The quantile-quantile (Q-Q) plot [18], is a visual goodness-of-fit test that can be applied to 1-D distributions.Corresponding to any ordinate value p there are two quantile values q f (p) and q g (p).A Q-Q plot of samples from f and g is then just a scatter plot of q f (p) versus q g (p) for various p.If f and g are drawn from the same distributions, then the plot of f -quantiles versus g-quantiles will be a straight line configuration with slope 1, pointed towards the origin.If g is a linear function of f then the corresponding Q-Q plot will still be linear but with possibly changed location and slope.It is this linear invariance property which has made the use of Q-Q plots appealing.For the case in which the variables have heavy tails, the Q-Q plot tends to emphasize the comparative structure in the tails and to blur the distinctions in the 'middle' where the densities are high.The reason for this is that the quantile is a rapidly changing function of p when the density is sparse (in the tails) and a slowly changing function of p where the density is high (in the middle).[18].
+Analysis of Collected 1-D Time DistributionsIn this section we analyze the data that was collected by evaluating the results of the KS test, the kernel two sample test and the Q-Q plot. Figure 3 and Fig. 4 show the results of the different statistical tests applied to aircraft from all gates and applied to aircraft from the middle Gates B6-B12 and C7-C13 respectively.Each figure contains 3 rows of figures where the top row analyzes the push back data, the middle row analyzes the stop data and the bottom row analyzes the taxi data.Both Fig. 3 and Fig. 4 contain 3 columns of subfigures.In the first column we show the raw data with a histogram and also show the three different parametric distributions that were fitted to the data including a normal distribution, gamma distribution, and log-normal distribution.The second column of subfigures represent the analysis of the three 1-D statistical tests which compare the collected data in each row to a gamma distribution.The third column of subfigures represent the analysis of the three 1-D tests which compare the collected data to a log-normal distribution.Within each subfigures in column 2 and 3 we show the Q-Q plot, followed by the results of the kernel two sample test using confidence level α = 0.05, followed by the results of the KS test using confidence level α = 0.05.The statistical tests are run for 100 independently drawn samples of the two distributions For example in Fig. 3, the subfigure in row 1 and column 2 contains the results of the three statistical tests applied to the push back data for trajectories originating from all gates.In contrast, in Fig. 4, the subfigure in row 2 and column 2 contains the results of the three statistical tests applied to the stop data for trajectories originating from the middle gates B6-B12 and C7-C13.The results in Fig. 3 and Fig. 4 appear to illustrate that there is a trade-off between the two distributions that we fit in column 2 (gamma distribution) and column 3 (log-normal distribution).The results of the KS test and kernel 2 sample test estimate that the log-normal distribution is a better fit for the collected data of push back and stop.The Q-Q plots, however, show a different result as the tails of Q-Q plot for the log-normal distribution are more nonlinear than the tails of the Q-Q plot for the gamma distribution.This implies the right tail of the log-normal distribution is heavier than the data that was collected.Although the log-normal distribution might do a good job at fitting the mean and CDF of the distribution, the gamma distribution appears to provide a better fit along the right tail of the distribution.Fitting the tails of the distribution is important because overestimating the density along the right tail of the distribution can impact ramp area throughput as the separation at the taxiway spot should be increased to accommodate for the greater uncertainty.
+IV. Sampled Ramp Area Trajectories and Sampled Conflict DistributionsWe begin this Section by defining the stochastic model of aircraft trajectories.The essence of the idea is to capture data influenced by the human operator and use the observed distributions as input to a stochastic model.We use the model to sample [19,20] a large number of realistic trajectories.The sampled aircraft trajectories are used to generate a probabilistic measure of conflict within the ramp area.These distributions will be analyzed in Section V.The discrete states i = 0, 1, .., 4 defined as gate, push back, stop, taxi, and spot are the building blocks of our stochastic hybrid automaton model [21].A single ramp area departure trajectory for aircraft i is described by our automaton where each discrete state is defined by the continuous time evolution: For q = 0(gate), q = 2(stop), q = 4(spot):dx i = 0, dy i = 0, dθ i = 0, dv i = 0(7)For q = 1(push back):dx i = -v i cos(θ i )dt, dy i = -v i sin(θ i )dt, dθ i = u i θ (t)dt, dv i = u i v (t)dt + σ i v dW i v(8)where dW i v is an increment of a unit intensity Wiener process and σ i v is a scaling factor for the intensity of the variations in the velocity v i .The control u i θ (t) and u i v (t) defines the evolution of the heading angle and velocity of the deterministic component of the trajectory, respectively.The deterministic component of the trajectory is defined by the path aircraft follow within the NASA Ames Future Flight Central (FFC) [22] simulations of the CLT ramp area.Given that we are interested in sampling unimpeded trajectories, we believe this provides a good representation of the path a pilot would follow in the absence of other aircraft.For q = 3(taxi):dx i = v i cos(θ i )dt, dy i = v i sin(θ i )dt, dθ i = u i θ (t)dt + σ i θ dW i θ , dv i = u i v (t)dt + σ i v dW i v (9)where dW i θ is an increment of a unit intensity Wiener process and σ i θ is a scaling factor for the intensity of the variations in the heading angle θ i .The introduction of the terms dW i v and dW i θ in the equations transforms the otherwise deterministic system of equations into a stochastic process.We use the data that was captured during the three day experiment at CLT to provide a distribution over the continuous interval of time that we could spend in any given discrete state.We use these distributions of time to fit parametric distributions for push back, stop and taxi.Sampling from these distributions provides the time each trajectory should spend within each discrete state.We then sample a large number of realistic trajectories, see Fig. 5.In the first column we illustrate the evolution of trajectories in time and Algorithm 1 Conflict Distribution: Aircraft i vs Aircraft j Assume t i = 0 Set N = 1,000 for t j = -200:1:200 do for k = 1:N do • Randomly sample aircraft i and j from their respective family of trajectories.• Measure the spatial proximity of the aircraft along the route and provide a conflict flag if aircraft lose spatial separation.end for • Return conflict ratio for the relative schedule t j -t i end for • Return conflict ratio for all relative schedules at a resolution of 1 second.in the second column we show the distribution of the different discrete states push back, stop and taxi.The distributions of push back, stop and taxi are color coded to represent the relationship to the middle and front gate distributions of Fig. 2, Fig. 3, and Fig. 4. The sample distributions of stop and taxi were accepted by the K-S test and the kernel 2 sample test when analyzed for goodness-of-fit.The sample distribution of push back were accepted by the K-S test and rejected for the kernel 2 sample test.After sampling trajectories, we estimate the probability density function for trajectory duration of aircraft i in the absence of any other aircraft in the ramp area.We refer to this type of distribution as natural since the aircraft is unimpeded.We are interested in computing push back windows for aircraft i such that the aircraft arrives at the terminal node at the scheduled time t i .Therefore, we enforce a terminal condition in time for the sampled trajectories and this generates a distribution for the push back time.In addition, enforcing this terminal condition in time provides us with a set of departing trajectories that all enter the FAA controlled taxiway at the same time.Using the family of trajectories defined by the natural distributions of aircraft i and j, we generate a measure of conflict defined by the ratio of conflicting trajectories to total trajectories.We compute the measure of conflict by fixing the terminal time of aircraft i in time such that t i = 0. Next we fix the terminal time of aircraft j in time, e.g.t j = -200.Given the relative schedule defined by t j -t i , there exists a family of trajectories for both aircraft i and j that push back from their respective gates and taxi to the terminal node as required.For the relative schedule t j -t i , we sample a single trajectory from the family of trajectories for aircraft i and j, measure their spatial proximity along the route, and provide a conflict flag if the aircraft lose spatial separation.If we continue this process of randomly sampling from the family of trajectories with fixed terminal times, we compute a conflict ratio for the relative separation in time at the taxiway spot, see Algorithm 1.The fixed terminal times are considered for every whole second and the estimated conflict distribution provides a measure of conflict at a resolution of 1 second, see Fig. 6a.
+V. Statistical Testing of Sampled Two-Dimensional Conflict DistributionsIn this Section we use the hypothesis testing method described in Section III to assess the goodness-offit of a multivariate Gaussian, Gaussian Copula, and t-Copula to the sampled distribution of red conflict points seen in Fig. 6b.We begin this section by describing the two new test statistics derived from the Wald-Wolfowitz Runs Test and the Kolmogorov Smirnov Test which we use to analyze the fit of the twodimensional distributions.
+Wald-Wolfowitz Two-Dimensional Runs Test:The Wald-Wolfowitz runs test [23] is a 1-dimensional distribution free test.Consider two random samples F ∼ f and G ∼ g.A run of a sequence is defined as a maximal non-empty segment of the sequence consisting of adjacent equal elements.For example the ranked list distributed as F F F F GGF F consists of 3 runs, 2 of which are F and the other G.Under the null hypothesis H 0 : f = g, the number of runs in a sequence of N elements is a random variable whose conditional distribution given n observationsσ 2 = (µ -1)(µ -2) N -1(11)This test can be extended to d-dimensions with the multivariate runs test proposed by [24] and later shown to be consistent against the general alternative hypothesis H 1 [25].The multivariate runs test extends the notion of a run to d-dimensional space by constructing the minimum spanning tree (MST) of the data set F ∪ G.After the MST has been constructed, we eliminate all edges where the vertices of the edge are from different families of the data.We define the multivariate run test statistic R = 1 + Number of cross matches = Number of disjoint trees (12) Under the null hypothesis H 0 the expected value of R and variance can be derived through a combinatorial argument asE[R] = 2mn N + 1 (13) V ar[R] = 2mn N (N -1) 2mn -N N + C -N + 2 (N -2)(N -3) [N (N -1) -mn + 2] (14)where C is the number of edge pairs that share a common node in the MST.Define the new statisticW = R -E[R] (V ar[R]) 1 2(15)Under the null hypothesis H 0 : f = g, the test statistics W approaches the standard normal distribution.Similar tests can be run where we use optimal non-bipartite matching [26], matching based on minimum energy [27], or nearest neighbors [28].
+Kolmogorov-Smirnov Two-Dimensional Test:Extending this test from 1-dimension to d-dimensions is not straightforward.The KS test requires the definition of a probability function that is independent of the direction of ordering, which is not possible given that there are 2 d -1 ways of defining a CDF in a d-dimensional space [29].Furthermore, tests based on binning face the hurdle of "the curse of dimensionality": a high dimensional space is mostly empty and binning tests can only start to be effective when the data sets are very large [30].To address this, authors [31,32] defined the statistic independent of any particular ordering by finding the largest difference under all possible orderings.
+Analysis of Goodness-of-Fit for Two-Dimensional Conflict DistributionsNow we analyze the distribution of red conflict points illustrated in Fig. 6b.This type of analysis is important because understanding the conflict distributions help us better understand the risk of conflicts.From a visual perspective, the conflict distributions for some values such as t j -t i = 0 or t j -t i = 20 seem "regular" and familiar.For some relative schedules, such as t j -t i = -20 or t j -t i = 150, the distributions exhibit odd shapes and skew that do not appear so "regular".These difference in distribution could be reasonable given the difference in ratio of conflicts for the two schedules.In order to assess the goodness-of-fit of the conflict distributions and potential qualitative differences, we select the conflict distributions for t j -t i = 20 and t j -t i = -20 seen in Fig. 6b.We analyze the goodnessof-fit of a multivariate Gaussian, Gaussian Copula, and t-Copula distribution to the samples.Figure 7 shows the results of the hypothesis testing applied to the two distributions.In the figure, the first row of subfigures are the statistical tests applied to the conflict distribution t j -t i = -20 and the second row are the tests applied to the conflict distribution t j -t i = 20.The first column is assessing the fit of the multivariate Gaussian, the second column assessing the fit of the Gaussian Copula, and the third column assessing the fit of the t-Copula.Within each subfigure the top image shows the distribution of the Wald-Wolfowitz test statistic W for 100 independent tests.Below the distribution is the binary decision for the Wald-Wolfowitz test to reject the null hypothesis H 0 for a confidence interval α = 0.05.Below that we have the binary decision for the K-S test to reject the null hypothesis H 0 for a confidence interval α = 0.05.Lastly we illustrate the kernel threshold and the kernel statistic for the kernel two sample test for with confidence level α = 0.05.The second row of subfigures within Fig. 7 analyzes the conflict distributions for t j -t i = 20.As can be seen in the figure, the multivariate Gaussian does not seem to be a good fit for the distribution.The Wald-Wolfowitz test statistic has a mean less than -1 which indicates that we are getting less cross-matches on average than we should if the null hypothesis H 0 were true.For the Gaussian Copula and the t-Copula the test statistic W mean is approaching 0, indicating that we are getting the expected number of crossmatches if the null hypothesis H 0 were true.The distributions of the test statistic W seem different for the Gaussian Copula and t-Copula.Given that the test statistic W should approach the standard normal, this could indicate the Gaussian Copula is a better fit.Next, consider the first row of subfigures within Fig. 7 which analyze the conflict distributions for t j -t i = -20.As can be seen, the Wald-Wolfowitz test statistics W is rejected for a large number of test for the relative schedule t j -t i = -20 regardless of the distribution that is being fit to the data.This indicates that the conflict distributions are statistically different from the parametric distributions we are considering.This is in contrast to the K-S test statistic and kernel two sample test statistic which are accepted for a large number of tests for the Gaussian Copula and t-Copula distributions, but not the multivariate Gaussian.This indicates that the multivariate Gaussian is not a good fit, but the Gaussian Copula and t-Copula may be a good fit.To get a better understanding why the Wald-Wolfowitz test statistic W would reject tests when the K-S and kernel two sample test accept the test, we visually analyze the distributions.The first row of subfigures in Fig. 8 show the sampled conflict points for t j -t i = -20 in red and samples from the fitted distributions in blue.Compare the first row of subfigures to the second row of subfigures, which are defined the same way for the conflict distribution t j -t i = 20.From Fig. 8 it seems like there may be a reason that the Wald-Wolfowitz test rejects the samples for t j -t i = -20 but accept samples for t j -t i = 20.For the figures analyzing distributions from t j -t i = 20 the concentration of blue and red points seems rather uniform.There are no large concentrations of blue or red points throughout the domain, instead they are equally distributed amongst themselves.In contrast, the figures analyzing the distributions from t j -t i = -20 have higher concentrations of blue or red points where individual colonies seem to appear.The Wald-Wolfowitz test statistic W is able to catch on to this difference in concentration and reject the tests.Overall, this analysis suggests that the conflict distributions can be qualitatively different depending on the conflict ratio.For conflict distributions that have a high ratio of conflicts the distributions seem more "regular" and this notion was confirmed when we found that the Gaussian Copula and t-Copula distributions are strong candidates to fit the distributions.For conflict distributions that have a low ratio of conflicts the distributions do not seem so "regular" and this notion was confirmed with one of the statistical tests, but was not supported by two other statistical tests.This contradiction between statistical tests introduces an interesting question.What is the most appropriate metric for our purposes to assess the goodness-of-fit of a two-dimensional distribution?Whereas the K-S test and kernel two sample test can confirm that the mean and CDF are a good fit, we know that the tails may be poorly accounted for.Intuitively, the Wald-Wolfowitz Runs test seems like a more natural candidate in two-dimensions and our results showed the test to distinguish between two distributions more stringently.Understanding the conflict distribution is important because it plays a critical role in computing aircraft push back time windows that ensure conflict free trajectories [6].For example, if we underestimate the density along the tails of the conflict distributions this can lead to unexpected conflicts in the ramp area and a greater likelihood of aircraft having to slow down or stop to avoid a loss of separation.Moreover, knowing the conflict distributions are a good fit to a parametric distribution can help us derive confidence intervals related to the push back windows that are ultimately computed.
+VI. ConclusionIn this paper, we analyzed the time duration distributions of different processes within the ramp area transit time.The processes that we consider are push back, stop, and taxi.The analyzed data were collected between August 23-25, 2015 at the Charlotte Douglas (CLT) airport.Specifically, we assessed the goodnessof-fit of a gamma distribution and log-normal distribution to the different processes.The analysis of data shows that the log-normal and gamma distributions are reasonable fit to push back, stop, and taxi.The analysis also show that there exists a trade-off between the two distributions.Whereas the log-normal may better fit the mean and CDF of the distribution, the gamma distribution provides a better fit along the right tail of the distribution Next, we analyzed the goodness-of-fit of a multivariate Gaussian, Gaussian Copula, and t-Copula distribution to the sampled two-dimensional conflict distributions.The analysis showed that the conflict distributions may be qualitatively different depending upon the conflict ratio.For conflict distributions with a high ratio of conflicts, the Gaussian Copula and t-Copula are reasonable fits.For conflict distributions with a low ratio of conflicts, the statistical tests contradict each other and it is not clear if these distributions are a good fit.The results show that parametric distributions can be a good fit for the duration of the different processes within the ramp area transit time and the two-dimensional conflict distributions.This is important because the better that we understand these distributions, the more we can anticipate the uncertainty that is intrinsic within them.Furthermore, knowing the conflict distributions are a good fit to a parametric distribution can help us derive confidence intervals related to the push back time windows that are eventually computed.Future work will use more ramp area data to analyze whether the distributions of the different processes within the ramp area are time dependent or stationary processes.Understanding the uncertainty within the distributions is critical for the safe and efficient execution of ramp area operations.For example, overestimating the right tail of the distributions for the different processes within ramp area transit time can impact throughput as the separation at the taxiway spot should be increased to accommodate for the greater uncertainty.Similarly, if we underestimate tails of the conflict distributions this can lead to conflicts that were not anticipated and a greater likelihood of aircraft having to slow down or stop to avoid a loss of separation.Ultimately, these distributions are important because they play a critical role in computing push back time windows that ensure aircraft can travel unimpeded from their gate to the departure runway queue in the presence of other aircraft and trajectory uncertainties.Figure 1 .1Figure 1.a) CLT airport surface.b) Zoomed in view of CLT south sector and illustration of the experiment set up.Data was collected by observer located in the ramp tower.
+Fand m observations G is approximately normal with
+Figure 2 .2Figure2.The processed data is illustrated using histograms.The x-axis represents the time spent in seconds to complete each process and the y-axis represents the number of aircraft within each bin.Data from all gates is shown in the first column, data from the middle gates B6-B12 and C7-C13 is shown in the second column and data from the back gates B2-B4 and C3-C5 is shown in the second column.Data that was collected over all three days is shown in the first row, data collected on the first day is shown in the second row, data collected on the second day is shown in the third row and data collected on the third day is shown in the fourth row.
+Figure 3 .3Figure 3. Analysis of collected data from all gates over all days.The push back data is in the first row, the stop data the second row, and the taxi data the third row.The first column shows the histogram of data and the fitted distributions, the second column show the results of the three different statistical tests assessing the goodness-of-fit of the gamma distribution to the collected data, and the third column show the results of the three different statistical tests assessing the goodness-of-fit of the log-normal distribution to the collected data.
+Figure 4 .4Figure 4. Analysis of collected data from middle gates B6-B12 and C7-C13.The push back data is in the first row, the stop data the second row, and the taxi data the third row.The first column shows the histogram of data and the fitted distributions, the second column show the results of the three different statistical tests assessing the goodness-of-fit of the gamma distribution to the collected data, and the third column show the results of the three different statistical tests assessing the goodness-of-fit of the log-normal distribution to the collected data.
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+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3899
+		
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+			
+The first column assesses the goodness-of-fit of a multi-variate distribution, the second column assesses the goodness-of-fit of a Gaussian Copula, and third column assess the goodness-of-fit of a t-Copula.Figure 8. Analysis of the sampled conflict points (red) with the samples from the parametric distribution (blue) and the edges of the MST that do not include cross matches (green).The first row analyzes the sampled distribution t B14 -t B10 = -20 and the second row assesses the goodness-of-fit to the sampled distribution t B14 -t B10 = 20.The first column analyzes a multi-variate distribution, the second column analyzes a Gaussian Copula, and third column analyzes a t-Copula.			
+			
+
+				
+
+
+	
+		Wheels-Off Time Prediction Using Surface Traffic Metrics
+		
+			GanoChatterji
+		
+		
+			YunZheng
+		
+		10.2514/6.2012-5699
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		
+			American Institute of Aeronautics and Astronautics
+			2012
+			5699
+		
+	
+	Chatterji, G. B. and Zheng, Y., "Wheels-Off Time Prediction Using Surface Traffic Metrics," 12th AIAA Aviation Tech- nology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2012, p. 5699.
+
+
+
+	
+		Improving departure taxi time predictions using ASDE-X surveillance data
+		
+			AmalSrivastava
+		
+		10.1109/dasc.2011.6095989
+	
+	
+		2011 IEEE/AIAA 30th Digital Avionics Systems Conference
+		
+			IEEE
+			2011
+			
+		
+	
+	2011 IEEE/AIAA 30th
+	Srivastava, A., "Improving Departure Taxi Time Predictions Using ASDE-X Surveillance Data," Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th, IEEE, 2011, pp. 2B5-1.
+
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+		Robot Experiment Analysis of Airport Ramp Area Time Constraints
+		
+			WilliamJCoupe
+		
+		
+			DejanMilutinovic
+		
+		
+			WaqarAMalik
+		
+		
+			GautamGupta
+		
+		
+			YoonCJung
+		
+		10.2514/6.2013-4884
+	
+	
+		AIAA Guidance, Navigation, and Control (GNC) Conference
+		Boston, MA
+		
+			American Institute of Aeronautics and Astronautics
+			2013
+		
+	
+	Coupe, W. J., Milutinović, D., Malik, W., Gupta, G., and Jung, Y., "Robot Experiment Analysis of Airport Ramp Area Time Constraints," AIAA Guidance, Navigation, and Control Conference (GNC), Boston, MA, 2013.
+
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+	
+		Integration of Uncertain Ramp Area Aircraft Trajectories and Generation of Optimal Taxiway Schedules at Charlotte Douglas (CLT) Airport
+		
+			JeremyCoupe
+		
+		
+			DejanMilutinovic
+		
+		
+			WaqarMalik
+		
+		
+			YoonCJung
+		
+		10.2514/6.2015-2591
+	
+	
+		15th AIAA Aviation Technology, Integration, and Operations Conference
+		Dallas, TX
+		
+			American Institute of Aeronautics and Astronautics
+			2015
+		
+	
+	Coupe, W. J., Milutinović, D., Malik, W., and Jung, Y., "Integration of Uncertain Ramp Area Aircraft Trajectories and Generation of Optimal Taxiway Schedules at Charlotte Douglas (CLT) Airport," AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Dallas, TX, 2015.
+
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+
+	
+		Detailed estimation of fuel consumption and emissions during aircraft taxi operations at Dallas/Fort Worth International Airport
+		
+			TasosNikoleris
+		
+		
+			GautamGupta
+		
+		
+			MatthewKistler
+		
+		10.1016/j.trd.2011.01.007
+	
+	
+		Transportation Research Part D: Transport and Environment
+		Transportation Research Part D: Transport and Environment
+		1361-9209
+		
+			16
+			4
+			
+			June 2011
+			Elsevier BV
+		
+	
+	Nikoleris, T., Gupta, G., and Kistler, M., "Detailed Estimation of Fuel Consumption and Emissions During Aircraft Taxi Operations at Dallas Fort Worth International Airport," Published in the journal Transportation Research Part D: Transport and Environment, Vol. 16D, Issue 4 , June 2011.
+
+
+
+	
+		Optimization of Push Back Time Windows That Ensure Conflict Free Ramp Area Aircraft Trajectories
+		
+			JeremyCoupe
+		
+		
+			DejanMilutinovic
+		
+		
+			WaqarMalik
+		
+		
+			YoonCJung
+		
+		10.2514/6.2015-3028
+	
+	
+		15th AIAA Aviation Technology, Integration, and Operations Conference
+		Dallas, TX
+		
+			American Institute of Aeronautics and Astronautics
+			2015
+		
+	
+	Coupe, W. J., Milutinović, D., Malik, W., and Jung, Y., "Optimization of Push Back Time Windows That Ensure Conflict Free Ramp Area Aircraft Trajectories," AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Dallas, TX, 2015.
+
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diff --git a/file173.txt b/file173.txt
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index 0000000000000000000000000000000000000000..8b8b7239e4ec8665011dad31878c19730fe42667
--- /dev/null
+++ b/file173.txt
@@ -0,0 +1,956 @@
+
+	
+	
+		
+I. IntroductionUnlike aircraft maneuvers on taxiways, ramp area aircraft movements are typically not confined to welldefined trajectories.The shape and timing of the trajectories are subject to uncertainties resulting from pilots decisions as well as other factors involved in ramp area operations, which can impede upon an optimal taxiway schedule plan.Most of the previous research [1][2][3][4][5][6][7][8][9][10][11][12] on taxiway scheduling has modeled the FAA controlled runways and taxiways as a graph, i.e. a connected network, and authors have neglected to account for the planning and execution of aircraft maneuvers within the ramp area.Although the surface operations can be improved by adopting an optimal taxiway schedule, its execution ultimately depends on human controllers who control aircraft maneuvers in both the ramp area and taxiways [13].Ramp area aircraft have been incorporated in [14,15], but the trajectories are considered to be deterministic.The main difficulty in the integration of ramp area operations into an optimal taxiway scheduling solution is in addressing uncertainties of ramp area aircraft trajectories.To address the uncertainty, a stochastic model of aircraft trajectories [16,17] was proposed.The model was used to sample a large number of feasible ramp area aircraft trajectories within the ramp area.A feasible trajectory is any sampled trajectory from the stochastic model that arrives at the target movement area within a predefined range of heading angles.The set of feasible trajectories for each aircraft is sampled in the absence of other aircraft.Therefore, the set of trajectories represent the feasible ways in which the aircraft can push back from their gate and taxi to the target movement area unimpeded by other aircraft.Using the sampled trajectories, we computed conflict distributions among any two aircraft defined by their relative target movement area schedule [16,17].The conflict distributions are defined by a measure of conflict estimated from the ratio of conflicting trajectories to total trajectories for every relative target movement area schedule.The conflict distributions were used to compute conservative conflict separation constraints that were passed to a mixed integer linear program (MILP) [18][19][20] which incorporated the Spot and Runway Departure Advisor (SARDA) [7][8][9][10][21][22][23] design approach.During time periods of heavy traffic, SARDA advises departing aircraft to remain at the gate with engines off, and when cleared, they can proceed straight from the gate to the departure runway queue minimizing slowing down or stopping for other traffic [8] and still meet their target movement area and take-off times.This technique has the effect of significantly reducing fuel burn and engine emissions.A Recent study [24] estimated as much as 18% of fuel consumption during taxi operations was due to stop-and-go activity.The study also concluded that under the assumption of 15 knots or greater speed for all unimpeded aircraft, there is the potential to reduce overall fuel consumption on the surface by at least 21%.For departing aircraft to proceed along the route unimpeded, it is critical the aircraft arrive at the target movement area at the scheduled time.This research proposes a tool to aid ramp area controllers in meeting the scheduled target movement area times by computing the push back time window for each departing aircraft.The push back time window is defined by the range between the earliest feasible push back time and latest feasible push back time.Initiating the push back within these bounds ensures there exists a feasible trajectory that arrives at the target movement area at the required time, which is defined by a higher level optimal taxiway scheduler.The main contribution of the tool is to compute push back time windows that allow for aircraft to taxi unimpeded from the gate to the departure runway queue in the presence of other aircraft and trajectory uncertainties.This allows for ramp controllers to better manage surface traffic, reduce fuel consumption, and execute conflict free ramp area aircraft trajectories.For relative target movement area schedules resulting in conflict free aircraft trajectories, computing the push back time windows are straightforward and can be estimated from the sampled trajectories.To compute the push back time windows we estimate the maximum trajectory duration and minimum trajectory duration for each aircraft, and then subtract from the scheduled target movement area time for the given aircraft.These computed times represent the start and finish of the feasible push back time window for each aircraft, respectively.When the schedule may lead to aircraft trajectory conflicts, an optimization procedure which solves for push back time windows in the presence of aircraft trajectory uncertainties is needed.Previously, we proposed a MILP approach to solve for the optimal combination of push back time windows [25].The optimal combination of push back time windows was defined to maximize the minimum push back time window of a set of all aircraft being scheduled.The solutions were based on conflict points that represent combinations of push back times that result in aircraft trajectory conflicts.We used the idea that no conflict point should be a convex combination [26] of the points in time that define the start and finish of the push back time window.In one-dimension, a convex combination of two points lies in between the two points.The number of constraints that are passed to the MILP is a function of the number of conflict points and for every conflict point, we need five constraints.The distribution and/or the boundary of the conflict points is not known a priori.The MILP that we developed in the previous paper is able to solve for the optimal combination of push back time windows given any distribution of conflict points.We pay for this in terms of the number of constraints and ultimately in the computation time.As the number of aircraft we solve for increases, the number of constraints hinders the solution technique.The resulting algorithm is too slow for real-time decision making and thus we need to develop techniques to reduce the execution time.Here we propose a new MILP approach that uses a significantly smaller number of constraints.Using fewer constraints, the problem can be solved with less computation time and the MILP model can be incorporated in real-time decision making.This paper is organized as follows.In section II we formulate the problem that we are considering.In section III we describe the techniques that we use to reduce the number of constraints that are passed to the MILP.In section IV we formulate the MILP approach we use to solve for the optimal combination of time windows.Next, in section V, we analyze the multi-criterion objective function that defines our solutions.In the last section, we conclude with a discussion of our findings and provide directions for future work.
+II. Problem FormulationIn this section we formulate the problem of computing the optimal ramp area aircraft push back time windows for each departing aircraft.The combination of push back time windows will be constrained to contain zero conflict points.We begin by defining the variables and parameters that we use:Symbol Description iA family of departing trajectories originating from a single gate and characterized by a single left or right push back maneuver pattern
+DThe set of all departing trajectory families available to push back from their gate A A specific family of trajectories available to push back from gate A t iThe scheduled time for family i to arrive at the target movement area t S i The start of the computed push back sub-window for family i t F i The finish of the computed push back sub-window for family i JThe objective function that is being maximizedt S0 iThe start of the feasible push back time window for family i scheduled att i = 0 t F 0 iThe finish of the feasible push back time window for family i scheduled att i = 0 T iThe set of all sampled trajectory duration data for departing trajectory family i P B iThe push back time of a single aircraft trajectory from family i κ A combination of push back times (conflict point) that lead to conflict among family i and j KThe number of discrete clusters of conflict points s(κ)The silhouette value for conflict point κ sThe averaged silhouette value over all conflict points A A vertex used to define quadrilateral that bounds conflict pointsQ A Quadrilateral that bounds a set of conflict points (conflict quadrilateral) V 1An edge that defines the conflict quadrilateral Q z A unit vector orthogonal to the x-y plane N 1A normal vector generated from V 1 × z T κ 1 A vector whose tail originates from vertex A and tip ends at conflict point κ
+CA clustering algorithm used to cluster a set of conflict pointsQ(C)The area of all conflict quadrilaterals using clustering algorithm C M A continuous variable representing the minimum push back time window δ min A parameter representing the minimum acceptable push back time window A parameter in the objective function J which influences the shape of the push back time windows z nE A binary variable that is one if edge E of the time window is selected as a separating axis, zero otherwise z mE A binary variable that is one if edge E of the conflict quadrilateral Q is selected as a separating axis, zero otherwise f mEThe function that defines the line for edge E of the conflict quadrilateral Q A departing aircraft is parked at the gate and scheduled to arrive at the target movement area.In this paper, we assume the target movement area time is provided from a higher level taxiway scheduler.Upon receiving the push back clearance, a tug (operated by ground crew) pushes back the aircraft from the gate.At the end of the push back procedure, the aircraft stops and the tug disengages.This stop period lasts for some time during which the pilot goes through a checklist and then starts the aircraft engine(s).When ready the pilot requests taxi approval, and after the approval, taxies the aircraft until arriving at the target movement area.The target movement area is the point in space where departing aircraft transition from the ramp area into the Federal Aviation Administration (FAA) controlled taxiway and is illustrated in Fig. 1a.During the departure maneuvers the duration of the trajectory, the transitions over the motion phases, and the trajectory path are determined by human operators and are considered to be stochastic in nature.Modeling ramp area aircraft departure maneuvers as stochastic processes, we sample a large number of departing trajectories from the stochastic model [16,17].The sampled trajectories define a family i of feasible trajectories that originate from a single gate and arrive at the target movement area at the scheduled time t i .We sample families of trajectories for each unique push back maneuver pattern i ∈ D where the set of D = {A, BL, BR} denotes a set of all possible push back maneuver patterns illustrated in Fig. 1a.The figure shows a single trajectory from each unique maneuver pattern that is available to push back.As shown in the figure, the aircraft parked at gate B is available to push back with a left (BL) or a right (BR) push back maneuver.Using the family of trajectories i and j, we generate a conflict ratio defined by their relative schedule t j -t i [17].A conflict ratio is estimated by fixing the relative schedule of the two families of trajectories and computing the ratio of conflicting trajectories to the total number of trajectories.A conflict is characterized by individual trajectories from the families i and j coming into close spatial proximity along their route.The conflict distribution is estimated by computing a conflict ratio at every whole second, as shown in Fig 1b .In the figure the y-axis represents the ratio of conflicting trajectories to conflict-free trajectories and the x-axis represents the relative target movement area schedule t j -t i .For departing trajectory family i with scheduled target movement area time t i = 0, the start of the push back time window is defined by t S0 i = -max i (T i ) and the finish of the push back time window is defined by t F 0 i = -min i (T i ) .The variable T i is the set of all trajectory duration data for family i that is sampled from the stochastic model.For any given relative schedule, the earliest and latest feasible push back times define the green edges of the rectangle that are seen in Fig. 1c.The distribution in trajectory duration is estimated from the robot experiment data which is directly influenced by the human operator [16,17].We use data from a scaled down robot experiment of the ramp area because trajectory data are not readily available mainly due to the lack of surveillance equipment in the ramp area.Investments in collecting such data are unlikely unless the usefulness of the data in increasing airport efficiency is illustrated.For the scheduled target movement area time differences that have a non-zero ratio of conflicts, we can store and plot the combination of push back times that lead to conflicts.In Fig. 1c we fix departing trajectory family i = A and family j = BR and the vertical axis represents the push back time of an individual trajectory from family A, P B A , and the horizontal axis represents the push back time of and individual trajectory from family BR, P B BR .In Fig. 1b we color select cross sections of the conflict distribution to demonstrate the relationship between the ratio of conflicts (Fig. 1b) defined by the difference between their scheduled target movement area times and the set of red conflict points (Fig. 1c) defined by the combination of push back times that lead to conflicts for the given schedule.The combinations of push back times that lead to conflicts among individual trajectories from family A and family BR are plotted (see Fig 1c) in 10s increments for the target movement area time differences ranging from t BR -t A = -70 to t BR -t A = 40.Given that we are interested in the relative scheduled difference between the two families, we fix the target movement area time of family A such that t A = 0, and the relative schedule difference is defined by the target movement area time of family BR.Associated with each difference in scheduled target movement area time, e.g., t BR = -70, is a green rectangle that is defined by the earliest and latest feasible push back times for each family such that the target movement area time schedule is satisfied.Thus, in order to satisfy the target movement area time t A = 0, any individual trajectory from family A must push back within the window P B A ∈ [-162, -102] and to satisfy the target movement area time t BR = -70 any individual trajectory from family BR must push back within P B BR ∈ [-217, -180].For -70 there is a set of combination of push back times that lead to conflicts.These combinations are labeled as red conflict points κ = (P B BR , P B A ) within the green rectangle (see Fig. 1c).Consider the distribution of conflict points for the scheduled difference of -60 seen in Fig. 1c.We observe that in the bottom right of the green rectangle there is a large area that does not contain any red conflict points.If we restrict an individual trajectory from family A and family BR to push back within the lower right corner of the green rectangle then we can ensure conflict free trajectories.Two potential solutions are shown where the first solution is shown with a solid black line and the second solution with a dotted black line.Among all possible solutions, we define the optimal combination of push back time windows to be the combination where we maximize the minimum time window.By maximizing the minimum time window we compute solutions that ensure any single aircraft's push back time window is not excessively reduced in size to accommodate other aircraft.The optimization problem for aircraft trajectory families i and j is defined asmax t S i ,t F i ,t S j ,t F j J := min{t F i -t S i , t F j -t S j }(1)subject to:∀ κ = (P B j , P B i ) : P B j ∈ [t S j , t F j ] ∨ P B i ∈ [t S i , t F i ](2)where the objective function J is a function of the four variables t S i , t F i , t S j , t F j which represent the start and finish of the push back sub-window for departing trajectory families i and j, respectively.The four variables together define a combination of push back sub-windows such as the window labeled with the solid (dotted) black line in Fig. 1c.The optimization problem is subject to the constraints that any given conflict point κ = (P B j , P B i ) can not be contained within the optimal combination of push back sub-windows.For any given relative target movement area schedule, at a resolution of 1[s], we consider computing the optimal combination of push back sub-windows that are constrained to contain zero conflict points.
+III. Reducing the Number of Constraints Passed to the MILPFor a real time application we envision a scenario where an optimal taxiway scheduler such as SARDA provides the target movement area time schedule for multiple aircraft.This schedule would be updated once every ten seconds.Our algorithm should return the feasible push back time windows or an infeasible flag at the same rate.The main difficulty in solving for the optimal combination of push back time windows is that the distribution and/or the boundary of the conflict points is not known beforehand.If one were to know the linear boundaries that separate each distinct cluster of conflict points, then a mixed integer linear program could be used in real-time to solve for the optimal combination of push back sub-windows.In general, solving for the number of distinct clusters K contained in a cloud of points and the linear boundaries that define the distinct clusters can be a challenging task.For some relative target movement area time schedules, the red conflict points form a single cluster whereas for other schedules the single cluster breaks apart into separated clusters, as seen in Fig. 1c.The well separated clusters seem to appear for schedules in-between the two modes of the conflict distribution (e.g.-50, -40 and -30 in Fig. 1c) where there is a potential mixing of two distinct sources of conflict.We can reduce the overall number of constraints in two steps described in the subsections below.In the first step we use a clustering algorithm to convert the cloud of points into K distinct clusters.Next, in the second step each cluster is bounded within an optimal polygon.The clustering of conflict points (step 1) and the computing of linear boundaries (step 2) can be done offline and the cluster boundaries stored in memory.A small number of boundaries, i.e., the constraints, allows for computing the optimal time windows in significantly less computation time when compared to our previous method.This enables the MILP we develop in this paper to compute the push back time windows online for real-time decision making.
+A. Clustering of Conflict PointsThe first task is to identify the most natural number of clusters to fit to the data.For our analysis any clustering algorithm is sufficient and we implement the following routines implementing the MATLAB machine learning toolbox: (1) support vector machine (2) naive-bayes estimator (3) k-Means clustering (4) hierarchical clustering and (5) Gaussian mixture model.We also used a clustering algorithm implementing (6) a minimum spanning tree.The minimum spanning tree clustering algorithm is equivalent to the hierarchal clustering algorithm except for the original tree is built utilizing Prim's algorithm [27] while the hierarchal clustering algorithm utilizes Kruskal's algorithm [28] .We first cluster the data into K distinct clusters.In order to interpret and validate the natural fit of the clusters with the data we use the silhouette [29] method.The silhouette method assumes that the data has been clustered via any technique into K ≥ 2 separate clusters.For each data point κ, let a(κ) be the average squared euclidean distance of κ to all other data points within the same cluster.We can interpret a(κ) as how well the point κ is assigned to its own cluster (the smaller the value, the better the assignment).Let b(κ) be the lowest average squared euclidean distance of κ to any other cluster which κ is not a member.The cluster with the lowest average squared euclidean distance is said to be the "neighboring cluster" because it is the next best fit cluster for point κ.We now define the silhouette value s(κ):s(κ) = b(κ) -a(κ) max{a(κ), b(κ)}(3)which can be written as From this definition we know that -1 < s(κ) < 1.For s(κ) to be close to 1 we require a(κ) << b(κ).As a(κ) is a measure of how dissimilar κ is to its own cluster, a small value a(κ) means it is well matched.A large b(κ) implies that κ is badly matched to its neighboring cluster.Thus an s(κ) close to one means that the data point κ is appropriately clustered.If s(κ) is close to negative one, we see that κ would be more appropriate if it was clustered in its neighboring cluster.An s(κ) near zero means that the data is on the border of two natural clusters.s(κ) =      1 -a(κ)/b(κ) if a(κ) < b(κ) 0 if a(κ) = b(κ) b(κ)/a(κ) -1 if a(κ) > b(κ) (4)Figure 2 shows the clusters identified by the support vector machine and the k-Means algorithm.We show these results to illustrate how different the identified clusters can be.The top row shows the results of the different methods applied to one distribution and the bottom row shows the results of the different methods applied to a second distribution.The associated silhouette value for each point κ is plotted below the clusters.As can be seen in the figures, the clusters for the k-Means algorithm seem less natural in comparison to the clusters identified by the support vector machine.This intuition is verified as we see the silhouette values for all points κ are greater than zero for the support vector machine whereas there exist points κ for the the k-Means algorithm that have negative silhouette values.For our application, the worst case scenario is to identify conflict points that have a large gap in space between them to the same cluster because this will cut off otherwise feasible portions of the domain.The average s(κ) over all points κ of a single cluster is a measure of how tightly grouped the data is within the cluster.Define s as the average s(κ) over all data points of the entire dataset as a measure of how appropriately the data has been clustered overall.For algorithms that can fit data to more than two see Fig. 3a.In two dimensions the absolute value of the cross product can be interpreted as twice the area of the quadrilateral defined by vertices A, B, C and D.The four constraints ( 6) -( 9) should be generated for each conflict point κ = (P B j , P B i ).Enforcing constraint (6) ensures that the conflict point κ and the normal vector N 1 are on the same side of vector V 1 , i.e. the conflict point κ is to the right of vector V 1 .Together the four constraints ( 6) -( 9) ensure that the conflict point is to the right of every vector V 1 , V 2 , V 3 and V 4 and therefore inside the quadrilateral.Expanding the objective function we have:J := AC × BD 2 = (x C -x A )(y D -y B ) -(x D -x B )(y C -y A ) 2Similarly expanding the constraints we get:N 1 • T κ 1 =(y B -y A )(P B j -x A ) -(x B -A )(P B i -y A ) =P B j y B -x A y B -P B j y A + x A y A -x B P B i + x B y A + x A P B i -x A y A = x B y A -x A y B + x A P B i -P B j y A -x B P B i + P B j y B = x B y A -x A y B + P B i (x A -x B ) + P B j (y B -y A ) N 2 • T κ 2 = x C y B -x B y C + P B i (x B -x C ) + P B j (y C -y B ) N 3 • T κ 3 = x D y C -x C y D + P B i (x C -x D ) + P B j (y D -y C ) N 4 • T κ 4 = x A y D -x D y A + P B i (x D -x A ) + P B j (y A -y D )The minimum perimeter quadrilateral is formulated as a quadratically constrained quadratic program [32,33] minA,B,C,D J := (x B -x A ) 2 + (y B -y A ) 2 + (x C -x B ) 2 + (y C -y B ) 2 + (10) (x D -x C ) 2 + (y D -y C ) 2 + (x A -x D ) 2 + (y A -y D ) 2subject to:N 1 • T κ 1 ≥ 0 (11) N 2 • T κ 2 ≥ 0 (12) N 3 • T κ 3 ≥ 0 (13) N 4 • T κ 4 ≥ 0 (14)where expression (10) is the objective function to minimize the perimeter of the quadrilateral defined by vertices A, B, C and D. The four constraints ( 11) -( 14) should be generated for each conflict point κ, and together they ensure that the conflict point is contained within the optimal bounding quadrilateral Q. Figure 3b illustrates the solution of the minimum area quadrilateral with a red dashed line, the minimum perimeter quadrilateral with a blue line and the convex hull with a black line for a random distribution of conflict points.Let us define the optimal clustering algorithm as the algorithm that minimizes the summation of quadrilateral area (perimeter) over all possible schedules Opt Algorithm = minC Σ k Q(C k ) (15)where Q(C k ) is the area of all optimal bounding quadrilaterals Q using cluster algorithm C for the relative schedule defined by t j -t i = k.We apply this definition to compute the total area Q of the optimal bounding quadrilaterals for each algorithm for each relative schedule.Then for each algorithm the total area is summed over all possible relative schedules.Figure 4 illustrates the performance of the clustering algorithms from best to worst: support vector machine, hierarchical / minimum spanning tree, naive bayes, k-Means and lastly Gaussian mixture model.
+IV. MILP for Real-Time Computing the Optimal Time WindowsAfter identifying the linear boundaries of each cluster, we use the boundaries to solve for the optimal combination of push back sub-windows.To ensure that our time windows do not intersect our conflict quadrilaterals we apply the separating axis theorem.The theorem states that in two dimensions, two convex polygons do not intersect if and only if one of the axis of one of the polygons is a separating axis [34].This implies that if we are given a convex n-gon and a convex m-gon, we can build a MILP that requires n+m+1 constraints to ensure the polygons do not intersect.There will be m+n linear inequality constraints; satisfying any one of these particular constraints ensures that the unique edge of the polygon associated with that constraint is a separating axis.The extra constraint will ensure that a minimum of one edge of one polygon is indeed a separating axis.Here we formulate the MILP that will be used to separate the optimal push back sub-windows from the conflict quadrilaterals.Given two departing aircraft trajectory families i, j ∈ D, the objective function is defined asmax t S i ,t F i ,t S j ,t F j J := M + (t F i -t S i + t F j -t S j )(16)where the continous value M is equivalent to the minimum time window among both aircraft i and j and is a scalar valued parameter.For departing aircraft trajectory families i, j ∈ D we introduce the two constraintst F i -t S i -M ≥ 0 (17) t F j -t S j -M ≥ 0(18)that ensure the push back time window for family i and the push back time window for family j are both greater than the minimum time window M .We note that the value M is not a fixed value, but a continuous variable passed to the model that is solved for in the optimization problem.Similarly, for departing aircraft trajectory family i, j ∈ D we introduce the two constraintst F i -t S i -δ min ≥ 0 (19) t F j -t S j -δ min ≥ 0(20)that ensure the push back time windows for family i and j are both larger than a predefined value δ min .The value δ min is the minimum acceptable push back time window.For example, pilots and ramp controllers could find a schedule that requires aircraft to initiate push back within a time window of 5 seconds too restrictive to consistently execute.In this paper we use the value δ min = 25[s] when solving for the optimal sub-windows.The correct value should be determined in conjunction by pilots and ramp controllers.For departing aircraft trajectory family i, j ∈ D we introduce the four constraintst S i -t i -t S0 i ≥ 0(21)t F i -t i -t F 0 i ≤ 0 (22) t S j -t j -t S0 j ≥ 0 (23) t F j -t j -t F 0 j ≤ 0(24)Constraints ( 21) -( 24) ensure that for any given combination of target movement time schedules, given by t i and t j , the start and end of the push back sub-windows defined by t S and t F must be within the bounds defined by the earliest and latest feasible push back times.This implies that there exists a feasible trajectory from family i and j that meets the scheduled target movement area times t i and t j without accounting for conflicts.These four constraints describe that the push back time windows that we solve for, which are illustrated in black solid (dotted) lines in Fig. 1, are proper sub-windows of the original green rectangle.Our problem is defined by a time window with n = 4 edges and a quadrilateral with m = 4 edges, giving us a total of m + n = 8 edges to select from when choosing a separating axis.To separate the optimal time window from a single conflict quadrilateral Q, we introduce the additional nine constraintsz n1 (t S i -max[y Q ]) ≥ 0(25)z n2 (t F i -min[y Q ]) ≤ 0 (26) z n3 (t S j -max[x Q ]) ≥ 0 (27) z n4 (t F j -min[x Q ]) ≤ 0 (28)z m1 (f m1 ) ≤ 0 (29)z m2 (f m2 ) ≤ 0 (30)z m3 (f m3 ) ≤ 0(31)z m4 (f m4 ) ≤ 0(32)z n1 + z n2 + z n3 + z n4 + z m1 + z m2 + z m3 + z m4 = 1(33)The variable z nE is a binary variable that is defined as z nE = 1 if edge E of the optimal time window is selected as the separating axis, else z nE = 0.The variable z mE is a binary variable that is defined as z mE = 1 if edge E of the conflict quadrilateral is selected as the separating axis, else z mE = 0.The numbering of the edges nE, mE ∈ [1, 2, 3, 4] of the time window and the conflict quadrilateral is not important as long as each edge has a unique number.Expression (33) ensures that one edge of the optimal time window or one edge of the conflict quadrilateral must be a separating axis.Expression (25) constrains the bottom edge of the optimal time window to be above the maximum y value of the conflict quadrilateral Q.This provides that the bottom edge of the time window is a separating axis and the quadrilaterals do not intersect.Similarly, Expression (26) constrains the top edge of the optimal time window to be below the minimum y value of the conflict quadrilateral Q.This provides that the top edge of the time window is a separating axis.Expressions (27)(28) apply similar reasoning to ensure that the left and right edge of the time window is selected as a separating axis.Whereas expressions used in (25)(26)(27)(28) do not change depending upon the conflict quadrilateral Q, equations (29)(30)(31)(32) are dependent upon the unique edges that define the conflict quadrilateral.Select any unique edge mE of Q, to generate the function f mE we need to obtain two pieces of information.First, we need to know if we should constrain the optimal time window above or below this edge, and second, we need the equation of the line that defines edge mE, Y = MX + B. The blue line is the MILP defined in our previous paper [25] and the black line is the real-time MILP defined in this paper.Given these two pieces of information we can generate the functions that ensure the edges of the quadrilateral are a separating axis asf mE =          -t S i + Mt F j + B if M ≥ 0 AND above t F i -Mt S j -B if M ≥ 0 AND below -t S i + Mt S j + B if M ≤ 0 AND above t F i -Mt F j -B if M ≤ 0 AND below(34)The program defined by objective (16) and constraints (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) solves for the optimal time window that is constrained to not intersect the conflict quadrilateral Q.For every additional conflict quadrilateral, the nine constraints (25)(26)(27)(28)(29)(30)(31)(32)(33) are required to separate the time window.These non-linear constraints can be linearized [25,35,36] and the resulting MILP can be solved using the Gurobi Optimizer [37] libraries.Figure 5a shows the optimal time window solution of the program given the two conflict quadrilaterals shown in blue and red. Figure 5b shows the execution time for schedules of n = 4, 5, 6 aircraft for the new MILP compared to the MILP that was defined in our previous paper [25].As can be seen in the figure, the improvement in computation time is significant.Whereas the previous MILP took roughly 10 seconds to solve for schedules of five aircraft, the MILP developed in this paper can solve for the optimal time windows of five aircraft in less than 0.5 seconds.
+V. Analysis of Objective FunctionConsider the objective function maxt S i ,t F i ,t S j ,t F j J := M + (t F i -t S i + t F j -t S j )(35)This objective function formulates a multi-criterion optimization problem.If we set the value of to zero, we solve for the combination of push back sub-windows where the minimum time window is maximized.In contrast, if we set the value of sufficiently large, then we solve for the the combination of push back sub-windows where the summation of all push back time windows is maximized.Figure 6a illustrates the solution of the objective function where is set to zero.The red and blue parts of the domain represent the conflict quadrilaterals.In Fig. 6b a solution where is sufficiently large is shown for the same input.Solutions can be quite different depending upon the value of as can be seen in the figure.The number of aircraft that we solve for in combination with the value of can also influence the quality of solution.For example, if we limit ourselves to solutions of two aircraft i and j, the most intuitive solutions are generated with small values of .This can be seen in Fig. 6 where the maximum perimeter solution (Fig. 6b) for the given input seems quite unappealing compared to the maximum minimum edge solution (Fig. 6a).We believe the solutions seen in Fig. 6b are unappealing compared to the solution in Fig. 6a because the maximum perimeter solutions excessively reduce a single aircraft's push back time window to accommodate the other aircraft's large push back time window.The inverse is true, however, for schedules of multiple aircraft.If we are to solve for a schedule of 4 aircraft using an objective defined to maximize the minimum edge, then it is easy to generate unappealing solutions.This occurs because reducing a push back time window for a single aircraft can have a dramatic affect on all aircraft, as seen in the black dashed line solutions of Fig. 7. Maximizing the minimum edge will return a solution where the push back time windows for all the aircraft are equal to the minimum push back time window.If we solve the same schedule of 4 aircraft with the objective defined to maximize the summation of push back time windows (equivalent to the perimeter), then the model will try to find solutions where it maximizes the push back time windows of all aircraft systematically, as seen in the grey dotted line solutions of Fig. 7.When we solve for multiple aircraft together, we find solutions that maximize the minimum edge unappealing because a single aircraft's push back time window can dramatically affect the other push back time windows.In addition to influencing the quality of solution, the value of has an impact on the computation time.We find that smaller values of help Gurobi solve the problem in less time.Consider Fig. 8 which shows the computation time as a function of the number of conflict domains that we solve for.A conflict domain is a single pairwise conflict that is being solved for.When we solve for multiple aircraft the coupled conflict domains must be solved together simultaneously to ensure optimal solutions.In this figure we report the computation time using the linear boundaries defined from the conflict quadrilaterals and also report the computation time using the linear boundaries defined by the convex hull.For both approaches, the figure shows that a smaller value of epsilon reduces computation time.As expected, the MILP based on the linear boundaries of the conflict quadrilateral outperforms the MILP based on the linear boundaries of the convex hull regardless of the value of epsilon.These results show that there is a tradeoff between the quality of the optimal time window solutions and the value of .When solving for multiple aircraft with value of = 0, then the solver simply has to find a feasible solution where the minimum edge is maximized.All other edges can be equivalent to the minimum edge, generating a relatively unappealing solution as seen in the grey dotted line solutions in Fig. 7. Instead we prefer the model to stretch out the feasible time windows to maximize the time window perimeter.This however, is not a straightforward task, as solving the perimeter problem can be computationally expensive and the solutions for two aircraft can generate relatively unappealing combinations of push back time windows such as the ones seen in Fig. 6b.
+VI. Conclusions and Future WorkIn this paper, we formulated a MILP to solve for the optimal combination of push back time sub-windows.The main contribution of the MILP is to compute in real-time the push back time sub-windows that allow for aircraft to taxi unimpeded from their gate to the departure runway queue in the presence of other aircraft and trajectory uncertainties.This allows for ramp controllers to better manage surface traffic, reduce fuel consumption, and execute conflict free ramp area aircraft trajectories.The MILP is designed for real-time decision making so the computational runtime is a critical component of the tool.The MILP formulation is based on a small number of constraints to ensure that its solution can be computed at speeds compatible with the NASA Ames SARDA scheduler.The small number of constraints results from the two-step processing.We first clustered the conflict points and then bounded each cluster of conflict points with linear boundaries.The linear cluster boundaries were used in the MILP where we exploited the separating axis theorem to ensure the push back time sub-windows do not intersect the conflict quadrilaterals.Solutions were provided and the objective function was analyzed to reveal the dependence of the parameter in the quality of solution and the computation time.Now that we understand how to exploit the structure of the conflict point distribution, we can integrate the information of the size of the push back time window into the logic that schedules aircraft at the target movement area.There is a likely tradeoff between the throughput of the schedule and the size of the push back time windows.A MILP approach using a multi-criterion objective function could account for this tradeoff.This can help with the optimal planning and execution of surface operations from the gates all the way to the departure runways.Figure 1 .1Figure 1.a) Layout of DFW ramp area and an illustration of the A, BL and BR aircraft trajectory.An individual aircraft i, j can be selected from the set of possible aircraft parked at the gate i, j ∈ {A, BL, BR} b) DFW conflict distribution with select cross sections colored for aircraft i = A and aircraft j = BR.c) Plot of combinations of push back times (red points) resulting in conflicts between aircraft i = A and j = BR for schedules ranging from t BR -t A = -70 to t BR -t A = 40 at a resolution of 10 [s].The vertical axis and the horizontal axis represent the push back times of aircraft A and BR, respectively.Each red conflict point is defined as κ = (P B BR , P B A ).If we do not account for conflicts the green rectangle represents the feasible push back domain.For the schedule t BR -t A = -60 two feasible push back sub-windows are plotted in black solid and dotted lines.
+Figure 2 .2Figure2.Clusters identified by the support vector machine and k-Means algorithm.The top row is the two methods applied to one distribution of conflict points defined by t j -t i = -20 and the bottom row is the methods applied to a second distribution of conflict points defined by t j -t i = -27.The associated silhouette value for each conflict point κ is plotted below the clusters.
+Figure 4 .4Figure 4. Top: The area of the bounding quadrilaterals for the different clustering algorithms as a function of the relative schedule.Bottom: The summation of total area of all the optimal bounding quadrilaterals.
+Figure 5 .5Figure 5. a) Optimal sub-windows using the two conflict quadrilaterals as constraints.b) The reduction in computation time that is achieved by reducing the number of constraints for schedules of n = 4, 5, 6 aircraft.The blue line is the MILP defined in our previous paper[25] and the black line is the real-time MILP defined in this paper.
+Figure 6 .6Figure 6.a)The optimal time window defined to maximize the minimum edge is illustrated with a dotted black line.b) The optimal time window defined to maximize the perimeter is not unique.There are four time windows with equivalent perimeter illustrated.Two solutions are represented as tall and skinny time windows with dashed black lines and two solutions are illustrated as short and wide time windows with dotted black lines.
+Figure 7 .7Figure 7. Solutions using a value = 0 (minimum time window maximized) are shown with a grey dotted line for a schedule of 4 aircraft.Solutions using a value = 10 (summation of time windows maximized) are shown with a black dashed line for the same input.In each sub-figure the vertical represents the push back time window of aircraft i, P B i , and the horizontal axis represents the push back time window of aircraft j, P B j .
+Figure 8 .8Figure 8. a) Computation time of the MILP with = 0.01.The red line illustrates the computation time using the minimum area quadrilateral linear boundaries and the blue line illustrates the computation time using the convex hull linear boundaries.b) Computation time of the MILP with = 0.001.The red line illustrates the computation time using the minimum area quadrilateral linear boundaries and the blue line illustrates the computation time using the convex hull linear boundaries.
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+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI:
+			10.2514/6.2016-3751
+			Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
+			of 16 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3751
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+		
+			
+clusters, we fit the data to K = 2, 3, 4 and 5 clusters and select the number of clusters K that provides the maximum silhouette value s.We define the optimal number of clusters as one if the averaged silhouette value s over all the data points is less than 0.8.If the value s is greater than or equal to 0.8 the optimal number of clusters is selected as the number of clusters K that maximized the silhouette value.Using this method, we compute that the optimal number of clusters is two within the interval t j -t i ∈ [-53, -17] for the example in Fig. 1c and one everywhere else.The interval where we find two clusters the most natural fit is consistent with the mixing of distinct conflict sources that are seen in the conflict distribution in Fig. 1b.
+B. Conflict Cluster Linear BoundariesAfter associating the points to K distinct clusters, the next step is to bound each cluster in an optimal bounding polygon.We would like to bound the conflict points with the convex hull of the cluster of points.In two dimensions, the convex hull of a set of points is the minimum area convex polygon that contains the points [30].However, each unique side of our polygon will translate into additional constraints and complexity for the MILP.Therefore we consider constraining the bounding polygon to a quadrilateral instead of the convex hull.The minimum area quadrilateral Q defined by vertices A = (x A , y A ), B = (x B , y B ), C = (x C , y C ) and D = (x D , y D ) labelled in a clockwise fashion is formulated as the non-linear optimization problem [26,31]:subject to:Define V 1 as the vector whose tail originates at vertex A and tip terminates at Vertex B as seen Fig. 3a.Vectors V 2 , V 3 and V 4 are defined in a similar fashion between vertices B, C, and D respectively and N 1 is the normal vector to V 1 generated by V 1 × z.For each conflict point κ we define vector T κ 1 whose tail originates at vertex A and tip terminates at the conflict point κ.Vectors T κ 2 , T κ 3 and T κ 4 are defined in the same way with their tails originating from vertices B, C and D respectively.Expression (5) is the objective function to minimize the square of the scalar value associated with the cross product of the vector AC × BD,			
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+	
+		Linear forms of nonlinear expressions: New insights on old ideas
+		
+			WarrenPAdams
+		
+		
+			RichardJForrester
+		
+		10.1016/j.orl.2006.08.008
+	
+	
+		Operations Research Letters
+		Operations Research Letters
+		0167-6377
+		
+			35
+			4
+			
+			2007
+			Elsevier BV
+		
+	
+	Adams, W. P. and Forrester, R. J., "Linear Forms of Nonlinear Expressions," Operations Research Letters, Vol. 35, No. 4, 2007, pp. 510-518.
+
+
+
+	
+		Gurobi announces Gurobi Optimizer 3.0
+		10.1287/orms.2010.03.09
+		
+			2015
+			Institute for Operations Research and the Management Sciences (INFORMS)
+		
+	
+	Gurobi Optimization, Inc., "Gurobi Optimizer Reference Manual," 2015.
+
+
+				
+			
+		
+	
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+I. IntroductionDuring periods of heavy surface traffic, the NASA Ames Spot and Runway Departure Advisor [1][2][3][4][5][6][7] (SARDA) directs departing aircraft to remain at the gate with their engines off, and when cleared, they can proceed straight from the gate to the departure runway queue minimizing slowing down or stopping for other traffic [2] and still meet their target movement area and take-off times.This technique has the capability of significantly reducing fuel burn and engine emissions.A recent study [8] estimated as much as 18% of fuel consumption during taxi operations was due to stop-and-go activity.The study also concluded that under the assumption of 15 knots or greater speed for all unimpeded aircraft, there is the potential to reduce overall fuel consumption on the surface by at least 21%.In order to execute unimpeded surface trajectories, it is required that airports have the necessary tools to meet the assigned target movement area times.Although surface operations can be improved by adopting an optimal taxiway schedule, the execution ultimately depends on human controllers who control aircraft maneuvers in both ramp area and taxiways [9].This research proposes a tool to aid ramp area controllers in meeting the scheduled target movement area times by computing the push back time window for each departing aircraft.The push back time window is defined by the range between the earliest feasible push back time and latest feasible push back time.Initiating the push back within these bounds ensures there exists a feasible trajectory that arrives at the target movement area at the required time, which is defined by a higher level optimal taxiway scheduler.The main contribution of the tool is to compute push back time windows that allow for aircraft to taxi unimpeded from their gate to the departure runway queue in the presence of other aircraft and trajectory uncertainties.This allows for ramp controllers to better manage surface traffic and reduce fuel consumption while scheduling the push back for ramp area aircraft.Ideally, the tool will be used for real-time decision making by controllers so the computational runtime becomes a critical component of the tool.For relative target movement area schedules resulting in conflict free aircraft trajectories, computing the push back time windows are straightforward and can be estimated from the sampled trajectories.To compute the push back time windows we estimate the maximum trajectory duration and minimum trajectory duration for each aircraft, and then subtract from the scheduled target movement area time for the given aircraft.These computed times represent the start and finish of the feasible push back time window for each aircraft, respectively.When the schedule may lead to aircraft trajectory conflicts, an optimization procedure which solves for push back time windows in the presence of aircraft trajectory uncertainties is needed.To account for the uncertainty of ramp area operations, we proposed a stochastic model of ramp area aircraft trajectories [10,11].The stochastic model was used to sample a large number of feasible ramp area aircraft trajectories.A feasible trajectory is any sampled trajectory from the stochastic model that arrives at the target movement area within a predefined range of heading angles.The set of feasible trajectories for each aircraft is sampled in the absence of other aircraft.Therefore, the set of trajectories represent the feasible ways in which the aircraft can push back from their gate and taxi to the target movement area unimpeded by other aircraft.Using the sampled trajectories, we computed combinations of push back times that lead to conflict of trajectories between any two aircraft, defined by the time separation of aircraft at the target movement area.The conflict distributions are defined by a measure of conflict estimated from the ratio of conflicting trajectories to total trajectories for every relative target movement area schedule.The conflict distributions were used to compute conservative conflict separation constraints that were passed to a mixed integer linear program (MILP) [12][13][14] which incorporated the Spot and Runway Departure Advisor (SARDA) design approach.Previously, we proposed a MILP approach to solve for the optimal combination of push back time windows [15].The optimal combination of push back time windows was defined to maximize the minimum push back time window of a set of all aircraft being scheduled.The solutions were based on conflict points that represent combinations of push back times that result in aircraft trajectory conflicts.We used the idea that no conflict point should be a convex combination [16] of the points in time that define the start and finish of the push back time window.In one-dimension, a convex combination of two points lies in between the two points.The number of constraints that are passed to the MILP is a function of the number of conflict points and for every conflict point, we need five constraints.This approach was conservative in nature because we solve for the combination of push back time windows that allow for zero conflict points inside.Given that we sample ramp area aircraft trajectories from a stochastic model, it is possible to sample trajectories and resulting conflict points that are extremely rare.These rare events can cut off otherwise feasible portions of the push back domain.To address this, in this paper we propose a new MILP to solve for the optimal chance-constrained push back time windows.Chance-constrained programming is defined to maximize an objective function subject to constraints on variables that must be held at prescribed levels of probability [17].The time windows are chance-constrained because they allow for a non-zero but bounded number of conflict points inside them.We find these solutions acceptable as the conflicts may be extremely rare.Furthermore, we do not expect that executing a schedule that leads to a sampled conflict will result in a conflict in real life.Pilots always have the option to slow down or stop along the route to avoid a loss of separation between aircraft.Therefore, we expect there to be a trade-off between the number of conflict points allowed inside the time window and the likelihood that pilots will have to slow down or stop aircraft to avoid a loss of separation.This paper is organized as follows.In Section II we formulate the optimization problem.Next, in Section III we formulate a MILP approach to solve for the chance-constrained optimal time windows.This approach is similar in nature to our previous approach that solved for the time windows which allowed for zero points inside the time windows.Then in Section IV we analyze the solutions and runtime of the MILP and suggest ways to speed up the algorithm.In the last Section, we conclude with a discussion of our findings and provide directions for future work.
+II. Problem FormulationIn this section we formulate the problem of computing the optimal chance-constrained ramp area aircraft push back time windows for each departing aircraft.The combination of push back time windows will be constrained to allow a non-zero but bounded number of conflict points inside the time windows.We begin by defining the variables and parameters that we use:Symbol Description iA family of departing trajectories originating from a single gate and characterized by a single left or right push back maneuver pattern
+DThe set of all possible departing aircraft push back maneuver patterns t iThe scheduled time for family i to arrive at the target movement area t S i The start of the computed push back sub-window for family i t F i The finish of the computed push back sub-window for family i JThe objective function that is being maximizedt S0 iThe start of the feasible push back time window for family i scheduled att i = 0 t F 0 iThe finish of the feasible push back time window for family i scheduled att i = 0 T iThe set of all sampled trajectory duration data for departing trajectory family i P B iThe push back time of a single aircraft trajectory from family i κ A combination of push back times (conflict point) that lead to conflict among family i and j M A continuous variable representing the size of the minimum push back time window δ min A parameter representing the minimum acceptable push back time window A parameter in the objective function J which influences the shape of the push back time windows v κ A binary variable that is one if the conflict point κ is to be constrained outside the time window, otherwise zero z κ1A binary variable that is one if the time window is to be constrained below the conflict point κ, otherwise zero z κ2A binary variable that is one if the time window is to be constrained above the conflict point κ, otherwise zero z κ3A binary variable that is one if the time window is to be constrained left of the conflict point κ, otherwise zero z κ4A binary variable that is one if the time window is to be constrained right of the conflict point κ, otherwise zero N A constant representing the total number of conflict points κ p A constant representing the total number of conflict points κ allowed inside the time windows S A constant that is used in the linearization of nonlinear constraints κ A conflict point κ that has been allowed inside the time window A departing aircraft is parked at the gate and scheduled to arrive at the target movement area.In this paper, we assume the target movement area time is provided from a higher level taxiway scheduler.Upon receiving the push back clearance, a tug (operated by ground crew) pushes back the aircraft from the gate.At the end of the push back procedure, the aircraft stops and the tug disengages.This stop period lasts for some time during which the pilot goes through a checklist and then starts the aircraft engine(s).When ready the pilot requests taxi approval, and after the approval, taxies the aircraft until arriving at the target movement area.The target movement area is the point in space where departing aircraft transition from the ramp area into the Federal Aviation Administration (FAA) controlled taxiway.During the departure maneuvers the duration of the trajectory, the transitions over the motion phases, and the trajectory path are determined by human operators and are considered to be stochastic in nature.Modeling ramp area aircraft departure maneuvers as stochastic processes, we sample a large number of departing trajectories from the stochastic model [10,11].The sampled trajectories define a family i of feasible trajectories that originate from a single gate and arrive at the target movement area at the scheduled time  Using the family of trajectories i and j, we generate a conflict ratio defined by their relative schedule t j -t i [10,11].A conflict ratio is estimated by fixing the relative schedule of the two families of trajectories and computing the ratio of conflicting trajectories to the total number of trajectories.A conflict is characterized by individual trajectories from the families i and j coming into close spatial proximity along their route.The conflict distribution is estimated by computing a conflict ratio at every whole second, as shown in Fig. 1a.In the figure the y-axis represents the ratio of conflicting trajectories to conflict-free trajectories and the x-axis represents the relative target movement area schedule t j -t i .For departing trajectory family i with scheduled target movement area time t i = 0, the start of the feasible push back time window is defined by t S0 i = -max i (T i ) and the finish of the feasible push back time window is defined by t F 0 i = -min i (T i ) .The variable T i is the set of all trajectory duration data for family i that is sampled from the stochastic model.For any given relative schedule, the earliest and latest feasible push back times define the green edges of the rectangle that are seen in Fig. 1b.The distribution in trajectory duration is estimated from the robot experiment data which is directly influenced by the human operator [10,11].We use data from a scaled down robot experiment of the ramp area because trajectory data are not readily available mainly due to the lack of surveillance equipment in the ramp area.For the scheduled target movement area time differences that have a non-zero ratio of conflicts, we can store and plot the combination of push back times that lead to conflicts.In Fig. 1b the vertical axis represents the push back time of an individual trajectory from family i, P B i , and the horizontal axis represents the push back time of and individual trajectory from family j, P B j .In Fig. 1a we color select cross sections of the conflict distribution to demonstrate the relationship between the ratio of conflicts (Fig. 1a) defined by the difference between their scheduled target movement area times and the set of red conflict points (Fig. 1b) defined by the combination of push back times that lead to conflicts for the given target movement area schedule.The combinations of push back times that lead to conflicts among individual trajectories from family i and family j are plotted (see Fig. 1b) in 10s increments for the target movement area time differences ranging from t j -t i = -70 to t j -t i = 40.Given that we are interested in the relative scheduled difference between the two families, we fix the target movement area time of family i such that t i = 0, and the relative schedule difference is defined by the target movement area time of family j.Associated with each difference in scheduled target movement area time, e.g., t j = -70, is a green rectangle that is defined by the earliest and latest feasible push back times for each family such that the target movement area time schedule is satisfied.Thus, in order to satisfy the target movement area time t i = 0, any individual trajectory from family i must push back within the window P B i ∈ [-162, -102] and to satisfy the target movement area time t j = -70 any individual trajectory from family j must push back within P B j ∈ [-217, -180].For -70 there is a set of combination of push back times that lead to conflicts.These combinations are labeled as red conflict points κ = (P B j , P B i ) within the green rectangle (see Fig. 1b).Consider the distribution of red conflict points for the scheduled target movement area time difference of -60 shown in Fig. 1b.We observe that in the bottom right of the green rectangle there is a large area that does not contain any red conflict points, only the purple star conflict point.If we restrict aircraft trajectory families i and j to push back within the lower right corner of the green rectangle, then with high probability the families of trajectories will be conflict free.Two potential solutions are shown in the lower right of the green feasible domain where the first solution is shown with a solid black line and the second solution with a dotted black line.Among all possible solutions, we define the optimal combination of push back time windows to be the combination where we maximize the minimum time window.By maximizing the minimum time window we compute solutions that ensure any single aircraft's push back time window is not excessively reduced in size to accommodate other aircraft.The objective function for aircraft trajectory families i and j is defined asmax t S i ,t F i ,t S j ,t F j J := (1 -)M + (t F i -t S i + t F j -t S j ) (1)where M is a continuous value representing the size of the minimum push back time window among both aircraft i and j and ∈ [0, 1].The value M is not known a priori and is a variable in the program which is solved for.In order for the problem to be well defined, we include the variable M in the constraints to ensure that each individual time window is greater than or equal to the value M .The cost function J is a function of the four variables t S i , t F i , t S j , t F j which represent the start and finish of the push back sub-window for aircraft trajectory families i and j, respectively.The four variables together define a combination of push back sub-windows such as the windows labeled with the solid (dotted) black lines in Fig. 1b.The selection of parameter = 0 defines the objective function to maximize the minimum push back time window (min edge of time window) and the selection = 1 defines the objective function to maximize the summation of push back time windows (perimeter of time window).The optimization problem is subject to the constraints that no more than p conflict points can be contained within the optimal combination of push back sub-windows.For any given relative target movement area schedule, at a resolution of 1[s], we consider computing the optimal combination of push back subwindows as defined above.
+III. MILP for Computing Optimal Chance-Constrained Push Back WindowsHere we provide the mathematical formulation for the constraints of the optimization problem.For departing aircraft trajectory families i, j ∈ D we introduce the two constraintst F i -t S i -M ≥ 0 (2) t F j -t S j -M ≥ 0(3)that ensure the push back time window for aircraft trajectory family i and the push back time window for aircraft trajectory family j are both greater than the size of the minimum time window M .We note that the value M is not a fixed value, but a continuous variable that we pass to the solver.Similarly, for departing aircraft trajectory families i, j ∈ D we introduce the two constraintst F i -t S i -δ min ≥ 0 (4) t F j -t S j -δ min ≥ 0 (5)that ensure the push back time windows for aircraft trajectory family i and j are both larger than a predefined value δ min .The value δ min is the minimum acceptable push back window.For example, pilots and ramp area ground crew could find a schedule that requires aircraft to initiate push back within a time window of 5 seconds too restrictive to consistently execute.In this paper we use the value δ min = 25[s] when solving for the optimal combination of sub-windows.The correct value should be determined in conjunction by ramp area controllers and pilots.For departing aircraft trajectory families i, j ∈ D we introduce the four constraints ( 6) -( 9)t F i -t i -t F 0 i ≤ 0 (6) t S i -t i -t S0 i ≥ 0 (7) t F j -t j -t F 0 j ≤ 0 (8) t S j -t j -t S0 j ≥ 0 (9)where t i is the target movement area time of aircraft trajectory family i and t S0 i and t F 0 i are the earliest and latest feasible push back times for aircraft i such that the scheduled target movement area time t i = 0 is enforced.The same definitions apply to the variables for aircraft trajectory family j.To ensure that for any given combination of target movement area time schedules, given by t i and t j , the start and finish of the push back sub-windows defined by t S and t F must be within the bounds defined by the start and finish of the feasible push back window.This implies that there exists a feasible trajectory from family i and j that meets the scheduled target movement area times t i and t j without accounting for conflicts.These four constraints describe that the push back time windows that we solve for, which are illustrated in black solid (dotted) lines in Fig. 1b, are proper sub-windows of the original green rectangle.For each conflict point κ = 1, 2, ..., N we generate the set of five constraintsv κ z κ1 t F i -P B i ≤ 0 (10)v κ z κ2 t S i -P B i ≥ 0 (11) v κ z κ3 t F j -P B j ≤ 0 (12) v κ z κ4 t S j -P B j ≥ 0(13)z κ1 + z κ2 + z κ3 + z κ4 = 1(14)where v κ is a binary variable associated with the conflict point κ.It is one if the constraints are valid and the point is to be outside the time window, zero otherwise.The variables z κ1 , z κ2 , z κ3 and z κ4 are binary variables associated with the conflict point κ that are one if the time window is to be constrained below, above, left or right of the conflict point, zero otherwise.Therefore, for any individual constraint (10)-( 13) to be valid, both binary variables v κ and z κ associated with the constraint must be equal to one.Otherwise, the constraint will be automatically satisfied because the left hand side of the equation will evaluate to zero.This implies that for any conflict point κ, we can set the value of v κ to zero and automatically satisfy constraints (10)- (13).This ensures that the optimal solution will not constrain the conflict point κ to be outside the optimal time window.Next, we introduce the constraintΣ N κ=1 v κ = N -p (15)where p is the number of conflict points not constrained to be outside the optimal time window.By constraining the summation of the valid bits, we ensure that the number of conflict points that are assigned valid constraints are equal to the value N -p.The four nonlinear constraints ( 10)-( 13) can be linearized [18,19] ast F i -P B i -(1 -z κ1 )S -(1 -v κ )S ≤ 0 (16) t S i -P B i + (1 -z κ2 )S + (1 -v κ )S ≥ 0 (17) t F j -P B j -(1 -z κ3 )S -(1 -v κ )S ≤ 0 (18) t S j -P B j + (1 -z κ4 )S + (1 -v κ )S ≥ 0 (19)where the value of S is sufficiently large.When we linearize the constraints, the value of S should be chosen to generate a constraint that is automatically satisfied for any optimal solution.For instance, if the feasible push back window is defined within the range [0, 100], then generating the constraint that the start of the window should be greater than -10 is automatically satisfied by any optimal solution.As an example, consider the expression ( 16) for aircraft it F i -P B i -(1 -z κ1 )S -(1 -v κ )S≤ 0 If we fix the value z κ1 = 1 and v κ = 0 the constraint simplifies tot F i ≤ P B i + S(20)This constraint should be automatically satisfied by any optimal solution given v κ = 0.For every aircraft i and any conflict point κ, the push back time that generates a conflict will be realized within the domainP B i ∈ [t i + t S0 i , t i + t F 0 i ].The worst case for the less than or equal constraint is to realize the lower bound of P B i = t i + t S0 i .We use the value S = (t F 0 i -t S0 i + B) and plug in the lower bound realization of the push back time into expression (20) to gett F i ≤ t i + t F 0 i + B Given B ≥ 0 theconstraint is automatically satisfied by expression (6).Similar reasoning can be applied to show the constraints are automatically satisfied when z κ1 = 1 and v κ = 0 or when z κ1 = 0 and v κ = 1.Next, consider the expressiont S i -P B i + (1 -z κ2 )S + (1 -v κ )S≥ 0 If we fix the value z κ2 = 1 and v κ = 0 the constraint simplifies to t S i ≥ P B i -S substituting for S = (t F 0 i -t S0 i + B) and the worse case P B i = t F 0 i for the greater than or equal to constraint we get t S i ≥ t i + t S0 i -B Given B ≥ 0 the constraint is automatically satisfied by expression (7).Similar reasoning can be applied to show the constraints are automatically satisfied when z κ2 = 1 and v κ = 0 or when z κ2 = 0 and v κ = 1.
+IV. Analysis of MILP for Optimal Chance-Constrained Time WindowsThe MILP can solve for the optimal chance-constrained time windows given any distribution of conflict points.In this paper we analyze two sample problems that are qualitatively different.The distribution of conflict points are selected to analyze the performance of the algorithm and are not representative of sampled conflict distribution from our stochastic model.The first sample domain can be seen in Fig. 2a -Fig.2c where we provide solutions that allow zero, three, and ten conflict points inside the time window.We call this domain the easy domain since there is only one main cluster of conflict points.Aside from the main conflict cluster, there are several rare samples within the otherwise empty domain.The second domain that we analyze in this paper can be seen in Fig. 2d -Fig.2f where we provide solutions that allow zero, three, and ten conflict points inside the time window.This domain we define as the hard domain.This domain is difficult because there exist symmetries that could produce near optimal solutions subject to chance.Aside from the two main conflict clusters, we introduce a couple of rare samples within the otherwise empty domain.As can be seen in Fig. 2, the quality of solution can be dramatically impacted by the few samples that we introduce into the otherwise empty domain.Particularly we can see that in Fig. 2a the solution is affected by the presence of three conflict points.By allowing these conflict points inside the time window the solution becomes much more appealing.These rare conflict points within the time window would likely be resolved by pilots slowing down or stopping along the route.This introduces an intriguing trade-off.The solutions are influenced by the choice of parameter that appears in objective function 1.In particular, setting = 0 solves for the optimal time window that is defined to maximize the minimal edge.Setting = 1 solves for the time window that maximizes the summation of push back time windows (perimeter of time window).Any value ∈ [0, 1] can also be selected allowing us to mix the two objectives.2c show the optimal time window solution on the "easy" domain allowing 0, 3, and 10 points inside the time window respectively.The red (blue) points are color coded to illustrate which points are outside (inside) the optimal time window.Figure 2d -Fig.2f show the optimal time window solution on the "hard" domain allowing 0, 3, and 10 points inside the time window respectively.The red (blue) points are color coded to illustrate which points are outside (inside) the optimal time window.
+A. Runtime of MILPThe runtime of the Gurobi Optimizer [20] solver is influenced by the distribution of the red conflict points.Figure 3a illustrates that both objectives can be efficiently solved on the easy domain.For the "easy domain", the maximum min edge computation ( = 0) is done faster than the maximum perimeter ( = 1) computation time on average, but the outperformance is not dramatic.For the "hard domain," the solver is not able to efficiently solve the maximum perimeter objective when we allow 30 points inside the time window.Allowing only 10 points inside the time window can take up to 100,000 seconds for the solver to return an answer.Because of this, we omit the maximum perimeter computation time on the hard domain in Fig. .3a so that we do not lose perspective.Figure 3b and Fig. 3c illustrate the sensitivity of the runtime to the selection of parameter in the objective function (1). Figure 3b shows the average runtime in solid blue of the min edge objective solving on the "hard domain" for 20 random inputs, allowing p = 0, 1, 2, ..., 50 conflict points inside the time window.The dotted blue lines represent the average computation time plus or minus one standard deviation.Figure 3c shows the average runtime in solid blue of the perimeter objective solving on the "hard domain" for 20 random inputs, allowing p = 0, 1, 2, ..., 5 conflict points inside the time window.The dotted blue lines represent the average computation time plus or minus one standard deviation.The runtime of the algorithm is also influenced by the number of points allowed inside the time window.Given N conflict points, and allowing p inside the time window, the number of combinatorial possibilities we must consider is given by N p .As the number of points p inside the time window increases, the computational complexity of the problem increases.Figure 3b and Fig. 3c illustrate the increase in runtime as the number of points inside the time window increases.The increased difficulty of the problem is dramatic in Fig. 3c where increasing the number of conflict points inside the time window from two to five increased the average runtime of the algorithm from less than 100 seconds to 1000 seconds.for i = 2:N doj = i -1 z i4 ≥ z j4 end for
+B. Improving the Runtime of the MILP ApproachThe performance of the MILP can be improved by enforcing cutting planes [13,[21][22][23][24] to the solution space.A cutting plane is a valid inequality that improves the linear relaxation of the problem to more closely approximate the integer programming problem.This topic is important because improving formulations with cutting planes is of interest independently of the algorithm used to solve the problem [24].A particularly interesting algorithm is the branch-and-cut method where the cutting plane method improves the relaxation of the problem, and branch-and-bound algorithms proceed by a sophisticated divide and conquer approach to solve the problem [22].Figure 4a shows four red conflict points and a blue time window.The conflict points have been labeled in decreasing order of their x-coordinate using the black labels.Furthermore, we imagine a situation in which the constraint that enforces the time window should be to the right of κ = 1 has been activated, i.e. z 14 = 1.Given the ordering of conflict points in decreasing order, we can immediately generate a set of linear constraints that define cutting planes.Every conflict point to the left of conflict point κ = 1 is by definition also to the left of the time window.Therefore, we can enforce z κ4 ≥ 1 for all conflict points κ > 1 and cut the feasible solution space.If we were to apply this cutting method we would generate O(N 2 ) additional constraints, looping through every conflict point twice.Instead, consider implementing the cut: if the time window is to the right of conflict point κ = 1, then the time window is also to the right of conflict point κ = 2.In algebriac terms this cut takes the form z 24 ≥ z 14 .Next, apply the same logic to the conflict point κ = 2. Iterating through, the loop eventually hits the left most conflict point and every conflict point to the left of conflict point κ = 1 is set with binary variable z κ4 = 1.Given that conflict point κ = 1 is to the left of the time window and the ordering of the conflict points in descending values of x coordinate; setting the binary variable z κ4 = 1 for the conflict points κ = 2, 3, 4 seen in Fig. 4a would satisfy the system of constraints (14,(16)(17)(18)(19).This cuts the feasible solution space while using only O(N ) constraints, see Algorithm 1.Notice the value of j = i -1 fixes the binary variable z κ4 for only the adjacent conflict point as opposed to looping through the indexes j = 1, 2, ..., i -1.The time window will be constrained to be both above conflict point κ = 4 (magenta) and to the right of conflict point κ = 4 (black).This violates constraint (14) and the solution is no longer feasible without a modification using constraints (21 -22) instead.c) If the solver assigns z κ2 = 1 for the conflict point κ = 3 that is inside the time window.This implies that the constraints enforce the time window to be above the conflict point κ = 3. Applying cascading constraints to constrain the window above κ = 3 would enforce the time window to also be above conflict point κ = 4 because the constraints will enforce z 42 >= z 32 .The solver will then either assign the valid bit v 4 = 0, or constrain the time window to be above the conflict point 4, both of which are undesired.Algorithm 2 Cascading MILP Cuts: Constrain Window Above (z κ2 ) And Right (z κ4 )for i = 2:N do j = i -1 z i2 ≥ z j2 z i4 ≥ z j4 end for Algorithm 3 Cascading MILP Cuts: Constrain Window All Directions for i = 2:N do j = i -1 1 + z i1 ≥ z j1 + v j 1 + z i2 ≥ z j2 + v j 1 + z i3 ≥ z j3 + v j 1 + z i4 ≥ z j4 + v j end forWe can apply cuts to the solution space in orthogonal directions at the same time.Figure 4b shows a time window that has been constrained above the magenta conflict point κ = 3 (vertical ordering labeled with magenta) and to the right of the black conflict point κ = 2 (horizontal ordering labeled with black).The time window will be constrained to the right of the black conflict point 3 enforced by the black conflict point 2 (z 34 ≥ z 24 for black κ) , and the time window will be constrained to the right of the black conflict point 4 enforced by the black conflict point 3 (z 44 ≥ z 34 for black κ).The time window will also be constrained to be above the magenta conflict point 4 enforced by the magenta conflict point 3 (z 42 ≥ z 32 for magenta κ).The cuts as described above can be implemented by the algebraic constraints seen in Algorithm 2.Enforcing cuts at the same time in orthogonal directions will lead to unfeasible solutions.An example is conflict point 4 (labeled in both magenta and black) located in the lower left hand corner of the domain of Fig. 4b.The Algorithm 2 will enforce z 42 = 1 and z 44 = 1; this is because the conflict point κ = 4 is indeed both below and to the left of the time window.When the value of both binary variables are set to 1, we violate constraint (14) and the solution is no longer feasible.This problem can be addressed by replacing constraint (14) with the two constraints which allow for any given conflict point to be assigned to two orthogonal directions in relation to the time window at the same time.Applying Algorithm 2 to the conflict points in Fig. 4b will satisfy the system of constraints (16)(17)(18)(19)(21)(22) To enforce cuts in all directions at the same time, a modification must be made to the cutting algorithm.In Algorithm 1 anytime the binary variable z j4 = 1, the binary variable z i4 = 1 was enforced, and this generated a cut to the solution space.This cut is generated even if the valid bit v κ = 0.When we generate cuts in all directions, a conflict point κ that is allowed inside the time window will be assigned a f alse value for either z κ1 , z κ2 , z κ3 , or z κ4 .Since we are cutting in all directions, any of these false assignments will generate unwanted cuts to conflict points outside the time window.If we were not cutting in all directions, the conflict point κ that is inside the time window can assign z κ1 , z κ2 , z κ3 , or z κ4 equal to 1 in the direction that will not be cutting the solution space.This ensures that no unwanted cuts are generated which could eliminate feasible solutions.z κ1 + z κ2 + z κ3 + z κ4 ≥ 1(21)z κ1 + z κ2 + z κ3 + z κ4 ≤ 2(22)For example, see Fig. 4c where the conflict point κ = 3 is inside the time window.To satisfy constraints (21)(22) the solver must assign the value one to any of the binary variables z κ1 , z κ2 , z κ3 or z κ4 .Imagine the solver has selected the value z κ2 = 1.This implies that the constraints enforce the time window to be above the conflict point κ = 3. Applying cascading constraints to constrain the window above κ = 3 would enforce the time window to also be above conflict point κ = 4 because the constraints will enforce z 42 >= z 32 .The solver will then constrain the time window to be above the conflict point 4 or assign the valid bit v 4 = 0, both of which are unwanted solutions.We can address this issue by modifying the algorithm that cuts our solution space.Algorithm 3 shows the adjusted cutting scheme.Instead of enforcing the constraint z 42 ≥= z 32 we now enforce the constraint 1 + z 42 ≥ z 32 + v 3 .This implies that the binary variable z 42 associated with conflict point κ = 4 is only turned on if z 32 = 1 and the valid bit associated with κ = 3 is turned on with v 3 = 1.In the example shown in Fig. 4c, the valid bit v 3 = 0 will not apply cascading cuts to the conflict point κ = 4, and we do not constrain the time window to be above the conflict point 4 or assign the valid bit v 4 = 0 Figure 5a -Fig.5f which shows the average runtime of the various cutting methods.In the top row Fig. 5a -Fig.5c report the results for the min edge objective ( = 0) and the bottom row Figure 5d -Fig.5f report the results for the maximum perimeter objective ( = 1).In the first column we plot the average runtime of the various computation methods in solid colors and plot the average runtime plus or minus the standard deviation in dotted lines.In Fig 5a -Fig.5f the average runtime and the standard deviation are calculated for 20 random inputs of conflict points.In the second column we focus on the cutting methods only and plot the average runtime in solid colors and plot the average runtime plus or minus the standard deviation in dotted lines.In the third column we plot the standard deviation of the various methods.Applying cuts to the solution space helped reduce the average runtime of both the min edge objective and the perimeter objective.As can be seen in Fig. 5a and Fig. 5d, cutting the solution space in the right, left-right, and left-right-up directions provided much better computation results than applying no cuts to the solution space.From Fig. 5b and Fig. 5e we can conclude that left-right cuts were the most efficient for the min edge objective and left-right-up cuts were the most efficient for the max perimeter objective.From Fig. 5c and Fig. 5f we can conclude that cutting the solution space not only improves the average runtime, but also reduces the standard deviation in computation time for the solver.Cutting the solution space in all directions had a negative impact on the runtime and underperformed applying no cuts to the solution space.We do not display the results in Fig. 5a -Fig.5f so that we can focus on the cutting methods that improved the runtime.We find the slowdown of cutting in all directions to be counterintuitive.Applying cuts should reduce the size of the solution space, which eliminates non-integer solutions to the relaxation problem, and therefore help the solver.The increase in runtime could be an artifact of the modified constraints 1 + z 42 ≥ z 32 + v 3 as opposed to the constraints z 42 ≥ z 32 .
+V. Discussion and Future WorkIn this work, we formulated a MILP model to solve for the optimal chance-constrained push back time windows.The time windows are chance-constrained because they allow for a non-zero but bounded probability of conflicts among the sampled aircraft trajectories.These solutions are acceptable because the conflicts may be extremely rare.Solutions of the MILP were shown to be significantly impacted by the presence of even a few conflict points within an otherwise empty domain.By allowing for some conflict points inside the time windows, the solutions become much more attractive and the trade-off between the increased size of the push back window and the small risk of conflict becomes appealing.The main contribution of the MILP is to compute push back time windows that allow for aircraft to taxi unimpeded from their gate to the departure runway queue in the presence of other aircraft and trajectory uncertainties.This allows for ramp controllers to better manage surface traffic and reduce fuel consumption while scheduling the push back for ramp area aircraft.Ideally, the MILP will be used for real-time decision making by controllers so the computational runtime is a critical component of the tool.The runtime of the MILP was shown to be most influenced by a parameter within the objective function, the distribution of conflict points, and the number of conflict points that are allowed inside the time window.Maximizing the minimum time window was found to be much more efficient than maximizing the perimeter of the time window.This is true even though all the constraints and formulation of the MILP is the same, the only difference is the selection of parameter within the objective function.In addition, the runtime of the algorithm was shown to be sensitive to the distribution of conflict points, and the algorithm was shown to execute much more efficiently on an "easy" domain than a "hard" domain.In order to address the the issues with the runtime, we introduced cutting planes to cut the solution space.A cutting plane is a valid inequality that improves the linear relaxation of the problem to more closely approximate the integer programming problem.Various cutting methods were investigated and applied to the MILP.Overall, the analysis showed that the cutting methods reduced the runtime and standard deviation of the runtime for both the maximum min edge objective and the maximum perimeter objective.Future work will consider techniques to reduce the overall execution time of the MILP.The algorithm must execute in less time if we are to implement the solutions for real-time decision making.Future work will also investigate the trade off between the number of points that are allowed inside the time window and the frequency at which pilots have to slow down or stop to avoid a loss of separation.Figure 1 .1Figure 1.a) DFW conflict distribution with select cross sections colored.b) Plot of combinations of push back times (red points) resulting in conflicts between aircraft i and i for schedules ranging from t j -t i = -70 to t j -t i = 40 at a resolution of 10 [s].The y-axis represents the push back time of aircraft i and the x-axis represents the push back time of aircraft j.If we do not account for conflicts the green rectangle represents the feasible push back domain.For the schedule t j -t i = -60 two feasible push back sub-windows are plotted in black solid and dotted lines.
+t i .We sample families of trajectories for each unique push back maneuver pattern i ∈ D where the set D denotes a set of all possible push back maneuver patterns, i.e. a left or right push back from the gate.
+Figure 2 .2Figure 2.Figure 2a -Fig.2cshow the optimal time window solution on the "easy" domain allowing 0, 3, and 10 points inside the time window respectively.The red (blue) points are color coded to illustrate which points are outside (inside) the optimal time window.Figure2d-Fig.2fshowthe optimal time window solution on the "hard" domain allowing 0, 3, and 10 points inside the time window respectively.The red (blue) points are color coded to illustrate which points are outside (inside) the optimal time window.
+Figure 3 .Algorithm 131Figure 3. a) Runtime of the Gurobi solver for different objective function applied to the easy and hard problems allowing 30 conflict points inside the window.The runtime of the maximum perimeter objective applied to the hard domain is omitted as it can take up to 100,000 [s] to execute.b) The average runtime is plotted in solid blue of the min edge objective solving on the "hard domain" for 20 random inputs, allowing p = 0, 1, 2, ..., 50 conflict points inside the time window.The dotted blue lines represent the average computation time plus or minus one standard deviation.c) The average runtime is plotted in solid blue of the perimeter objective solving on the "hard domain" for 20 random inputs, allowing p = 0, 1, 2, ..., 5 conflict points inside the time window.The dotted blue lines represent the average computation time plus or minus one standard deviation.
+Figure 4 .4Figure 4. a) Conflict point 1 will activate the cascading constraints which ensure the time window is constrained to the right of conflict point 2 (z 24 = 1), 3 (z 34 = 1), and 4 (z 44 = 1).b)The time window will be constrained to be both above conflict point κ = 4 (magenta) and to the right of conflict point κ = 4 (black).This violates constraint (14) and the solution is no longer feasible without a modification using constraints(21 -22)  instead.c) If the solver assigns z κ2 = 1 for the conflict point κ = 3 that is inside the time window.This implies that the constraints enforce the time window to be above the conflict point κ = 3. Applying cascading constraints to constrain the window above κ = 3 would enforce the time window to also be above conflict point κ = 4 because the constraints will enforce z 42 >= z 32 .The solver will then either assign the valid bit v 4 = 0, or constrain the time window to be above the conflict point 4, both of which are undesired.
+Figure 5 .5Figure 5.In the top row Fig. 5a -Fig.5cwe report the results for the min edge objective and the bottom row Fig. 5d -Fig.5fwereport the results for the maximum perimeter objective.In the first column we plot the average runtime of the various computation methods in solid colors and plot the average runtime plus or minus the standard deviation in dotted lines.In the second column we plot the average runtime of the cutting methods only in solid colors and plot the average runtime plus or minus the standard deviation in dotted lines.In the third column we plot the standard deviation of the various methods.
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
+			of 13 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 17, 2016 | http://arc.aiaa.org| DOI: 10.2514/6.2016-3752
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+I. IntroductionAirport runways and taxiways have been identified as a bottleneck of the National Airspace System, and the major inhibiting factor for serving an increasing air traffic demand.In order to keep up with the increase of traffic density, new techniques are required to increase airport throughput while maintaining safe separation constraints.Since key airports that accommodate a large portion of traffic operate at or close to their maximum capacity, an optimization of runway and taxiway operations is necessary. 1However, once their operations are improved by an optimal taxiway schedule, its execution ultimately depends on the ramp controllers who control gate push backs and the aircraft maneuvers within the ramp area. 2 Most of the previous taxiway scheduling research has focused on modeling an airport as a graph, i.e., a connected network, with aircraft travelling along the graph edges.In order to solve the optimization problem on the graph authors have used genetic algorithms, 3,4 Mixed Integer Linear Programs (MILPs), 5 hybrids of these, 6,7 constrained search algorithms, 8 and generalized dynamic programming algorithms. 9The MILP approach has been used 10,11,12,13,14,15,16,17 to optimize the routing and scheduling of airport surface traffic.The approach has been applied 18,19 where an optimization model is formulated for taxi scheduling at Dallas-Fort Worth International Airport (DFW).Similar work 20 has formulated the problem to include uncertainties related to constraint satisfaction while uncertainties in aircraft taxiing has been considered in. 21,22,23 Tese previous works have addressed uncertainties in the active movement area (runways and taxiways), but do not consider the ramp area.Ramp area aircraft have been incorporated in, 24,25 but the trajectories are considered to be deterministic.This paper attempts to address the integration of uncertain ramp area aircraft trajectories with a state-of-the-art optimal taxiway scheduler.To the best of our knowledge, this research is the first attempt to address the taxiway scheduler problem assuming stochastic ramp area aircraft trajectories. 26n this paper we develop a general methodology to integrate uncertain ramp area aircraft trajectories within the framework of an optimal taxiway scheduling problem.In our previous work 26 we designed a scaled down robot experiment to generate data that was used as input to a stochastic model of aircraft trajectories.Using the stochastic aircraft trajectories we computed a probabilistic measure of conflicts among departing aircraft within the DFW airport ramp area.While in this paper we apply a similar approach to the Charlotte Douglas International Airport (CLT) ramp area, here we extend our previous work by a simultaneous consideration of departing and arriving aircraft.Moreover, the layout of the CLT ramp area presents new challenges due to the unique geometric constraints and high density of aircraft.The main difficulty in the integration of ramp area aircraft maneuvers into an optimal taxiway scheduling solution is in uncertainties of ramp area trajectories.Unlike aircraft maneuvers on taxiways, ramp area aircraft maneuvers are frequently not confined to well-defined trajectories.The shape and timing of the trajectories are subject to uncertainties resulting from pilots decisions as well as other factors involved in ramp area operations, which can impede an optimal taxiway schedule plan.To account for these uncertainties, we model the trajectories as stochastic processes.However, ramp area trajectory data that could be used to build maneuver models are not readily available mainly due to the lack of surveillance data in the ramp area.To address the lack of data, we collect the critical data on how a human operator navigates an aircraft within the ramp area using an inexpensive scaled down wheeled robot experiment.We use e-puck robots 27 controlled by a human operator to simulate the movement of aircraft from the gate to the taxiway spot and collect the trajectory data with a standard webcam.The data is then processed in MATLAB to provide a time series of position and heading angle measurements of the simulated aircraft.By using the robot experiments, we are in position to go beyond the limits imposed by the lack of surveillance data and are able to collect realistic data that is physical in nature and includes variabilities due to human pilots.The collected data is critical because it is used to estimate parameters of our stochastic model of aircraft trajectories.The stochastic trajectories replicate the statistical properties of the simulated aircraft data and allow us to find optimal taxiway schedules that account for uncertainties generated from the presence of a human pilot.Finally, our work demonstrates the utilization of spatiotemporal data that could be captured from ramp area surveillance equipment if it existed.It is unlikely that airports will invest in the surveillance system unless its usefulness to the efficiency of operations can be shown.This paper is organized as follows.In section II, we formulate the problem under consideration.In section III, we provide information regarding specific operational procedures at the CLT airport.Then, in section IV, we describe the methodology for our stochastic model of aircraft trajectories.We present data associated with the sampled trajectories and conflict distributions in section V. Next, in section VI, we provide the mathematical formulation of the Mixed Integer Linear Program and in section VII we provide illustrative examples.In the last section VIII, we conclude with a discussion of our findings and provide directions for future work.
+II. Problem FormulationDeparting aircraft i is parked at a gate.Upon receiving the push back clearance, a tug (operated by ground crew) pushes back the aircraft from the gate.At the end of the push back procedure, the aircraft stops and the tug disengages.This stop period lasts for some time during which the pilot goes through a checklist and then starts the aircraft engine(s).When ready, the pilot requests taxi approval, and after the approval, the aircraft taxies until arriving at time t i at the terminal node (P1), as shown Fig 1 .During the departing maneuvers, the duration of the trajectory, the transitions over the motion phases, and the trajectory path are determined by human operators and are stochastic in nature.Arriving aircraft i begins its trajectory at the initial node (P2), see Fig 1 .After being released from node P2 at time t i , the aircraft taxies to the assigned gate.During the arriving maneuvers the duration of the trajectory and the trajectory path are considered to be stochastic.The ramp area is defined as the aircraft moving area between the taxiway spots and the gates.For both departures and arrivals, the locations of the initial node and the terminal node in our model define a boundary between the domain where trajectories are well-defined and the domain where trajectories are stochastic.In this paper, we assume the graph extends beyond the taxiway spot to include the blue and yellow structure roadways contained within the ramp area.The uncertain nature of ramp area trajectories between gates and initial/terminal nodes can can impede upon an optimal schedule that is defined on the graph.It is possible to compute a feasible schedule on the graph where aircraft will have to slow down or stop along their route to avoid a loss of separation in the ramp area.In contrast, we consider computing an optimal schedule on the graph that anticipates the uncertainty in such a way that every aircraft can proceed along their route without having to slow down or stop for other traffic.Modeling trajectories as stochastic processes, we generate a probabilistic measure of conflict among aircraft i and j defined by their relative schedule t jt i .A conflict ratio is estimated by fixing the relative schedule of the two aircraft and computing the ratio of conflicting trajectories to the total number of sampled feasible trajectories.Conflicts are defined when trajectories come into close spatial proximity along their route.The conflict distribution is estimated by computing a conflict ratio at every whole second, see Fig 5.  We use the conflict distributions to calculate conservative conflict separation constraints that provide for safe separation in the presence of trajectory uncertainties.The constraints are conservative in nature because they ensure a zero ratio of conflict.The conservative separation constraints are integrated into an optimization problem on the graph that outputs a scheduled time t i at the node P1/P2 for every departing/arriving aircraft.The computed schedule is constrained to optimize the flow of surface traffic such that every aircraft i can proceed along the route without having to slow down or stop for other traffic.For departure aircraft i to proceed unimpeded, it is critical that the aircraft arrive at the terminal node P1 (boundary between the ramp area and the graph) at the scheduled time t i .In order to aid ramp area controllers in meeting the scheduled times, we consider computing the feasible push back time window for each departing aircraft.The push back window is defined by the range between the earliest feasible push back time t S and latest feasible push back time t F .Initiating the push back within the bounds [t S , t F ] ensures there exists a feasible trajectory that arrives at the terminal node P1 at the scheduled time t i , as required by the optimal schedule.
+III. CLT Airport Surface OperationsIn this paper, we consider the center alley of the CLT ramp area with the terminal B on the left and terminal C on the right, see Fig. 1.The gates under analysis are highlighted in red and include B6, B8, B10, C7, C9 and C11.Each gate that we consider can either contain an aircraft ready for departure or receive an arrival aircraft if not currently occupied.We assume departing flights exit the ramp area at taxiway spot 2 and arriving flights enter the ramp are at taxiway spot 4. At CLT, taxiing aircraft can enter or leave the ramp area through the other spots depending on the runway used, in addition to the taxi routes we are considering in this paper.Departure flights begin their trajectories by pushing back from their gates, entering an uncertain waiting period, followed by taxiing to spot 2. The taxiway spot is used as a hold short node and aircraft are required to receive approval from controllers before transitioning between the ramp area and FAA taxiway.Along the taxi route to spot 2, departing aircraft travel over the ramp area merge node P1, see Fig. 1.The ramp area merge node P1 is introduced under an assumption that between merge node P1 and spot 2 there exists a well-defined roadway for the aircraft to follow.In this paper, departing aircraft are not held after arriving at node P1 and are allowed to proceed along the route to spot 2 without having to slow down or stop.Therefore, we assume that providing separation for departing aircraft along the route from their gates to merge node P1 ensures separation along the entire route from their gates to taxiway spot 2.Arrival flights enter the ramp area at taxiway spot 4 and taxi to their assigned gates through the center alley.Arrival trajectories follow the well-defined yellow structure from spot 4 to merge node P2.After arriving at P2, arrival aircraft are released into the center alley and taxi to their gates.We assume that providing the necessary separation for arriving aircraft along the route from merge node P2 to their gates ensures separation along the entire route from spot 4 to their gates.
+IV. MethodologyData related to aircraft ramp area trajectories are not available, or the existing data contains only the average value of trajectory duration.The available information is not sufficient to capture the evolution of individual aircraft trajectories.To account for this we use an inexpensive robot experiment setup where the movement of a Boeing 747-400 (Boeing 747) 28 is simulated within the ramp area. 26Data from experiments are captured on video and processed in MATLAB to provide positions and orientations of simulated aircraft in time.Collecting this data for multiple trajectories provides a distribution over the continuous interval of time that a trajectory can spend in the discrete states such as push back, stop, and taxi. 26We assume that the time spent in each discrete state is defined by a gamma distribution of the formX ∼ Γ(k q , ω q ) (1)with shape parameter k q and scale parameter ω q .Using the MATLAB function gamfit, we estimate parameters that fit the data from our robot experiments.The estimated gamma distributions for aircraft departure trajectories are depicted in Fig. 2.Our data captures the influence of a human operator and we use the collected data to fit parameters of our stochastic model of aircraft trajectories.Once the model is defined we use it to sample 29,30 a large number of realistic trajectories.The sampled trajectories are used to build a probabilistic measure of conflict within the ramp area.After generating the measure of conflict, we calculate the necessary separation constraints in time among aircraft that ensure conflict free trajectories within the ramp area.A single ramp area departure trajectory for aircraft i is described by five discrete states q, q = 0, 1, .., 4. Each discrete state is defined by the continuous time evolution of the aircraft i's position and heading angle described by x i , y i coordinates and θ i , respectively: For q = 0 (gate), q = 2 (stop), q = 4 (merge node P1):dx i = 0, dy i = 0, dθ i = 0(2)For q = 1 (push back): where R i is the radius of the circle of curvature that aircraft i is pushing back along and v i P is the push back velocity.dx i = -v i P cos(θ i )dt, dy i = -v i P sin(θ i )dt, dθ i = - v i P R i dt(3)For q = 3 (taxi):dx i = v i T cos(θ i )dt, dy i = v i T sin(θ i )dt, dθ i = σ i dW i(4)where dW i is an increment of a unit intensity Wiener process, σ i is a scaling factor for the intensity of the variations in the heading angle θ i , and v i T is the forward taxi velocity for aircraft i.In a similar fashion we can define the three discrete states q defined by the continuous time evolution for an arriving aircraft i (q=0, 1, 2):For q = 0 (merge node P2), q = 2 (gate)dx i = 0, dy i = 0, dθ i = 0(5)For q = 1 (taxi):dx i = v i cos(θ i )dt, dy i = v i sin(θ i )dt, dθ i = σ i dW i(6)Transitions between discrete states are considered to be stochastic.In order to simulate a single departure trajectory, we sample the times for states q = 1, 2, 3 from the gamma distribution that was fitted to the robot experiment data.Transitions between the states are defined by the values of the sampled times.In general, the times that we sample for the discrete states will never match exactly between two unique trajectories.For the set of successful samples, this temporal uncertainty will produce a distribution over the trajectory duration.This distribution in trajectory duration is directly influenced by the human operator.In addition to the initial conditions and parameters, we also define a terminal condition the trajectory sample must satisfy.Given that our trajectories are described by an uncontrolled stochastic processes, in general we do not expect the departure (arrival) samples to terminate at the merge node P1 (gate).However, if we sample enough departure (arrival) trajectories, we do expect for some samples to arrive at the merge node P1 (gate) as desired.Conditioning the trajectories to terminate within the goal region provides a set of feasible ramp area departure (arrival) trajectories that terminate (initiate) their trajectory at time t i at merge node P1 (P2).After sampling trajectories we estimate the probability density function for trajectory duration of aircraft i in the absence of any other aircraft in the ramp area.We refer to this type of distribution as natural since the aircraft is unimpeded.We are interested in computing push back windows for aircraft i such that the aircraft arrives at node P1 at a scheduled time t i .Therefore, we enforce a terminal condition in time for the sampled trajectories and this generates a distribution for the push back time.In addition, enforcing this terminal condition in time provides us with a set of departing trajectories that all enter the FAA controlled taxiway via spot 2, see Fig. 1, at the same time.Using the family of trajectories defined by the natural distributions of aircraft i and j, we generate a probabilistic measure of conflict.We compute the measure of conflict by fixing the terminal time of aircraft i in time such that t i = 0. Next we fix the terminal time of aircraft j in time, e.g.t j = -200.Given the relative schedule defined by t jt i , there exists a family of trajectories for both aircraft i and j that push back from their respective gates and taxi to merge node P1 as required.For the relative schedule t jt i , we sample a single trajectory from the family of trajectories for aircraft i and j, measure their spatial proximity along the route, and provide a conflict flag if the aircraft lose spatial separation.If we continue this process of randomly sampling from the family of trajectories with fixed terminal times, we compute a conflict ratio for the relative separation in time at the taxiway spot, see Algorithm 1.The fixed terminal times are considered for every whole second and the estimated conflict distributions provide a measure of conflict at a resolution of 1 second, see Fig. 5.
+V. Sampled Trajectories and Conflict DistributionsAircraft trajectories sampled from the stochastic model are shown in Fig. 3 and Fig. 4.These sampled trajectories are used to compute conflict distributions using Algorithm 1.The conflict distributions provide a conflict ratio among aircraft i and j as a function of the difference between their merge node times, see Fig. 5.In this figure we assume departing aircraft i always arrives at the merge node P1 at time t i = 0 and departing aircraft j arrives at the merge node defined by the value on the horizontal axis.For conflicts that arise between two aircraft that travel through the same merge node(departure vs departure or arrival vs arrival conflicts) there exists a well-defined sequence such that either aircraft i comes prior to aircraft j, or vice versa.When aircraft i is followed by j, define the minimum-time separation constraint δ ij (δ * ij ) that ensures departure (arrival) pairwise separation constraints.This value can be estimated from the upper bound of the conflict distributions, see left image of Fig. 5.The value δ ji (δ * ji ) can be estimated from the lower bound of the conflict distribution.The minimum-time separation constraints are defined as strictly non-negative.Therefore, if departing aircraft i is followed by departing aircraft j we should separate the aircraft at the merge node by the value δ ij , else we separate the aircraft by the value δ ji .For conflicts that arise between two aircraft that do not travel through the same merge node(departure vs arrival conflicts) there does not exist a well defined sequence, see right image of Fig. 5.In this figure we assume departing aircraft i always arrives at the merge node P1 at time t i = 0 and arriving aircraft j is and ∆ U B ij can both be negative, i.e. whether using the lower or upper bound separation constraint we release the arrival from P2 prior to the time that the departure is scheduled at node P1.Therefore, we can not simply select which separation constraint to use defined by the sequencing of aircraft at the merge node as we did before.This implies that to separate the aircraft we should release the arriving aircraft to the left of the value ∆ LB ij or to the right of the value ∆ U B ij .
+VI. Mixed Integer Linear Program (MILP)Given a set of departing aircraft i ∈ D available to push back from their gates at time α i and a set of arriving aircraft i ∈ A that are available to be released from node P2 into the ramp area at time β i , we consider finding a sequence of merge node times t i that ensure conflict free trajectories.The optimal sequence of merge node times is defined as the schedule that minimizes the sum of aircraft hold time for both departing and arriving aircraft.The objective function is given bymin i∈D t i -(α i + |t S0 i |) + i∈A (t i -β i ) (7)where t S0 i is the earliest feasible push back time for departing aircraft i such that the scheduled time t i = 0 is enforced.The value |t S0 i | is equal to the longest duration feasible trajectory that is sampled from the stochastic model.For departing aircraft i, the difference between the scheduled terminal time t i and the earliest available push back time plus duration of the longest feasible trajectory, (α i + |t S0 i |), describes the hold time for the individual aircraft.Departing aircraft are only held at the gate.After being cleared to push back, departing aircraft are not held and are assumed to begin the taxi when they finish spooling the engines.For arriving aircraft i, the difference between the scheduled time t i and the earliest available release time β i describes the hold time for the individual aircraft.Arrival aircraft are assumed to be held at merge node P2 prior to being released into the ramp area.Thus, within the objective function the total aircraft hold time for departing and arriving aircraft are given by the summations.For all departing aircraft i ∈ D we introduce the constraintt i -(α i + |t S0 i |) ≥0 ∀i ∈ D (8)where this constraint ensures that for departing aircraft i the scheduled time of arrival t i at merge node P1 is greater than the earliest available push back time α i plus the duration of the longest feasible trajectory |t S0 i |.Similarly for all arriving aircraft i ∈ A we have the constraintt i -β i ≥0 ∀i ∈ A(9)which ensures that for arriving aircraft i the scheduled time t i that we release the aircraft from merge node P2 into the ramp area is greater than the earliest time β i that the aircraft is available to be released.Constraints ( 8) and ( 9) in conjunction ensure that the hold time of any individual departing or arriving aircraft within the objective function is strictly non-negative, i.e., the minimum hold time for any aircraft i is equal to zero.For all departing aircraft i, j ∈ D we introduce a sequencing constraint defined at merge node P1 given byz ij + z ji =1 ∀i, j ∈ D(10)where z ij is a binary variable that is 1 if departing aircraft j follows departing aircraft i at merge node P1, else z ij = 0.For all departing aircraft i, j ∈ D we have the separation constraintz ij (t j -t i -δ ij ) ≥0 ∀i, j ∈ D(11)which ensures that if departing aircraft j follows departing aircraft i at merge node P1 they should be separated by a minimum of δ ij , else the constraint is automatically satisfied.Given the sequencing constraint defined in (10), the value δ ij should be non-negative else the constraint can not be satisfied.Similarly for all arriving aircraft i, j ∈ A we introduce the sequencing constraintZ * ij + Z * ji =1 ∀i, j ∈ A(12)where Z * ij is a binary variable that is 1 if arriving aircraft j follows arriving aircraft i at merge node P2, else Z * ij = 0.For all arriving aircraft i, j ∈ A we have the separation constraintZ * ij (t j -t i -δ * ij ) ≥0 ∀i, j ∈ A(13)which ensures that if arriving aircraft j follows arriving aircraft i at merge node P2 they should be separated by a minimum of δ * ij , else the constraint is automatically satisfied.Given the sequencing constraint defined in (12), the value δ * ij should be non-negative else the constraint can not be satisfied.For all departing aircraft i ∈ D and arriving aircraft j ∈ A we introduce the constrainta LB ij + a U B ij =1 ∀i ∈ D, j ∈ A(14)where a LB ij is a binary variable that is 1 for departing aircraft i and arriving aircraft j if we release the arriving aircraft j into the center alley to the left of the lower bound of the conflict with departing aircraft i, see Fig. 5, else a LB ij = 0. Similarly a U B ij is a binary variable that is 1 for departing aircraft i and arriving aircraft j if we release the arriving aircraft into the center aley to the right of the upper bound of the conflict with departing aircraft i.For all departing aircraft i ∈ D and arriving aircraft j ∈ A we have the separation constrainta LB ij (t j -t i -∆ LB ij ) ≤0 ∀i ∈ D, j ∈ A(15)when a LB ij = 1 this ensures the scheduled time t j that we release arriving aircraft j into the ramp area is a minimum of ∆ LB ij prior to the scheduled terminal time t i that we require departing aircraft i to arrive at merge node P1.For all departing aircraft i ∈ D and arriving aircraft j ∈ A we also have the separation constrainta U B ij (t j -t i -∆ U B ij ) ≥0 ∀i ∈ D, j ∈ A(16)Algorithm 1 Conflict Distribution: Aircraft i vs Aircraft j Assume t i = 0 Set N = 1,000 for t j = -200:1:200 do for k = 1:N do • Randomly sample aircraft i and j from their respective family of trajectories.• Measure the spatial proximity of the aircraft along the route and provide a conflict flag if aircraft lose spatial separation.end for • Return conflict ratio for the relative schedule t jt i end for • Return conflict ratio for all relative schedules at a resolution of 1 second.when a U B ij = 1 this ensures the scheduled time t j that we release arriving aircraft j into the ramp area is a minimum of ∆ U B ij after the scheduled terminal time t i that we require departing aircraft i to arrive at merge node P1.t j t i [s] t j t i [s]This formulation solves for the optimal schedule that minimizes the summation of aircraft hold time while also ensuring conflict free trajectories.The program is in the form of a Mixed Integer Quadratic Program due to quadratic constraints (11), (13), and (15-16).In order to pass this program to a MILP solver we linearize the quadratic constraints ast j -t i -δ ij + (1 -z ij )M ≥0 ∀i, j ∈ D (17) t j -t i -δ * ij + (1 -Z * ij )M ≥0 ∀i, j ∈ A (18) t j -t i -∆ LB ij -(1 -a LB ij )M ≤0 ∀i ∈ D, j ∈ A (19) t j -t i -∆ U B ij + (1 -a U B ij )M ≥0 ∀i ∈ D, j ∈ A (20)where the constant M is chosen to be sufficiently large.Constraints ( 17), ( 18), ( 19), (20) are linear separation constraints that replace the quadratic constraints ( 11), ( 13), ( 15), (16), respectively.After formulating the program as a MILP we solve for the optimal time schedule by utilizing the Gurobi Optimizer 31 solver.1.An optimal solution for CLT example scenario 1 containing three departing aircraft at gates B6, B10, and C9 and two arriving aircraft that terminate their trajectories at gates B8 and C7.The earliest available time α i or β i that aircraft i is available to initiate its trajectory is sampled from the uniform distribution defined as U (0, 100).For departing trajectories the feasible window in time to initiate their push back is defined by the values ts and t f .The scheduled times and aircraft hold for a FCFS scheduling approach are shown on the right for comparison.
+VII. MILP Example SolutionsIn this section, we provide example solutions of the MILP.In order to output an optimal schedule the first thing we do is to select the set of departing and arriving aircraft.For this paper we define a scenario as a set of three departing aircraft and two arriving aircraft.After the departing (arriving) aircraft are defined, we sample the parameters α i or β i for each aircraft i from the uniform distribution defined as U(0, 100).For a departing aircraft with the scheduled time t i = 0, the earliest feasible push back time is defined by t S0 i =max i (T i ) and the latest feasible push back time is defined by t F 0 i =min i (T i ) .The variable T i is the trajectory duration of aircraft i that is sampled from the stochastic model.Using the values t S0 i and t F 0 i , the push back bounds t S i and t F i for any given scheduled spot time t i can be computed as t S i = t i + t S0 i and t F i = t i + t F 0 i .All other necessary parameters for the MILP are computed from the conflict distributions as previously mentioned.The output of the program is a schedule of merge node times t i that minimizes the summation of aircraft hold time.Furthermore, the model provides the feasible push back windows for each departing aircraft, as shown in Table 1.The right hand side of Table 1 shows the scheduled times and aircraft hold for a First-Come, First-Served (FCFS) scheduling approach.The FCSC scheduling approach is defined to schedule the aircraft at the taxiway spot in the same sequence that the aircraft become available to initiate their trajectories.The conservative conflict constraints are applied to the FCFS sequence of aircraft and the schedule is computed.As can be seen in the table, the FCFS scheduling approach is sub-optimal in comparison to the MILP approach.The example scenario that we consider in Table 1 is defined by three departing aircraft at gates B6, B10, and C9 and two arriving aircraft that terminate their trajectories at gates B8 and C7.For the given scenario, the optimal schedule and the associated aircraft hold times are dependent upon the set of sampled earliest available times α i and β i .For a different set of earliest available times α i and β i , the optimal schedule and aircraft hold times can be quite different.For a given scenario (set of departing and arriving aircraft), we would like to understand how our scheduling algorithm performs under a variety of different sets of earliest available times.In order to understand the overall performance of the MILP we fix a scenario, compute the optimal schedule for many different sets of earliest available times, and then calculate the average hold times for each aircraft, as seen in each sub figure in Fig. 6.Solutions for each scenario are computed for 300 randomly sampled sets of earliest available input parameters and the average hold time for each aircraft within the six different example scenarios is plotted.In the upper left most figure (scenario 1), the average hold time for departing aircraft from gates B6, B10 and C9 are shown with blue bars while the average hold time for arriving aircraft from gates B8 and C7 are shown with red bars.We apply the same analysis of averaging the hold time over many different sampled sets of earliest available times and apply it to five additional scenarios (sets of departing and arriving aircraft).
+VIII. DiscussionIn this work we used sampling methods to build sets of feasible ramp area aircraft trajectories.These sampled trajectories were used to compute conflict distributions among aircraft i and j.Once the conflict distributions were computed, we estimated minimum-time separation constraints between any two aircraft i and j.Using these separation constraints we formulated a MILP and solved the optimal taxiway scheduling problem.The separation constraints that we used in the MILP formulation are conservative in nature.The constraints are conservative because providing the minimum-time separation at the taxiway spot ensures a zero ratio of conflict.Using the conservative conflict constraints, we solved for the average hold time for six different scenarios defined by three departing aircraft and two arriving aircraft.For schedules that have a non-zero ratio of conflict, future work will include techniques that can eliminate the conflicts between aircraft i and j by shrinking the push back time windows.Using these techniques, the throughput of the conservative schedule can be improved.We would also like to improve upon the time data that we use as input to the stochastic model of aircraft trajectories.To get more accurate time data, we plan to use the data from real-time human-in-the-loop simulations performed at NASA Ames' FutureFlight Central. 32Figure 1 .1Figure 1.Center alley of the CLT airport.The gates under consideration are highlighted in red and include gates B6, B8, B10, C7, C9 and C11.Departing aircraft push back from their gates, enter into an uncertain stopped period, and then taxi to the merge node P1.Arriving aircraft are released from the merge node P2 and taxi to their assigned gates.
+Figure 2 .2Figure 2. Distribution of time spent in discrete states for departing trajectories.The time spent in push back, wait, and taxi is shown in red, green, and blue, respectively.
+Figure 3 .3Figure 3. Sampled departure trajectories.For each gate, we generate a family of feasible departure trajectories.For each gate, the family of trajectories contains uncertainty within both the spatial path taken and trajectory duration.
+Figure 4 .4Figure 4. Sampled arrival trajectories.For each gate, we generate a family of feasible arrival trajectories.For each gate, the family of trajectories contains uncertainty within both the spatial path taken and trajectory duration.
+Figure 5 .5Figure 5. Conflict distributions computed using Algorithm 1. Left: Conflict distribution for CLT departure from gate B6(i) VS.CLT departure from gate B8(j).The terminal time of departing aircraft i is fixed at time t i = 0 and the terminal time for departing aircraft j is given by the value on the horizontal axis.Right: Conflict distribution for CLT departure from gate C9(i) VS.CLT arrival from gate B6(j).The terminal time of departing aircraft i is fixed at time t i = 0 and the release time for arriving aircraft j is given by the value on the horizontal axis.
+9 Figure 6 .96Figure 6.Top: Example scenario 1, 2, and 3 from left to right.Bottom: Example scenario 4, 5 and 6 from left to right.The average hold time for various departing (blue) and arriving (red) aircraft operating within the CLT center alley.Each sub figure is defined by fixing a different scenario of three departing and two arriving aircraft.The earliest available time α i or β i that aircraft i is available to initiate their trajectory is sampled from the uniform distribution defined as U (0, 100).
+TableAircraftα i (β i ) Merge Node Time: t i Aircraft Holdt St FFCFS t i FCFS HoldDeparture: B651742126591530Departure: B10101290103822899Arrival: C726260NA NA229203Arrival: B845169124NA NA234189Departure: C96530993158 187369218Total Hold238709
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI:
+			10.2514/6.2015-2591
+			Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2591
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diff --git a/file176.txt b/file176.txt
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@@ -0,0 +1,763 @@
+
+	
+	
+		
+I. IntroductionIn order to keep up with the increase of traffic density and reduce congestions on the surface of airports, new techniques are required to improve airport throughput while maintaining safe separation among taxiing aircraft.Since key airports that accommodate a large portion of traffic operate at or close to their maximum capacity, an optimization of runway and taxiway operations is necessary. 1However, although their operations can be improved by adopting an optimal taxiway schedule, its execution ultimately depends on human controllers who control aircraft maneuvers in both ramp area and taxiways. 2ost of the prior research on taxiway scheduling has focused on modeling an airport as a graph, i.e., a connected network, with aircraft travelling along the graph edges.In order to solve the optimization problem on the graph authors have used genetic algorithms, 3,4 Mixed Integer Linear Programs (MILPs), 5 hybrids of these, 6,7 constrained search algorithms, 8 and generalized dynamic programming algorithms. 9The MILP approach has been used in 10,11,12,13,14,15,16,17 to optimize the routing and scheduling of airport surface traffic.The approach has been applied in 18,19 where an optimization model is formulated for taxi scheduling at Dallas-Fort Worth International Airport (DFW).Similar work 20 has formulated the problem to include uncertainties related to constraint satisfaction while uncertainties in aircraft taxiing has been considered in. 21,22,23 Tese previous works have addressed uncertainties in the active movement area (runways and taxiways), but do not consider the ramp area.Ramp area aircraft have been incorporated in, 24,25 but the trajectories are considered to be deterministic.This paper attempts to address the integration of uncertain ramp area aircraft trajectories with a state-of-the-art optimal taxiway scheduler.To the best of our knowledge, this research is the first attempt to address the taxiway scheduler problem assuming stochastic ramp area aircraft trajectories.The main difficulty in the integration of ramp area aircraft trajectory characteristics into an optimal taxiway scheduling solution is in addressing uncertainties of ramp area trajectories.Unlike aircraft maneuvers on taxiways, ramp area aircraft maneuvers are typically not confined to well-defined trajectories.The shape and timing of the trajectories are subject to uncertainties resulting from pilots decisions as well as other factors involved in ramp area operations, which can impede upon an optimal taxiway schedule plan.In our previous work 26 we accounted for trajectory uncertainties by developing a stochastic model of aircraft trajectories.The stochastic model was used to generate a probabilistic measure of conflicts within the ramp area, see Fig. 1.The conflict distributions were then used to conservatively schedule conflict free trajectories at the taxiway spot.This method was applied to the DFW Terminal C ramp area to generate optimal schedules for departing aircraft. 26The method was also applied to the center alley of the Charlotte Douglas International Airport ramp area to generate optimal schedules for both departing and arriving aircraft. 27ur previous work has taken a conservative approach to separating aircraft.Using the conservative scheduling approach, we only consider schedules for aircraft i and j that have a zero ratio of conflict.A conflict ratio is estimated by fixing the relative schedule of the two aircraft and computing the ratio of conflicting trajectories to the total number of feasible trajectories.In this paper we build upon our previous work and develop an optimization framework that exploits the structure of the conflict distributions in order to increase the throughput of the optimal schedule.The throughput of the schedule can be improved upon by considering schedules that have a non-zero ratio of conflicts.For schedules that have a non-zero ratio of conflict, we formulate a MILP that returns the optimal combination of push back sub-windows that ensure conflict free trajectories.We apply the MILP to scheduling departing aircraft within the DFW Terminal C ramp area.We then analyze the increase of throughput that is available when compared to our previous conservative scheduling approach.This paper is organized as follows.In section II we formulate the optimization problem under consideration.In section III we define the MILP model that we use to solve for the optimal combination of push back sub-windows.Then we provide solutions of the MILP and demonstrate the increase of throughput that can be achieved over the conservative schedule.We then analyze the computational performance of the MILP in comparison to a brute force algorithm that solves for the optimal combination of push back sub-windows.In the last section, we conclude with a discussion of our findings and provide directions for future work.
+II. Problem FormulationThe right panel of Fig. 1 shows the DFW Terminal C ramp area.Departing aircraft i begins parked at one of three possible gates labeled with A, B and C. Trajectories that begin from gate B can push  back with either a left or a right push back maneuver, labeled as BL and BR, respectively.The stochastic model trajectory samples contain spatial and temporal uncertainty and are colored to illustrate the family of possible trajectories.Using the stochastic trajectories we compute a probabilistic measure of conflict among aircraft i and j defined by the difference between their scheduled times at the spot, t jt i .The computed conflict distribution is defined by a ratio of the number of pairs of conflicting trajectories to the total number of sampled trajectory pairs.The conflict distribution is estimated by computing the conflict ratio at every whole second, see Fig 2 .For the scheduled spot time differences that have a non-zero ratio of conflicts, we can store and plot the combination of push back times that lead to conflicts.In Fig. 2B the vertical axis represents the push back time of aircraft A(i), P B A , and the horizontal axis represents the push back time of aircraft BR(j), P B BR .In Fig. 2 we color select cross sections to demonstrate the relationship between the ratio of conflicts (Fig. 2A) defined by the difference between their scheduled spot times and the set of red conflict points (Fig. 2B) defined by the combination of push back times that lead to conflicts for the given difference between their scheduled spot times t jt i .t j sch t i sch [s] t j sch t i sch [s] t j sch t i sch [s] (A, BR) : i = 1, j = 2 (BR, A) : i = 2, j = 1 (A, BL) : i = 1, j = 3 (A, C) : i = 1, j = 4 (BR, C) : i = 2, j = 4 (BL, C) : i = 3, j = 4The combinations of push back times that lead to conflicts between aircraft A(i) and BR(j) are plotted (see Fig 2B) in 10s increments for the spot time differences ranging from t jt i = -70 to t jt i = 40.Given that we are interested in the scheduled spot time difference between two aircraft, we fix the spot time of aircraft A(i) such that t A = 0, and the difference in the scheduled spot time is defined by the spot time of aircraft BR(j).Associated with each difference in scheduled spot time, i.e., t BR = -70, is a green rectangle that is defined by the earliest and latest feasible push back times for each aircraft such that the spot time of the schedule is satisfied.Thus, in order to satisfy the spot time t A = 0, aircraft A(i) must push back within the window P B A ∈ [-162, -102] and to satisfy the spot time t BR = -70 aircraft BR(j) must push back within P B BR ∈ [-217, -180].For -70 there is a set of combination of push back times that lead to conflicts.These combinations are labeled as red points within the green rectangle (see Fig. 2B).Consider the distribution of red conflict points for the scheduled spot time difference of -60 seen in Fig. 2 right panel.We observe that in the bottom right of the green rectangle there is a large area that does not contain any red conflict points.If we restrict aircraft A(i) and B(j) to push back within the lower right corner of the green rectangle then we can ensure conflict free trajectories.Two potential solutions are shown where the first solution is shown with a solid black line and the second solution with a dotted black line.Among all possible solutions we would like to find a combination of push back time windows where the minimum time window is maximized.The abstract form of the optimization problem is defined asmax t S i ,t F i ,t S j ,t F j J := min{t F i -t S i , t S j -t F j }(1)subject to:∀ κ = (x, y) : x ∈ [t S j , t F j ] ∨ y ∈ [t S i , t F i ](2)where the cost function J is a function of the four variables t S i , t F i , t S j , t F j which represent the earliest and latest push back times for aircraft i and j, respectively.Thus, for aircraft i the variables t S i and t F i represent the start and finish of the push back window.The four variables together define a combination of push back sub-windows such as the windows labeled with the solid (dotted) black lines.The optimization problem is subject to the constraints that any given conflict point κ = (x, y) can not be contained within the optimal combination of push back sub-windows.For any given schedule, at a resolution of 1[s], we consider solving for the optimal combination of push back sub-windows that are constrained to contain no conflicts.
+III. Mixed Integer Linear Program (MILP)Here we provide the mathematical formulation of the abstract optimization problem formulated by expressions (1) and (2).Given any two aircraft i and j, the objective function ( 1) is used to maximize the minimum time window for both aircraft.While this seems like a reasonable objective function there is a slight problem with this formulation.With this objective function we can not distinguish between two time windows that have equal minimum edge length, as illustrated in Fig. 3A.In Fig. 3A the minimum edge of the orange combination of time windows and the minimum edge of the blue combination of time windows are equal and defined by t S j and t F j .Clearly, we would prefer the orange combination of time windows to the blue as aircraft i has a much larger time window to push back within.In order to distinguish between these two solutions we introduce the objective function maxt S i ,t F i ,t S j ,t F j J := M + (t F i -t S i + t F j -t S j )(3)where M = min{t F i -t S i , t S j -t F j } is the minimum time window among both aircraft i and j and is sufficiently small.In the objective function the extra term multiplied by is added in order to distinguish between two combinations of time windows that have equal minimum edge lengths.Using the objective function (3) with the example depicted in Fig. 3A, we can distinguish between the orange and blue combination of time windows and the orange combination would be selected as optimal.The optimization constraints are described in sequel.For departing aircraft i, j ∈ D we introduce the two constraintst F i -t S i -M ≥ 0 (4) t F j -t S j -M ≥ 0 (5)that ensure the push back time window for aircraft i and the push back time window for aircraft j are both greater than the minimum time window M .We note that the value M is not a fixed value, but a function of the four variables we solve for.Similarly, for departing aircraft i, j ∈ D we introduce the two constraintst F i -t S i -δ min ≥ 0 (6) t F j -t S j -δ min ≥ 0 (7)that ensure the push back time windows for aircraft i and j are both larger than a predefined value δ min .The value δ min is the minimum acceptable push back window.For example, pilots and ramp area ground crew could find a schedule that requires aircraft to initiate push back within a window of 5 seconds too restrictive to consistently execute.In this paper we use the value δ min = 25[s] when solving for the optimal sub-windows.The correct value should be determined in conjunction by ramp area controllers and pilots.For departing aircraft i, j ∈ D we introduce the four constraintst S i -t i -t S0 i ≥ 0 (8) t F i -t i -t F 0 i ≤ 0 (9) t S j -t j -t S0 j ≥ 0 (10) t F j -t j -t F 0 j ≤ 0 (11)where t i is the taxiway spot time of aircraft i and t S0 i and t F 0 i are the earliest and latest feasible push back times for aircraft i such that the scheduled spot time t i = 0 is enforced.The same definitions apply to thet S j t S j t F j t F j t S i t S i t F i t F i z 3 h t F j P B BR i  0 z 4 h t S j P B BR i 0 z 1 h t F i P B A i  0 z 2 h t S i P B A i 0 t S j t F j t F j t S i t S i t F i t F i A) B)Figure 3. A) The cost function in objective ( 3) is a function of 4 variables (t S i , t F i , t S j , t F j ).The minimum edge length of the blue combination of time windows is equal to the minimum edge of the orange combination of time windows.By adding the extra term Σ i,j t F i/j -t S i/j in the cost function we can distinguish between the two rectangles and the orange rectangle is selected as optimal.B) Set of 4 constraints that ensure the optimal combination of push back sub-windows is either above, below, left or right of any single conflict point κ = (P B BR , P B A ).variables for aircraft j.For the scheduled spot time t i = 0, the earliest feasible push back time is defined by t S0 i = -max i (T i ) and the latest feasible push back time is defined by t F 0 i = -min i (T i ) .The variable T i is the trajectory duration of aircraft i that is sampled from the stochastic model.For any given relative schedule, the earliest and latest feasible push back times define the green edges of the rectangle that are seen in Fig. 2. The distribution in trajectory duration is estimated from the robot experiment data which is directly influenced by the human operator.Constraints ( 8) - (11) ensure that for any given combination of spot time schedules, given by t i and t j , the start and end of the push back sub-windows defined by t S and t F must be within the bounds defined by the earliest and latest feasible push back times.This implies satisfying these constraints ensures that there exists a feasible trajectory for aircraft i/j that is capable of meeting the scheduled spot times t i /t j without accounting for conflicts.These four constraints provide that the push back windows that we solve for, which are illustrated in black solid (dotted) lines in Fig. 2, are indeed sub-windows of the original green rectangle.To solve for the optimal push back sub-windows we need information related to the set of conflict points.For example, the conflict points could be used to estimate a distribution that defines the probability of conflict as a function of the combination of push back times.The conflict points could also be used to fit a piecewise linear boundary that separates the level set of conflicting combination of push back times from the level set of conflict-free combination of push back times.In contrast to these approaches, we use the conflict points directly to generate constraints.The constraints we use are based on an idea that no conflict point should be a convex combination of the optimal combination of time window endpoints.The conflict point κ = (x, y) is a convex combination of the optimal time window endpoints of aircraft i if and only ifαt F i + (1 -α) t S i = y, α ∈ [0, 1]This implies that we can enforce that point κ is not a convex combination of t S i and t F i by choosing a value of α that is either smaller than 0 or greater than 1.For departing aircraft i, j ∈ D, we enforce the following set of seven constraints for each conflict point κ = (P B BR , P B A ).α κ1 t F i + (1 -α κ1 ) t S i = P B A (12) z κ1 α κ1 -1 ≥ 0 (13) z κ2 α κ1 ≤ 0 (14)α κ2 t F j + (1 -α κ2 ) t S j = P B BR (15) z κ3 α κ2 -1 ≥ 0 (16)z κ4 α κ2 ≤ 0 (17)z κ1 + z κ2 + z κ3 + z κ4 = 1 (18)where z κ is a binary variable.The constraints ( 12)-( 18) ensure that the conflict point κ = (P B BR , P B A ) is not a convex combination of the optimal time window endpoints t S i and t F i nor a convex combination of the optimal time window endpoints t S j and t F j .By enforcing this set of constraints we ensure that the conflict point κ is not a convex combination of the optimal combination of push back time window endpoints.The constraints in equations ( 12)-( 17) can be simplified.From constraints ( 12)-( 14) we can write α κ1 as a function of the conflict point κ and the start and end of the push back sub-windows, t S i and t F i , respectively.α κ1 (P B A , t S i , t F i ) = P B A -t S i t F i -t S iSubstituting the function α κ1 (P B A , t S i , t F i ) into constraint (13) provides us with the equationP B A -t S i t F i -t S i ≥ 1which can be enforced by satisfying the inequalityP B A ≥ t F iSimilarly we can substitute the function α κ1 (P B A , t S i , t F i ) into constraint ( 14) which provides us with the equationP B A -t S i t F i -t S i ≤ 0which can be enforced by satisfying the inequalityP B A ≤ t S iFollowing the same reason, we can transform the three constraints (15)-( 17) into the two constraintsP B BR ≥ t F j P B BR ≤ t S jPutting everything together we obtain the following set of five constraints that can be used instead of the seven constraints (12-18) for each conflict point κ = (P B BR , P B A ).z κ1 t F i -P B A ≤ 0 (19)z κ2 t S i -P B A ≥ 0 (20) z κ3 t F j -P B BR ≤ 0 (21)z κ4 t S j -P B BR ≥ 0 (22)z κ1 + z κ2 + z κ3 + z κ4 = 1(23)The set of constraints ( 19)-( 23) enforces that the conflict point κ is not a convex combination of the start and end times of the optimal combination of sub-windows defined by (t S i , t F i , t S j , t F j ).Geometrically speaking, this set of five constraints ensures that any feasible combination of sub-window is either above, below, left or right of the conflict point κ, shown in Fig. 3.In the optimization problem defined by objective (3) subject to constraints ( 4)-( 11) and ( 19)-( 23), for every conflict point κ we have four nonlinear constraints ( 19)- (22).These constraints can be linearized ast F i -P B A -(1 -z κ1 )S ≤ 0 (24)t S i -P B A + (1 -z κ2 )S ≥ 0 (25) t F j -P B BR -(1 -z κ3 )S ≤ 0 (26) t S j -P B BR + (1 -z κ4 )S ≥ 0 (27)where the value of S is sufficiently large.By replacing the nonlinear constraints ( 19)-( 22) with the linear constraints ( 24)-( 27) we obtain a MILP problem that we can pass directly to Gurobi Optimizer 28 to solve.The mixed integer linear program that we pass to Gurobi is defined as maxt S i ,t F i ,t S j ,t F j J := M + (t F i -t S i + t F j -t S j ) (28)for aircraft i, j we generate the eight constraintst F i -t S i -M ≥ 0 (29) t F j -t S j -M ≥ 0 (30) t F i -t S i -δ min ≥ 0 (31) t F j -t S j -δ min ≥ 0 (32) t S i -t i -t S0 i ≥ 0 (33) t F i -t i -t F 0 i ≤ 0 (34) t S j -t j -t S0 j ≥ 0 (35) t F j -t j -t F 0 j ≤ 0 (36)and for each conflict point κ = (P B j , P B i ) we generate the five constraintst F i -P B i -(1 -z κ1 )S ≤ 0 (37) t S i -P B i + (1 -z κ2 )S ≥ 0 (38) t F j -P B j -(1 -z κ3 )S ≤ 0 (39) t S j -P B j + (1 -z κ4 )S ≥ 0 (40) z κ1 + z κ2 + z κ3 + z κ4 = 1 (41)
+IV. MILP Optimal Time Window SolutionsFigure 4 illustrates the optimal combination of push back sub-windows for aircraft A(i) and BR(j).Solutions are computed for the differences of taxiway spot times of departing aircraft with a resolution of 1 second.The optimal solution for the scheduled spot time difference t jt i = 23[s] is shown in Fig. 4A and the solution for the scheduled spot time difference t jt i = -39[s] in Fig. 4B.The optimal combination of push back sub-windows are labeled by the purple rectangles, which are by definition within the green rectangles and contain no red conflict points.The two solutions in Fig. 4 demonstrate a key property of our MILP problem formulation.In Fig. 4A the conflict points appear as a single cloud while the conflict points in Fig. 4B appear to form two disjoint clouds.Our MILP approach addresses the challenge of computing the boundaries around conflict points.Our MILP approach is appealing as the complexity and structure of the clouds of conflict points is not known a priori.Figure 5 provides the minimum time-separation at the taxiway spot between aircraft i and j that ensures conflict free trajectories.In the graph, the directed edge e i-j represents the minimum time separation when   .LEFT: Minimum time separation at the taxiway spot using the conservative conflict separation constraints.RIGHT: Minimum time separation at the taxiway spot using the optimal combination of push back sub-windows.Here we assume that the minimum push back time window that we are willing to accept is given by δ min = 25.t j t i [s] t j t i = 39[s]t j t i [s] B) A)scheduling aircraft i followed by j. Figure 5A and 5B provide the minimum time separation for a conservative approach and the MILP approach, respectively.The conservative approach is defined to separate aircraft at the taxiway spot such that there is a zero ratio of conflicts.For the conflict distribution seen in Fig. 2A, for example, we can see that edge e A-BR = 37 and e BR-A = 123.The MILP formulation exploits the structure of the conflict points and allows us to reduce the minimum time separation between aircraft i and j for all possible sequences.The minimum-time separation graph in Fig. 5 can be used to schedule aircraft at the taxiway spot. 26Using a MILP approach, the minimum-time separation can be enforced with constraints, and schedules can be computed to optimize the uninterrupted flow of departure traffic from the gate to the departure queue.Using the conservative graph as constraints, the optimal schedule is computed as t A = 0, t B = 37 and Algorithm 1 Brute Force Algorithm Set OptCost = 0 Set left = t jt S0 j Set right = t jt F 0 j for t S j = left:rightδ min do for t F j = left + δ min : right do • Solve for t S i and t F i that provides the largest vertical window that contains no conflict points.t C = 37 + 41 = 78 where aircraft B pushes back with a right push back maneuver.Using the MILP graph as constraints the optimal schedule is defined as t B = 0, t A = 13 and t C = 13 + 26 = 39 where aircraft B pushes back with a left push back maneuver.In this scenario the MILP approach provides an increase of throughput of 2 times over the conservative approach.This increase in throughput comes at the cost of smaller push back windows for each aircraft.• cost = min[t F i -t S i , t F j -t S j ] + (t F i -t S i + t F j -t S j ) if cost > OptCost then • OptCost = cost • OptWindows = (t S i , t F i ,
+V. Computational Performance of the MILPHere we compare the performance of the MILP with a brute force algorithm for computing the optimal combination of push back sub-windows.The brute force algorithm systematically walks through the green domain searching for feasible combinations of sub-windows.The brute force algorithm pseudo code is presented in Algorithm 1.We initialize the optimal cost to zero.Given the scheduled spot time of aircraft j, t j , we know the value of the left and right borders of our green rectangle and store those values as left and right.We then enter a nested for loop where the outer loop goes through the values of t S j and the inner loop goes through t F j .Once the values for t S j and t F j have been selected, we can solve for the variables t S i and t F i that provide us the largest push back interval that is conflict free for the fixed values of t S j and t F j .Once we have the four values (t S i , t F i , t S j , t F j ) we can use the cost function in objective (3) to compute a cost.We then compare this cost to the previously known optimal cost.If the computed cost is greater than the previously known optimal cost we store the computed cost as the known optimal and store the four variables (t S i , t F i , t S j , t F j ) that define the combination of sub-windows.When the algorithm ends we return the optimal sub-windows that contain no conflicts.We measure the computation time of the two algorithms where the area of the domain is variable and the number of conflict points is variable.Because we are interested in the computation time we solve a sample problem where the area of the feasible domain is considered to be d 2 for d = 100s, 200s, .., 500s.For a fixed domain size, we randomly sample k = 100, 200, .., 500 points from the uniform distribution defined within the domain and use both the MILP and brute force algorithm to solve for the optimal combination of push back sub-windows.For a fixed domain size and fixed number of points, we repeat the routine of randomly sampling points from the uniform distribution a total of fifty times and average the computation time.Figure 6A reports the average computation time of the two algorithms on a 1.6 GHz Intel(R) Core(TM) i7 running MATLAB 2011b.Figure 6B illustrates the difference in the computation time of the brute force algorithm and the MILP.The contour lines are plotted illustrating the gradient in the difference in computation time.A positive value indicates that the MILP executed in less time than the brute force algorithm.The contour plot is color coded where the color red (blue) illustrates where the MILP outperforms the most (least).Figure 6B shows that the brute force only outperforms the MILP for a small subset of problems defined by a small area of domain and a large number of conflict points.As can be seen by the shape of the contour lines, the difference in computation time is affected by changes in both the area of the domain and the number of conflict points.
+VI. MILP Computation Time for Solutions of Multiple AircraftIn section III we formulated a MILP that returns the optimal combination of push back sub-windows for fixed taxiway spot schedules of two aircraft.Here we apply the MILP for fixed taxiway spot schedules of more than two aircraft.We assume that we are given the taxiway spot times for n aircraft, t 1 , t 2 , .., t n , and the optimal solution is a combination of push back windows for all aircraft 1, 2, .., n.The objective function is defined by (28) where the summation is over n aircraft.Constraints that are seen in equations ( 29)-(36) are generated using Algorithm 2 and constraints that are seen in equations (37)-(41) are generated using Algorithm 3. The number of constraints that are passed to the MILP is a function of the number of conflict points.For every conflict point κ, we generate five constraints that ensure the conflict point is not a convex combination of the optimal push back intervals.Figure 7 illustrates the computation time on a 1.6 GHz Intel(R) Core(TM) i7 running MATLAB 2011b for taxiway spot schedules of n = 4, 5, 6 aircraft.The computation time is shown as a function of the maximum number of pairwise conflict points that we allow within the domain.As can be seen in the figure, the computation time of the MILP significantly increases with the increase in the number of aircraft.
+VII. Conclusion and Future WorkIn this paper we formulated a MILP to solve for the optimal combination of push back sub-windows.Solutions were constrained to be conflict free in the presence of trajectory uncertainties.The MILP was used to solve for the optimal combination of push back sub-windows for any scheduled spot time difference at a resolution of 1 second.We analyzed example solutions that illustrate the ability of the MILP to solve for the push back subwindows regardless of the complexity of the distribution of conflict points.This is critical as the shape and structure of the conflicts is not known a priori.Using the computed solutions we generated a minimum time-separation graph for different sequence of aircraft.We compared the MILP minimum-time separation graph to the conservative minimum-time separation graph.We found that the throughput of the conservative schedule can be significantly increased within the ramp area by exploiting the structure of the conflict points.Now that we understand how to exploit the structure of the conflicts, we would like to integrate the MILP with a state-of-the-art optimal taxiway scheduler.This would allow for the optimal planning of surface operations from the runways all the way to the gate.We would also like to improve upon the computational performance of the MILP.We will also investigate new techniques to reduce the computation time of the MILP, a critical component for any real time application.
+Algorithm 2 Multi Aircraft Initial Constraint GenerationFor every aircraft i we generate the initial constraints for i = 1:NumAircraft do t F it S i -M ≥ 0 t F it S iδ min ≥ 0 t S it it S0 i ≥ 0 t F it it F 0 i ≤ 0 end for
+Algorithm 3 Multi Aircraft Conflict Constraint GenerationFor every pairwise conflict between aircraft i and j we generate the constraints for i = 1:NumAircraft -1 do for j = i+1:NumAircraft do for κ =1:NumConflicts do t F i -P B i -(1z κ1 )S ≤ 0 t S i -P B i + (1z κ2 )S ≥ 0 t F j -P B j -(1z κ3 )S ≤ 0 t S j -P B j + (1z κ4 )S ≥ 0 z κ1 + z κ2 + z κ3 + z κ4 = 1 end for end for end for2626
+Figure 1 .1Figure 1.A) Layout of Dallas-Fort Worth International Airport (DFW) with ramp area outlined in green.Departing aircraft push back from their gates and taxi to the departure queue via the taxiway spot.B) Zoomed in view of the green Terminal C ramp area.The departure trajectories from the gate to the taxiway spot (blue) that were sampled from the stochastic model of aircraft trajectories are shown.
+Figure 2 .2Figure 2. A) Conflict distributions with select cross sections color coded.B) Plot of conflicts between aircraft A(i) and BR(j) for schedules ranging from t BR -t A = -70 to t BR -t A = 40 at a resolution of 10 seconds.For the scheduled difference t BR -t A = -60 two conflict free sub-windows are shown in black solid(dotted) lines.
+t j t i = 23[s]
+Figure 4 .4Figure 4. A) Optimal combination of push back sub-windows for the scheduled spot time difference t j -t i = 23[s].B) Optimal combination of push back sub-windows for the scheduled spot time difference t j -t i = -39[s].
+Figure 55Figure 5. LEFT: Minimum time separation at the taxiway spot using the conservative conflict separation constraints.RIGHT: Minimum time separation at the taxiway spot using the optimal combination of push back sub-windows.Here we assume that the minimum push back time window that we are willing to accept is given by δ min = 25.
+Figure 6 .6Figure 6.A) Average computation time in seconds of the MILP and the brute force algorithm for problems with variable domain area and variable number of points.B) Contour plot of the difference between the computation time of the brute force algorithm and MILP.A positive value implies that the brute force algorithm took longer to execute than the MILP.
+Figure 7 .7Figure 7. Computation time of solutions for taxiway spot schedules of n = 4, 5, 6 aircraft.
+11 of 12 American1112Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
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+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July
+			6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
+			of 12 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 6, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-3028
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diff --git a/file177.txt b/file177.txt
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@@ -0,0 +1,305 @@
+
+	
+	
+		
+IntroductionNASA's vision for Advanced Air Mobility (AAM) is to help emerging aviation markets develop a safe air transportation system that would allow moving people and cargo between places previously not served or underserved by aviation [1].Advanced Air Mobility (AAM) encompasses a range of innovative aviation technologies (small drones, electric aircraft, automated air traffic management, etc.) that are transforming aviation's role in everyday life, including the movement of goods and people.Urban Air Mobility (UAM) represents one of the AAM concepts with highly automated aircraft, providing commercial services to the public over densely populated cities to improve mobility.The improvement of UAM envisages a future in which advanced technologies and new operational procedures enable practical, cost-effective air travel as an integral mode of transportation in metropolitan areas.This includes flying to local, regional, intra-regional, and urban locations using revolutionary new electric Vertical TakeOff and Landing (eVTOL) aircraft that are only just now becoming possible.The previous research (X1) investigated the operational capabilities in the current day ATC environment.The results of X1 showed that ATC communication and workload are bottlenecks for scalability of the UAM operations [3].The objective of X2 was to utilize the UTM's Technical Capability Level-4 (TCL-4) capabilities for UAM operations with one industry partner.It unraveled the challenges for using operational volumes for UAM operations that are not standardized across the industry [4].Both NASA and the FAA have been collaborating to describe the innovative UAM operations through a Concept of Operations (Conops) document.The document also describes the challenges to these operations, which range from integration of UAM operations in the National Airspace (NAS), safety, noise impacts, public acceptance, and many more.The FAA's Conops v1.0 on UAM operations [2] describes near term to mid-term UAM operations that define airspace structures, such as corridors, that would allow UAM operations without Air Traffic Control (ATC) services.These corridors could be defined in any class of airspace as well as defined from vertiport to vertiport.The UAM operations would require minimum performance requirements to operate within the corridors and to traverse them.The Conops also defines an architecture where Providers of Services for UAM (PSUs) would provide the relevant services for UAM operations that would utilize eVTOL vehicles.NASA's Vision Conops [1] focuses on the mature UAM operations and describes a different set of airspace structures -a UAM Operational Environment (UOE) that would be utilized for more complex and higher density UAM operations.In support of the AAM mission to accelerate the integration of UAM operations in the NAS, a series of test activities that are focused on flight and simulation are planned by the National Campaign Sub Project (NC SP).The NC flight test series will guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment.NC SP plans to conduct flight tests over the next several years.The first flight test, referred to as National Campaign -Development Test (DT), focuses on testing with helicopters in March 2021.The airspace partners with NC are referred to in this document as NC-DT airspace partners or just airspace partners.The UAM Sub-Project (SP) conducted initial lab simulations with NC developmental testing (NC-DT) airspace partners to evaluate and demonstrate their capabilities and components prior to NC flight activities.The X3 series of simulations focuses on the development of technologies, capabilities, and procedures with the objective of integrating the UAM operations in the NAS via simulation test activities.It does not utilize the airspace structures defined by FAA Conops or the NASA Vision Conops.The goal of these tests was to provide insight into the evolving regulatory, operational, and safety environment.The insights generated by these tests are necessary to gather crucial data about the UAM concept while promoting public confidence in safety.This document defines the process leading up to the X3 simulation test events, the execution of the X3 simulation tests, and their results.
+ScopeThese simulation activities, referred to as X3, were conducted during the second half of 2020 and completed in December 2020.X3 was an initial opportunity to assess the UAM airspace system developed by the Air Traffic Management -eXploration (ATM-X) Project's UAM Sub-Project (SP) in collaboration with National Campaign Sub Project's Airspace Test Infrastructure (ATI) team.The capabilities provided by the airspace partners were tested during these simulation activities.In X3, the UAM SP executed initial lab simulations with NC-DT airspace partners to test and demonstrate their capabilities and components prior to flight activities.The NC SP's ATI team was responsible for providing data collection capabilities for X3.In addition, the ATM-X UAM SP facilitated the connection of NC-DT airspace partners to the UAM airspace system and simulation platform.The X3 simulation started with eleven NC-DT airspace partners.None of the 11 airspace partners completed all the available testing with UAM Airspace Simulation Platform.More details are provided about the number of completed tests in the Results section.
+Requirement ProcessThe UAM SP and NC SP's ATI teams collaborated to gather initial internal (NC, NASA) and external (FAA, Airspace Partners) requirements and capabilities based on the NC goals, objectives, success criteria, and scenarios, which served as the foundation of the X3 simulation activities.Figure 1 shows the system architecture used to meet these requirements.
+Figure 1 X3 System Architecture DiagramComponents in the Emulated Environment and UAM Core Services comprised the UAM Airspace Simulation Platform.Each component in the Figure 1 architecture is defined in Table 1.
+Table 1 Components in the Emulated Environment
+Component DescriptionEmulated Urban Layer Provides adaptation data (e.g.routes and airspace constructs) to the Airspace Partners.
+Flight Information ManagementSystem -Authorization (FIMS AZ)Authorizes and authenticates PSU to ensure access is provisioned to those permitted to use the system.
+Discovery and Synchronization Service (DSS)Enables a PSU to identify other PSUs with active operations or subscriptions in the area of interest.
+Constraint SubmitterGenerates an airspace constraint with defined spatial and temporal boundaries and distributes it to the PSU Network.
+Provider of Services for UAM (PSU)A PSU is an entity that supports UAM operators with meeting UAM operational requirements that enable safe, efficient, and secure use of the airspace [2].A PSU:1. Provides a communication bridge between federated UAM actors, PSU to PSU via the PSU Network, to support the UAM operator's ability to meet the regulatory and operational requirements for UAM operations 2. Provides the UAM operator with information gathered from the PSU Network, about planned UAM operations so that UAM operators can ascertain the ability to conduct safe and efficient missions The Application Programming Interfaces (API) for each interface identified in the system architecture were defined in a GitHub repository which was available to all airspace partners.Links to all applicable API are included in Table 2 GitHub links for the different interfaces in X3 simulation environment.The ASTM API [6] was originally written for Unmanned Aircraft System (UAS) Traffic Management (UTM) [5] and the UAS Service Supplier (USS) in that architecture.For X3, the same API was used, and 'USS' and 'PSU' were used interchangeably.In future, 'USS' and 'PSU' will not be interchangeable in actual applications and operations.
+System RequirementsTo support the System Architecture, there were two categories of System Requirements; UAM Airspace Simulation Platform Requirements and Airspace Partner Requirements.The first category consisted of requirements to provide the UAM Airspace Simulation Platform with the services necessary to execute and collect data for the scenarios to meet the minimum success criteria.These included requirements for providing airspace definitions (such as airspace classes and nominal/off-nominal routes); providing authorization, discovery, and constraint submission services; and receiving and storing data.As depicted in the System Architecture (Figure 1), these systems were primarily developed by the ATM-X / UAM SP, and the NC Sub-Project / ATI teams.The DSS was developed by industry and hosted by one of the airspace partners.The second category of requirements were capabilities the NC-DT airspace partners needed in order to connect to the simulated airspace services and exercise the capabilities necessary for the scenarios.This included a PSU that interfaced with the UAM Airspace Simulation Platform so that operations could be planned and re-planned and enabled simulated vehicles to fly the planned operations.These capabilities, while not strictly necessary to meet the success criteria of X3, were beneficial because they supported data collection, and could be used for the development and refinement of the requirements for future NC simulations and flights tests.
+ScenariosThree simulated scenarios were tested in X3 as part of a joint effort between NASA, the FAA, and airspace partners.All industry partners in X3 were encouraged to execute any or all of the NC Scenarios 1 through 3 out of the 7 total NC Scenarios.The UAM SP maintained flexibility to enable data collection and validation of NC scenarios for partners that were ready to test.General assumptions which apply to all scenarios are described in Table 3.
+Simulation EnvironmentOnly one airspace partner will run the scenario at any given time in X3.Scenarios were designed such that complexity increased in each scenario.Adaptation files in KML format were provided to the airspace partners for each scenario and included details such as airspace classes, nominal routes, and off-nominal routes.
+Scenario 1 DescriptionScenario 1 included flight and operation planning for nominal operations.Additional assumptions specific to Scenario 1 are included in Table 4.
+Air traffic control (ATC) CommunicationsCurrent day (verbal) communications not required.Adaptation UAM airspace/routes are pre-defined and shared with partners as adaptation (files), including terrain elevation data along the route.The airspace objectives for Scenario 1 were to demonstrate a PSU's ability to perform predeparture flight planning for UAM aircraft, including scheduled departure and arrival times and strategic deconfliction.In Scenario 1, the PSUs planned an operation using the provided routes and interfaced with the UAM Airspace Simulation Platform to announce the operation.This was followed by a simulation of vehicle(s) which conformed to that plan.A generic representation of this scenario is shown in Figure 2. The line indicates the route, and the arrows indicate the intended direction of the aircraft.
+Scenario 2 DescriptionScenario 2 included en-route operation re-planning in response to an announced airspace constraint.Additional assumptions specific to Scenario 2 are included in Table 5. Adaptation UAM airspace/routes are pre-defined and shared with partners as adaptation static files.Generic airspace with terrain data along the route and locations of Class D, E/G airspace boundaries.
+ATC CommunicationsCurrent day (verbal) communications are not required.Exit of the UAM route will normally prompt UAM vehicle and ATC interaction in Class D airspace.Presume that ATC has communicated to and pre-authorized the UAM aircraft regarding the re-route around the Constraint and re-joining the corridor.
+Constraint CreationWill be announced by a NASA service using the USS-USS and USS-DSS APIs.
+UAM Re-routeThe UAM re-route flight path is pre-authorized by ATC and provided as updated waypoints to the PSU.In addition to the airspace objectives listed in Scenario 1, the objectives of this scenario are to demonstrate:• Interfaces for generating, and announcing airspace constraints to operations that may be impacted by the constraints both pre-departure and in flight • PSU's ability to receive airspace constraints and re-plan operations accordingly In Scenario 2, the PSUs planned the operation(s) using the provided routes, interfaced with the UAM Airspace Simulation Platform to announce the operation(s), and then simulated vehicle(s), which were expected to conform to that plan.While the operation(s) were in-flight, an airspace constraint (a UAS Volume Reservation, or UVR) was announced by the UAM Airspace Simulation Platform using the defined APIs.The PSU:1. Updated the operation plan(s) using the waypoints around the constraint which were provided in the adaptation files, 2. Announced the updated plan(s) to the UAM Airspace Simulation Platform, 3. Simulated the vehicle(s) that were expected to conform to the updated operation plan(s).A generic representation of this scenario is shown in Figure 3.
+Scenario 3 DescriptionIn order to develop a scalable Vertiport design and procedures, and explore influencing factors, Scenario 3 was split into three test cases: Scenarios 3A, 3B, and 3C.The influencing factors that were explored included the impacts of go-arounds and landing on an unplanned vertipad or location on the surface of the airport The overarching airspace objectives for this scenario were to demonstrate:• Vertiport operations including density of landing/takeoffs, traffic flow management, and operations at closely spaced UAM vertipads • A PSU's ability to safely and efficiently support UAM aircraft that perform a goaround with another approach/landing attempt • A PSU's ability to safely and efficiently support UAM aircraft with emergency states that require changing landing locations
+Scenario 3AScenario 3A included en-route operation re-planning in response to an occupied or obstructed vertipad.Additional assumptions specific to Scenario 3A are included in Table 6.Adaptation UAM airspace/routes are pre-defined and shared with partners as adaptation (files).Generic airspace with terrain data along the route and locations of Class D, E/G airspace boundaries.
+ATC CommunicationsCurrent day (verbal) communications not required.Go-Around is a published procedure that does not require ATC communication.
+Element AssumptionGo-Around trigger PSU detects that the landing pad is unavailable and triggers the go-around
+UAM Go-Around RouteThe UAM go-around route is a pre-defined contingency plan (similar to the loiter path) and is taken by the flight.In Scenario 3A, the PSUs planned operation(s) using the provided routes, interfaced with the UAM Airspace Simulation Platform to announce the operation(s), and then simulated vehicle(s) that were expected to conform to that plan.Prior to arrival of the operation, the PSU was alerted that the intended landing location was unavailable.As a result, the PSU:1. Generated an updated operation plan to perform a go-around using the provided route, 2. Announced the updated operation plan to the UAM Airspace Simulation Platform, 3. Simulated vehicle(s) that were expected to conform to the updated operation plan.Following the go-around, the operation was expected to be re-sequenced with the other operations planned to land at the same vertiport.A generic representation of this scenario is shown in Figure 4.
+Scenario 3BScenario 3B included en-route operation re-planning in response to an occupied or obstructed vertipad similar to Scenario 3A.Additional assumptions specific to Scenario 3B are included in Table 7.
+Table 7 Scenario 3B Assumptions for X3
+Element AssumptionAirspace Class D/E/G, Day VMC/ VFR.Adaptation UAM airspace/routes are pre-defined and shared with partners as adaptation (files).Generic airspace with terrain data along the route and locations of Class D and E/G airspace boundaries.
+ATC CommunicationsCurrent day (verbal) communications not required.Landing on the same vertiport but to a different vertipad is assumed to be managed by the operators
+Alternate Landing TriggerPSU detects the landing pad is unavailable and the aircraft is unable to do a go-around, requiring landing on a different pad.
+Element Assumption
+Alternate Landing LocationThe PSU will update its operation volume and land at the new location.The alternate vertipad is a pre-defined contingency planIn Scenario 3B, the PSUs planned operation(s) using the provided routes, interfaced with the UAM Airspace Simulation Platform to announce the operation(s) plan, followed by simulation of the vehicle(s) that were expected to conform to that plan.Prior to the arrival of the operation at the intended vertipad, the PSU was alerted that the intended landing location was unavailable.The vehicle was not be able to perform a go-around (like in Scenario 3A) due to the vehicle status and needed to land at an alternate vertipad on the same vertiport.As a result, the PSU:1. Generated an updated operation plan to land at the provided alternate vertipad, 2. Announced the updated operation plan to the UAM Airspace Simulation Platform, 3. Simulated the vehicle, which was expected to conform to the updated operation plan.A generic representation of this scenario is shown in Figure 5.
+Scenario 3CScenario 3C included en-route operation re-planning in response to an emergency landing request made by the UAM aircraft.Additional assumptions specific to Scenario 3C are included in Table 8.
+ATC CommunicationsCurrent day (verbal) communications are not required.Emergency landing will normally prompt UAM vehicle and ATC interaction in Class D. Presume that ATC has communicated to and pre-authorized the landing and location.Emergency Landing UAM flight is unable to use the vertiports and the operator triggers the emergency landing on the airport surface (not a vertipad).Assumes that landing location was authorized by the ATC.
+Element Assumption
+Landing to RunwayThe PSU will update its operation volume and land at the new location.In Scenario 3C, the PSUs planned operation(s) using the provided routes, interfaced with the UAM Airspace Simulation Platform to announce the operation(s), and then simulated vehicle(s) that were expected to conform to that plan.Prior to arrival of the operation at the planned landing location, the PSU was alerted that the operation needed to perform an emergency landing which required a contingency state and diversion to a runway landing location.As a result, the PSU:1. Generated an updated operation plan to land at the provided runway landing location, 2. Announced the updated operation plan to the UAM Airspace Simulation Platform, 3. Simulated the vehicle that was expected to conform to the updated operation plan.The PSU also indicated the contingency state of the operation to the UAM Airspace Simulation Platform.A generic representation of this scenario is shown in Figure 6.
+Method Airspace DefinitionFor each scenario described in the Scenarios section of this document, adaptation files were used to define a common airspace for all the airspace partners.Included in the files were applicable airspace definitions, available landing / takeoff vertipads, nominal routes between the vertipads, and off-nominal routes.Airspace definitions included the applicable Class D airspace, and a portion of the Class D airspace referred to as 'UAM Airspace' in which UAM operations were allowed to occur under an assumed predefined agreement with ATC.The airspace partners were not required to plan their operation at a specific cruise altitude while in Class G airspace.Points along the routes were provided and included the World Geodetic System (WGS) 84 altitude of the terrain at that location.All adaptation data was provided to the airspace partners in Keyhole Markup Language (KML) format.For each scenario test, one route was identified in the test procedure as the primary route.Operations identified as part of the test procedure were required to use this primary/nominal route as shown in Figure 8.Similarly, if there was a scripted off-nominal event, that off-nominal route was also identified in the procedure.Both the nominal and off-nominal routes were provided to exercise control over the scenarios and allow comparisons where possible among different airspace partners.The adaptation for the routes and classes of airspace created a generic airspace based on Class D airports in Dallas area.The two class D airspaces that were emulated included Arlington and Alliance airspaces.These airspaces were then transposed to Edwards Airforce Base (EDW) for terrain because the NC flight test was planned at that location.The emulation of the route planned for NC flight test was referred to as the nominal route that all airspace partners were required to fly as shown in Figure 7.
+Test ApproachThe general sequence of test events for a scenario is shown in Figure 8.This sequence was used for each scenario, based on that scenario's test plan.Connectivity and validation tests were followed by functional tests that focused on required functionality for the scenarios.Lastly scenario tests were used for data collection with every partner who was ready and available to test.
+Figure 8 X3 Testing Sequence Per ScenarioValidation tests were performed on individual subsystems to exercise the applicable API endpoints without connecting to other subsystems.This approach was based on the testing performed in UTM TCL-4 [8].The primary focus of the validation test was verifying the expected HTTP response that was returned when the validation criteria identified in the API failed.Only endpoints exercised by a simulated PSU were tested.The FIMS Authorization Server (FIMS-AZ) Validation Test was developed by NASA.The DSS-USS and USS-USS validation tests were developed jointly by NASA and the airspace partners.These tests included two categories: tests which required an airspace partner operation, and tests which did not.The test that could be run without an airspace partner operation were run routinely.Tests which required an active operation from the airspace partner were run during the connectivity tests at the beginning of a test session.Connectivity Tests were performed with multiple connected subsystems at the beginning of each test session to ensure that the exchanged data were as expected.In general, this was limited to posting an operation and/or constraint, and not necessarily flying a route.Once the operation/constraint was active, the additional Validation Tests were performed.If the expected data were exchanged successfully during the connectivity test, the functional tests and/or the scenario tests were performed.Functional tests were intended to be run before the scenario tests as a preliminary assessment of the capabilities needed by the scenario tests.This provided additional confidence that the scenario test would be successful and collected data would be of good quality.The functional tests also exercised capabilities that may not be specifically identified in a scenario test but may be encountered.One example of this was correctly reporting a nonconforming state, which is when an operation goes out of its planned operation volume.Following completion of the applicable functional tests, the scenario tests were performed.In some cases, all functional tests were not performed leading up to the scenario test.The reasons for not performing functional tests were resource constraints, time constraints or lack of capability.In the event of time constraints, priority was given to the scenario tests.An example of the impact of not performing a functional test prior to the scenario test is provided in the NonConforming Announcement Section of this document.
+ScheduleThe X3 simulation tests started on July 30, 2020 and completed on December 11, 2020.X3 testing was intended to be as flexible as possible to allow partners with different levels of maturity and capabilities to be able to test at their own pace.To support this, partners were able to test any procedure or capability that was available up until the scheduled end date of the simulation.
+Test ConfigurationsEach test scenario built on the functionality tested in the previous scenario.As a result, the configuration of the system under test evolved with the added functionality.The test configuration was documented for every test procedure that was conducted with the airspace partners.The Scenario 1 configuration included testing with the FIMS-AZ server, Discovery System, Data Collection PSU, and the Data Pipeline.The Scenario 2 configuration included the Scenario 1 configuration, and the Constraint Submitter used for submitting the temporary flight restriction.These capabilities were not included prior to the start of Scenario 1 testing, resulting in the need for additional validation, connectivity, and functional testing for Scenario 2. The Scenario 3 configuration did not add any additional subsystems.
+Test SessionsAll airspace partners were allowed to reserve a test session for up to 2 hours, where they would have full access to the designated test area and could run any procedure available to them at that time.Generally, each test session began by running the Connectivity test, followed by the applicable Functional or Scenario test.Results and notes from each test were recorded for the test procedures.
+Data Collection ApproachData were collected throughout the testing and stored in the Data Pipeline Database.All collected data were per the USS-USS API or the Data Collection API referenced in the Requirement Process section.For each of the functional and scenario tests, the Data Collection PSU collected all PSU-to-PSU data models identified in the ASTM API.Data received by the Data Collection PSU were then provided to the Data Pipeline where they were processed for preliminary data validation checks and organized into a database structure before being added to a PostgreSQL database for storage.Airspace partners also provided additional data models which were not included as part of the USS-USS API.These data models were directly added to the Data Pipeline using the Data API.The Data Pipeline processed the data by performing preliminary data validation checks and organizing those data into a database structure and stored the data in the same PostgreSQL database.Table 9 includes definitions of data models that were used during X3 and the API used to submit them.ConstraintOccurrence Data API Data regarding the enactment of a constraint from the perspective of PSU to operator interaction.Includes the time the PSU is alerted to the constraint, the time the operator is alerted to the constraint by the PSU, and the time the operator responds to the PSU about the constraint.
+FlightRunMetadataData API Provides context for the corresponding operation.Includes metadata such as scenario ID, run number, and PSU name.OffNominalResponse Data API Data regarding messages between an operator and a PSU when an off nominal event occurs.Includes the time the operator alerts the PSU to the off nominal event, and the planned response (such as go-around, use alternate landing location, or announce contingent state), and the revised landing location as a result of the event.Operation
+USS-USS APIThe information of a planned or active operation.Includes the owning PSU, the state of the operation (e.g.Activated, NonConforming, etc.), the applicable operation volumes, and the version of the operation (used to track updates to the operation volumes/states,etc).
+PSUExchangeData API Performance and interoperability data for a PSU.Includes the type of request (e.g.operation, constraint, etc.) and method (e.g.Get, Post, etc.), time the request was initiated, time the request was completed, who initiated the request, and the HTTP response.This model was correlated to the operation data received to identify the times at which operations were submitted and/or modified.
+VehicleTelemetry
+USS-USS APIVehicle telemetry data at a given timestamp.Includes the position and velocity of the vehicle.
+Model Applicable API
+Description
+WaypointData API A single waypoint.When these waypoints are grouped by operation ID, they provide the planned route which will be flown by the operation.Includes the phase of flight (e.g., takeoff, cruise, landing, etc.), target time, and target speed associated with the waypoint.
+MetricsThe system requirements, as described in the Requirement Process section, were used in conjunction with the planned scenario test events, as described in the Scenarios section, to identify metrics.Metrics served two main purposes during X3: 1.To assess the success of a given test procedure by identifying if the events expected in the procedure occurred, and if the expected data were received.2. To analyze the Airspace Partner's ability to perform the capabilities needed by the scenarios.To support these purposes, 38 metrics were identified, and corresponding SQL queries were developed to access the data stored in the PostgreSQL database, and perform the necessary associations / transformations.Observations from a partial set of these metrics are discussed in the Results section.
+ResultsThe X3 simulation started with eleven NC-DT airspace partners.Out of the 11 airspace partners, nine were able to perform testing with UAM Airspace Simulation Platform.Of those nine airspace partners, seven were able to complete Scenario 1, four of those seven partners were able to complete Scenario 2, and only two were able to complete Scenario 3, as shown in Table 10.The total number of times the scenario test was performed, and the total number of UAM operations flown in that scenario irrespective of their origin and destination was counted along with the Number of airspace partner who participated and is included in Table 10.For all the runs of the three scenario tests, metrics were calculated to help understand how the test unfolded, and how the airspace partners interpreted and interacted with the UAM Airspace Simulation Platform.The following sections describe a subset of the metrics.The metrics relied heavily on the PSUExchange data model to provide timestamps for message exchanges / events.If this model was not populated by the airspace partner, or was inaccurate, not all metrics could be calculated.To maintain anonymity, the names of the airspace partners have been removed in the following sections.For each metric, a result range observed or calculated across all test runs for all airspace partners who participated in the identified test is provided.
+Airspace ConformanceConforming to airspace is an important consideration for UAM.Airspace conformance means that the operations are expected to stay within the boundaries of authorized airspace as well as operational volume.The factors that contribute to airspace conformance include the size of the operational volume and the awareness of the authorized airspace, this will have an effect on how future airspace structures like corridors are defined and disseminated.For X3, every airspace partner received the definition of the airspace via a set of adaptation files for each scenario as described in the Airspace Definition section.With this information, the airspace partners planned their own operations to conform to this airspace.
+Size of Operation VolumesOperation volumes were used in UTM and continued to be used for UAM operations in X3.Design of UAM volumes is not standardized, and the airspace partners were allowed to design them based on the simulated or intended vehicle performance.The size of the volume has an impact on conformance monitoring and performance requirements for the vehicle.Generally, conformance to a larger volume is easier than conformance to a smaller volume but may impose constraints on surrounding airspace availability.Figure 9 shows three operations from three different airspace partners.The sizes for the volumes shown in Figure 9 (separated by horizontal size, vertical size, and volume duration) are shown in Table 11.Despite the differences in sizes between A (blue) and B (green) operation volumes, the corresponding PSUs supported five concurrent operations with no volume intersections with other operations along the route.This was primarily a result of the short volume durations.The C (purple) operation may have similarly been able to support multiple operations.However, the duration of C's operation volumes was 2 hours, which caused time overlaps of the volumes between operations.Figure 10 shows the size of the volumes of new operations near a vertiport location.It was observed that the size of operation volumes in some cases were large enough to encompass multiple vertipads.As seen in Figure 10, the last volumes of the operation were large enough to encompass both the 'KGKY-a' and 'KGKY-b' landing pads, which were separated by approximately 230 feet.
+Figure 10 Operation Volumes Near VertipadAdditional requirements regarding volume sizes and use of the volumes by multiple vehicles operating concurrently in the same airspace is required.Additional requirements are also be needed to further define the size of an operation volume over a vertipad or an increase the spacing between vertipads.
+Operations Near Class D AirspaceMetrics used to assess airspace conformance relative to the defined UAM and Class D airspace are included in Table 12.These metrics evaluated the operation volumes provided by the airspace partner via the PSU-PSU API and compared them to the airspace definitions in the adaptation files.In cases where multiple operations are compared to each other, only pairs or operations with versions that had overlapping active time ranges were used.The active time ranges were determined using the PSUExchange data, where the start of the range was the time the version was announced, and the end time was the time the next sequential version was announced.For each run of Scenario 1 and 2, a total of five operations were required on the route concurrently.Scenario 3 required a minimum of two concurrent operations.Scenario 2 and Scenario 3 required the partners to re-plan their operations and utilize the provided reroute.In general, the airspace partners were able to meet these requirements, as seen by the frequent upper range of 100% in the metric results in Table 12.It was observed that some airspace partners did not conform to the UAM airspace inside Class D as shown by lower range of 0% for metric #5 in Table 12.These operations entered the Class D airspace above the UAM airspace as shown in Figure 11.The operation volume in Figure 12 is entering the Class D airspace (green area) instead of the expected UAM Airspace (blue area).Several factors potentially contributed to this, including difficulty parsing the KML files, and not considering the separate airspace definition files that were provided in their entirety.In the future, a service that provides adaptation data to PSUs could be designed so that the operators have access to all the information in one place and updates to the adaptation are well managed.Operations submitted by airspace partners conformed to the UAM Airspace horizontally, as this conformance was factored into the design of the routes that were provided by NASA as shown by Figure 13.In this case, the route was designed to have adequate separation from the edge of the UAM Airspace to allow the operation volumes to be well separated.Separation from the edge of the UAM Airspace also needs to be considered when planning contingency operations within the UAM Airspace, such as a go-around.While the exact route into and through the UAM Airspace was provided for X3, if this is not provided in future tests, additional requirements for operation volumes within the UAM Airspace may be needed.Additional requirements for how to manage operations in UAM airspace may also be needed to ensure that they do not enter other classes of airspace.Exploration of 4D trajectories instead of volumes is also suggested especially if pre-defined routes are not designed in UAM airspace.
+Submissions to DSSThe DSS enables a PSU to identify other PSUs with active operations or subscriptions in an area of interest.In order for an operation to be accepted by the DSS, the submitting PSU must prove that it is aware of the other operations in the airspace at the time the operation is submitted.This is done using an Opaque Version Number (OVN) which is assigned to the operation version when it is accepted by the DSS. Figure 13 shows a general depiction of the events around the initial submission of an operation, relative to the OVN, as they were understood for X3.This figure assumes that the PSU has no knowledge of the airspace prior to submitting the operation.Prior to submitting an operation, the PSU would query the DSS for all operations in the area (indicated by the 'POST operation_references' line), then contact the owning PSU of the returned operations (indicated by the 'GET operations' line) to obtain the applicable details, such as operation volumes and OVN.With those details, the PSU could then submit its operation to the DSS with the list of received OVNs (indicated by the 'PUT operation_references' line).If the list of OVNs matched the list held by the DSS at that time, then the DSS accepted the operation.DSS played an important role in identifying potential operational conflicts.
+Overlapping Operation VolumesMetrics used to identify operation intersections are included in Table 13.These metrics compared the operation volumes provided by the airspace partner via the USS-USS API to other operations in the airspace.For X3, all operations in the airspace belonged to a single PSU in a given test run.Only operation pairs with versions that had overlapping active time ranges were compared.In these metrics, 'intersect' means that the volumes shared a portion of the same horizontal and/or vertical space at the same time.When the 'PUT operation_references' is performed, per the ASTM API [6], the operation was expected to be "deconflicted from all relevant features in the airspace."Based on that statement, NASA gave its airspace partners a requirement for operations to not intersect the 4D volumes (i.e., share the same space) of other operations.As can be seen in the results of the Table 13 metrics, some airspace partners met this requirement and had zero overlapping operation volumes.While this requirement was based on the description in the API, it was not a validation check performed by the DSS, as it was an 'expectation' that the PSU would perform this check.If an operation proved to the DSS that it was aware of the other operations in the airspace (via the provided OVN values), the operation would be accepted by this DSS implementation, regardless of the possibility that the operation intersects with other operations.This can also be seen in the results of the Table 13 metrics where some airspace partners had operations with intersecting operation volumes.These overlaps between operation volumes occurred for varying reasons.One occurrence was during a test of Scenario 2, where the airspace constraint was announced in a way that it did not impact all operations.As a result, the impacted operations replanned to reduce speed and join the route around the constraint.The last operation was not impacted by the constraint and continued to fly its original route at its original speed.The unimpacted operation proceeded to pass the impacted operations at the same altitude.In this example, the operation re-plans were created by the PSU, and accepted by the DSS under the expectation that they were deconflicted, even though they were not.Similar issues regarding assumed operation deconfliction and re-plans due to airspace constraints were observed during UTM TCL-4 testing [7].Another series of occurrences was due to airspace partners having their own business logic for how to manage their operations in the systems using the defined APIs.They may have alternate means to deconflict their own operations internally, and not require non-overlapping volumes.This is allowed by the DSS, as 'deconflicted' is not fully defined, and can be interpreted multiple ways.Additional requirements regarding the definition of 'deconflicted' and right-of-way among UAM operations is needed for future testing.
+Constraint ResponseOperations faced with a constraint needed to re-plan to avoid the constraint and potentially de-conflict with other operations.Metrics used to identify operation volume intersections in Scenario 2 and Scenario 3, in which a constraint was submitted, are included in Table 14.These metrics compared the operation volumes provided by the airspace partner via the USS-USS API to the announced airspace constraint.In addition to the volumes, the PSUExchange model provided by the airspace partner PSU was used to identify the time at which the operation version changes occurred as compared to the time the Constraint was announced, and the number of exchanges to both the DSS and the Data Collection PSU.If the PSUExchange model was not provided for a test run, that run was not included in the Table 14 results.Note that in Scenario 3, not all operations were expected to be impacted by the Constraint.During the Scenario 2 functional test, an airspace partner initially tried to re-plan all operations at the same time when the constraint was announced.When this happened, the first operation was accepted but the other operations were rejected by the DSS.This appeared to be because the other operation exchanges submitted to the DSS did not include the OVN for the newly accepted operation re-plan.There were several possible ways to correct this behavior.One possibility was to add a several second delay between each of the exchanges with the DSS to stagger the change to the new route.This solution will not be scalable when there are multiple PSUs operating in the airspace at the same time, as there will be less ability to coordinate the re-plans using the current architecture.When an operation is rejected due to the provided OVNs, the HTTP response from the DSS indicates that "the provided key did not prove knowledge of all current and relevant airspace Entities" [6].Using this error response, another approach would be to re-perform the operation requests to get the updated OVNs as identified earlier in this section.This approach, however, would still lead to additional race conditions, albeit with fewer operations, until all operations were eventually able to successfully re-plan.Additional requirements may be needed to handle events where multiple operations re-plan simultaneously or provide additional detail on when a re-plan due to a constraint should occur since constraints are generally announced by regulatory authorities with a lead time.
+NonConforming AnnouncementMetrics used to identify vehicle non-conformance to an operation plan are included in Table 15.These metrics compared the provided vehicle position data (including the location at a timestamp) to the corresponding volumes for that operation.The vehicle position was only compared to the volumes of the operation version that was active at that time.The PSUExchange model was used to identify the time ranges for each operation version.To be within a volume, the vehicle position data needed to be both horizontally within a volume's polygon and vertically within a volume's altitude range, as well as temporally within a volume's time range.Included in the Operation model was a State property for a PSU to indicate if its vehicle is NonConforming.The scenarios expected all operations to remain conforming to their plans for the entire operation.The scenario tests did not specifically force a NonConforming state to occur, but conformance was monitored by NASA at all times during the tests.Per the API definitions, the NonConforming state was announced when the operation left its volume spatially or temporally.Data used in this analysis showed a small number of operations that inadvertently left their volumes; thus, these data were available from a limited number of partners and is anecdotal in nature.For one airspace partner, vehicle telemetry was only inside the volumes briefly at the beginning of the operation, then it was ahead of the volume for the remainder of the flight.The vehicle was identified as out of its volume as shown by metric #1 in Table 15.Even though the telemetry data were outside of the volume, the operation never reported as NonConforming as calculated by metric #2 in Table 15.As part of the functional testing leading to the scenario tests, there was a procedure to test a partner's ability to announce a NonConforming state after commanding the vehicle to leave its volumes.This capability (commanding a vehicle to leave its volume) was not specifically included as part of the scenario tests and, as a result, was not implemented and/or not tested by all partners.It is recommended that future events test the NonConforming state of the operations prior to proceeding into data collection.During a Scenario 2 test when the operation re-plan occurred, a single position message was outside of the new operation volume for several operations.In these cases, there was minimal overlap in time or volume before and after the re-plan.There was also a potential time difference between when the PSU considers its operation updated (i.e., when it was received and accepted by the DSS) to when other PSUs in the network receive / are aware of the update.Due to these factors, the position may have been within a volume from the perspective of the PSU but outside of the volume by the time the Data Collection PSU was aware of the new volumes.According to the ASTM protocols and specifications used in this test, the operation was conforming to its plan.However, there was no process available for another PSU to validate the conformance using the specification, because there were no synchronized timestamps indicating the start of a modified operation plan.Additional requirements to provide timestamps for an operation and its updates may be needed in future.In another Scenario 2 test, an airspace partner encountered an issue when their operation re-plans were rejected by the DSS.The simulated vehicles for the operations which failed to replan continued to fly the expected re-plan route, even though it wasn't accepted by the DSS, resulting in the operations becoming NonConforming.If these operations continued to fly the original route, the operations would have flown through the airspace constraint.The resolution of this issue may be related to how operations re-plan as a result of a constraint injection.
+SummaryNASA's UAM SP under ATM-X project has been investigating UAM operations over the last few years.The first experiment-X1 that explored DFW operations under the current day air traffic management paradigm and found that UAM operations are not scalable due to air traffic control workload.The X2 simulation researched using UTM paradigm for UAM operations and how advanced services would be utilized for UAM operations with one industry partner.As part of X3, the UAM Sub-Project (SP) conducted initial lab simulations with NC Developmental Testing (NC-DT) airspace partners to evaluate and demonstrate their capabilities and components prior to NC flight activities.As part of this effort, the UAM SP facilitated the connection for nine NC-DT airspace partners to the UAM Airspace Simulation Platform.Out of the 11 original airspace partners, nine were able to perform testing with UAM Airspace Simulation Platform.Of those nine airspace partners, seven were able to complete Scenario 1, four of those seven were able to complete Scenario 2, and two of those four were able to complete Scenario 3. Requirements for running the three NC scenarios were provided to the airspace partners and testing spanned several months.A single airspace partner tested using the UAM Airspace Simulation Platform at any given time.All seven airspace partners were able to run at least the five concurrent operations as required by the simulations.The testing provided insights into how the operations were able to conform to the requirements of the airspace.For instance, many operations flew within the horizontal boundaries of the UAM airspace but exited the vertical boundaries and entered controlled airspace.In the real world, this would have implications for contacting ATC since the operations were in the positively controlled environment.It was also observed that there is no standardization for operation volumes design, some airspace partners created large volumes and others made them really small.The size of the volume has an effect on the conformance of the volumes as was also found in X2 studies [4].It is suggested that usage of 4D trajectories and standardization of volumes for the operations should be explored for future tests related to UAM operations.Another insight related to rejection of an operation re-plan by the DSS while in flight.This could lead to operations becoming 'NonConforming', especially if the re-plan was to avoid an airspace constraint.These insights have been valuable and will aid in building rigorous requirements for future tests and simulations planned for NC in pursuit of preparing the airspace partners for future flight tests.The next series of tests will focus on information exchange requirements between different UAM actors and new airspace structures like corridors for UAM operations.Figure 22Figure 2 Scenario 1 Generic Representation
+Figure 33Figure 3 Scenario 2 Generic Representation
+Figure 44Figure 4 Scenario 3A Generic Representation
+Figure 55Figure 5 Scenario 3AB Generic Representation
+Figure 66Figure 6 Scenario 3C Generic Representation
+Figure 77Figure 7 Airspace Adaptation used for X3
+Figure 99Figure 9 Operation Volumes for different airspace partners
+Figure 1111Figure 11 Operation Volumes in Class D Airspace
+Figure 1212Figure 12 Operation Volumes in UAM Airspace inside Class D
+Figure 1313Figure 13 Steps in Operation Submission to DSS
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+Table 22GitHub links for the different interfaces in X3 simulation environmentData Pipelinehttps://github.com/nasa/uam-apis/blob/master/datacollection/openapi/X3/uam-data-collection-X3.yamlFIMShttps://github.com/nasa/utm-apis/blob/master/fimsauthz-api/fims-authz.yamlUSS-to-DSS and USS-to-USShttps://github.com/nasa/uam-apis/blob/master/datacollection/nasa-astm-(Based on ASTM API)utm.yamlVehicle Telemetryhttps://github.com/nasa/uam-apis/blob/master/datacollection/openapi/X3/utm-telemetry.yaml
+Table 33General Assumptions for X3ElementAssumptionUAM Airspace ManagementPre-authorization to submit operations; does not include airspace and/orSystem Authorizationperformance authorization. Letter of Authorization (LOA) authorizes flight to enterClass D.Weather ConditionsDaytime Visual Meteorological Conditions (VMC).UAM Routes Interaction withUAM airspace/routes are designed to be de-conflicted with Instrument FlightInstrument Flight Rules (IFR)Rules (IFR) and Visual Flight Rules (VFR) routes using current day separationand Visual Flight Rules (VFR)requirements. UAM airspace/routes are expected to be high density routes thatare notified to the rest of the VFR traffic in Class G for awareness.No interaction is assumed between UAM and IFR/VFR flights.Background TrafficNone.UAM Routes SharingEach UAM operator manages its own set of UAM routes (i.e., UAM routes arenot shared among multiple UAM operators at the same time).Vertiport SharingEach UAM operator manages its own set of Vertiports (i.e., Vertiports are notshared among multiple UAM operators at the same time).Small Unmanned AircraftNot included in the traffic.Systems (sUAS) and Non-Transponder Flights
+Table 4 Scenario 1 Assumptions for X34ElementAssumptionAirspaceClass E/G, Day VMC.
+Table 5 Scenario 2 Assumptions for X3 Element Assumption5AirspaceClass D/E/G, Day VMC.
+Table 66Scenario 3A Assumptions for X3ElementAssumptionAirspaceClass D/E/G, Day VMC/ VFR.
+Table 88Scenario 3C Assumptions for X3ElementAssumptionAirspaceClass D/E/G, Day VMC/ VFR.AdaptationUAM airspace/routes are pre-defined and shared with partners as adaptation (files). Genericairspace includes terrain data along the route and locations of Class D, E/G airspaceboundaries.
+Table 9 X39Data Model Definitions and applicable APIsModelApplicableDescriptionAPIAuxiliaryOperationData APIOperation data which are not specifically included in the operation model,such as actual takeoff / landing times.ConPreRunOpData APIData regarding planned takeoff and landing locations, including alternate /contingency landing locations.ConstraintUSS-USSDetails of the airspace constraint. Includes the volume, start time, and endAPItime of the constraint.
+Table 11 Operation Volume Sizes Operation (Color) Min Horizontal Size (ft 2 ) Max Horizontal Size (ft 2 ) Min Vertical Size (ft) Max Vertical Size (ft) Min Duration (s) Max Duration (s)11A (Blue)6,384172,18849241145142B (Green)525,2693,794,08465627552083C (Purple)54,770269,1202005577,2007,200
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+		(2021) Volume 2, Issue 4 Cultural Implications of China Pakistan Economic Corridor (CPEC Authors: Dr. Unsa Jamshed Amar Jahangir Anbrin Khawaja Abstract: This study is an attempt to highlight the cultural implication of CPEC on Pak-China relations, how it will align two nations culturally, and what steps were taken by the governments of two states to bring the people closer. After the establishment of diplomatic relations between Pakistan and China, the cultural aspect of relations between the two states also moved forward. The flow of cultural delegations intensified after the 2010, because this year was celebrated as the ‘Pak-China Friendship Year’. This dimension of relations further cemented between the two states with the signing of CPEC in April 2015. CPEC will not only bring economic prosperity in Pakistan but it will also bring two states culturally closer. The roads and other communication link under this project will become source of cultural flow between the two states. Keyswords: China, CPEC, Culture, Exhibitions Pages: 01-11 Article: 1 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)01 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)01 Download Pdf: download pdf view article Creative Commons License Political Persona on Twittersphere: Comparing the Stardom of Prime Minister(s) of Pakistan, UK and India Authors: Maryam Waqas Mudassar Hussain Shah Saima Kausar Abstract: Political setup demands to use Twittersphere for preserving its reputation because of significant twitter audience, which follows celebrities and political figures. In this perspective, political figures frequently use twitter to highlight their political as well as personal lives worldwide. However, political figures take the stardom status among the twitter audience that follow, retweet and comment by their fans. The purpose of this study is, to analyze what kind of language, level of interest is made by political figures while communicating via twitter, text, phrases and languages used by political figures, and do their tweets contribute in their reputation. The qualitative content analysis is used for evaluation of the interests shared by PM Imran Khan, PM Boris John Son and PM Narendra Modi with the key words of tweets. A well-established coding sheet is developed for the analysis of text, phrases and words in the frames of negative, positive and neutral from March 2020 to May 2020. The results are demonstrating on the basis of content shared by Prime Ministers of three countries i.e., From Pakistan, Imran Khan, United Kingdom, Johnson Boris and India, Narendra Modi on twitter. The findings also reveal that varied issues discussed in tweets, significantly positive and neutral words are selected by these political figures. PM Imran tweeted more negative tweets than PM Boris Johnson and PM Narendra Modi. However, PM Boris Johnson and PM Narendra Modi make significant positive and neutral tweets. It is observed that political figures are conscious about their personal reputation while tweeting. It also revealed that the issues and tweets shared by these leaders contribute to their personal reputation. Keyswords: Imran Khan, Johnson Boris, Narendra Modi, Political Persona, Stardom, Twittersphere Pages: 12-23 Article: 2 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)02 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)02 Download Pdf: download pdf view article Creative Commons License An Empirical Relationship between Government Size and Economic Growth of Pakistan in the Presence of Different Budget Uncertainty Measures Authors: Sunila Jabeen Dr. Wasim Shahid Malik Abstract: Relationship between government size and economic growth has always been a debated issue all over the world since the formative work of Barro (1990). However, this relationship becomes more questionable when policy uncertainty is added in it. Hence, this paper presents evidence on the effect of government size on economic growth in the presence of budget uncertainty measured through three different approaches. Rather than relying on the traditional and complicated measures of uncertainty, a new method of measuring uncertainty based on government budget revisions of total spending is introduced and compared with the other competing approaches. Using time series annual data from 1973-2018, the short run and long run coefficients from Autoregressive Distributed Lag (ARDL) framework validate the negative effect of budget uncertainty and government size on economic growth of Pakistan regardless of the uncertainty measure used. Therefore, to attain the long run economic growth, along with the control on the share of government spending in total GDP, government should keep the revisions in the budget as close to the initial announcements as it can so that uncertainty can be reduced. Further, the uncertainty in fiscal spending calculated through the deviation method raises a big question on the credibility of fiscal policy in Pakistan. Higher will be the deviation higher will be the uncertainty and lower the fiscal policy credibility hence making fiscal policy less effective in the long run. Keyswords: Budget Uncertainty, Economic Growth, Government Size, Policy Credibility Pages: 24-38 Article: 3 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)03 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)03 Download Pdf: download pdf view article Creative Commons License Despair in The Alchemist by Ben Jonson Authors: Dr. Fatima Syeda Dr. Faiza Zaheer Numrah Mehmood Abstract: This research aims to challenge the assumption that The Alchemist by Ben Jonson is one of the greatest examples of the “explicit mirth and laughter” (Veneables 86). The paper argues that The Alchemist is a cynical and despairing play created in an atmosphere not suitable for a comedy. This is a qualitative study of the text and aims at an analysis of the theme, situations, characters, language, and the mood of the play to determine that Jonson is unable to retain the comic spirit in The Alchemist and in an attempt to “better men” (Prologue. 12) he becomes more satirical and less humorous or comic. This research is important for it contends that the play, termed as a comedy, may be read as a bitter satire on the cynical, stinky, and despairing world of the Elizabethan times. Keyswords: Comedy, Despair, Reformation Pages: 39-47 Article: 4 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)04 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)04 Download Pdf: download pdf view article Creative Commons License Analysis of Principles of Coordinated Border Management (CBM) in articulation of War-Control Strategies: An Account of Implementation Range on Pakistan and Afghanistan Authors: Dr. Sehrish Qayyum Dr. Umbreen Javaid Abstract: Currently, Border Management is crucial issue not only for Pakistan but for the entire world due to increased technological developments and security circumstances. Pakistan and Afghanistan being immediate states have inter-connected future with socio-economic and security prospects. Principles of Coordinated Border Management (CBM) approach have been extracted on the basis of in-depth interviews with security agencies and policymakers to understand the real time needs. The current research employs mixed method approach. Process Tracing is employed in this research to comprehend the causal mechanism behind the contemporary issue of border management system. A detailed statistical analysis of prospect outcomes has been given to validate the implication of CBM. Implication range of CBM has been discussed with positive and probably negative impacts due to its wide range of significance. This research gives an analysis of feasibility support to exercise CBM in best interest of the state and secure future of the region. Keyswords: Afghanistan, Coordinated Border Management, Fencing, Pakistan, Security Pages: 48-62 Article: 5 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)05 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)05 Download Pdf: download pdf view article Creative Commons License The Belt and Road Initiative (BRI) vs. Quadrilateral Security Dialogue (the Quad): A Perspective of a Game Theory Authors: Muhammad Atif Prof. Dr. Muqarrab Akbar Abstract: Containment is the central part of the U.S.'s foreign policy during the cold war. With the application of containment Policy, the U.S. achieved much success in international politics. Over time China has become more powerful and sees great power in international politics. China wants to expand and launched the Belt and Road Initiative (BRI). The primary purpose of The Belt and Road Initiative (BRI) is to achieve support from regional countries and save their interests from the U.S. In 2017, the American administration launched its Containment policy through Quadrilateral Security Dialogue (the Quad) to keep their interest from China. The Quadrilateral Security Dialogue (Quad) is comprising of Australia, the United States, Japan, and India. This Study is based on Qualitative research with theoretical application of Game theory. This research investigates both plans of China (BRI) and the U.S. (the Quad) through a Game Theory. In this study, China and the U.S. both like to act as gamers in international politics. This study recommends that Game theory can predict all developments in the long term. Keyswords: Containment, Expansionism, Quadrilateral Security Dialogue, The Belt and Road Initiative (BRI) Pages: 63-75 Article: 6 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)06 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)06 Download Pdf: download pdf view article Creative Commons License Narendra Modi a Machiavellian Prince: An Appraisal Authors: Dr. Imran Khan Dr. Karim Haider Syed Muhammad Yousaf Abstract: The comparison of Narendra Modi and Machiavellian Prince is very important as policies of Modi are creating problems within India and beyond the borders. The Prince is the book of Niccolo Machiavelli a great philosopher of his time. If Indian Prime Minister Narendra Modi qualifies as a Prince of Machiavelli is a very important question. This is answered in the light of his policies and strategies to become the undisputed political leader of India. Much of the Machiavellian Prince deals with the problem of how a layman can raise himself from abject and obscure origins to such a position that Narendra Modi has been holding in India since 2014. The basic theme of this article is revolving around the question that is following: Can Modi’s success be attributed to techniques of The Prince in important respects? This article analyzed Narendra Modi's policies and strategies to develop an analogy between Machiavellian Prince and Modi in terms of characteristics and political strategies. This research work examines, how Narendra Modi became the strongest person in India. Keyswords: Comparison, India, Machiavelli, Modus Operandi, Narendra Modi Pages: 76-84 Article: 7 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)07 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)07 Download Pdf: download pdf view article Creative Commons License Analyzing Beckett's Waiting for Godot as a Political Comedy Authors: Muhammad Umer Azim Dr. Muhammad Saleem Nargis Saleem Abstract: This study was devised to analyze Samuel Beckett’s play Waiting for Godot in the light of Jean-Francois Lyotard’s theory of postmodernism given in his book The Postmodern Condition (1984). This Lyotardian paradigm extends a subversive challenge to all the grand narratives that have been enjoying the status of an enviable complete code of life in the world for a long time. Even a cursory scan over the play under analysis creates a strong feel that Beckett very smartly, comprehensively and successfully questioned the relevance of the totalizing metanarratives to the present times. Being an imaginative writer, he was well aware of the fact that ridicule is a much more useful weapon than satire in the context of political literature. There are so many foundationalist ideologies that he ridicules in his dramatic writing. Christianity as a religion is well exposed; the gravity of philosophy is devalued; the traditional luxury that the humans get from the art of poetry is ruptured and the great ideals of struggle are punctured. He achieves his artistic and ideologically evolved authorial intentions with a ringing success. It is interesting to note that he maintains a healthy balance between art and message. Keyswords: Beckett, Lyotard, The Postmodern Condition, Waiting for Godot Pages: 85-94 Article: 8 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)08 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)08 Download Pdf: download pdf view article Creative Commons License Effect of Parenting Styles on Students’ Academic Achievement at Elementary Level Authors: Hafsa Noreen Mushtaq Ahmad Uzma Shahzadi Abstract: The study intended to find out the effect of parenting styles on students’ academic achievement. Current study was quantitative in nature. All elementary level enrolled students at government schools in the province of the Punjab made the population of the study. Multistage sampling was used to select the sample from four districts of one division (Sargodha) of the Punjab province i.e., Sargodha. A sample size i.e., n=960; students and their parents were participated in this study. Research scales i.e. Parenting Styles Dimension Questionnaire (PSDQ) was adapted to analyze and measure parents’ parenting styles and an achievement test was developed to measure the academic achievement of the elementary students. After pilot testing, reliability coefficient Cronbach Alpha values for PSDQ and achievement test were 0.67 and 0.71 Data was collected and analyzed using frequencies count, percentages, mean scores and one way ANOVA. Major findings of the study were; Majority of the parents had authoritative parental style, a handsome number of parents keep connection of warmth and support with their children, show intimacy, focus on discipline, do not grant autonomy to their children, do not indulge with their children and as well as a handsome number of students were confident during their studies and study, further, found that parental style had positive relationship with academic achievement. Recommendations were made on the basis of findings and conclusion such as arrangement of Parents Teachers Meetings (PTM‘s), parents’ training, provision of incentives and facilities to motivate families might be an inclusive component of elementary education program. Keyswords: Academic Achievement, Elementary Education, Parenting Styles Pages: 95-110 Article: 9 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)09 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)09 Download Pdf: download pdf view article Creative Commons License Kashmir Conflict and the Question of Self-Determination Authors: Izzat Raazia Saqib Ur Rehman Abstract: The objective of this paper is to explore relations between Pakistan and India since their inception in the perspective of Kashmir conundrum and its impact on the regional security. Kashmir is the unfinished agenda of partition and a stumbling block in the bilateral relations between Pakistan and India. After the partition of sub-continent in 1947, Pakistan and India got their sovereign status. Kashmir conflict, a disputed status state, is the byproduct of partition. Pakistan and India are traditional arch-foes. Any clash between Pakistan and India can bring the two nuclear states toe-to-toe and accelerate into nuclear warfare. Due to the revulsion, hostility and lack of trust between the two, the peaceful resolution of the Kashmir issue has been long overdue. Ever-increasing border spats, arms race and threat of terrorism between the two have augmented anxiety in the subcontinent along with the halt of talks between India and Pakistan at several times. Additionally, it hampers the economic and trade ties between the two. India, time and again, backtracked on Kashmir issue despite UN efforts to resolve the issue. Recently, Indian government has responded heavy-handedly to the Kashmiri agitators’ demand for sovereignty and revocation of ‘Special Status’ of Kashmir impacting the stability of the region in future. Keyswords: India, Kashmir Conundrum, Pakistan, Regional Security, Sovereignty Pages: 111-119 Article: 10 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)10 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)10 Download Pdf: download pdf view article Creative Commons License Exploring Image of China in the Diplomatic Discourse: A Critical Discourse Analysis Authors: Muhammad Afzaal Muhammad Ilyas Chishti Abstract: The present study hinges on the major objective of analyzing Pakistani and Indian diplomatic discourses employed in portrayal of image of China. Data comprises the official discourse which is used in diplomatic affairs of both the states. The extensive investigation seeks insights from the fundamentals of Critical Discourse Analysis propounded by van Dijk, Fairclough and Wodak with a special focus on Bhatia’s (2006) work. The study reveals that the image of China has always been accorded priority within Indian and Pakistani diplomatic discourse even though nature of bilateral relations among China, India and Pakistan is based on entirely different dynamics; Indian and Pakistani diplomatic discourses are reflective of sensitivities involved within the bilateral relations. Through employment of linguistic techniques of ‘positivity’, ‘evasion’ and ‘influence and power’, Indian diplomats have managed not to compromise over the fundamentals in bilateral relations with China despite Pakistan’s already strengthened and deep-rooted relations with China. While Pakistani diplomatic fronts have been equally successful in further deepening their already strengthened relations in the midst of surging controversies on CPEC, BRI and OBOR. Hence, diplomatic fronts of both the counties, through employment of ideologically loaded linguistic choices, leave no stone unturned in consolidation of the diplomatic relations with China. Keyswords: CDA, China Image, Corpus, Language of Diplomacy, Political Discourse Analysis Pages: 120-133 Article: 11 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)11 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)11 Download Pdf: download pdf view article Creative Commons License Students’ Perception about Academic Advising Satisfaction at Higher Education Level Authors: Rukhsana Sardar Zarina Akhtar Shamsa Aziz Abstract: The purpose of the study was to examine the students’ perception about academic advising satisfaction at higher education level. All the students from two years master (M.A) degree programme and four years (BS) degree programme of eight departments from International Islamic University Islamabad (IIUI), Faculty of Social Sciences were taken as a population of the study. 475 students were randomly selected as a sample of the study. The Academic Advising Inventory (AAI) was used to assess Academic Advising Style. For measuring level of the satisfaction, descriptive statistics was used. To compare the mean difference department-wise and gender-wise about academic advising satisfaction t.test was applied. It was concluded that from the major findings of the study those students who received departmental academic advising style are more satisfied as compared to those students who provided prescriptive academic advising style. Female students seemed more satisfied as compared to male students regarding the academic advising style provided to them. Students who satisfied from developmental academic advising style and they were also highly satisfied from the advising provided to them at Personalizing Education (PE) and this is the subscale of developmental academic advising whereas students who received prescriptive academic advising they were also satisfied from the advising provided to them regarding personalizing education and academic decision making but their percentage is less. It is recommended to Universities Administration to focus on Developmental Academic Advising Style and establish centers at universities/department level and nominate staff who may be responsible to provide developmental academic advising. Keyswords: Academic Advising, Higher Level, Students’ Perception Pages: 134-144 Article: 12 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)12 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)12 Download Pdf: download pdf view article Creative Commons License Perceptions of Sexual Harassment in Higher Education Institutions: A Gender Analysis Authors: Ruhina Ghassan Dr. Subha Malik Nayab Javed Abstract: Sexual harassment is a social issue which is present in every society, globally, which interferes in an individual’s social and professional life. It happens almost everywhere i.e. at workplaces, public places or institutes as well. The focus of the present study was to explore the differences of male and female students’ perception of sexual harassment. This study was a quantitative research. Sample of the study included of 400 students (200 males and 200 females) from two government and two private universities. In the present study, Sexual Harassment Perception Questionnaire (SHPQ) was used to find out these differences in perceptions as every person has his own view for different situations. The study revealed the significant differences in perception of students. Study showed that both genders perceived that female students get more harassed than male students. The factors that affect the perception frequently were gender and age. The findings recommended that regulations for sexual harassment should be implemented in universities; laws should be made for sexual harassment in higher education institutes. Students should be aware of sexual harassment through seminars, self-defense classes and awareness campaigns. And every institute should have a counseling center for the better mental health of students. Keyswords: Gender Differences, Higher Educational Institutions, Sexual Harassment Pages: 145-158 Article: 13 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)13 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)13 Download Pdf: download pdf view article Creative Commons License Role of IMF Over the Governance Structure and Economic Development of Pakistan Authors: Ali Qamar Sheikh Dr. Muhammad Imran Pasha Muhammad Shakeel Ahmad Siddiqui Abstract: Developing countries like Pakistan seeks for financial assistance in order to fulfil their deficits. IMF is one of the largest financial institution who give loans to countries who need it. This research has studied the IMF role and the effects of IMF conditions on the economy of Pakistan. To carry out this research, both quantitative data from primary sources has been gathered and qualitative analysis has been made to signify whither this borrowing creating and maintaining dependency of Pakistan on West and financial and governance structure constructed to curtail Countries like Pakistan. The results concluded that there is negative and insignificant relationship between GDP and IMF loans in the long run. The short-term dynamic shows that weak economic and Political Institutions in Pakistan. The Development dilemma constitutes dependency even today. The Current Budget Deficit Pakistan's fiscal deficit climbs to Rs 3.403 trillion in 2020-21 needs to be readdressed in such a manner that Pakistan can counter Balance of Payments and import/export imbalance. Keyswords: Dependency, Development, IMF, Loans, Debt, Pakistan, Governance structure Pages: 159-172 Article: 14 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)14 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)14 Download Pdf: download pdf view article Creative Commons License Climate Change and the Indus Basin: Prospects of Cooperation between India and Pakistan Authors: Sarah Saeed Prof. Dr. Rana Eijaz Ahmad Abstract: Climate change is transforming the global societies. The shift in average temperature is putting negative impacts on human health, food production and the natural resources. In the wake of the altered climate, water flow in the river systems is experiencing variability and uncertainty. This paper aims at studying the negative impacts of climate change on the water resources of the Indus Basin and investigate the prospects of cooperation between India and Pakistan; two major riparian nations sharing the basin. Adopting the case study approach, a theoretical framework has been built on the ‘Theory of the International Regimes’. It has been argued that institutional capacity and the dispute resolution mechanism provided in any water sharing agreement determine the extent of cooperation among the member states. Since India and Pakistan are bound by the provisions of the Indus Waters Treaty, this study tries to assess the effectiveness of this agreement in managing the negative consequences of the climate change. Keyswords: Climate Change, Cooperation, Dispute Resolution Mechanism, Institutional Capacity Pages: 173-185 Article: 15 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)15 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)15 Download Pdf: download pdf view article Creative Commons License Translation, Cultural Adaptation and Validation of Behavioral-Emotional Reactivity Index for Adolescents Authors: Saima Saeed Farah Malik Suzanne Bartle Haring Abstract: Measuring differentiation of self in terms of behavioral/emotional reactivity towards parents is important because of the complex parent-child connection. This needs a valid and reliable measure to assess the differentiation of self particularly in a relationship with parents. Behavior\Emotional Reactivity Index is such a tool that fulfills this purpose. The present study was carried out to culturaly adapt and translate BERI into the Urdu language and establish the psychometric properties of Urdu version. A sample of 303 adolescents of age (M = 16.07, SD = 1.77) was taken from different schools and colleges. Scale was split into Mother and father forms for the convenience of respondents. Findings supported the original factor structure of the BERI-original version. Higher-order factor analysis showed good fit indices with excellent alpha ranges (α= .91 to α=.80). BERI scores were compared for the adolescents who were securely attached with parents and insecurely attached with parents which showed a significant difference between the groups. BERI-Urdu version was found to be a valid and reliable measure in the Pakistani cultural context which gives researchers new directions to work with adolescents. Keyswords: Adolescence, Differentiation of Self, Behavioral, Emotional Reactivit, Index, Parental Attachment Pages: 186-200 Article: 16 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)16 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)16 Download Pdf: download pdf view article Creative Commons License Notion of Repression in Modern Society: A Comparative Analysis of Sigmund Freud and Herbert Marcuse Authors: Khadija Naz Abstract: One of the fundamental issues for modern civilized man is how to adapt a modern society without losing his individual status. Is it possible for an individual to adjust in a society where he/she loses his/her individuality and becomes part of collectivity? One point of view is that for society to flourish, man needs to be repressed. But to what extent is repression necessary for societies to rise and survive? This paper shall examine the above given questions from the standpoint of two thinkers who greatly influenced twentieth-century thought: Sigmund Freud and Herbert Marcuse. To undertake this task, first the term Repression shall be examined and then the notions of Freud and Marcuse will be discussed to determine the degree of repression required for the development of modern society. Keyswords: Modern Society, Performance Principle, Repression, Surplus-Repression, The Pleasure Principle, The Reality Principle Pages: 201-214 Article: 17 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)17 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)17 Download Pdf: download pdf view article Creative Commons License Perceptions of Teacher Educators about Integration of (ESD) in Elementary Teachers Education Program Authors: Dr. Rukhsana Durrani Dr. Fazal ur Rahman Dr. Shaista Anjum Abstract: Education and sustainable development have a close relationship as education provides sustainability to society. This study explored the perceptions of teacher educators for integration of Education for Sustainable Development (ESD) in B.Ed. 4 years’ elementary program. Four major components of ESD i.e., Education, Social & Culture, Economic and Environment were included in study. 127 teacher educators from departments of education were randomly selected from public universities of Pakistan who were offering B.Ed. 4 years’ elementary program. Data was collected through questionnaires from teacher educators. The findings recommended the inclusion of the components of Education for Sustainable Development (ESD) in curriculum of B.Ed. 4 years’ elementary program. Keyswords: B.Ed. 4 Years Elementary Curriculum, Sustainable Development, Integration, Teacher Education Pages: 215-225 Article: 18 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)18 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)18 Download Pdf: download pdf view article Creative Commons License Exploring TPACK skills of prospective teachers and challenges faced in digital technology integration in Pakistan Authors: Tariq Saleem Ghayyur Dr. Nargis Abbas Mirza Abstract: The current study was aimed to explore TPACK skills of prospective teachers and challenges faced in digital technology integration in Pakistan. The study was qualitative in nature and semi structured interview schedule was developed to collect data from prospective teachers. Purposive sampling technique was employed to collect data from 20 prospective teachers of 7 public sector universities. It was concluded that majority of the prospective teachers used general technological and pedagogical practices (GTPP), technological knowledge practices (TKP), Technological Pedagogical Knowledge practices (TPKP), Technological Content Knowledge practices (TCKP). Majority of prospective teachers reported multiple challenges in integration of digital technology in teacher education programs including lack of teacher training as one of the largest hurdle in digital technology integration, lack of digital technology resources or outdated digital technology resources, inadequate computer lab, lack of learning apps (courseware), financial constraints, lack of teachers’ motivation to use digital technology, slow computers available at computer labs, and unavailability of technical support. It was recommended that digital technology infrastructure should be improved across all teacher education institution and it was further recommended that TPACK model of digital technology integration should serve digital technology integration in teacher education programs in Pakistan. Keyswords: Challenges, Digital Technology Integration, Digital Technology Resources, Digital Technology, TPACK Pages: 226-241 Article: 19 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)19 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)19 Download Pdf: download pdf view article Creative Commons License Revisiting the Linkage between Money Supply and Income: A Simultaneous Equation Model for Pakistan Authors: Zenab Faizullah Dr. Shahid Ali Muhammad Imad Khan Abstract: A reliable estimate of the money supply is an important sign of the Gross Domestic Product (GDP) and many other macroeconomic indicators. It is widely discussed that over a long period of time, there is a strong link between GDP and money supply. This link is significantly important for formation of monetary policy. The main aim of this study is to estimate the income-money supply model for Pakistan. This study estimates the income-money supply model for Pakistan over the period of 2009 to 2019. The study uses Two Stage Least Square (2SLS) econometric technique due to the presence of endogeneity problem in the model under consideration. The existence of simultaneity between money supply (M2) and income (GDP) is also clear from the results of Hausman Specification test for simultaneity between M2 and GDP. The results further show that there exists a strong money-income relationship in case of Pakistan. Keyswords: Money Supply, Income, Simultaneous Equations Pages: 242-247 Article: 20 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)20 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)20 Download Pdf: download pdf view article Creative Commons License Analyzing the Mechanism of Language Learning Process by the Use of Language Learning Strategies Authors: Shafiq Ahmad Farooqi Dr. Muhammad Shakir Sher Muhammad Awan Abstract: This analytical research study involves the use of learning strategies to know the mechanism of learning a second language. People acquire their native language (L1) without any conscious effort and they have a complete knowledge of L1 and are competent in their native language even without going to school. It is believed that language learning is a process as well as an outcome and the focus of current study is to understand the process of learning a second language. The population in this study comprised of 182 boys and Girls Govt. Higher Secondary Schools studying at intermediate level in the 11 Districts of the Southern Punjab. The sample was selected through random probability sampling and consisted of 40 subject specialists teaching the subject of English in Govt. higher secondary schools with 400 students studying English at Intermediate level. A questionnaire comprising some common and easily accessible learning strategies was designed to determine the frequency of these strategies used in the classrooms by the language learners through the specialists of the subject. The data was collected from the selected sample through the subject specialists teaching in these schools. The data was collected quantitatively and was analyzed in the statistical package for social sciences (SPSS) version 20. The most common 27 language learning strategies (LLS) were applied to analyze the process of language learning. In the light of the results of the study, it was concluded that application of the learning strategies according to the nature of the text is helpful in understanding the language functions and its application. Keyswords: Language Acquisition, Learning Strategies, Mechanism of Language Learning Pages: 249-258 Article: 21 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)21 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)21 Download Pdf: download pdf view article Creative Commons License Secondary School Science Teachers’ Practices for the Development of Critical Thinking Skills: An Observational Study Authors: Dr. Muhammad Jamil Dr. Yaar Muhammad Dr. Naima Qureshi Abstract: In the National curriculum policy documents, to produce rationale and independent critical thinkers, different pedagogical practices have been recommended like cooperative learning, questioning, discussion, etc. This qualitative case study aimed at analyzing secondary school science teachers’ practices for the development of critical thinking skills in secondary school students. There were twelve classrooms (four from each subject of Physics, Chemistry and Biology) selected as cases. Video recording was used for the observations for six lessons in each classroom. In this way, a total of 72 observations were conducted lasting for approximately 35 minutes. Qualitative content analysis was used for data analysis through Nvivo 12. The findings of the observations revealed that all the teachers used the lecture method. They used this to cover the content at a given specific time. There was not much focus on the development of critical thinking. In a few of the classrooms, the students were engaged and active during learning different specific topics. Whiteboard was used as a visual aid by most of the teachers. Furthermore, to some extent, discussion, questioning, and daily life examples were used in different classrooms. It is recommended that teachers’ professional development should be conducted to focus on the development of critical thinking skills through pedagogical practices which have been recommended by the national education policy documents. Keyswords: Analysis, Critical Thinking, Curriculum Policy, Pedagogy, Secondary Level Pages: 259-265 Article: 22 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)22 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)22 Download Pdf: download pdf view article Creative Commons License Historical Development of Clinical Psychology in Pakistan: A Critical Review-based Study Authors: Muhammad Nawaz Shahzad Dr. Mushtaq Ahmad Dr. Muhammad Waseem Tufail Abstract: Clinical Psychology is clinical and curing psychological practices in Pakistan. The present research study endeavors to examine the contemporary status of Clinical Psychology in the country and descriptively analyzes the significant contribution of various psychologists in its development. The study also elaborates the emergence of Clinical Psychology and its treatment aspects in the country. The experimental approach of the treatment psychology has also been defined. The role of different scholars to set and promote the Clinical Psychology as discipline and dealing about treatment of Human mind has also been discussed here. The study also presented the scenario of the issues of legislative acknowledgment, qualifications mandatory for practice, communal awareness of cerebral treatment, the tradition of ethnic and native practices about the clinical psychological treatments has also been discussed. Keyswords: Approaches, Clinical Psychology, Psychologist, Therapist Pages: 266-272 Article: 23 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)23 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)23 Download Pdf: download pdf view article Creative Commons License Impact of Devolution of Power on School Education Performance in Sindh after 18th Constitutional Amendment Authors: Abdul Hafeez Dr. Saima Iqbal Muhammad Imran Abstract: Devolution of the authority from central units of empowering authorities to the local level to develop and exercise policies at local or organizational level is under debate in various countries of the world. The legation in with the name of 18th constitutional amendment in constitution of 1973 of Pakistan ensures more autonomy to federal units. The difference between province and federation mostly creates misunderstanding in the belief of cooperation and universalism of education standards, expenditures and service delivery. Very currently the ministry of education and local government encoring principles and headmasters to adopt self-management skills to be updated to accept the spin of power from higher authorities to lower authorities’ pedagogical and local schools. In this qualitative research semi structured questioner were incorporated as data collection tool equally, the data was analyzed by usage of NVivo software. In this regard Government of Sindh has introduced various reforms and new trends like objectives and policy pillars, better government schools, improved learning outcomes and increased and improved funding in the education sector Sindh government has so far been unable to effectively use its resources to implement effective governance system which provides quality and sustained education in the province. To achieve this basic universal education, equally fourth objective of Sustainable Development Goal (SDG) the educational leaders must develop a comparative education setup that help to educate planers to plan and design standards for school leaders, instruction, appropriate professional development of teachers, ways to support school leaders to change in mission. Parallel, develop new program for early childhood, school and class size and ensure school enrollment. Keyswords: 18th Constitutional Amendment, Devolution of Power, Sindh Education Performance Pages: 273-285 Article: 24 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)24 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)24 Download Pdf: download pdf view article Creative Commons License Legal Aspects of Evidence Collected by Modern Devices: A Case Study Authors: Muhammad Hassan Zia Alvina Ali Abstract: This paper is a qualitative research of different case laws dealing with modern technological evidence. Courts were required to adopt new methods, techniques and devices obtained through advancement of science without affecting the original intention of law. Because of modern technology, a benefit could be taken from said technology to preserve evidences and to assist proceedings of the Court in the dispensation of justice in modern times. Owing to the scientific and technological advancements the admissibility of audio and visual proofs has grown doubtful. No doubt modern evidence assist the court in reaching out to the just decision but at the same time certain criteria need to be laid down which must be satisfied to consider such evidence admissible. Different Case laws are discussed here to show how the cases were resolved on the basis of technological evidence and when and why such evidence have been rejected by the court, if it did. Moreover, legal practices developed in various countries allow our Courts to record evidence through video conferencing. The Honorable Supreme Court of Pakistan directed that in appropriate cases statement of juvenile rape victims and other cases of sensitive nature must be recorded through video conferencing to avoid inconvenience for them to come to the Court. Nevertheless, it has some problems. The most important among them is the identification of the witness and an assurance that he is not being prompted when his statement is recorded. In this paper protocols that are necessary to follow while examining witness through video link are discussed Keyswords: DNA Profiling, Finger Prints, , Telephone Calls, Video Tape Pages: 286-297 Article: 25 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)25 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)25 Download Pdf: download pdf view article Creative Commons License The Political Economy of Terrorisms: Economic Cost of War on Terror for Pakistan Authors: Muhammad Shakeel Ahmad Siddiqui Dr. Muhammad Imran Pasha Saira Akram Abstract: Terrorism and its effect on contemporary society is one of the core and vital subjects of International Political Economy (IPE) during the last years. Despite the fact that this is not a new phenomenon, special attention has been given to this issue, specifically after the terrorist attacks of 9/11, 2001. The objective of this paper analyzes to what dimensions terrorism affects the global economy mainly the two predominant actors of the conflict i.e. Pakistan and the United States. For this purpose, this article will take a look at the financial cost of War for Pakistan and how Pakistan’s decision to become frontline State has affected its Economy, its effect on agriculture, manufacturing, tourism, FDI, increased defense costs The normative and qualitative methodology shows a significant disadvantage between terrorist activities and economic growth, social progress, and political development. The results shows that Pakistan has bear slow economic growth while facing terrorist activities more than US. In this last section, the paper suggests ways and means to satisfy people around the world not to go in the hands of fundamentals and terrorists. Keyswords: Cost of War, Economic Growth, Frontline States, Pak Us Relations, Terrorism Pages: 297-309 Article: 26 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)26 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)26 Download Pdf: download pdf view article Creative Commons License A Comparative Study of Grade 10 English Textbooks of Sindh Textbook Board and Cambridge “O Level” in the perspective of Revised Bloom’s Taxonomy Authors: Mahnoor Shaikh Dr. Shumaila Memon Abstract: The present study evaluated the cognitive levels of reading comprehension questions present in grade 10 English Textbooks namely English Textbook for grade 10 by Sindh Textbook Board and compared it to Oxford Progressive English book 10 used in Cambridge “O Level” in the perspective of Revised Bloom’s Taxonomy. Qualitative content analysis was used as a methodology to carry out the study. To collect the data, a checklist based on Revised Bloom’s taxonomy was used as an instrument. A total of 260 reading comprehension questions from both the textbooks were evaluated. The findings of the study revealed that reading comprehension questions in English textbook for grade 10 were solely based on remembering level (100%) whereas the questions in Oxford Progressive English 10 were mainly based on understanding level (75.5%) with a small percentage of remembering (12.5%), analyzing (11.1%) and evaluating level (0.74%). This suggests that the reading comprehension questions in both the textbooks are dominantly based on lower-order thinking skills. Keyswords: Bloom’s Taxonomy, Content Analysis, Reading Comprehension, Textbook Evaluation Pages: 310-320 Article: 27 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)27 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)27 Download Pdf: download pdf view article Creative Commons License Assessing the Preparedness of Government Hospitals: A Case of Quetta City, Balochiatan Authors: Sahar Arshad Syed Ainuddin Jamal ud din Abstract: Earthquake with high magnitude is often resulting in massive destruction with more causalities and high mortality rate. Timely providence of critical healthcare facilities to affected people during an emergency response is the core principle of disaster resilient communities. The main objective of this paper is assessing the hospital preparedness of government hospitals in Quetta. Primary data was collected through questionnaire survey. Total of 165 sample size chosen via simple random sampling. Relative important index (RII) is used to analyze the overall situation of hospitals preparedness in term of earthquake disaster. Findings of the study showed that the preparedness level of government hospitals in Quetta is weak to moderate level. Based on the findings this study recommends the necessary measures to minimize the risk of earthquake disaster including training and exercise programs for the staff of hospital, proper resource management to efficiently use the existing machinery and equipment in the meeting of disaster to enhance employee’s performance and preparedness of government hospitals in Quetta to deal with earthquake disaster. Keyswords: Earthquake, Preparedness, Relative Important Index Pages: 321-329 Article: 28 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)28 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)28 Download Pdf: download pdf view article Creative Commons License Development of Reasoning Skills among Prospective Teachers through Cognitive Acceleration Approach Authors: Memoona Bibi Dr. Shamsa Aziz Abstract: The main objectives of this study were to; investigate the effects of the Cognitive Acceleration approach on the reasoning skills of the prospective teachers at the university level and compare the effects of the Cognitive Acceleration approach and traditional approach concerning reasoning skills of prospective teachers’ at the university level. The study was experimental and followed a pre-test post-test control group experimental design. The sample of the study included the experimental group and control group from the BS Education program in the Department of Education at International Islamic University Islamabad. A simple random sampling technique was used to select the sample after pre-test and pairing of prospective teachers. CTSR (classroom test for scientific reasoning) developed by A.E. Lawson (2000) was used to collect the data through pre-tests and post-tests. The experimental group’s perception about different activities of the experiment was taken through a self-made rating scale. Collected data were analyzed by calculating mean scores and t-test for hypothesis testing by using SPSS. The main findings of the study revealed that the Cognitive Acceleration teaching approach has a significant positive effect on the reasoning skills development of prospective teachers at the university level. Findings also showed that participants found this teaching approach effective and learned many new concepts and skills with the help of thinking activities. Based on findings it has been concluded that the Cognitive Acceleration teaching approach might be encouraged for training prospective teachers at the university level and training sessions about the use of the Cognitive Acceleration approach must be arranged by teacher education programs and institutions. Keyswords: Cognitive Acceleration Approach, Prospective Teachers, Reasoning Skills, Traditional Approach Pages: 330-342 Article: 29 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)29 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)29 Download Pdf: download pdf view article Creative Commons License Spatial Injustice in Shamsie’s Kartography Authors: Syeda Hibba Zainab Zaidi Dr. Ali Usman Saleem Sadia Waheed Abstract: Social space under postmodernism and wave of globalization have suffered in and its idealistic representations are lost and deteriorated which ultimately led to discursiveness in the lives of postmodern man, especially Karachiites. The boundaries of geographies play a significant role in shaping fates, biographies, social superstructures and shared collective histories of its residents. Considering this, Henri Lefebvre and Edward William Soja, argue that space is something which determines the living circumstances within the particular social framework and instigates and controls various societal happenings. City space of Karachi suffers from appalling distortions as a part of postmodern, globalized and capitalist world. By employing Lefebvre’s idea of spatial triad and Soja’s views of the trialectrics of spaciality, this paper foregrounds how social space enforces spatial injustice and serves for the inculcation of spatial cleansing in the lives of inhabitants of urban space. Using Shamsie’s Kartography as an interpretive tool for contemporary urban environment, this paper inquires the engrafting of spatial cleansing in the lives of Karachiites resulting in multiple standardization and segregation on the basis of living standards among different social strata. This research substantiates how in Kartography, Materialism nibbles the roots of social values and norms while sequentially administering Spatial Injustice in the lives of Karachiites. This paper proclaims the scarcity of execution of Spatial Justice in the lives of common people in this postmodern globalized capitalist era. This paper urges the possibility of a utopian urban space with enforced spatial justice where people can be saved from dilemmas of injustice and segregation, especially Karachiites. Keyswords: Capitalistic Hegemony, City Space, Globalization, Spatial Cleansing, Spatial Injustice Pages: 343-352 Article: 30 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)30 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)30 Download Pdf: download pdf view article Creative Commons License A Quasi-Experimental Study on the Performance and Attitudes of Pakistani Undergraduate Students towards Hello English Language Learning Application Authors: Wafa Pirzada Dr. Shumaila Memon Dr. Habibullah Pathan Abstract: With the advancement of technology, more and more avenues of bringing creativity and innovation in language learning have opened up. These exciting advances have given rise to a new field of study within linguistics, termed Mobile Assisted Language Learning (MALL). This paper aims to fill the gap of MALL research in the area of grammar teaching in the Pakistan. Two BS Part 1 classes from University of Sindh, Jamshoro, were chosen for this quasi-experimental study. In total, 62 out of 101 students volunteered to use the Hello English application for 2 months, making up the experiment group, and the remaining 39 students were put in a control group. Paired Samples T-Test was run on pretest and posttest results which revealed no significant difference in both groups’ performances, proving that Hello English application could not significantly improve students’ grammar performance. However, in spite of the lack of a significant difference between the test results, the data gathered through the attitudinal survey showed that students still found mobile application very easy to use and effective in language learning. Keyswords: Attitudes, Grammar Learning, Hello English, Mobile Language Learning, Technology In Language Learning Pages: 353-367 Article: 31 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)31 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)31 Download Pdf: download pdf view article Creative Commons License Impact of Determinants on the Profile Elevation of Secondary School Teachers in Pakistan Authors: Zahida Aziz Sial Dr. Farah Latif Naz Humaira Saadia Abstract: The foremost purpose of this research paper was to interrogate the effects of determinants on the educational and social profile of secondary school teachers in Pakistan. The key question taken was related to determinants that affect teachers’ profile. The Population of the study was secondary school teachers of Punjab province. A questionnaire was used as research instrument. The researcher personally visited the schools to administer the questionnaire. E-Views software was used for data analysis. Moreover, OLS regression model and LOGIT regression model were carried out. It was found that the variable years of teaching experience (EXPYR) (*** 0.03) can have a vital concrete effect upon the societal figuration of teachers as the experience of teachers grows, so does their social interactions with officials, colleagues, students and friends increases. The said variable is significant at 10 percent level. The variable, Residence (RESIDE) (** 0.53) have a significant impact upon civic links. This obviously associated with less community connection of country side teachers than the teachers residing in urban areas. Keyswords: Determinants, Elevation, Educational Profile, Social Profile, Secondary School Teacher Pages: 368-372 Article: 32 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)32 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)32 Download Pdf: download pdf view article Creative Commons License Impact of War on Terror on the Tourism Industry in Swat, Pakistan Authors: Sabir Ihsan Prof. Dr. Anwar Alam Aman Ullah Abstract: The present study was designed to ascertain the status of tourism before insurgency, during insurgency and after insurgency in District Swat-KP Pakistan. The study is quantitative and descriptive in nature. A diverse sample size of 370 out of 9014 was selected through convenient sampling strategy. Notwithstanding, the objectives of the study was achieved through structured questionnaire. Data was analysed through chi-square at Bi Variate level. Findings of the study revealed that earning livelihood in swat was significantly associated (P=0.016), (P=0.003) with tourism industry prior 2009 and present time respective, but the same statement was observed non-significant (P=0.075) at the time of insurgency. Arranging different festivals in the study area and establishment of different showrooms for local handcrafts, artificial jewellery and woollen shawl are some of the recommendations of the study. Keyswords: Business, Insurgency, Swat, Tourism Pages: 373-385 Article: 33 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)33 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)33 Download Pdf: download pdf view article Creative Commons License Challenges and Prospects of Pak-China Economic Corridor Authors: Muhammad Mudabbir Malik Prof. Dr. Muqarrab Akbar Abstract: Pak-China has historic relationships from the emergence of both states, and were proved long-lasting in every thick and thin times. In initial times they supported each other in foreign policies and regional issues. Pakistan and China have border disputes with India, which forced them to come close to counter India, letter on the economic interests strengthened these relations. In order to maximize the economic benefits, China announced economic corridor with the name China Pakistan Economic Corridor (CEPC). It was thought it will boost the economic growth of China, and as a prime partner Pakistan will also get economic benefits. In order to completely understand how Pakistan and China came on the same page and decided to put CPEC into reality we have to understand the Geo-political Importance of Pakistan, Strategic and economic importance of CPEC for China and Pakistan, Influence and concerns of West and neighboring countries including India. Domestic limitations and all the possible benefits and risks involved in this project for both Pakistan and China, this research acknowledges all these questions. Keyswords: Challenges, China, CPEC, Domestic Limitations Economic Growth, Pakistan, Western and Regional Concerns Pages: 386-404 Article: 34 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)34 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)34 Download Pdf: download pdf view article Creative Commons License An Analysis of Learning Practices and Habits of Children at Early Childhood Education: Students’ Perspective Authors: Masood Ahmad Sabiha Iqbal Shaista Noreen Abstract: The study was designed to analysis learning practices and habits of children at early childhood education. The major objective of the study was to find out the learning practices and habits of children. Problem was related to current situation, so survey method was exercised, 220 students were selected with the help of convenient sampling technique. Self-constructed questionnaire were exercised. The collected data was analyzed and calculate frequency, percentage, mean score, standard deviation and t-test of independent variable. The major findings of the study were; students learn from the pictures, cartoons and funny face; student’s eyes get tired of reading. When student read context continuously then they feel that their eyes get tired. There was a significance difference between male and female student about learning practices and habits of children. Keyswords: Early Childhood Education, Learning Practices and Habits, Pre-School Students Pages: 405-416 Article: 35 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)35 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)35 Download Pdf: download pdf view article Creative Commons License Gender Identity Construction in Akhtar’s Melody of a Tear Authors: Dr. Amna Saeed Hina Quddus Abstract: This study aims to discuss the notion of gender in terms of performativity and social construction. It also draws upon the idea of gender identity construction and how it relates to the society, performativity and biology. As its theoretical framework, the study relies upon the Performative Theory of Gender and Sex (1990) presented by Judith Butler and studies the gender identity construction in the female protagonist of Akhtar’s Melody of a Tear. Zara is a girl who is raised as a boy from his father and there is a kind of dilemma in Zara’s personality related to being masculine and feminine. The cultural norms of a particular gender are also a cause of this dilemma. Throughout the novel, she is in a conflicting state whether she should behave feminine or masculine. She is being depicted as an incomplete person until she finds and resolves this issue of gender identity. The paper discusses the gender performativity, social construction, cultural norms and identity as these are all contributing to the confusion and construction of the protagonist’s identity. Character analysis is used as the methodology of analysis. Keyswords: Cultural Norms, Femininity And Identity Confusion, Gender, Performativity, Masculinity, Social Construction Pages: 417-427 Article: 36 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)36 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)36 Download Pdf: download pdf view article Creative Commons License The Level of Impulsivity and Aggression among Crystal Meth and Cannabis Users Authors: Dr. Umbreen Khizar Muhammad Shafique Sana Nawab Abstract: Cannabis and crystal meth use is pervading in our society. Present study was conducted to explore the relationship between level of impulsivity and aggression among crystal meth and cannabis users. The sample of the present study was comprised of 100 participants. There were 50 cannabis and 50 crystal meth users who were diagnosed on the basis of DSM-V without any comorbidity. The sample were taken from all age range of population. The minimum education level was primary and maximum education level was graduation and above. The sample was selected from different drug rehabilitation centers of Rawalpindi and Islamabad, Pakistan. Demographic Performa was used to collect the initial important information, The “Barratt Impulsiveness Scale was used to measure the impulsivity and “Aggression Questionnaire” were used to measure the level of aggression. Finding of the study showed that there are significant differences among crystal meth and cannabis users on level of aggression. The calculated mean value for crystal meth user and for cannabis users indicates that crystal meth users have higher level of aggression as compared to the cannabis user. Over all analysis indicates a significant positive correlation of impulsivity with the variable aggression. The alpha coefficient value for all scale is acceptable. Keyswords: Aggression, Cannabis Users, Crystal Meth, Impulsivity Pages: 428-439 Article: 37 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)37 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)37 Download Pdf: download pdf view article Creative Commons License Impact of Social Factors on the Status of Tribal Women: A Case Study of the (Erstwhile) Mohmand Agency Authors: Sadia Jabeen Prof. Dr. Anwar Alam Muhammad Jawad Abstract: This study investigates the impact of socio-economic and cultural factors on the status of tribal women in the erstwhile Mohmand agency of the Ex-Federally Administered Tribal Area (FATA), Pakistan. Cultural practices and illiteracy impede the role of women in socio-economic development. The respondents were randomly selected from tehsil Ekka Ghund and Pindialai with a sample size of 370, through stratified random sampling. Data collected through structured interview schedule, FGD and observation technique. The study reveals that tribal practices early marriages, joint family system, tradition of forced marriages, compensation/Swara, exchange, purchase marriages, hampers women’s socioeconomic status. The illiteracy rate is high among the tribal women and it further undermines their role and negatively affects their socio-economic status. However, improvement in women status needs peace and stability, reforms in the constitution for women empowerment and active participation, improvement in the quality and quantity of education, women employability, skills development and women entrepreneurship Keyswords: Empowerment and Education, Marriage Types, Tribal Women Role, Tribal Women Status, Violence against Women Pages: 440-455 Article: 38 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)38 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)38 Download Pdf: download pdf view article Creative Commons License Effects of Heavy School Bags on Students’ Health at Primary Level in District Haveli (Kahutta) Azad Jammu and Kashmir Authors: Dr. Muhammad Mushtaq Shamsa Rathore Mishbah Saba Abstract: Heavy school bags is a very serious issue for the health of the primary level students throughout the world particularly in Azad Jammu and Kashmir. This study intends to explore the effect of heavy school bags on students’ health at primary level in district Kahuta. Naturally the study was descriptive and survey method was used, the population consists of one hundred ninety teachers and a sample of one hundred twenty seven teachers was selected using non probability sampling technique. A likert scale questionnaire was developed validated and distributed among the sampled respondents. The researcher personally visited the schools and collected the filled questionnaire. The data was coded and fed to the SPSS to analyze and interpret. The Chi Square test was applied to see the effect of heavy school bags on student’s health and academic achievement. The study found that heavy bags have negative effect on their health as well as their academic achievement. Students were found complaining their sickness, body and back pain. They were also found improper in their gait and their body postures. The researcher recommended the policy makers to take and develop strategies to decrease the heavy school bags. The school administration needs to make alternate days’ time tables of the subjects. Keyswords: Health, Primary Level, School, Bags, Students Heavy Pages: 456-466 Article: 39 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)39 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)39 Download Pdf: download pdf view article Creative Commons License Exploring the ‘Civil Repair’ Function of Media: A Case Study of The Christchurch Mosques Shootings Authors: Ayaz Khan Dr. Muhammad Junaid Ghauri Riffat Alam Abstract: This research endeavor is an attempt to explore and analyze the discourse produced by The New Zealand Herald; a newspaper from New Zealand and by The News International; a Pakistani newspaper. The researchers intend to determine whether and to what extent both the newspapers have the role of ‘civil repair’ played after the Christchurch mosques shootings. The researchers have incorporated the ‘lexicalization’ and the ‘ideological square’ techniques proposed by Tuen A. van Dijk within the scope of Critical Discourse Analysis. The findings of this study show that both the selected newspapers assuming the social status of ‘vital center’ performed the role of ‘civil repair’ in the aftermath of the shootings by producing the ‘solidarity discourse’. The ‘solidarity discourse’ has been produced in terms of the ‘we-ness’, harmony, understanding, and by mitigating the conflicting opinions. Keyswords: Christchurch Mosque Shootings, Civil Repair, Civil Sphere Theory, Lexicalization, Solidarity Discourse Pages: 467-484 Article: 40 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)40 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)40 Download Pdf: download pdf view article Creative Commons License China Pakistan Economic Corridor: Regional Dominance into Peace and Economic Development Authors: Tayba Anwar Asia Saif Alvi Abstract: The purpose of this qualitative study was to investigate the true motivations behind CPEC idea and the advantages it delivers to Pakistan and China. It also recognizes the Corridor's potential for mixing regional economies while dissolving geographical borders. The study is deductive in character, since it examines financial, political, and military elements of Pakistan and China's positions and situations. Enhancing geographical linkages through improved road, train, and air transport systems with regular and free exchanges of development and individual’s interaction, boosting through educational, social, and regional civilization and wisdom, activity of larger quantity of investment and commerce flow, generating and moving energy to provide more optimal businesses for the region. Keyswords: Geographical Linkages, Globalized World, Landlocked, Regional Connectivity, Regionalization Pages: 485-497 Article: 41 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)41 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)41 Download Pdf: download pdf view article Creative Commons License China’s New Great Game in Central Asia: Its Interest and Development Authors: Bushra Fatima Rana Eijaz Ahmad Abstract: Central Asia is rich in hydrocarbon resources. It’s geostrategic, geopolitical, and geo-economic significance has grasped the attention of multiple actors such as China, the USA, Russia, Turkey, the European Union, Pakistan, Afghanistan, and India. Due to its location, the Central Asian region appeared as a strategic hub. In the present scenario, China’s strategy is massive economic development, energy interest, peace, and stability. This article highlights China’s interest, political and economic development, and its role as a major player in the New Great Game in Central Asia. Shanghai Cooperation Organization (SCO) which presents as a platform where China is playing an active role in political, economic, and security concerns for achieving its objectives in Central Asia. The new step of the Belt and Road Initiative (BRI) sheds light on China’s progressive move in this region via land and sea routes, which creates opportunities for globalization. Keyswords: Belt and Road Initiative, Central Asia, China, New Great Game Pages: 498-509 Article: 42 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)42 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)42 Download Pdf: download pdf view article Creative Commons License Personality Traits as Predictors of Self-Esteem and Death Anxiety among Drug Addicts Authors: Umbreen Khizar Saira Irfan Iram Ramzan Abstract: This study seeks to investigate whether personality traits predict self-esteem and death anxiety among drug addicts. The sample consisted of 100 drug addicts taken from the two hospitals in Multan city. Only men between the ages of 20 and 65 were included in the study. Data was collected through reliable and valid questionnaires. Results revealed positive relationship between conscientiousness, openness to experience and self-esteem. Moreover, findings showed positive relationship between extraversion and death anxiety, and negative correlation between neuroticism and death anxiety. Findings also showed that self-esteem and death anxiety are significantly and negatively correlated. Additionally, findings revealed that conscientiousness positively predicted self-esteem and neuroticism negatively predicted death anxiety. Furthermore, significant differences were observed in self-esteem, and death anxiety based on age. Significant differences were also found in extraversion, agreeableness, openness to experience, and death anxiety based on location. Understanding how personality traits affect behavior can help drug addicts get the support they need to live a better life and reduce their risk of death anxiety and premature death. Keyswords: Death Anxiety, Drug Users, Personality Traits, Self- Esteem Pages: 510-524 Article: 43 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)43 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)43 Download Pdf: download pdf view article Creative Commons License Middle East: A Regional Instability Prototype Provoking Third Party Interventions Authors: Waseem Din Prof. Dr. Iram Khalid Abstract: Third party interventions always prolong the interstate or civil wars with unending sufferings and devastations. The entire Middle East region is fraught with tensions, conflicts, civil wars and rivalries. From strategic interests to power grabbing, sectarian divisions, flaws in the civil and social structure of the state and society, ethnic insurrections, and many other shapes of instability syndromes can be diagnosed in this region. In the post-Arab Spring, 2011, the emerging new regional hierarchical order for power/dominance, in addition to the weakening/declining dominant US power in the region, changed the entire shape of already conflict-ridden region. New weak or collapsing states and bifurcation of the ‘status quo’ and ‘counter-hegemonic’ states along with their respective allies, made this region a prototype of instability in the regional security complex of the Middle East, as a direct result of these developments. The perpetuation of these abnormalities would not recede this instability conundrum from the region, provoking third party intervention, if not contained. Keyswords: Conflicts/Civil Wars, Dominant Power, Instability, Intervention, Middle East, Middle Powers, Regional Hierarchy, Regional Powers, Security Complex, Weak State Pages: 525-542 Article: 44 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)44 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)44 Download Pdf: download pdf view article Creative Commons License Impact of Classroom Environment on Second Language Learning Anxiety Authors: Zohaib Zahid Abstract: Second language learning anxiety has attained the attention of the researchers in almost every part of the world. Pakistan is a country where English is taught as a second language from the very beginning of school education. Second Language learning anxiety is a phenomenon which has been prominently found among the learners because of their less proficiency in learning English language. This study has been conducted to investigate the effect of anxiety in learning and using English language in classroom, university and outside the classroom. There are variables that affect language learning performance of the learners but this paper has solely investigated the effect of anxiety. The paper has concluded that anxiety is a variable which has a striking affect in second language learning and its use inside classrooms. Keyswords: Effect of Anxiety, Proficiency, Second Language Learning Anxiety, Striking Affect Pages: 485-497 Article: 45 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)45 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)45 Download Pdf: download pdf view article Creative Commons License Struggling for Democracy: A Case of Democratization in Pakistan Authors: Ammara Tariq Cheema Dr. Rehana Saeed Hashmi Abstract: The objective of this research paper is to review the challenges for democratization in Pakistan. The problem of democratization and consolidation refers to the structure of democracy following the collapse of non-democratic regime. Ten factors as given by Michael J. Sodaro are considered effective in helping a democratically unstable state to stabilize its system in other words helps in the democratic consolidation. It is argued in this research that the ten factors of democratization as given by Michael J. Sodaro have been absent in the political system of Pakistan and working on these factors can lead Pakistan to the road of democratization. This study uses qualitative method of research and proposes a novel framework for the deed of parliament, because the effectiveness of parliament can contribute positively to democratization/consolidated democracy. Keyswords: Electoral Politics, General Elections, Political Participation, Women Empowerment Pages: 554-562 Article: 46 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)46 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)46 Download Pdf: download pdf view article Creative Commons License Impact of Dependency Ratio on Economic Growth among Most Populated Asian Countries Authors: Dilshad Ahmad Salyha Zulfiqar Ali Shah Abstract: Demographic transition through different channels significantly influences economic growth. Malthusian view postulated as dependency ratio adversely affects economic growth while Julian Simon's view is quite different, highlighted the long-run benefits of the population in the range of 5 to15 years on economic growth. This study can be a valuable addition in research to analyzing the association of dependency ratio and economic growth of the five most populated Asian countries (Bangladesh, China, Indonesia, India, and Pakistan). Empirical findings of the study indicated that a total dependency and younger dependency ratio has a positive and significant influence on economic growth in both short-run and long-run scenarios while the old dependency ratio shows a negative influence on economic growth in the long run while short-run results are unpredictable. There is a need for state-based proper policy measures in focusing the higher financing in human capital development specifically in education and health. Keyswords: Economic Growth, Gross Saving, Old Dependency Ratio, Young Dependency Ratio Pages: 563-579 Article: 47 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)47 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)47 Download Pdf: download pdf view article Creative Commons License Chinese Geo-Strategic Objectives and Economic Interests in Afghanistan under President Xi Jinping Authors: Farooq Ahmed Prof. Dr. Iram Khalid Abstract: China has its own distinctive interests, concerns and strategies with respect to the changing security dynamics in Afghanistan. China has taken an active interest, though retaining a low profile and avoiding direct military interaction. China has exclusively relished on economic engagement actively and provided numerous financial aid and financial support in the rebuilding of Afghanistan's economy. The aim of this research study is to analyze the geo-strategic objectives and economic interests of China under the leadership of President Xi Jinping. This study looks at the actual diplomatic, economic and protection commitments of both countries as well as the basis of the geopolitical complexities – core variables that form China's current foreign policy to Afghanistan. Keyswords: Afghanistan, BRI, China, NATO Withdrawal Pages: 580-592 Article: 48 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)48 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)48 Download Pdf: download pdf view article Creative Commons License The Argument Structure of Intransitive Verbs in Pashto Authors: Abdul Hamid Nadeem Haider Bukhari Ghani Rehman Abstract: This study focuses on the description and categorization of intransitive verbs in terms of its argument structure. The study concludes that the unaccusative verbs only project an internal argument. It does not require the event argument. However, the said verb can be causativised by adding external argument and at the same time the event argument gets included in the valency of the derived causative of the unaccusative root. The unergative, on the other hand, requires an external argument as an obligatory argument while the internal argument is not the obligatory argument of the verb. The event argument is also a part of the valency of the verb. The APFs require one argument which is the internal argument of the verb. However, since the external argument is not available, the internal argument of the verb gets realized as the subject of the verb. The verb does not project event argument. The ergative predicates are derived by the suppression of the external argument and by the externalization of the internal argument. Keyswords: Argument Structure, Ergative Case, Event Argument, External Argument, Internal Argument, Valency Pages: 593-610 Article: 49 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)49 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)49 Download Pdf: download pdf view article Creative Commons License Positive, Negative and Criminal Orientation of Beggars in Okara: Perspective of Students Authors: Shahzad Farid Saif-Ur-Rehman Saif Abbasi Hassan Raza Abstract: This study aimed to measure the perspective of students about the criminal orientation of beggars. The sample size of the study (i.e., 100 students) was explored using Taro Yamane’ equation from the university of Okara, Punjab, Pakistan. The respondents were approached using simple random sampling and interviewed using face to face interview schedule. The data was collected using a structured questionnaire. The analysis was administered through SPSS-20.The study explored that parental illiteracy is associated with the high criminal and negative orientation of students towards beggars. It was also explored that females and respondents from rural background have low negative orientation towards beggars. However, males and respondents from urban background have medium criminal orientation and low positive orientation towards beggars, respectively. The study is useful for the government of Punjab, Pakistan campaign and policy for anti-begging. The study introduced the geometrical model of youth’s orientation toward begging. The study also contributed to the literature on begging by extending its domain from Law and Criminology to sociology as it incorporated social variables e.g., parents’ education, gender, etc., to explore their association with the youth’s socialization about begging. Keyswords: Begging, Crime, Education, Gender, Students Pages: 611-621 Article: 50 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)50 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)50 Download Pdf: download pdf view article Creative Commons License Relationship between Entrepreneurial Export Orientation and Export Entrepreneurship through Mediation of Entrepreneurial Capabilities Authors: Muhammad Saqib Nawaz Masood ul Hassan Abstract: Export led growth is prominent paradigm in developing world since decades. Exports play vital role in the economy by improving the level of balance of payments, economic growth and employment. Due to strategic importance of exports, organizational researchers focused on finding antecedents of export performance of the organizations. To line with this, current study aims to find the impact of entrepreneurial export orientation on export entrepreneurship through mediation of entrepreneurial capabilities in the Pakistani context. For this purpose, data was collected from 221 exporting firms of Pakistan by using questionnaire. Collected data was analyzed with the help of Smart PLS. In findings, measurement model confirmed the validity and reliability of measures of variables. Additionally, structural model provides the positive impact of entrepreneurial export orientation on export entrepreneurship. Similarly, entrepreneurial capabilities mediate the relationship between entrepreneurial export orientation on export entrepreneurship. The findings provide important implications for the managers of exporting firms to improve export performance. Keyswords: Entrepreneurial Capabilities, Entrepreneurial Export Orientation, Export Entrepreneurship Pages: 622-636 Article: 51 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)51 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)51 Download Pdf: download pdf view article Creative Commons License China Pakistan Economic Corridor: Explaining U.S-India Strategic Concerns Authors: Nasreen Akhtar Dilshad Bano Abstract: Regional and International political and economic landscape is being changed owing to China Pakistan Economic Corridor (CEPEC)-the new security paradigm has taken place-that has increased the strategic concerns of the U.S. and India. This research paper attempts to re-examine China-Pakistan relations in the new emerging geo-political compass. This paper has investigated the question that how regional, and global developments have impacted the China-Pakistan relationship? And why China – Pakistan have become partners of CPEC? In the global context, this paper assesses the emerging International Order, Indo-U. S strategic narrative vis-à-vis CPEC, and the containment of China through the new alliances and their impacts on China -Pakistan vis-à-vis the Belt Road Initiative (BRI). Quadrilateral (Quad) alliances is shaping the new strategic political and security paradigms in the world politics. Keyswords: BRI, China, CPEC, India, Pakistan, Silk Road, Strategic Concerns Pages: 637-649 Article: 52 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)52 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)52 Download Pdf: download pdf view article Creative Commons License The Structure of Domestic Politics and 1973 Constitution of Pakistan Authors: Dr. Fida Bazai Dr. Ruqia Rehman Amjad Rashid Abstract: Pakistan is located in a pivotal region. Its geo-strategic location affects its national identity as a nation state. Unlike Europe in South Asia security dilemma, proxy warfare and nuclear arms race are consistent features of the regional politics. The identity of Pakistan as security-centric state gives its army disproportional power, which created institutional imbalance that directly affected constitutionalism in the country. The constitution of Pakistan is based on principles of civilian supremacy and separation of power but in reality Pakistan’s army is the most powerful institution in country. This paper argues that the structure of Pakistani politics; created institutional imbalances by the disproportionate distribution of resources is the key variable in creating dichotomy. The structure of domestic politics is based upon the principles of hostility to India, use of Islam for national unity and strategic alliances with major powers to finance defense against the neighboring countries. Keyswords: Constitutionalism, Identity, Islam, South Asia Pages: 650-661 Article: 53 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)53 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)53 Download Pdf: download pdf view article Creative Commons License National Integration and Regionalism in Pakistan: Government’s Strategy and Response toward Regionalist Demands 1947-77 Authors: Najeeb ur Rehman Mohammad Dilshad Mohabbat Muhammad Wahid Abstract: The countries of South Asian region have pluralistic societies with different language, religious, and ethnic identities. Pakistan is no exception who is facing the challenge of regionalism since its inception. Different ethnic groups have been consistently raising their voices for separatism or autonomy within the frame work of an existing territorial state. The issues of provincialism, ethnicity, and regionalism is posing a serious challenge to the integrity of the country. This paper aims to explore the causes of the regionalism in Pakistan and intends to analyze the policies and strategies of different political governments which they launched to tackle this all important issue. The paper follows the historical method of research and analyzes different types of qualitative data to conclude the finding of the research. The paper develops the theory of “Regionalists Demand and Government Response” which shows how different regionalist forces put their demands and how the governments react on these demands. It recommends the grant of greater regional autonomy to the regionalists to enhance internal security and to protect the country from disintegration. Keyswords: Demands, Ethnicity, Government Strategy, National Integrity, Nationalism, Regionalism Pages: 662-678 Article: 54 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)54 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)54 Download Pdf: download pdf view article Creative Commons License Fostering Entrepreneurial Mindset through Entrepreneurial Education: A Qualitative Study Authors: Saira Maqbool Dr. Qaisara Parveen Dr. Muhammad Hanif Abstract: Research on entrepreneurial mindset has flourished in these recent years. Its significance lies in a critical suspicion and its matters for inventive behavior. Entrepreneurship joined with innovative abilities, seen as one of the most wanted in this day and age. This study aims to determine the perceptions about entrepreneurial mindset, its importance, and the role of entrepreneurship education and Training in developing the entrepreneurial mindset. This is a qualitative study based on interviews conducted by professors of Pakistan and Germany. The analysis was determined through content analysis. The results determine that 'Making Entrepreneurial Mindset' assists with seeing better all parts of business venture, which will undoubtedly influence their view of business venture, pioneering abilities, and mentalities. Keyswords: Entrepreneurship Education, Entrepreneurial Mindset Pages: 679-691 Article: 55 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)55 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)55 Download Pdf: download pdf view article Creative Commons License Benefits of Implementing Single National Curriculum in Special Schools of Lahore city for Children with Intellectual Disability: Teachers’ Perception Authors: Dr. Hina Fazil Khurram Rameez Sidra Ansar Abstract: Single national curriculum (SNC) is an important issue across the Punjab Province of Pakistan. Making and implementing SNC is not only focusing the education of normal pupils, but also focusing students with disabilities (SWD). The field of special education experienced an increased discussion of curriculum for students with intellectual disabilities (SID). The present research aimed to know the benefits to implement first stage of single national curriculum for students with Intellectual disability and to know the differences about the benefits between public and private schools regarding SNC for students with ID based on demographic characteristics. Likert type researchers-made questionnaire with reliability) Cronbach alpha .922) was used. 90 special educationists from public and private schools were chosen through random sampling technique. The findings raised some benefits such as: SNC will bridge the social and economic disparities which will increase the acceptance of ID students. It was recommended that SNC should include areas of adaptive skills, motor, and vocational skills to get involved in work activities. Keyswords: Benefits, Children with Intellectual Disability, Single National Curriculum Pages: 692-703 Article: 56 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)56 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)56 Download Pdf: download pdf view article Creative Commons License Last Rituals and Problems Faced by the Hindu Community in Punjab: A Case Study of Lahore Authors: Sabir Naz Abstract: Lahore is the provincial capital of Punjab, where a sizeable population of the Hindus has been residing there since the inception of Pakistan. There had been many crematoriums in the city but with the passage of time, one after another, disappeared from the land after partition of the Sub-continent. Those places were replaced by commercial or residential sites. There is also a graveyard in the city which is in the use of Hindu Valmik Sect. However, it was encroached by some Muslims due to very small size of population and indolence of the Hindus. Later on, the encroachments were removed by the District Government Lahore in compliance of order of the Supreme Court of Pakistan. Presently, there is a graveyard as well as a crematorium in the city. The community remained deprived of a place to dispose of a dead body according to their faith for a long period which is contravention with the guidelines of the Quaid-e-Azam, founder of the nation Keyswords: Crematorium, Graveyard, Hindu community, Last Rituals Pages: 704-713 Article: 57 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)57 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)57 Download Pdf: download pdf view article Creative Commons License Estimating Growth Model by Non-Nested Encompassing: A Cross Country Analysis Authors: Benish Rashid Dr. Shahid Razzaque Dr. Atiq ur Rehman Abstract: Whether models are nested or non-nested it is important to be able to compare them and evaluate their comparative results. In this study six growth models have been used for analyzing the main determinants of economic growth in case of cross countries, therefore by using these six models we have tested them for non-nested and nested encompassing through Cox test and F-test respectively. Data from 1980 to 2020 were used to analyze the cross country growth factors so therefore, the current study looked at about forty four countries with modelling these different comparative studies based on growth modelling. So, we can make these six individual models and we can estimate the General Unrestricted Model with the use of econometric technique of Non-Nested Encompassing. By evaluating the data using the Non-Nested Encompassing econometric technique, different sets of economic variables has been used to evaluate which sets of the economic variables are important to boost up the growth level of the country. And found that in case of nested model or full model it is concluded that model with lag value of GDP, trade openness, population, real export, and gross fix capital formation are the main and potential determinants to boost up the Economic Growth in most of the countries. Keyswords: Cross Country, Economic Growth, Encompassing, Nested, Non-nested Pages: 714-727 Article: 58 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)58 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)58 Download Pdf: download pdf view article Creative Commons License Assessment of Youth Buying Behaviour for Organic Food Products in Southern Punjab: Perceptions and Hindrances Authors: Ayousha Rahman Asif Yaseen Muhammad Arif Nawaz Abstract: This research examined the cognitive antecedental effects on organic food purchase behaviour for understanding the perceptions and hindrances associated with purchasing organic food products. Theory of Planned Behaviour (TPB) was adopted as a theoretical framework. A total of 250 young consumers in the two cities of Southern Punjab, Pakistan was randomly sampled and data were collected via a face-to-face survey method. Partial least square technique was employed to test the model. The results showed that attitude towards organic food purchasing motivated when moral norms were activated to consume organic food products. Further, environmental knowledge moderated the relationship of organic food purchase intentions and behaviour significantly. The findings highlighted the importance of moral norms as a meaningful antecedent that could increase the TP-based psychosocial processes if consumers have sufficient environmental knowledge. Therefore, farmers, organic products marketers, government administrators, and food retailers should take initiatives not only to highlight the norms and values but also when promoting organic food production and consumption. Keyswords: Environmental Knowledge, Organic Food Purchase Behaviour, Personal Attitude, PLS-SEM, Subjective & Moral Norms Pages: 728-748 Article: 59 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)59 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)59 Download Pdf: download pdf view article Creative Commons License An Analysis on Students Ideas about English and Urdu as Medium of Instructions in the Subjects of Social Sciences studying in the Colleges of the Punjab, Pakistan Authors: Ashiq Hussain Asma Amanat Abstract: The worth and usefulness of English education as a foreign language is of great concern to language rule and planning (LRP) researchers compared to teaching their native language globally in higher education. The study under research examines the perspectives of two similar groups of the final year students of at Higher Education Institutions of Pakistan. The first group consists of art students who received the Urdu medium of instruction (UMI), and the second group received the English medium of instruction (EMI). An empirical methodology was carried out in the present year, students answered questionnaires to find out the benefits and challenges of learning subject-based knowledge, what subject-based knowledge means to them, and their understanding of language as a teaching language. Interviews were conducted with the selected group of students who wished to participate in research. Additional information is available from the tests and results obtained in the two equivalent courses. Although many similarities have been identified between the two groups, the overall knowledge of disciplinary knowledge of English medium instruction students was not very effective, while that of UMI students was very effective. It explains the implications of the findings to continue the language rule as policy experience for teaching in higher education institutions. Keyswords: English as Medium of Instruction (EMI), Higher Education Institutions (HEIs), Urdu as Medium of Instruction (UMI) Pages: 749-760 Article: 60 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)60 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)60 Download Pdf: download pdf view article Creative Commons License Environment and Women in Kurt Vonnegut’s ‘Happy Birthday Wanda Juny’: An Eco- Critical and Feminist Analysis Authors: Dr. Muhammad Asif Safana Hashmat Khan Muhammad Afzal Khan Janjua Abstract: This is an Eco-feminist study of Vonnegut’s ‘Happy Birthday Wanda Juny’ and focuses on how both women and environment are exploited by patriarchy. Ecofeminism critiques masculine dominance highlighting its role in creating and perpetuating gender discrimination, social inequity and environmental degradation. Women suffer more because of power disparity in society. Environmental crises affect women more than men because of their already precarious existence and subaltern position. There is affinity between women and nature are victims of climate change and other environmental hazards. Cheryl Glotfelty introduced interdisciplinary approach to the study of literature and environment. Literary ecology as an emerging discipline explores the intriguing relationship between environment and literature. Ecofeminism draws on feminist critique of gender inequality showing how gender categories inscribed in power structure exploit both women and nature. Francoise d‘Eaubonne coined the term ecofeminism to critique the prevalent exploitation of both women and environment. Ecofeminism asserts that exploitation of women and degradation of the environment are the direct result of male dominance and capitalism. Ecofeminism argues for redressing the plight of women and protection of environment. Vonnegut’s play ‘Happy Birthday Wanda June’ was written at a time when the movement for the right of women and protection of environment were gaining momentum. The play shows how toxic masculinity rooted in power and capitalism exploit both women and environment. Keyswords: Eco-Feminism, Eco-Criticism, Ecology, Environment, Exploitation Pages: 761-773 Article: 61 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)61 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)61 Download Pdf: download pdf view article Creative Commons License Critical Analysis of Social Equity and Economic Opportunities in the Light of Quranic Message Authors: Prof. Dr. Muhammad Yousuf Sharjeel Mahnaz Aslam Zahida Shah Abstract: This study critically evaluated the key verses of Surah Al-Baqarah -the second chapter of Quran, a sacred scripture of Islam- which specifically relates to social equity opportunities and a code of conduct in the context of economics. The Quran claims that it is a book which explains every situation; therefore, the aim of this study remained to extract those verses of Surah Al-Baqarah which can guide us in Economics. The authentic and approved Islamic clerics and their translations were consulted for the interpretations of the Holy verses. The researchers chiefly focused and studied Surah Baqarah with regards to social equity and economic opportunities. The translations were primarily in the regional language Urdu so the interpretations must not be related exactly equitable in English. The study engaged the document analysis research strategy. This study is only an endeavour to decipher Holy Quran’s message from Allah for the mankind so it must not be considered as the full and complete solution to the all the economic issues, challenges and opportunities. Ahadees and the saying of the Holy prophet were referred to where ever required and available. The researcher also considered the Tafasir (detail intellectual interpretations) of the Quran done by the well-known scholars of Islam for the verses studied therein and any statements and/or material - such as ideas, studies, articles, documentation, data, reports, facts, statistics etc. For the study, data was collected and analyzed qualitatively. On the basis of the study, recommendations were also primed. Keyswords: Economic Issues and Challenges, Social Equity, Surah Al-Baqarah, Al Quran Pages: 774-790 Article: 62 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)62 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)62 Download Pdf: download pdf view article Creative Commons License A Critical Discourse Analysis of Dastak by Mirza Adeeb Authors: Muhammad Afzal Dr. Syed Kazim Shah Umar Hayat Abstract: The present research aims to explore ideology in Pakistani drama. The drama, “Dastak”, written by Mirza Adeeb, has been taken for exploration ideologically. Fairclough’s (1992) three-dimensional model has been used for analyzing the text of the above-mentioned drama which includes textual, discursive practice and social practice analyses. The linguistic and social analyses of the drama reveal the writer’s ideology about socio-cultural, conventional and professional aspects of life. The study has also explored the past and present states of mind of Dr. Zaidi, the central and principal character of the drama, Dastak. The text implies that the writer has conveyed personal as well as social aspects of his times through the drama of Dastak. Keyswords: Dastak, Drama, Ideology, Semiotics Pages: 791-807 Article: 63 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)63 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)63 Download Pdf: download pdf view article Creative Commons License Linking Job Satisfaction to Employee Performance: The Moderating Role of Islamic Work Ethics Authors: Dr. Shakira Huma Siddiqui Dr. Hira Salah ud din Khan Dr. Nabeel Younus Ansari Abstract: The most pervasive concern in public sector organizations is declining employee performance and workforce of these organizations are less satisfied with their jobs. The aim of this study is to investigate the impact of Job Satisfaction on employee’s performance and how Islamic work ethics moderates the above mentioned direct relationship in the public sector organizations of Pakistan. The data were collected from the sample of 193 permanent employees working in public sector organizations through stratified sampling technique. The results revealed that employees Job satisfaction is significantly related to higher performance. Further, the findings indicated that Islamic work ethics moderates the relationship between job satisfaction and employee performance. The present research has some theoretical and empirical implications for academicians, policymakers, especially of public sector organizations, for the improvement of performance of their workforce. Keyswords: Employee Performance, Islamic Work Ethics, Job Satisfaction, Person-Environment Fit Theory Pages: 808-821 Article: 64 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)64 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)64 Download Pdf: download pdf view article Creative Commons License Semantics of Qawwali: Poetry, Perception, and Cultural Consumption Authors: Rao Nadeem Alam Tayyaba Khalid Abstract: Semantics is about meanings and meanings are arbitrary and shared. Understanding qawwali context requires comprehension of semantics or process of meaning creation and meaning sharing among the qawwal party and the audience. This interactive activity might frequently be hindered when interrupted by subjective meanings creation during cultural consumption. Qawwali is a cultural tradition, its semantics are conditioned by axiological premises of poetry and perceptions which are transforming. The previous researches revealed that qawwali is associated with religion which provides the religious message by singing hamd and naat. It was a means to experience Divine; therefore, semantics are multi-layered and often crossroad with values and subjective experiences. It is novel due to its ritual of Sama. It has the therapeutic power that helps mentally disturbed people and they find refuge. This study is exploratory having a small sample size of twenty purposively selected audiences. This phenomenological inquiry used ethnographic method of conversational interviews at selected shrines and cultural spaces in Islamabad. The results indicate that qawwali is a strong refuge for people facing miseries of life and they attend Sama with a belief that attending and listening will consequently resolve their issues, either psychological or physiological. They participate in Sama which teaches them how to be optimistic in a negative situation; this paper brings forth this nodal phenomenon using the verbatim explanations by the interlocutors. Semantics of Qawwali are conditioned and some of these elements are highlighted including poetry and axiology based perceptions and cultural consumption of a cultural realm. Keyswords: Cognition, Culture, Poetry, Qawwal, Qawwali, Semantics Pages: 822-834 Article: 65 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)65 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)65 Download Pdf: download pdf view article Creative Commons License Political Economy of Smuggling: The Living Source for the Natives (A Case Study of Jiwani-Iran Border, Baluchistan) Authors: Abdul Raheem Dr. Ikram Badshah Wasia Arshed Abstract: This study explores the political economy of smuggling on Jiwani-Iran border. The natives are majorly involved in illegal transportation of goods and objects, therefore; the study sets to explain how significant smuggling for the local people is. It describes the kinship role in reciprocity of their trade and transportation. The qualitative methods such as purposive sampling and interview guide were employed for data collection. The research findings revealed that local people were satisfied with their illegal trading which is depended largely on their expertise and know-how of smuggling at borders. They disclosed that their total economy was predominantly based on smuggling of stuff like drugs, diesel, oil, gas, petrol, ration food from Iran, and human trafficking. They also enjoyed the privilege of possessing Sajjil (Iranian identity card), thus; the dual nationality helped them in their daily business and rahdari (border crossing agreement), enabling them to travel to Iran for multiple purposes. Keyswords: Drugs, Human, Navigation, Political Economy, Reciprocity, Smuggling, Trafficking Pages: 835-848 Article: 66 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)66 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)66 Download Pdf: download pdf view article Creative Commons License The Vicious Circles of System: A Kafkaesque Study of Kobo Abe’s The Woman in the Dunes Authors: Imran Aslam Kainat Azhar Abstract: This paper analyses the Kafkaesque/Kafkan features of Kobo Abe’s novel The Woman in the as formulated by Kundera in “Kafka’s World.” For Kundera, in a Kafkaesque work human existence is bleakly represented through intermingling of tragedy and comedy in an indifferent world dominated by hegemonic systems. The Kafkaesque is characterised by the following: World is a huge forking labyrinthine institution where the man has been thrown to suffer its complexities, confrontation with the labyrinth makes his existence meaningless because freedom is a taboo in no man’s land, he is punished for an unknown sin for which he seeks justification from the superior authorities, but his efforts are viewed as ludicrous or comic despite the underlying sense of tragedy. (5) The Kafkaesque tendency to present tragic situation comically is also explored in Abe’s novel. The paper studies the effect of higher authorities exercising their power over man and the inscrutability of cosmic structures continuously undermining human freedom in nightmarish conditions. The paper establishes Kobo Abe in the literary world as a writer who portrays the hollowness and futility of human lives with a Kafkaesque touch. Keyswords: Authority, Institutions, Kafka, Kafkaesque, Kafkan, Kobo Abe, Kundera, The Trial, The Woman in the Dune Pages: 849-861 Article: 67 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)67 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)67 Download Pdf: download pdf view article Creative Commons License Subjectivity and Ideological Interpellation: An Investigation of Omar Shahid Hamid’s The Spinner’s Tale Authors: Hina Iqbal Dr. Muhammad Asif Asia Saeed Abstract: Louis Althusser’s concept of interpellation is a process in which individuals internalize cultural values and ideology and becomes subject. Althusser believes that ideology is a belief system of a society in which ideological agencies establish hierarchies in society through reinforcement and discrimination for cultural conditioning. These agencies function through ideological state apparatuses. These ideological agencies help to construct individual identity in society. The undesirable ideologies promote repressive political agendas. The non-repressive ideologies are inhaled by the individuals as a natural way of looking at the culture and society. This research seeks to investigate Omar Shahid Hamid’s novel The Spinners Tales through the lens of Althusser’s ideology and interpellation. This study examines how the characters of Shahid’s novel inhaled ideology and became its subjects. This research also depicts the alarming effects of cultural hegemony that creates cultural infidelity and hierarchies between the bourgeoisie and proletariat classes. Keyswords: Cultural Hegemony, Ideological State Apparatus, Ideology, Interpellation, Repressive Factors Pages: 862-872 Article: 68 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)68 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)68 Download Pdf: download pdf view article Creative Commons License Blessing in Disguise: Recommendations of Indian Education Commission (1882) and Christian Missionaries’ Educational Policy in the Colonial Punjab Authors: Mohammad Dilshad Mohabbat Muhammad Hassan Muhammad Ayaz Rafi Abstract: Woods Education Despatch is considered to be the Magna Carta of Indian Education. It controlled the Indian education field till the establishment of Indian Education Commission, 1882. The Despatch provided space to Christian missionaries by promising government’s gradual withdrawal from the education in favour of missionaries. It also facilitated the missionaries by offering system of ‘grants on aid’ to the private bodies. Consequently, the missionaries fancied to replace the government institutions in the Punjab and initiated their efforts to increase the number of their educational institutions. They tried to occupy the educational field by establishing more and more educational institutions. But after the Recommendations of the Indian Education Commission 1882, a change in their policy of numeric increase of educational institutions is quite visible. With the turn of the century, they are found to be eager to establish a few institutions with good quality of education. This paper intends to analyse different factors behind the change of their policy of quantitative dominance to qualitative improvement. It also attempts to evaluate how their change of policy worked and what steps were taken to improve the quality of their educational institutions. Following the historical method qualitative data comprising educational reports, missionaries’ autobiographies, Reports of missionaries’ conferences, and the other relevant primary and secondary sources has been collected from different repositories. The analysis of the data suggests that the attitude of the administration of the education department and the recommendations of Indian Education Commission were the major driving forces behind the change of missionaries’ educational policy in the 20th century. The missionaries, after adopting the new policy, worked on the quality of education in their institutions and became successful. Keyswords: Christian Missionaries, Indian Education Commission, Missionary Schools, Numeric Increase, Quality of Education. The Punjab, Woods Education Despatch Pages: 873-887 Article: 69 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)69 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)69 Download Pdf: download pdf view article Creative Commons License Basic Life Values of Prospective Special Education Teachers Authors: Dr. Maria Sohaib Qureshi Dr. Syeda Samina Tahira Dr. Muhammad Irfan Arif Abstract: Future teachers' preconceived values about how to live their lives and how that affects the lives of their students were the focus of this study. Descriptive research was used by the researchers. The study was carried out by using Morris's Ways to Live Scale. Researchers used this scale to study prospective special education teachers' gender, social status, personal relationships, aesthetics and mental approach using purposive sampling method. Descriptive and inferential stats were used to analyse the data collected from those who participated in the study on basic life values of prospective teachers. Results indicated that being social and sympathetic are the most important values among prospective special education teachers. It was also found that male and female prospective special education teachers living in urban and rural areas had no significant differences in their basic life values. Keyswords: Special Education, Teacher, Values Pages: 888-896 Article: 70 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)70 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)70 Download Pdf: download pdf view article Creative Commons License Perception of Dowry: Effects on Women Rights in Punjab Authors: Dr. Bushra Yasmeen Dr. Muhammad Ramzan Dr. Asma Seemi Malik Abstract: Dowry is a common tradition in south Asian countries, especially in Pakistan and India. Daughters became curses and liability for parents causing serious consequences. For control, there are legal ban/restrictions (Dowry and Wedding Gifts (Restriction) Act, 1976; Amendment in Act, 1993) on its practice in Pakistan. Despite the legal cover, the custom has been extended. Dowry amount seems to be increasing due to changing lifestyle and trends of society. To understand males’ and females’ perceptions about dowry; impacts of dowry; why dowry is essential; and how it is affecting women’s rights and eventually affecting women’s autonomy. A qualitative study was conducted. Data was collected by using unstructured interviews from males and females including social activists, economists, and married couples about wedding expenses, demands, society pressure, men’s support, and perception against dowry especially with regards to women’s rights and autonomy. The study concluded heavy dowry especially in terms of furniture, electronics, kitchenware, car, furnished houses, and cash highly associated with women’s development and their rights. General people’s perception showed that dowry is no longer remained a custom or tradition in Asian countries. It is just a trend and people follow it as a symbol of respect for parents and women as well. Keyswords: Dowry, Effects, Impacts Of Dowry, Perceptions, Women Autonomy, Women Rights Pages: 897-909 Article: 71 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)71 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)71 Download Pdf: download pdf view article Creative Commons License NCOC-An Emblem of Effective Governance: An analysis of Pakistan’s Counter Strategy for Covid-19 as a Non-Traditional Security Challenge Authors: Dr. Iram Khalid Abstract: COVID -19 affected the world unprecedentedly. Lack of capacity and poor standards of governance caused nontraditional security challenges to Pakistan too. The NCOC is the central nerve center to guide the national response to COVID-19 by Pakistan and can be best analyzed in the light of the decision-making theory of Naturalist Decision Making (NDM). The study points out the effective role performed by NCOC at policy formation through a more prosaic combination of science, data, decision making and execution of decisions at the level of federalism. The study highlights the changing patterns of government’s approach during the pandemic at various levels. Pakistan faced economic, political and social crisis during this phase. This study uses a survey and key informant interviews as the source of analysis for qualitative data collection. By applying the decision- making theory, the paper extends that there is a need to use a model to balance the existing gap within the system, to meet challenges. The study suggests a coordinating approach among various units and center; that might raise the level of performance to meet the nontraditional security challenges with innovation, creativity and boldness. Keyswords: COVID-19, Decision Making Theory, Governance, Nontraditional Threats, Strategy Pages: 910-930 Article: 72 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)72 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)72 Download Pdf: download pdf view article Creative Commons License Comparative Implications of Wednesbury Principle in England and Pakistan Authors: Safarat Ahmad Ali Shah Dr. Sara Qayum Arzoo Farhad Abstract: Wednesbury principle is one of the most important and useful grounds of the Judicial Review. Judicial review is a remedy provided by the public law and is exercised by the superior and higher courts to supervise administrative authorities' powers and functions. The main objective of the judicial review is to ensure the fair and transparent treatment of individuals by public authorities. The ground of the judicial review, i.e., Unreasonableness or irrationality or popularly known as Wednesbury Unreasonableness was introduced by lord Greene in the Wednesbury Corporation case in 1948. Initially, the scope of this ground of judicial review was very narrow and was allowed only in rare cases. However, with the development of administrative law and Human rights, it also developed. Its development resulted in different controversies and issues about the application of this ground. The main issue is about its encroachment in the jurisdiction of other branches of the government i.e., the parliament and executive. The free and loose application of this principle results in confusion and conflict between different organs of the government. The present paper is based on the implications of the limitations on the ground of Wednesbury Unreasonableness both on the judicial and administrative bodies in Pakistan to avoid the chaos and confusion that results in the criticisms on this ground of judicial review. Keyswords: Administrative Authorities, Critical Analysis, Illegality, Judicial Review, Pakistan, Wednesbury Unreasonableness Pages: 931-946 Article: 73 , Volume 2 , Issue 4 DOI Number: 10.47205/jdss.2021(2-IV)73 DOI Link: http://doi.org/10.47205/jdss.2021(2-IV)73 Download Pdf: download pdf view article Creative Commons License Water Sharing Issues in Pakistan: Impacts on Inter-Provincial Relations
+		10.47205/jdss.2021(2-iv)74
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+		Journal of Development and Social Sciences
+		JDSS
+		2709-6254
+		2709-6262
+		
+			2
+			IV
+			March 18, 2021
+			Pakistan Social Sciences Research Institute (PSSRI)
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+I. IntroductionCommercial air mobility for transportation of people and packages in urban environments is both strategically as well as tactically challenging.Heterogeneity of aerial vehicles coupled with their expected high density in highly fragmented airspace poses a significant challenge in implementing a coordinated urban air mobility strategy.Furthermore, varying individual business objectives and priorities of different aerial vehicle operators, make the tactical management of Urban Air Mobility (UAM) operations in tight and shared airspace difficult, specifically in a centralized manner as it can be quickly overwhelmed by the demand for decision making.On the other hand, a distributed management approach for the airspace usage provides scalability of services whereby these diverse aerial vehicles participate in providing services; however, it adds to the computational needs for vehicle decision making.Leveraging onboard artificial intelligence (AI) capabilities, promises a faster and commercially viable self/collaborative airspace management, as long as it does not compromise airspace safety.With over seven million unmanned aircraft systems (UAS) sales projected over the next five years, the national air space is destined to get more crowded and more challenging to manage.As it is almost impractical to foresee this fully scaled and widely heterogeneous air traffic being entirely human controlled or centrally managed, alternative approaches involving some level of autonomy and distributiveness are warranted.This paper presents such an approach for future air mobility, wherein a distributed network of vehicle-based artificial intelligence units are employed to assure tactical separation among cooperative and uncooperative air traffic.The work presented in this paper is inspired from National Aeronautics and Space Administration (NASA)'s rich aviation technology foundation, developed over the years in collaboration with the Federal Aviation Agency (FAA), to make utilization of the national airspace system (NAS) safer, structured and efficient.Among the many decisionmaking tasks in the conventional air traffic management (ATM), separation assurance and runway access are probably the two most critical and frequent tasks managed by the air traffic controllers at both strategic as well as tactical levels.This has led to several research and development efforts, including the Traffic Alert and Collision Avoidance System (TCAS) [1], which is an FAA implementation of International Civil Aviation Organization (ICAO)'s airborne collision avoidance system (ACAS) for short range systems (often mentioned in conjunction with the airborne separation assurance system (ASAS) for long range systems).ACAS serves as a non-centralized & automated conflict detection and avoidance system, operating independently of ground-based equipment.While older ACAS systems relied exclusively on an interrogation mechanism using on-vehicle transponders and beacons followed by issuance of potential threat advisory based on fixed set of rules, the newer ACAS X [2] system implements its alerting logic based on a numeric lookup table optimized with respect to a probabilistic model of the airspace and a set of safety and operational considerations.Furthermore, ACAS X uses multiple surveillance sources such as sensors and global positioning system (GPS) measurements, while maintaining full compatibility with newer generations of TCAS.Kuchar & Yang have reported a survey of multiple contemporary conflict detection & resolution (CDR) methods [3].In ATM, which is essentially a centralized management model, ACAS X (or TCAS) is expected to serve primarily as an emergency event handling tool with short and clear directions for the pilots to take preventive course of action.In this regard, the distributed control advisories issued by the ACAS X system are purely for physical conflict avoidance and not for any other business utility function optimization.Furthermore, such advisories are only issued on a case by case basis to resolve specific conflicts and aircraft return to centralized control after the conflict resolution.A variation of ACAS X, called ACAS Xu, has been studied [4] for unmanned aircrafts.ACAS Xu brings in certain special consideration such as handling of non-cooperative sensors and use of horizontal resolutions, especially in view of electronic Vertical Take-Off and Landing (eVTOL) vehicles [5].Specific to UAS, Skowron et al. have discussed recent advances in sense and avoid methods for small UAS [6].While these technological advancements are designed to ensure UAM operational safety, the presented work offers a supplementary edge-mounted AI technique for UAM optimization based on UAS operators' business utility functions within the operational safety boundaries, thereby, paving the pathway for UAM democratization, especially benefitting small & new market entrants.This paper utilizes conflict detection as one of the instantiations of such an overarching vision of pushing the smartness to the edge.The rest of the paper is organized as follows: Section II discusses the research focus followed by the proposed approach in section III that presents a graphical data aggregation and AI modelling method for collection behavior assessment of UASs.In section IV the concept of operation scenarios and corresponding metrics are introduced.Section V presents a custom simulator developed for UASs collective behavior simulation.Research findings are summarized in section VI.Finally, section VII concludes the paper with a discussion on the future directions.
+II. Research Focus: Heterogeneous Multi-Entity Collaboration for UAMMultiple market studies, concepts of operations (ConOps), and other research reports have collectively portrayed a UAM vision that is highly diverse in nature, comprising of a wide variety of automated flying entities (or agents), their operators with different utility functions driven by their business models, and a whole range of stakeholders such as operation support businesses, cities, emergency service providers, regulatory agencies, general public and so on.At present, there is no established infrastructure to enable and safely manage the widespread use of low-altitude airspace and UAS operations, regardless of the type and operation of UAS.Accordingly the FAA, NASA and several industry partners are collaborating on the conceptualization and development of a UTM system.Ref. [7] reflects the UTM architecture that introduces a layered/hierarchical framework for urban air traffic management with entities such as UAS service suppliers (USS), supplemental data service providers (SDSP) and UAS operators, working in coordination with the UASs for operational decision making.The industry components, including the UAS operators, USSs, and SDSPs, are expected to coordinate with each other for UAM operations.A critical capability that ensures overall safety of the airspace shared by conventional air vehicles such as airplanes, and helicopters, and UAS agents is the Flight Information Management System (FIMS).FIMS is a central, cloud-based data exchange gateway among ATM, UTM, public service, and regulatory entities.In the UTM architecture, connections to the FIMS are made by the USSs authorized for functionality, quality of service, and reliability.Logically, FIMS supports UTM functionalities such as registration and licensing, assurance of equitable use of airspace including information sharing about restrictions and notice to airmen (NOTAMs), identification and authentication of data/service providers, overall airspace health and status monitoring, and most importantly potential de-confliction of flight plans coming from different USSs servicing the same airspace.The deployment model for these functions, however, determines the efficacy of UTM.For example, taking tactical deconfliction into account in a centralized model may offer better control, but may also become a critical bottleneck as operation volume scales up.On the other hand, a federated model can become complex due to standardization requirements.A more practical solution, as considered by the UAM community, is a blended architecture using elements of centralized and federated models.In such a case, certain critical operations can be centralized while leaving the others to a federated model where the USSs can prosper in a fair market competition to deliver added value to their customers, while working within the Government defined regulations.Additionally, the centralized model can always serve as the fall-back plan in situations where the federated model could not resolve a particular issue.Assuming that contemporary and future UASs already come equipped with sophisticated onboard processing capability, or alternatively they can be augmented with auxiliary processing modules onboard, it is possible that they can take part in tactical decision making, thereby extending the earlier federated model that works at the USS level into a true distributed model that works on the edge at the UAS level, as shown in Fig. 1.
+Fig. 1 A distributed model for UTM utilizing edge computing capability onboard UASsNote that the UTM architecture described in Ref [7] includes the concept of vehicle-to-vehicle (V2V) communication capability that enables a UAS to broadcast relevant information (e.g., position) to nearby vehicles with cooperative equipment, allowing for impacted stakeholders (e.g., nearby operators in four-dimensional proximity to the UAS) to gain awareness of the situation and respond accordingly.In general, airborne separation is a critical aspect of air traffic management that has been studied by NASA and others, where vehicle technology was designed to predict and resolve conflicts based on ADS-B shared intent and similar information [8] [9] [10] [11] [12] [13].The work presented in this paper envisions enhancing this capability to not only share information to gain collective situational awareness but also reason upon this information to build knowledge about the projection of the situation into the future.Such knowledge coupled with a suitable onboard AI decision engine, will enable the UASs to implement remedial measures either simply through proactive action or active inter-UAS negotiations.There are situations when USS-USS negotiation may be slow, insecure, or ineffective in capturing vehicle utilities, and better aided with direct UAS-UAS negotiation with minimal USS role for approval.Note that these USS's are envisioned as highly automated bots, so the latencies are expected to be much less to begin with, in comparison to human-centric operations.Nevertheless, UAS level decision making alleviates most of the risks including communication network congestion, latency, and brownout, etc.While the aforementioned distributed model for UTM offers a more robust and low latency decision making framework that accommodates the utility functions of the UASs and their operators, the implementation of such a framework requires that the UASs have comprehensive knowledge about other UASs in addition to knowing their own individual states.Given the limited computing capability onboard the UASs, it is not realistic for them to process each element of such a big volume of data.Therefore, a custom data-to-intent synthesis approach, that allows rapid thickening of data into more relevant actionable information, is proposed in the following section.
+III. Data Aggregation for AI-based Cooperative ControlData sharing, aggregation, and interpretation are critical components of multi-agent, multi-nodal decision-making framework.A multitude of research approaches have been investigated in the past for decision making in 4D (three positional dimensions and one time dimension) trajectory management in ATC.Some examples are: trajectory based air traffic control and mixed operations with airborne self-separation [14], separation assurance, arrival sequencing and weather-cell avoidance solver called Autoresolver [15], 4D trajectory planning human-machine interface for flight decks [16] [17], distributed trajectory flexibility preservation [18], scenario complexity metric formulation [19], and so on.Adding to this rich technological foundation, this paper presents a holistic approach of synthesizing data into graphical data frames that carry information regarding the agents' behavior and intent, in conjunction with the physical data from the agents.The motivation for such data aggregation method comes from the opportunity to utilize cutting edge deep learning techniques for rapid assessment and classification of conflicts in the 4D traffic scenarios.Air traffic controllers today use a wide range of visual aids to monitor and manage the airspace (and ground facilities such as airports) traffic.These visual aids are designed to supplement the human intelligence with data, in an easily interpretable manner, so that the human controller can take timely decisions to ensure safety and efficiency.In the case of the distributed UTM model, where the AI does the decision making, the data ingestion framework needs to be optimized for the AI.For autonomous entities, such as UASs, the data aggregation needs to be AI-centric where large sets of information, both real-time and historical, can be quickly synthesized and processed onboard the vehicles.This AI-centric data aggregation can be seen as analogous to the way the human brain aggregates information for quick decision making, by using relational models reinforced by selected critical features in the data and making decisions based on the best made predictions from often incomplete data.Classical cooperative control involving multiple agents in a connected network generally assume that the agents have complete information about their neighbors, and if not, then a fairly deterministic or fixed trust vector is used to complete the analytical model for the cooperative control implementation.While such analytical control frameworks provide a sound foundation for distributed control in UAM scenarios, to make it commercially usable additional considerations need to be incorporated in the UAM cooperative control framework.Such considerations entail the business utility functions as well as agent-to-agent negotiation with a time-based reward system that incentivizes integrative conflict resolution.The following sub-sections introduce the foundation for agent-to-agent cooperative control with the additional considerations for real-world functionalization (in sub-section A), a graphical approach to data aggregation (in subsection B), and a deep neural net based scenario learning and assessment framework (in sub-section C).This paper specifically focuses on the data aggregation and deep neural net-based data ingestion, learning, and inference aspects.The deep learning-based decision engine for distributed conflict resolution via pro-active measures and active negotiations, built on the fundamental idea and modification for cooperative control framework as mentioned in subsection A below, will be discussed in a separate paper.
+A. Foundation of Cooperative Control Modeling for Multi-Agent ScenariosFor a set of N agents in a particular airspace with identical dynamics, the state space model for each agent can be represented as Eq. ( 1), ℎ   ∈ ℝ   ℎ ,   ∈ ℝ   ℎ ,   ∈ ℝ   ℎ     ̇=   +   ,   =   , ∀ ∈ ,In the general linear system in Eq. ( 1), it is assumed that the coefficients A, B, and C are stable and detectable.In a leader-based cooperative control, the leader dynamics can be represented with  0 ̇=  0 ,  0 =  0 , which generates the desired target trajectory and acts as a command generator, observable from a subset of agent nodes in graph .In leaderless model, such as the one in discussion in this paper,   = 0, ∀ ∈ , we require that all eigenvalues of A are in the closed left-half complex plane [20], in other words all agents operations exhibit asymptotic stability in the linearized system with (Ai, Bi) stabilizable. = [  ] is the adjacency matrix, where aij is the edge weight such that   > 0   (  ,   ) ∈      = 0 ℎ.Assuming that A is known for all agents, the distributed consensus algorithm can be represented as:𝑢 𝑖 = ∑ 𝑎 𝑖𝑗 (𝑥 𝑗 -𝑥 𝑖 ) 𝑗𝜖𝑁 𝑖(2)Eq. ( 2) is also referred to as local voting protocol.Here it is assumed that each agent can obtain information about the state only of itself and its in-neighbors in Ni.Defining the weighted in-degree of node vi as the i-th row sum of A, i.e.𝑑 𝑖 = ∑ 𝑎 𝑖𝑗 𝑁 𝑗=1(3)Eq. ( 2) can be rewritten as:𝑢 𝑖 = -𝑥 𝑖 ∑ 𝑎 𝑖𝑗 𝑗𝜖𝑁 𝑖 + ∑ 𝑎 𝑖𝑗 𝑥 𝑗 𝑗𝜖𝑁 𝑖 = -𝑑 𝑖 𝑥 𝑖 + [ 𝑎 𝑖1 … 𝑎 𝑖𝑁] [ 𝑥 1 ⋮ 𝑥 𝑁 ](4)Using block matrix notations,𝑢 = [ 𝑢 1 ⋮ 𝑢 𝑁 ] , 𝐷 = [ 𝑑 1 ⋱ 𝑑 𝑁 ] , 𝐴 = [𝑎 𝑖𝑗 ](5)the overall control input for the network can be represented as:𝑢 = -𝐷𝑥 + 𝐴𝑥 = -(𝐷 -𝐴)𝑥 = -𝐿𝑥(6)where L is the graph Laplacian matrix.As the UAM agents will operate under a set of guidance set forth by the FAA and delegated through the USSs, it can be assumed that these agents will most likely demonstrate uniform behavior and responses to the same events.Under this assumption, the overall system dynamics can be approximated with single-integrator dynamics, i.e.   ̇() =   ().The closed-loop dynamics, therefore, are given as:𝑥̇= -𝐿𝑥(7)The system matrix is -L and hence has eigenvalues in the left-half plane.Details regarding the above multi-agent cooperative control method can be found in [21].The takeaway from Eq. ( 7) is that for such cooperative control to work effectively, states of all agents need to be collectively known, and there must be no malicious or non-cooperating agents.In the real world, however, the states of an agent may not be known with absolute certainty and also some rouge agents might be present.Therefore, the distributed cooperative control method needs to incorporate inter-agent trust.Defining   [-1,1] as the trust that node i has for node j, the trust propagation can be represented as:𝜉 ̇𝑖 = ∑ 𝑎 𝑖𝑗 (𝜉 𝑗 -𝜉 𝑖 ) 𝑗𝜖𝑁 𝑖(8)Where (  -  ) represents the difference of opinion between neighboring agents.With this trust dynamics the state dynamics can be written as:𝑥̇𝑖 = ∑ 𝜉 𝑖𝑗 . 𝑎 𝑖𝑗 (𝑥 𝑗 -𝑥 𝑖 ) 𝑗𝜖𝑁 𝑖(9)Note that the trust can change over time, for example it can become stronger with recognizing a pattern in agent behavior over time, or it can degrade if the agent behavior is more erratic.In Eq. ( 9), the term   .  , therefore, represents time-varying edge weight.The distributed control problem then essentially becomes the problem of figuring out and optimizing these time-varying edge weights.Linearization of trust, however, is generally difficult as this is highly volatile and sensitive to a lot of variables and unanticipated events.Studies have been conducted to model herd behavior in panic situations [22], but such studies are done in the context of humans, and their portability to autonomous machines such as UASs is not absolute.On the other hand, Boyd [23] showed that if some edge weights are negative, convergence speed is faster.Knowing that   is a positive number, this means that if some trusts are negative or in other words there is distrust in the network then the consensus is reached sooner.UAM scenarios, staged by multiple operators with different utility functions and in market competition, often preclude the availability of complete contextual insight into the individual agents' intent unless explicitly shared.The collective state and intent of the agents can thus be broadly categorized into public and private sets of information.Furthermore, the private set of information can be approximated on the basis of the trust, which in turn can be formulated based on the knowledge about past behavior of the agent, preferably via patterns.Additionally, the agent behavior can be moderated; that is, the trust can be engineered, through a suitable reward system for exhibiting desired behavior.With these additional considerations for distributed cooperative control in UAM scenarios, the state update function can be expressed as:𝑥 𝑖 ̇= 𝑓 (𝑥 𝑖𝑝 , 𝑥 𝑖𝑞 , [ 𝑥 1𝑝 𝛿 1𝑝 𝑟(𝑡) 1 ⋮ ⋮ ⋮ 𝑥 𝑛𝑝 𝛿 𝑛𝑝 𝑟(𝑡) 𝑛 ] , 𝑐 0 , 𝜎 0 , 𝑟 0 , 𝑡)(10)where, xip and xiq are the public data and private utility functions in the current state of the i th agent.The public data is known to every other agent in the neighborhood, whereas the private utility functions are known only to the agents in the same carrier or operator group.As shown in Eq. ( 10), the state change of an agent is a function of its own utility functions, and the public information of other non-group agents.The public utility functions of the non-group agents has an associate local trust denoted by δip and an associated time-varying-reward denoted by r(t)i.Furthermore, all agents are required to operate within certain global constraints denoted by c0, which has an associated global trust σ0, and reward r0.This uncertainty could simply be a temporary communication brown out.While the vehicle dynamics are not explicitly expressed in Eq. (10), which is envisioned to serve as a high level decision model representation, it indirectly incorporates the vehicle specifications in the agents utility functions as well as the time budget t, which is derived from the agent's maneuverability and available resources.To summarize, the inner bracket in Eq. ( 10) can be seen as a black box that in conjunction constitutes the inter-agent trust   .While it is possible to mathematically model   based on the inner bracket parameters in Eq. ( 10), albeit with substantial complexity and consequent latency, this paper takes an approach to learn it via heuristic optimization method such as deep neural nets.
+B. Data Thickening via Graphical Data FramesFig. 2 shows an example UAM scenario where two agents make course adjustments to avoid a weather event or a dynamic airspace closure notice issued by the city.Such course adjustments, however, can give rise to a potential conflict between the two agents.For a distributed model to work in such a scenario, the agents first need to be able to predict their trajectories and detect the conflict using onboard resources.While it is feasible to compute mathematically the possible trajectories for the agents, such computations, especially for multiple bi-lateral conflict detections, can quickly become computationally very complex.Current approaches to conflict detection compare one nominal trajectory for each flight along an intent such as the flight plan using a common speed, or in some cases very few trajectories such as along state projection, commanded state in the flight management system and the flight plan [8].These assumptions may be suitable for traditional commercial operations where flights are forced to fly their intent as a contract and use consistent dynamics, however they are likely to prove brittle in UAM operations with volatile ondemand profiles that are unlikely to fly the same nominal trajectory every time.Furthermore, simple state projections for the agents can lead to more and more false positives, overwhelming the system.Alternatively, a large number of possible trajectories would need to be computed or separation buffers increased to account for the uncertainties, which is inefficient for high-tempo UAM operations.Machine learning offers an ability to test many possible trajectories and commit them to memory such that they are accounted for in the outcome of a neural network, without expensive re-computation every time.Finally, additional information regarding the agents' inherent utility functions, such as fuel usage preference, payload type, urgency etc., can improve the optimality of conflict resolution.Therefore, data enrichment with such utility function information is desirable as long as it does not significantly increase the computational burden on the onboard processor.
+Planned or known routeOne potential approach to answer the above question is to utilize pixel manipulation to encode and embed agent behavior.As the total number of pixels in fixed dimensional images is the same, regardless of the content of the image, the processing burden will remain the same.While the image's 2D surface is used to represent the physical status of the agent (such as position, speed, heading, waypoints etc.), careful manipulation of pixels for agent icon shapes and colors can be used to represent the agent's non-physical attributes (for example: known behavior based on past experience, preference for payload comfort over fuel saving, flexibility to maneuver etc.).In some sense, this behavior encoding approach through graphical data frames is comparable to existing data visualization techniques such as heat maps, histogram plots, network diagrams, and word clouds etc. used for overall context representation.The difference in the case of the presented data-intent-behavior encoding approach is that the resulting graphical data frames are designed to be ingested by machines and not humans.
+C. Steps for Learning and Interpretation of Graphical Data Frames by Deep Neural NetIn this paper, a deep neural net based heuristic optimization approach of integrated data synthesis and inference is used that allows localized derivation of spatiotemporal decisions with partial data from each agent in UAM scenarios.Given the high variability in data and multi-order solution space, the deep neural net learning approach is envisioned to enable the agent's onboard AI module to predict and resolve emerging operational conflicts by employing collaborative bi-/multi-lateral time-dependent strategies in real-time.Deep learning [24] utilizes large neural networks, with multiple layers between the input and output layers of an artificial neural network.In recent times, significant progress has been made in developing and tuning such large neural networks to learn visual data, such as images and videos.Ground traffic forecasting and incident detection using deep learning approach has been reported in multiple studies [25] [26] [27].Ma et al. have recently demonstrated the use of a convolutional neural network to learn the traffic as an image [28].The deep learning work flow used in this paper goes through the following steps:1) The data is sourced from a custom developed simulator that simulates different scenarios to gather the starting graphical data frames and the corresponding ground truths after simulating the scenario over time.2) The collected data is automatically labeled with the ground truth from simulation, for learning 3) A transfer learning technique is used where a deep neural net with proven performance is taken as the starting point and then it is trained and fine-tuned with the labeled graphical data frames.During training the model is tested periodically with an evaluation dataset to make sure that the model accuracy is gradually improving 4) Once the model accuracy plateaus, training is stopped and it is deployed for real time classification of data Note that the purpose of the deep learning approach is to capture the intent and behavior, and not just the operational data, of the entities in considerations.It is assumed that the governing dynamics and influencing parameters for the agent behavior is not publicly known, therefore, the behavior is only observable and not controllable globally.With an appropriate reward system, however, the behavior can be changed during bi-/multi-lateral negotiations.In fact, in some cases with making the information available and easily interpretable, the behavior can be altered.As mentioned earlier, the utilization of the presented deep learning based collective behavior learning and scenario evolution predictions for a distributed UTM decision engine will be discussed in a separate paper.For the deep neural learning and prediction feasibility study, in this paper, three specific neural nets were selected.The first one is a residual neural network or ResNet [29].ResNets utilize skip connections or shortcuts to jump over some layers to avoid the problem of vanishing gradient that effectively prevents the value change for weights.ResNets solve this issue by reusing activations from a previous layer until the adjacent layer learns its weight.This approach simplifies the network and speeds up learning as there are fewer layers used during the initial training stage.The network then gradually restores the skipped layers as it learns the feature space and towards the end of training all layers are expanded.ResNet was released in 2015 by Microsoft Research Asia with three architecture realizations i.e.ResNet-50, ResNet-101 and ResNet-152.For the study in this paper, ResNet101 was used, which has 101 deep layers.The second neural network that was selected for the presented study is developed by Oxford University's Visual Geometry Group in 2014, and known as VGG class of deep neural networks [30].Neural networks prior to VGG used bigger, such as 7 by 7 pixels or 11 by 11 pixels, receptive fields.The receptive field is the part of the image that is visible to one filter at a time.VGG networks implemented smaller receptive fields (3 by 3 pixels) and increased the depth of the neural network to show improvements to the classification accuracy.Two variations of VGG, i.e.VGG-16 and VGG-19 are used most widely.For the study in this paper, VGG-19 was used, which has 19 deep layers.The third neural network that was used in the presented study is called Inception [31].The inception deep convolutional architecture was introduced by Google in 2015.Multiple iteration of this architecture has been made following the first version or Inception-v1 to include batch normalization in Inception-v2 and additional factorization in Inception-v3.The main idea with Inception architecture is to reduce number of connections or parameter without decreasing the network efficiency.For the study in this paper, Inception-v3 was used which is 48 layers deep.
+IV. Concept of Operation Use Case and Performance MetricsFAA's NextGen Concept of Operations (ConOps) for UTM [7], in its appendix E, outlines an inventory of use cases in multiple technology capability levels (TCLs).For the feasibility analysis of the presented approach, this paper takes up an embodiment of a TCL2 use case from this inventory that entails beyond visual line of sight (BVLOS) operation in uncontrolled airspace.Furthermore, this paper performs such analysis with the distributed model, i.e. beyond the federated USS-based model, for de-confliction within operation volumes.Fig. 3 (a) shows a rendering for the TCL2 use case, where the airspace under consideration is used by UAM agents as well as conventional air vehicles.Two intersecting corridors in such environment are highlighted in Fig. 3 (b), which constitutes the use case scenario for the approach presented in this paper.The agent to agent separation threshold is defined as follows:  Intra-network separation threshold -Agents from the same operator/servicer, for example 'A' and 'C', can maintain a comparatively shorter separation distance assuming that such agents have complete knowledge about each other's state and intent through their common servicer. Inter-network separation threshold -Agents from different operators/servicers, for example 'A' and 'B', will need to maintain a comparatively longer separation distance assuming that such agents do not have complete knowledge about each other's state and intent.The intersection brings the agents closer, where some proximities are converging (for example: agents 'A' and 'C' in the scenario shown in Fig. 3 (b)) and some are diverging (for example: agents 'A' and 'B' in the scenario shown in Fig. 3 (b)).A custom simulator has been developed to create the scenario and simulate it with the above behaviors.
+V. Custom Simulator for Scenario Creation and Ground Truth CollectionA custom simulator and GUI (see Fig. 4) has been developed in the MATLAB ® App Designer environment, which allows multi-agent simulation in conops scenarios by selection of parameters and controls interactively.A data collection module of the custom simulator collects a set of samples labeled as "good", in which no violation of the separation standards occurred within a certain projected time period, and a set of samples labeled as "bad", in which a violation of the separation standards did occur.The separation standard is analogous to the same in terminal airspace traffic control, which is the designated center-to-center distance for two specific aircrafts.With N as the number of samples and T as the number of time step predictions, the program initializes the simulator and updates it T time steps.If a conflict occurs at or before time step T, the sample is labeled "bad" and if no conflict occurs, the sample is labeled "good" and moved into the respective dataset.This process repeats until at least N "good" and N "bad" samples have been labeled and stored.The save frame number specifies up to which time step to simulate to capture the states of the agents in the map.Using MATLAB ® Deep Learning Toolbox, the synthesized data is learnt by a convolutional neural network (CNN).The dataset is split into 70% training, 20% validation, and 10% testing sets.The validation dataset is used for testing throughout training to ensure the model's accuracy is improving.The testing dataset is put aside for testing after training to ensure the model is not over-fit to the training data, in other words the model does not lose its generality in classifying new data frames.The architectures of three commonly used CNNs in image classification are tested: ResNet-101, VGG-19, and Inception-v3.The final layers of each model are replaced to classify images labeled "good" and "bad".The models are trained on the full synthesized dataset with 6N samples, comprised of 2N white background, 2N transparent background, and 2N crossroad image background samples.The idea behind using three different backgrounds for the same data frame is to suppress the impact of the background as a feature and put more emphasis on the agents and their behavior represented by their shape, color, and future path projections.Results using data augmentation, Bayesian optimization, and grayscale-only images are discussed in the following section.More information about the deep neural networks used, and the overall learn-to-classify workflow can be found in [32].ResNet-101, VGG-19, and Inception-v3 architectures were used for comparison of results.Tests were done on each of these models with 4, 6, and 8 agents in the scenario.Graphical data frames were collected in RGB and grayscale image formats.A simple averaging method was applied to the RGB images to convert them to grayscale.Additionally, to improve the diversity of the data, a two phased data augmentation step was used.The phase I of the data augmentation is the use of three backgrounds, i.e. crossroad image, transparent, and white, as mentioned earlier.The phase II of the data augmentation involved random x-axis reflections, y-axis reflections, and rotations between [-15º, 15º].Finally, a Bayesian optimization step was used to find the following five training options that resulted in the lowest validation error: initial learning rate, learn rate drop period, learning rate drop factor, L2 regularization factor, and momentum.Training of the deep neural nets, i.e.ResNet-101 with 101 layer, VGG-19 with 19 layers, and Inception-v3 with 48 layers, was conducted using CUDA ® , which is a parallel computing platform and programming model developed by NVIDIA ® for general computing on graphical processing units (GPUs).
+VI. Simulation Results and Summary of FindingsThe following simulation results were obtained from the training of a dataset consisting of 4,000 graphical data frames with equal distribution of "conflict" and "no conflict" classes.Each image was included in the dataset with three different backgrounds as mentioned earlier.So the total number of images used in the deep neural net training and testing is 12,000.An NVidia Quadro RTX 6000 GPU with 24GB GDDR6 frame buffer and 4,608 CUDA cores, in conjunction with an Intel Xeon platinum 8160, 3.70 GHz, 24 core CPU was used for the training and classification.Table 1 includes a comparison between the three CNN networks with 4, 6, and 8 agents and shows the effects of data augmentation and Bayesian optimization on the model's accuracy.The phase II data augmentation applied to the training dataset generally decreased the overall accuracy of the models by 2%, with as much as an 11% decrease.This may be caused by the specific data augmentation method used where, unlike common augmentation, the training dataset does not increase in size but is simply altered, coupled with the possibility that the agents in the rotated images, especially in transparent or white background, loose their motion directionality resulting in training ambiguity.As a different improvement method, Bayesian optimization [33] was applied to the models trained with phase I data augmentation only.Hyper-parameter tuning generally increased the overall accuracy of the models by 4%.The drop in model accuracy due to the increase in the number of agents in the scenario was the smallest, i.e. 4%, for VGG19 network.This drop in accuracy can be attributed to multiple factors including agent representation, path projection, and the data augmentation methods used.These factors will be further investigated in our future work to improve the overall accuracy with higher number of agents in the scenario.Table 2 shows the performance of the three neural nets with 3-channel color data frames vs. single channel grayscale data frames.ResNet-101 and VGG-19 were more robust to this change in comparison to Inception-v3 model.Next a mixed scenario, where any number of agents can approach the corridor intersection, was evaluated.For this the three neural network models were trained with the combination of the datasets for 4, 6, and 8 agents, i.e. with a total of 36,000 data frames.Separately a test dataset with an equal number of data frames from each of the two classes was prepared using the simulator for scenarios consisting of 4 to 8 agents with the three backgrounds.This test dataset consists of 9,000 data frames that the trained models have not seen earlier.For each one of the three deep neural networks used in this study, Table 3 shows the training time per epoch, and classification accuracy and time.The maximum number of epochs for each model training was set to be 15 and an initial learning rate of 0.0002 was used.The overall accuracy can be boosted further with training to more epochs, and with a lower learning rate.
+VII. Conclusion and Future WorksThis paper presented a conflict prediction and classification approach using synthesized graphical data frames, learnt by deep neural networks.A custom simulator was developed to create the data frames by integrating the physical state as well as the utility functions of the agents.Ground truth regarding scenarios were collected by simulating the scenario up to a fixed time step into the future and detecting conflicts based on violation of agent-to-agent separation criteria.Based on these simulation results, the scenarios were labeled appropriately for training.Three different neural networks were trained with such datasets and their performance in term of accurately classifying new scenarios were recorded.Preliminary findings validate the feasibility of the proposed graphical data synthesis and AI-based holistic scenario assessment approach.Further fine-tuning of the deep neural network models and training hyper-parameters as well as data augmentation methods, is expected to significantly boost the overall classification accuracy.Being a heuristic machine learning method, the presented capability is not intended to replace other deterministic methods for safety-critical conflict detection and resolution, but rather to complement them with (a) accounting for much larger datasets potential futures learned into the neural net, (b) early precursor prediction with fast computation, and (c) elimination of false positives based on a wider set of behavioral as well as physical vehicle properties.Future work will involve refinement of the deep neural net structures to improve their efficiency.Additionally, the data synthesis process will be investigated further, in conjunction with feature learning, to identify the most influential ways to synthesize the collective behavior of the agents in particular scenarios.Last but not least, implementation of the presented technology on portable edge devices, such as NVidia Xavier edge-GPU module [34] with TensorRT optimization [35], will be explored to enable scenario classification and implementation of decision engines onboard the UAM agents.Fig. 22Fig. 2 Multi-agent scenario classification using graphical data frames
+Fig. 33Fig. 3 ConOps use case: (a) Broad view of the diverse airspace, (b) Corridor intersection scenario Fig. 3 (b) shows two pairs of corridors at two different flight levels (altitudes).In this scenario, the conflict assessment focus is put on one flight level alone.Agents not at this flight level are represented by hollow icons, for example: 'X' and 'Y' in Fig. 3 (b).Agents that are already at or going to be at the flight level of consideration are represented by color filled icons, for example: 'A', 'B', 'C' and 'D'.Furthermore, we assign certain baseline behavior for the agents approaching the corridor intersections, as follows:  Agents of designations 'A' and 'B' always (either to the left or to the right) at the intersection  Agents of designation 'C' always go straight through the intersection  Agents of designation 'D' go straight through the intersection in most cases (90% of the time). Agents turning left stay closer in for shortest turn  Agents turning right stay farther out for shortest turn  Agents can approach the intersection from any one of the eight directions from these two corridor pairs  Agents 'A' and 'B', and agents 'C' and 'D' always come from opposing directions  Additionally, agents 'A' and 'C' are managed by USS 1 and agents 'B' and 'D' are managed by USS 2The agent to agent separation threshold is defined as follows:  Intra-network separation threshold -Agents from the same operator/servicer, for example 'A' and 'C', can maintain a comparatively shorter separation distance assuming that such agents have complete knowledge about each other's state and intent through their common servicer. Inter-network separation threshold -Agents from different operators/servicers, for example 'A' and 'B', will need to maintain a comparatively longer separation distance assuming that such agents do not have complete knowledge about each other's state and intent.The intersection brings the agents closer, where some proximities are converging (for example: agents 'A' and 'C' in the scenario shown in Fig.3 (b)) and some are diverging (for example: agents 'A' and 'B' in the scenario shown in Fig.3 (b)).A custom simulator has been developed to create the scenario and simulate it with the above behaviors.
+Fig. 4 (4Fig. 4 (a) Custom developed simulator, (b) Data collection moduleEach agent is initialized with a position, altitude, speed, bearing, trust factor, and utility functions (or business priorities) for fuel emission, payload, and fuel burn rate.The simulator controls include the flight level of interest, time step, speed, airway size, agent spacing, agent size, and length of the leading path projection.Any agents outside of the specified flight level appear as hollow circles or squares to indicate they are out of the altitude range of interest.When initialized, the program chooses random values within the ranges specified in the left panel as the agents' starting state.When simulating begins, the state of each agent and map is updated once for each time step.The airway, with a total width set in the simulator control panel, is randomly set in a vertical and horizontal shape or in a diagonal cross shape.In this example, red and blue squares are used to indicate these agents' intent to continue straight ahead through the intersection and may choose either the left or right lane.Magenta and cyan circles are used to indicate the agents' intent to turn left or right and may only choose the left or right lane accordingly.The gradient rectangle in front of the agent in the direction of motion projects the path from the current position at time t to the agent's projected position at time ( + ∆), where ∆ is a user-defined offset.∆ = 40 in the presented study.A data collection module of the custom simulator collects a set of samples labeled as "good", in which no violation of the separation standards occurred within a certain projected time period, and a set of samples labeled as "bad", in which a violation of the separation standards did occur.The separation standard is analogous to the same in terminal airspace traffic control, which is the designated center-to-center distance for two specific aircrafts.With N as the number of samples and T as the number of time step predictions, the program initializes the simulator and updates it T time steps.If a conflict occurs at or before time step T, the sample is labeled "bad" and if no conflict occurs, the sample is labeled "good" and moved into the respective dataset.This process repeats until at least N "good" and N "bad" samples have been labeled and stored.The save frame number specifies up to which time step to simulate to capture the states of the agents in the map.Using MATLAB ® Deep Learning Toolbox, the synthesized data is learnt by a convolutional neural network (CNN).The dataset is split into 70% training, 20% validation, and 10% testing sets.The validation dataset is used for testing throughout training to ensure the model's accuracy is improving.The testing dataset is put aside for testing after training to ensure the model is not over-fit to the training data, in other words the model does not lose its generality in classifying new data frames.The architectures of three commonly used CNNs in image classification are tested: ResNet-101, VGG-19, and Inception-v3.The final layers of each model are replaced to classify images labeled "good" and "bad".The models are trained on the full synthesized dataset with 6N samples, comprised of 2N white background, 2N transparent background, and 2N crossroad image background samples.The idea behind using three different backgrounds for the same data frame is to suppress the impact of the background as a feature and put more emphasis on the agents and their behavior represented by their shape, color, and future path projections.Results using data augmentation, Bayesian optimization, and grayscale-only images are discussed in the following section.More information about the deep neural networks used, and the overall learn-to-classify workflow can be found in[32].ResNet-101, VGG-19, and Inception-v3 architectures were used for comparison of results.Tests were done on each of these models with 4, 6, and 8 agents in the scenario.Graphical data frames were collected in RGB and
+Table 1 Accuracy of three neural net models with varying number of agents, data augmentation, & optimization Model Accuracy with Phase I Data Augmentation Only Accuracy with Phase I & II Data Augmentation Accuracy with Phase I Data Augmentation and1Bayesian Optimization
+Table 2 Accuracy of three models trained on color versus grayscale images with four agents2ModelRGBGrayscaleResNet-10195.6%94.2%VGG-1997.2%98.3%Inception-v398.8%88.8%
+Table 3 Comparison of three deep neural networks in training and classification of mixed number of agents3ModelTraining Time/EpochClassification Accuracy Classification Time/Data FrameResNet-10130 minutes89.2%3.5 millisecondsInception-v326 minutes89.2%6.3 millisecondsVGG-198.5 minutes89.4%7 milliseconds
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+NASA STI Program. . . in ProfileSince its founding, NASA has been dedicated to the advancement of aeronautics and space science.The NASA scientific and technical information (STI) program plays a key part in helping NASA maintain this important role.The NASA STI Program operates under the auspices of the Agency Chief Information Officer.It collects, organizes, provides for archiving, and disseminates NASA's STI.The NASA STI Program provides access to the NASA Aeronautics and Space Database and its public interface, the NASA Technical Report Server, thus providing one of the largest collection of aeronautical and space science STI in the world.Results are published in both non-NASA channels and by NASA in the NASA STI Report Series, which includes the following report types:• TECHNICAL PUBLICATION.Reports of completed research or a major significant phase of research that present the results of NASA programs and include extensive data or theoretical analysis.Includes compilations of significant scientific and technical data and information deemed to be of continuing reference value.NASA counterpart of peer-reviewed formal professional papers, but having less stringent limitations on manuscript length and extent of graphic presentations.• TECHNICAL MEMORANDUM.Scientific and technical findings that are preliminary or of specialized interest, e.g., quick release reports, working papers, and bibliographies that contain minimal annotation.Does not contain extensive analysis.• CONTRACTOR REPORT.Scientific and technical findings by NASA-sponsored contractors and grantees.• CONFERENCE PUBLICATION.Collected papers from scientific and technical conferences, symposia, seminars, or other meetings sponsored or co-sponsored by NASA.• SPECIAL PUBLICATION.Scientific, technical, or historical information from NASA programs, projects, and missions, often concerned with subjects having substantial public interest.• TECHNICAL TRANSLATION.Englishlanguage translations of foreign scientific and technical material pertinent to NASA's mission.Specialized services also include organizing and publishing research results, distributing specialized research announcements and feeds, providing information desk and personal search support, and enabling data exchange services.For more information about the NASA STI Program, see the following:• Access the NASA STI program home page at http://www.sti.nasa.gov• E-mail your question to help@sti.nasa.gov• Phone the NASA STI Information Desk at 757-864-9658   surveillance, or radar.Manned aircraft traffic is projected on constant-velocity trajectories to a look-ahead time of 180 seconds.DAIDALUS provides discrete alert levels for all nearby traffic based on time to intersection of a given alert zone.These alerts align with alerting definitions defined in the Phase 1 DAA MOPS with buffered volumes and timelines.DAIDAlUS's guidance is of a suggestive nature in that it indicates a range of maneuvers (and non-maneuvers) that would lead to conflicts, without dictating a single maneuver for the pilot to follow.The types of maneuver include heading, altitude, climb rate, and airspeed for corrective and warning alert types as well as guidance to regain WC if a Loss of Well Clear (LoWC) is unavoidable [6].All possible maneuvers within the UAS's performance are calculated at a given time step to detect maneuver regions that would result in a LoWC with projected traffic within the look-ahead time.Figure 2 summarizes DAIDALUS's high-level design principles.Figure 2. DAIDALUS alert and guidance design principles [6] The Aircraft Collision Avoidance System X (ACAS-X) [7] is envisioned by the Federal Aviation Administration (FAA) to be a critical component that supports the safety of the Next-Generation Air Transportation System (NextGen).ACAS-X will replace the currently deployed Traffic Alert and Collision Avoidance System II (TCAS-II) [8] in the near future.The UAS-variant of ACAS-X, called ACAS-Xu, is currently under development.Recently, ACAS-Xu's capability has been extended from the collision avoidance time regime to longer look-ahead times so as to provide DAA alert and guidance.This extension aims at meeting the DAA functional requirement defined by the Phase 1 MOPS.In contrast to the deterministic approach used by DAIDALUS, ACAS-Xu uses cost analysis, dynamic programming, and probabilistic state distributions to calculate alerting statistics and guidance.ACAS-Xu represents aircraft states and dynamics non-deterministically [9].Aircraft and sensor models are applied to surveillance data sources to develop probability distributions of aircraft states.The Surveillance and Tracking Module (STM) correlates and associates aircraft states as well as their probability distributions.These state distributions are applied to pre-calculated dynamic programming tables to estimate relevant alerting information, such as time to LoWC or Near Mid-Air Collision (NMAC).The Threat Resolution Module (TRM) then uses a combination of state distributions and statistical output from dynamic programming to select the best maneuver based on several cost factors [7,10].Both systems interact with the pilot in a combination of alerts and suggestive guidance.ACAS-Xu also issues directive guidance called Resolution Advisories (RA), indicating the severity, sense, and strength of a necessary maneuver.These RAs can occur in horizontal, vertical, or mixed type maneuvers as selected by the Nucleus module [7].Complimentary to RAs, ACAS-Xu's suggestive guidance is calculated in the horizontal dimension, indicating ranges of headings that the PIC should avoid to prevent a LoWC in the near future.This report compares the DAA alerting and guidance performance of DAIDALUS and ACAS-Xu algorithms using flight test data flown with scripted encounters.The performance of both systems is also measured against the Phase 1 MOPS for alerting and guidance.The results reveal striking similarities and some differences between the two systems, and can inform further development of both.
+MethodsThe comparison in this work uses flight test data flown with scripted encounters.ACAS-Xu alerting and guidance data were generated and collected in real time during the flight test.DAIDALUS alerting and guidance data were generated after the flight test, by processing the flight test collected aircraft data through DAIDALUS.The following sub-sections describe the data generation and collection processes in detail.
+ACAS-Xu Flight Test 2ACAS-Xu Flight Test 2 (FT2) was conducted between June and August of 2017 over Edwards Air Force Base in order to test new capabilities implemented in ACAS-X Run 3. A total of 250 scripted encounters were flown.A small percentage of these encounters were flown with the intention to test the new DAA alerting and guidance capabilities.These DAA additions included suggestive and directive horizontal maneuver guidance to meet DAA corrective alert timelines.This work analyzes data from a total of seven of the 250 encounters, including six of the mitigated DAA encounters and one unmitigated (fly-through), not-DAA-specific encounter.Table 1 shows test cards for these seven encounters.Test cards with the prefix RWC were designed to test new DAA capabilities in ACAS-Xu Run 3.Among these encounters, RWC-03, RWC-12, and DA-62 were head-on (with a safety horizontal offset), RWC-09 and RWC-18 were at a 90 degree angle, and RWC-06, and RWC-15 were at a 45-degree angle.Test card DA-62 was included in order to examine the behavior of the DAA systems in an unmitigated scenario where a LoWC occurs.Among the six RWC encounters, RWC-03, RWC-06, and RWC-09 were flown with ACAS-Xu configured in the DAA Mode, whereas RWC-12, RWC-15, and RWC-18 were flown with ACAS-Xu configured in the Policy Mode.The DAA Mode was meant to capture the DAA caution alert and advisories times defined by the MOPS, whereas the Policy mode was for the unadulterated ACAS Xu alert and advisory time.Table 1.ACAS-Xu FT2 Scripted Flight Parameters [11] The seven encounters were flown with the three geometries outlined in Figure 3.Each encounter involved one UAS and a small manned utility aircraft similarly equipped to the UAS, running ACAS-Xa [7] and TCAS II [8].These UAS and manned aircraft are referred to as "Ownship" and "Intruder", respectively, throughout this analysis.The Ownship for each test was a Mode-S and ADS-B equipped Ikhana Predator-B UAS running ACAS-Xu controlled by a test pilot on the ground.During these encounters, ACAS-Xu perceived Ownship to be unable to maneuver vertically, consequently only horizontal guidance was calculated, and tracks were produced from the ACAS Surveillance and Tracking Module (STM) correlation tracker with an ADS-B transceiver as its source.RWC encounters were mitigated, as the Ownship pilot was instructed to follow ACAS-Xu corrective guidance after a response delay timer had expired.The DA-62 encounter had similar geometric and equipage parameters to those with the RWC prefix, but Ownship was not perceived to have limited maneuverability and RA dimension was determined by the ACAS-X Nucleus module.
+Flight Data ProcessingThis analysis was conducted using data recorded from the ACAS-Xu FT2 flight test.Surveillance data, Ownship states, and ACAS-Xu output were recorded onboard the Ownship, downlinked, and recorded via the Live Virtual Constructive Distributed Environment (LVC DE) interface.These LVC DE messages included absolute aircraft states for all traffic including Ownship, updated at 1 Hz, and ACAS-Xu guidance and alerting data payloads.DAIDALUS alerting and guidance was produced by feeding the LVC DE messages to DAIDALUS in a post-processing fashion.DAIDALUS requires that traffic states be aligned to Ownship time steps.To support this, blocks of traffic states were linearly interpolated forward in-track to Ownship time assuming constant velocity.This simple "last block" linear interpolation technique produced only modest errors in position, 97 ft from GPS location on average (see Figure 4).
+DAIDALUS SimulationThe behavior of DAIDALUS is highly configurable.Alerting thresholds, hazard zone parameters, and perceived aircraft performance can be configured to support different analysis scenarios.For this analysis, a standard configuration file2 designed to be MOPS compliant with a 3-degree-per-second turn rate was applied.This configuration defines three alert zones-preventive, corrective, and warning-in increasing severity.Only the corrective and warning alerts are investigated in this work.Both alert zones were buffered to a 1-nmi Horizontal Miss Distance threshold (HMD*), with 60 and 30 second time to alert thresholds to corrective and warning alert zones respectively.Additionally, guidance to regain Well Clear was calculated The DAIDALUS algorithm is provided as an open-source library3 maintained in Java and C++ versions.This analysis was conducted using DAIDALUS release V-1.0.1-FormalATM-v2.6.2.A basic framework for controlling DAIDALUS was written in MATLAB using the DAIDALUS Java library.This framework was used to calculate guidance and alerting statistics from input scenario files generated from flight test data.
+AnalysisDAA systems interact with the UAS pilot by displaying multi-level alerting and suggestive guidance information.The latter is based on the outcome of a range of maneuvers that the pilot should avoid, generally referred to as bands.These bands change severity based on an intruder's alert level and predicted time to intersection of an alert zone.Once the warning bands are saturated (meaning imminent loss of DWC) it is necessary for a DAA system to provide a range of maneuvers (recovery bands) that the PIC can maneuver to in order to regain DWC as soon as possible.Alerting and maneuver guidance performance and statistics of ACAS-Xu are compared against DAIDALUS.
+AlertingThe Phase 1 DAA MOPS defines alerting requirements using the Hazard Zone and non-Hazard Zone (see Table 2).Appendix A describes their definitions in detail.Phase 1 DAA MOPS outlines required performance for a DAA system applied to the test vectors outlined in Appendix P of the DAA MOPS [1].The MOPS requires that a DAA system provide corrective and warning level alerts at an average of 55 and 25 seconds before intersection of the corrective and warning hazard zones respectively.The MOPS, nonetheless, does not dictate a specific way of alerting algorithm implementation.This performance of DAIDALUS and ACAS-Xu in the analyzed scenarios is referenced to performance benchmarks outlined within the MOPS.DAIDALUS calculates alerts based on the predicted time to intersection of an alert zone by a projected intruder.The alert zone is chosen to be slightly larger than the alert's Hazard Zone to account for sensor and trajectory uncertainties.Appendix A describes the DAIDALUS alert zone parameters in detail.If this volume is to be violated within a specified minimum time an alert for that volume is presented with the highest level alert taking priority.Figure 5 shows the alerting and guidance timelines of ACAS-Xu and DAIDALUS.The version of ACAS-Xu analyzed here provides two levels of alert, Remain Well Clear (RWC) and Collision Avoidance (CA).ACAS-Xu's collision avoidance alerts are provided on similar timelines to TCAS II.The horizontal DAA additions to ACAS-Xu are designed to extend these timelines to support look-ahead times applicable to DAA requirements.The new DAA alert, the RWC alert, is expected to be issued at approximately the same time as the start of a DAA corrective alert threshold.It continues to be issued during the progression of an encounter until either the conflict is cleared or a CA alert is issued.DAIDALUS, on the other hand, provides discrete alerts for each DAA threshold and Regain-WC guidance calculated at approximately the DAA WC threshold.To facilitate comparison, the two ACAS-Xu alert levels are mapped to the corrective and warning alert levels for DAIDALUS, respectively (see Table 3.) It is important Figure 5. ACAS-Xu and DAIDALUS Alerting Timelines [10] to note that ACAS-Xu CA alert is not intended to provide DAA warning level alerts.The comparison between ACAS-Xu Collision Avoidance and DAA warning is imperfect as these refer to the alerts pertaining to NMAC and intersection of the warning hazard zone respectively.As such it is not expected that ACAS-Xu meet Phase 1 DAA warning thresholds requirements in alerting or guidance.Phase 2 of the DAA MOPS is expected to address the RWC guidance techniques used by ACAS-Xu in separate requirements for the proven robust ACAS-X method.
+Maneuver GuidanceOnly horizontal DAA guidance is compared between the two systems as the DAA vertical guidance of ACAS-Xu has not been implemented in Run 3. Inclusion of DAA guidance in the vertical dimension is planned for future releases of ACAS-Xu.Figure 7 shows the maneuver guidance for test card RWC-12.Horizontal maneuver guidance bands, in corrective and warning severities, with respect to Ownship heading are plotted along the Y-axis and time elapsed from the beginning of the test card along the X-axis.Negative headings correspond to left turns while positive headings correspond to right turns.These plots for test RWC-12 are representative of maneuver guidance performance observed across all scenarios.Similar plots for the remaining six scenarios can be found in Appendix B.It was observed that DAIDALUS's corrective guidance and ACAS-Xu's RWC guidance occur within 3 to 5 seconds of each other in the same region.ACAS-Xu was DAIDALUS produced warning level bands at 81 seconds elapsed, approximately 30 seconds before LoWC.ACAS-Xu produced smaller CA bands in the same regions 15 seconds later on average than DAIDALUS and later than the average DAA warning alert threshold.This is consistent with the onset delay in the warning-level alerts reported in Section 3.1.CA bands calculated by ACAS-Xu were consistently 25 to 35 degrees smaller than those calculated by DAIDALUS before saturation.Interesting behavior of ACAS-Xu's corrective level guidance bands is seen in several scenarios, including RWC-12 in Figure 7. Between approximately 75 and 90 seconds elapsed in RWC-12, ACAS-Xu's corrective bands were saturated in both directions, no directive CA heading was calculated, and no horizontal RA's were produced.During these 15 seconds there was no positive (i.e., recommended) guidance presented to the PIC.This indicates that there were no actions that the PIC could take in order to maintain or regain separation.This is in contrast to DAIDALUS's guidance bands which indicate possible maneuvers to the right and left during these times.Phase 1 DAA MOPS specifies that a DAA system shall always provide positive guidance information unless in the condition of NMAC4  [1].Future versions of ACAS-Xu should address this gap and make sure guidance is always available.
+Regain Well-Clear GuidanceIf the Ownship penetrates the RWC alerting zone deep enough, ACAS-Xu calculates horizontal directive guidance in the form of a heading to turn to.The horizontal directive guidance is also called a horizontal resolution advisory (RA).In general, ACAS-Xu may generate both horizontal and vertical resolution advisories.Its Nucleus module decides whether to issue a horizontal, a vertical, or a blended resolution advisory.In the case that a horizontal resolution advisory is issued, the PIC is expected to command the aircraft to these headings in order to maintain separation and avoid NMAC.In contrast, DAIDALUS calculates recovery bands once a loss of Well Clear is unavoidable (with horizontal maneuvers).These recovery bands indicate a range of maneuvers that the PIC should take in order to regain WC in a timely manner.Figure 7 shows the directive guidance of ACAS-Xu starting at 90 seconds, about 20 seconds before DAIDALUS issues the recovery bands.Among the other encounters analyzed, ACAS-Xu calculated CA maneuver guidance about 25 seconds before DAIDALUS issued the recovery bands (see Appendix B).Figure 8 shows the normalized amount of directive guidance headings calculated by ACAS-Xu that agree with DAIDALUS Regain-DWC guidance bands.
+Figure 8. Regain Well Clear Guidance OutcomesAgreement is considered to be when ACAS-Xu directive CA headings occur within a DAIDALUS DWC recovery band, or outside of DAIDALUS corrective or warning bands.For quantitative comparison of regain DWC guidance, probability of intersection was calculated as the ratio of headings in agreement to the total number of calculated CA hearings.These headings are agreeable with DAIDALUS guidance, falling within DAIDALUS non-alerting or recovery bands with a 0.87 probability globally and in complete agreement in 5/7 scenarios as seen in the histogram on the left in Figure 8.The right histogram of Figure 8 indicates the frequency of an ACAS-Xu directive CA heading occurring at a given distance to the nearest DIADLUS corrective or warning band edge.ACAS-Xu directive headings appear to deviate minimally from the Ownship's current heading, diverting 25 degrees on average from the nearest DAIDALUS corrective or warning band edge.
+Anomalies
+Alerting ToggleThe deterministic aircraft model used by DAIDALUS is susceptible to uncertainties in aircraft state data in some situations.Figure 9 shows how DAIDALUS was observed to drop maneuver guidance and decrease alert level due to perceived Intruder vertical divergence.This behavior was observed in all RWC scenarios.These scenarios occurred with Ownship offset vertically 300ft above the intruder.When linearly projected, these intruders appeared to not intersect the 450ft Vertical Miss Distance threshold (VMD*) of the corrective alert zone during one of these negative peaks.This effect becomes less pronounced as CPA gets nears, less time is allowed for the projected Intruder to descend resulting in steady alerting and guidance.The hysteresis provided by ACAS-Xu's probabilistic approach could be a significant advantage in combating surveillance sources with high uncertainty or low resolution such ADS-B or radar.
+Regain Well Clear ReversalEncounter DA-62 is an unmitigated encounter with a head-on trajectory and a 0.6 nmi lateral offset (see Figure 3).Ownship flew from west to east and the intruder offset was to the north of the ownship at CPA. Horizontal guidance bands and directive CA guidance from ACAS-Xu for this encounter are shown on the left side of Figure 11.ACAS-Xu suggested a slightly Northerly relative target heading at 87 seconds elapsed, approximately 28 seconds before a LoWC.Interestingly, the directive heading fell within the range of CA bands during this period of time, while there appears to be conflict-free Southerly headings available.The directive heading was therefore inconsistent with the CA bands, an undesirable behavior.This behavior, nonetheless, was not observed in any other encounters analyzed.This heading strengthened from -20 to -42 degrees relative to Ownship heading then reverses direction at 129 seconds elapsed, 7 seconds past DAIDALUS's time of regain DWC, to +48 degrees relative to Ownship heading.In comparison, DAIDALUS (shown on the right side of Figure 11) suggested a Southerly turn throughout the encounter, calculating recovery bands between +6 and +40 degrees to right saturation, i.e.only Southerly turns.
+ConclusionsThe analysis presented in this paper compares the DAA alerting and guidance behavior of both ACAS-Xu Run The Hazard Zone in Table 2 is defined in a similar way, using thresholds of the three variables HMD, h, and τ mod .The intruder and UAS are in the Hazard Zone when their HMD, h, and τ mod values all fall below the respective thresholds.The average, early, and late alert times are relative to the time at which the Hazard Zone is violated.DAIDALUS's alert zone is also defined in a similar way to the Well Clear and Hazard Zone, using thresholds of the three variables HMD, h, and τ mod .The HMD threshold is increased to 1.0 nmi to account for sensor and intruder intent uncertainties.The Non-Hazard Zone in Table 2 is also defined in a similar way, except that the UAS in a Non-Hazard zone when any of the three variables is above its threshold.
+RWC-06Figure B2 shows maneuver guidance and Regain-WC guidance for scenario RWC-06.RWC-06 is a mitigated scenario with Intruder enclosing on Ownship from 45 • North.Pilot mitigation prevents LoWC so DAIDALUS does not calculate recovery bands during this scenario.
+RWC-09Figure B3 shows maneuver guidance and Regain-WC guidance for scenario RWC-09.RWC-09 is a mitigated scenario with Intruder enclosing on Ownship from 90 • North.The differences between ACAS-Xu's CA and DAIDALUS's warning guidance can be seen clearly here.DAIDALUS produces warning guidance near the Ownship heading to left saturation throughout the scenario.In contrast, ACAS-Xu begins its CA guidance further from Ownship heading to left saturation as well.
+RWC-12Figure B4 shows maneuver guidance and Regain-WC guidance for scenario RWC-12.RWC-12 is a mitigated scenario with Ownship and Intruder on head-on trajectories.Very similar guidance performance is observed between ACAS-Xu and DAIDALUS.
+DA-62Figure B7 shows maneuver guidance and Regain-WC guidance for scenario DA-62.DA-62 is an unmitigated scenario with a head-on trajectory.Because of this, clear comparisons of ACAS-Xu and DAIDALUS alerting timelines can be made.ACAS-Xu's RWC guidance begins slightly before DAIDALUS and progresses to cover the same regions.CA guidance occurs later than DAIDALUS but occur over the same regions as well.
+Figure B7. DA-62 DAIDALUS and ACAS-Xu Guidance BandsA discrepancy between the sense of ACAS-Xu's CA headings and DAIDALUS recovery bands is seen midway through the scenario.This scenario occurs with a slight 0.6 nmi Northern horizontal offset.One hypothesized explanation for this behavior is the relatively large timeline that ACAS-Xu directive guidance occurs on would allow for significant time to divert Ownship trajectory in either direction.In this case, either sense being approximately equal in cost and probability.It is less costly to increase the strength of the maneuver than than inverting its sense so strength is increased as the encounter progresses.A limit is reached where such a turn would no longer be viable or is outside of Ownship performance limits so the maneuver's sense is inverted.Further analysis of this scenario is suggested.Figure 1 .1Figure 1.Illustration of Warning guidance information (a) heading (b) altitude (reprinted from MOPS DO-365 of RTCA with permission).
+Figure 3 .3Figure3.Scripted Flight Geometries[12]
+Figure 4 .4Figure 4. LVC DE and Interpolated Position Error
+Figure 66Figure6shows the alerting time before Closest Point of Approach (CPA) and LoWC in both alert levels for both systems during the seven encounters.The actual horizontal CPA was used as the reference point for CPA time.LoWC time is set to the time DAIDALUS's recovery bands start.This time is usually a few seconds before the aircraft enters the alerting zone.Both DAIDALUS and ACAS-Xu provide corrective level alerts before the corrective requirement threshold specified by the DAA MOPS.ACAS-Xu issues RWC alerts 3 seconds earlier than DAIDALUS's corrective alerts on average.This is before the 55 second corrective alert average threshold as expected.DAIDALUS issues warning alerts between 10 and 15 seconds earlier than ACAS-Xu's CA alerts.While ACAS-Xu's CA alerts fall outside of the DAA warning alert threshold, this is expected due to differences between the volumes of NMAC and DWC.
+Figure 6 .6Figure 6.Alerting Time Before CPA and WCV
+Figure 7 .7Figure 7. RWC-12 Maneuver Guidance
+Figure 9 .9Figure 9. DAIDALUS Bands and Alerting With LVC DE Source
+Figure 10 .10Figure 10.RWC-12: DAIDALUS and ACAS-Xu Bands
+Figure 11 .11Figure 11.ACAS-Xu Guidance Reversal Behavior
+FigureFigure B2.RWC-06 DAIDALUS and ACAS-Xu Guidance Bands
+Figure B3 .B3Figure B3.RWC-09 DAIDALUS and ACAS-Xu Guidance Bands
+FigureFigure B4.RWC-12 DAIDALUS and ACAS-Xu Guidance Bands
+Figure B5 .B5Figure B5.RWC-15 DAIDALUS and ACAS-Xu Guidance Bands
+Figure B6 .B6Figure B6.RWC-18 DAIDALUS and ACAS-Xu Guidance Bands
+
+
+
+
+Table of ContentsofACAS-Xu Flight Test 2 . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Flight Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3 DAIDALUS Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Alerting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 3.2 Maneuver Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . .9 3.3 Regain Well-Clear Guidance . . . . . . . . . . . . . . . . . . . . . .11 Alerting Toggle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 4.2 Regain Well Clear Reversal . . . . . . . . . . . . . . . . . . . . . . .141 Introduction12 Methods32.1 3 Analysis63.1 4 Anomalies124.1
+5 Conclusions 14 A Well Clear, Hazard Zone, Alert Zone, and Non-Hazard Zone 18 B Maneuver Guidance Plots 20 Nomenclature[1]7]][4][5]void (DAA) systems are a critical component of Unmanned Aircraft Systems (UAS) to remain Well Clear (WC)[1]from and avoid collisions with other airborne traffic.Manned aircraft rely on the pilot's sight to see and avoid aircraft in nearby airspace.DAA systems use surveillance sensors and algorithms to predict losses of DAA Well Clear (DWC) and provide alerting and guidance to the Pilot in Command (PIC) to ensure separation.Trade studies[2][3][4][5]and prototype DAA algorithms[6,7]have been developed to explore and characterize the technical challenges of DAA.This technical work among others has enabled RTCA Committee 228 (SC-228) to publish Minimum Operational Performance Standards (MOPS DO-365)[1]1 for DAA systems employed by UAS operating in non-terminal areas, referred to as Phase 1 MOPS.This MOPS applies to UAS equipped with Automatic Dependent Surveillance-Broadcast (ADS-B), active surveillance, and air-toair radar systems that detect aircraft without transponders.Phase 2 work to extend the MOPS to additional UAS categories and operations is currently underway.1 IntroductionACASAirborne Collision Avoidance SystemADS-BAutomatic Dependent Surveillance -BroadcastDAADetect And AvoidDAIDALUS Detect and AvoID Alerting Logic for Unmanned SystemsD modmodified distanceFAAFederal Aviation AdministrationFT2ACAS-Xu Flight Test 2GPSGlobal Positioning SystemHMD  *Horizontal Miss Distance ThresholdLoWCLoss of Well-ClearMOPSminimum operational performance standardPICpilot in commandRAResolution AdvisoryRWCRemain Well-ClearSTMSurveillance and Tracking ModuleTCASTraffic Alert and Collision Avoidance SystemCPAclosest point of approachTRMThreat Resolution ModuleUASunmanned aircraft systemVMD*vertical miss distance thresholdWCWell-ClearWCVWell-Clear Violationfpm Preventive and corrective alerts and guidance are caution-level (shown to the PIC Feet Per Minutenmi in yellow/amber symbols). They are intended to provide awareness to the PIC that nautical mileτ mod there is a predicted loss of DWC, but that there is sufficient time to coordinate with modified tauAir Traffic Control (ATC). The warning-level (shown to the PIC with a red symbol)alerts and guidance are intended to inform the PIC that an immediate maneuver isrequired. These caution and warning level alerts are in compliance with AdvisoryCircular guidance on the use of alerts. The guidance is of a suggestive nature,indicating a range of vertical and/or horizontal maneuvers predicted to result in aloss of DWC. Maneuvers outside the indicated range are suggested to the PIC toremain DWC. Figure 1 illustrates the heading and altitude guidance display to thePIC. The red color indicates ranges that are predicted to lead to a loss of WellClear. If a loss of DWC is imminent and unavoidable by any maneuver, the DAA isrequired to issue suggestive guidance in order to expedite regaining DWC.DAIDALUS [6] is a DAA algorithm developed by NASA Langley Research Centerto support Phase 1 MOPS development. It serves as a reference of a MOPS-compliant DAA algorithm. DAIDALUS takes a deterministic approach to alertingand guidance calculations [6]. Aircraft states are represented as linear projectionsof deterministic models obtained from surveillance sources such as ADS-B, active[1] alerting and guidance requirements in the DAA MOPS aim at avoiding losses of DWC.The Phase 1 MOPS define the DWC condition by the aircrafts' relative position and velocity.Specifically, it defines the DWC condition by three thresholds: projected horizontal miss distance, current altitude difference, and a nonlinear time to horizontal violation called modified tau.A detailed mathematical definition of DWC can be found in Appendix A. The Phase 1 MOPS defines three DAA alert and guidance levels, Preventive, Corrective, and Warning, in increasing severity[1].
+Table 2 .2Parameters for DAA Alerting Requirements (reprinted from MOPS DO-365 of RTCA with permission)
+Table 3 .3Equivalent DAA Alert Level
+3and DAIDALUS using flight test data from ACAS-Xu FT2 in July, 2017.Alerting comparison results show that ACAS-Xu's RWC alert starts at similar to slightly earlier times to DAIDALUS's corrective alert.ACAS-Xu's CA alert starts at 10-15 seconds later than DAIDALUS's warning alert, and overlaps with DAIDALUS's Regain-WC guidance times nearing LoWC.Guidance comparison results show ACAS-Xu's guidance is found to occur in similar locations and to be more conservative compared to DAIDALUS's, protecting a larger range of headings from maneuvering.In these test conditions, ACAS-Xu's horizontal directive guidance usually started while the RWC bands were saturated and 10-15 seconds before the CA bands begin.DAIDALUS's Regain-WC bands, on the other hand, are not calculated until violation of the warning alert zone is imminent or has occurred.ACAS-Xu's horizontal directive guidance sense agreed with that of DAIDALUS's Regain-WC bands in most analyzed encounters.However, there are notable discrepancies in the DA-62 where ACAS-Xu's directive heading cut well into both ACAS-Xu and DAIDALUS's bands.Further analysis of this scenario is suggested to uncover the cause of this disagreement.ACAS-Xu's alerting and guidance appeared to be more resilient under the tested sensor uncertainties, leaving no gaps in its time series of alert and guidance while DAIDALUS stopped issuing alerts due to noise in the predicted vertical trajectories moving out of the alerting zone.The gaps in DAIDALUS's alerting and guidance may be remedied by filters that reduce the vertical state's uncertainty, either in the surveillance tracker or in the DAA system itself.This comparative analysis reveals striking similarities and differences between the DAA performance of two distinct systems, ACAS-Xu and DAIDALUS.With the Phase 2 MOPS for DAA and MOPS for ACAS-Xu both in progress, the methodology and tools developed for this analysis will be useful for evaluation of upcoming versions of both systems.14.Mu ñoz, C.; and Narkawicz, A.: Formal Analysis of Extended Well-Clear Boundaries for Unmanned Aircraft.Proceedings of the 8th NASA Formal Methods Symposium, Springer, vol.9690, 2016, pp.221-226.
+			The complete RTCA DO-365 document referenced may be purchased from RTCA, Inc., 1150 18th Street NW Suite 910, Washington, DC
+			20036, (202) 833-9339, www.rtca.org
+			WC SC 228 nom b.txt
+			http://www.github.com/nasa/wellclear
+			MOPS 238
+		
+		
+			
+Appendix A Well Clear, Hazard Zone, Alert Zone, and Non-Hazard ZoneThe DAA Well Clear (DWC) zone for the UAS targeted in the Phase 1 MOPS is defined by thresholds of three parameters.It does not have distinct physical boundaries because the definition depends on two aircraft's relative position and velocity during an encounter.Figure A1  The Horizontal Miss Distance (HMD) represents the two aircraft's predicted minimum horizontal distance during an encounter assuming constant velocities.The parameter h represents the two aircraft's current altitude difference.The time metric modified tau, τ mod , is an estimated time taken for the two aircraft to intersect the "protection" disk.The range rate is negative for closing geometries.The positive incremental distance modifier D mod defines the radius of a "protection" disk around the Ownship such that any intruder with a horizontal range less than D mod is always considered "urgent".In this case, τ mod = 0.The thresholds, denoted by an asterisk, for the HMD, h, and τ mod are 4000 ft, 450 ft, and 35 sec, respectively.All three parameters must simultaneously fall below their respective thresholds during an encounter for the two aircraft to violate the DWC.Alerting algorithms are designed to reduce the probability of violating DWC to a value required by the MOPS.The definition of τ mod is [13]where r and ṙ are the horizontal range and range rate between the intruding aircraft and the UAS, respectively.The value of D mod must be equal to HMD * to avoid the undesirable on-and-off alert during an constant velocity encounter [14].
+Appendix B Maneuver Guidance Plots
+RWC-03Figure B1 shows maneuver guidance and Regain-WC guidance for scenario RWC-03.RWC-03 is a mitigated scenario with a head-on trajectory.The PIC conducted a right turn in accordance with ACAS-Xu suggestive guidance.Pilot mitigation prevents LoWC, meaning no DAIDALUS recovery bands are calculated for this scenario.The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302.Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
+PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.
+Standard Form 298 (Rev. 8/98)Prescribed by ANSI Std.Z39.18
+REPORT DATE (DD-MM-YYYY)01-02-2018
+REPORT TYPE
+Technical Memorandum
+DATES COVERED (From -To)
+TITLE AND SUBTITLEComparative Analysis of ACAS-Xu and DAIDALUS Detect-and-Avoid Systems
+AUTHOR(S)Jason Davies and Minghong G. Wu
+PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)NASA Ames Research Center Moffett Field, California 94035-1000
+PERFORMING ORGANIZATION REPORT NUMBER
+L-
+SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)National Aeronautics and Space Administration Washington, DC 20546-0001			
+			
+
+				
+
+
+	
+		Minimum Operational Performance Standard (MOPS) for Helicopter Hoist Systems
+		10.4271/as6342
+		
+		
+			20036. 2017
+			SAE International
+			Washington, DC
+		
+	
+	Minimum Operational Performance Standards (MOPS) for Detect-and-Avoid Systems. DO-365, 1150 18th Street NW, Suite 910, Washington, DC 20036, 2017. URL www.rtca.org.
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+		FAA Position on Building Consensus Around the SARP Well-Clear Definition
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+		Characteristics of a Well Clear Definition and Alerting Criteria for Encounters between UAS and Manned Aircraft in Class E Airspace
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+		the Eleventh UAS/Europe Air Trac Management Research and Development Seminar
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+	Johnson, M.; Mueller, E. R.; and Santiago, C.: Characteristics of a Well Clear Definition and Alerting Criteria for Encounters between UAS and Manned Air- craft in Class E Airspace. Proceedings of the Eleventh UAS/Europe Air Trac Management Research and Development Seminar , 2015.
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+		Characterizing the Effects of a Vertical Time Threshold for a Class of Well-Clear Definitions
+		
+			JasonUpchurch
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+			CésarMuñoz
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+			AnthonyNarkawicz
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+			MaríaConsiglio
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+		10.2514/atcq.23.4.275
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+	Upchurch, J.; Mu ñoz, C.; Narkawicz, A.; Consiglio, M.; Chamberlain, J.; : Char- acterizing the Effects of a Vertical Time Threshold for a Class of Well-Clear Definitions. Proceedings of the Eleventh USA/Europe Air Traffic Management Research and Development Seminar (ATM2015), 2015.
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+		Piloted Well Clear Performance Evaluation of Detect-and-Avoid Systems with Suggestive Guidance. Tm, NASA
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+		DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems
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+		the Digital Avionics Systems Conference (DASC)
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+		Proceedings of Lincoln Laboratory Air Traffic Control Workshop 2016 -2, no. RTCA Paper
+		Lincoln Laboratory Air Traffic Control Workshop 2016 -2, no. RTCA Paper
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+	Olson, W.: ACAS Xu UAS Detect and Avoid Solution. Proceedings of Lincoln Laboratory Air Traffic Control Workshop 2016 -2, no. RTCA Paper No.261- 15/PMC1400, Dec. 2016.
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+	Olson, W. A.: Airborne Collision Avoidance System X. , Massachusetts Insti- tute of Technology, Lincoln Laboratory, June 2015. Tech Notes.
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+	NASA, Honeywell, ACSS, FAA, General Atomics Aeronautical, Northrop Grum- man: FT2 Test Card Deck -Flight 9. 2017.
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+
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+	
+		STI Product Title
+		
+			FirstNameLast Name
+		
+		10.2172/1429240
+		
+			
+			Office of Scientific and Technical Information (OSTI)
+		
+	
+	nasa.gov
+	NAME OF RESPONSIBLE PERSON STI Information Desk (help@sti.nasa.gov)
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+		
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+INTRODUCTIONIn the past several years, the nation's air traffic control (ATC) system has become increasingly congested, delays have become common, and controller workload has increased.NASA Ames and other research laboratories are investigating methods for increasing the efficiency of terminal-area traffic management and decreasing controller workload.The current work focuses on the potential for using time-based trafficmanagement techniques.The success of this approach is dependent on its ability to handle aircraft regardless of the level of sophistication of the on-board equipment.Some new commercial aircraft are equipped with fightpath-management systems that are capable of generating and flying fourdimensional (4D) trajectories.Although these on-board systems will be an essential component of a time-based traffic management system, there will be a long transition period in which both equipped and unequipped aircraft will be flying.During this transition period, the success of time-based traffic management will be determined by how well controllers can control the arrival times of unequipped aircraft.The descent advisor (DA) is an automation tool that assists air traffic controllers in meeting arrival-time and spacing requirements for inbound traffic.The DA algorithm resides in a microprocessor-based workstation that interfaces with and receives aircraft surveillance data from the National Airspace Host Computer.As an unequipped aircraft approaches the descent area, the algorithm predicts the arrival time, taking into account the aircraft's performance capability, current wind and weather conditions, and the airline's standard operating procedure.The predicted arrival time is presented to the controllers, along with predictions for all other aircraft in their sector, by several graphical techniques.The controller chooses an optimum arrival time for an aircraft by adjusting the aircraft's descent-speed profile, using a mouse-based, menu-driven interface with the DA algorithm.When the controller accepts a descent-speed profile that will allow the aircraft to arrive at the desired time, the controller issues the speed profile advisory to the aircraft in the form of a clearance.A detailed description of the graphical controller interface for the DA algorithm is given in reference (1).For the work described in this paper, a piloted simulator was used in conjunction with an ATC simulation to evaluate the performance of the ground-based, 4D, DA algorithm for controlling the arrival time of conventional (unequipped) aircraft.Arrival time is controlled to a position about 30 n.mi.from the airport, referred to as the metering fix or feeder fix.This is an intermediate point between cruise and touchdown where commercial jet traffic undergoes a transition from enroute descent to terminal area operation.The desired arrivaltime accuracy for unequipped aircraft at the feeder fix is +_0 sec.An earlier study evaluated the DA performance for a single aircraft executing straight-in descents (2).Although the DA was shown to have considerable promise, pilots felt it was necessary to conduct an evaluation in an operational ATC environment with other traffic.In the current study, the piloted simulation was conducted in conjunction with a controller evaluation of the DA tool (3), to determine the precision with which airline pilots could fly curved-path, advisor-assisted descents in a realistic ATC environment.Additional evaluations included the effects of different wind conditions on pilot performance, and procedures for advisor-assisted route intercepts during descents.This paper first briefly reviews the 4D, DA algorithm and then describes the results of the piloted simulation studies.
+ALGORITHM DESCRIFFIONIn broad outline, the DA algorithm synthesizes a 4D trajectory in the following way.First, the DA predicts the arrival time of an aircraft following a defined arrival route, based on the aircraft's standard operating procedures.
+DA Trajectory ModelFor each descent speed profile selected in the iteration process, the corresponding descent trajectory is computed by integrating a set of point mass equations of motion along the route of arrival.The equations are derived with respect to an Earthfixed reference frame using a rectangular coordinate system based upon a fiat-Earth approximation.The trajectory is projected onto two planes--the horizontal, which defines the horizontal path, and the vertical, which defines the altitude profile along the path.The altitude profile and horizontal path are illustrated in Figures 1 and2.This paper will focus on the hor-
+The coordinatess and h are defined as distance along the path and altitude, respectively.The kinematic equations to be integrated are ds u e _-= V t cos(?a) + Uws (1)dh w =-_ = V t sin (?a) (2)where u and w are defined as the components of inertial veloc-ity
+RESULTS
+ComparisonsFig. 11Fig. 1 Altitude profile of DA trajectory model.
+Before each simulation session the pilots were given a briefing about the DA which included the procedures for anticipating and flying turns precisely and for executing the profile descents that were to be issued by the controllers. They were also briefed ononly change to current procedures for a descent to the Drako feeder fix was that the crossing restriction at Drako was changed from "cross Drako between 17 ,17Fig. 3 Simulated arrival routes to Denver.
+FiguresFigures 4-7 show the composite altitude profiles for the baseline, nominal, fast, and slow descents in calm wind conditions.Note that for the baseline runs, although all of the pilots were flying the same profile descent (Mach 0.8/280 KIAS), the range of top-of-descent points chosen by the pilots varied by as much as 25 n.mi.This is because pilots have their own simple "rules of thumb" for choosing a top-of-descent point.There are typically even greater variations in altitude profiles and arrival times for baseline descents when various wind conditions are present (2).Also note that for the slow descents, one aircraft began its descent 10 n.mi. earlier than necessary.In this descent, the pilot thought the procedure was to reduce speed
+Fig. 4 Fig. 5 Fig. 6456Fig. 4 Composite of all baseline descents flown in simulation.32.5 30.0 _o 27.5 X _= 25.0 22.5
+FigureFig. 8 Fig. 9 Fig. 118911Figure 8 shows arrival-time errors at feeder fix Drako for aircraft flying advisor-assisted descent-speed profiles for all three wind conditions.The mean arrival-time error was 13.4 sec late, with a standard deviation of 15.6 sec.This histogram shows that most aircraft arrived within :E20 sec of their scheduled arrival time.
+air- speed, Yo is the aerodynamic flightpath angle, and Uw, is theeffectivewindspeedin the flightpathdirection.The effectivewindspeedis def'medas the differencebetweenthe ground-speed and the true airspeed.Assumingthat the wind is knownas a function ors and h, equations(1) and (2) may be integratedonce expressionsfor Vt and _'a are known. The relationshipsforVt and Ya in terms of thrust, drag, weight, and Mach number orCAS are describedin reference(2). The determinationofthrust, drag, weight, and winds aloft will be discussedin a latersection on algorithm implementation.In its current implementationon a high-performancework-station, the DA algorithmcontainsmodels of two aircraft types,a Boeing 727-200and a Boeing 737-100.However,the soft-ware is structuredto accommodateany number of differentair-craft types. The performancemodel, which includesdetailedpropulsiveand aerodynamicinformation,is used to evaluatethe thrust and drag terms just discussed.The propulsivemodelrepresentsthrust as a functionof engine pressureratio (EPR),Machnumber,temperature,andpressure.Thethrust-managementmodel definesin the direction of s and h, respectively, Vt is the true either the EPR or the thrust value required during the descent for a
+particular thrust-management procedure. The Mach number is determined by the speed pro-(MVSRF)727-200full-missionsimulator to evaluateboth pilotperformanceand acceptanceof the DA clearances.The simu-file along with temperature,and the temperatureand pressurelator, which is FAA-certifiedphase II, has a six-degree-of-are determinedfrom atmosphericdata. The aerodynamicmodelfreedommotion system and a night/duskcomputer-generated-representsthe drag coefficientas a function of lift coefficient,imagery visual system. To generatea substantialand represen-Mach number, and control-surfacedeflection(speedbrake,tative statisticaldata base, 42 airline pilots,from four majorflaps, and gear). Here, the lift coefficientis determinedbyU.S. carriers,and 9 air traffic controllersparticipatedin theusing the approximationthat lift is equal to weight.Theevaluation.control-surfacedeflectionscheduleis based upon speed andposition. For the purposes of this paper, the aircraft is assumedThe piloted simulationwas conductedjointlywith an ATCto be in a clean configuration.simulationof the DA/controllerjnterfacesystem. The simula-tion scenariowas based on the Denver Air Route Traffic Con-With regard to the modelingof thrust managementduring atrol Center's(ATRCC)northwestarrival sectors.The piloteddescent,three cases have been identified.The first case is that727 simulatorwas one of several simulatedaircraft participat-of a constant-thrustsetting. The second and third cases involveing in the study. The other aircraftwere computer-generatedthe variationof thrust to maintain a constant rate of descent andpseudo-aircraft.The pseudo-aircraftare "flown"by pseudo-a constantinertialflightpathangle, respectively.The actualpilots who use a keyboardto initiatechangesin aircraftalti-implementationof the algorithmallows for the assignmentoftude, speed, and heading.The commandsare sent throughaany one of these cases to each distinct segmentin a descentcommunicationslink to the pseudo-aircraftdynamicsmodel(e.g., constantMach number,constantCAS, and so on) alongwhich providesdata for all pseudo-aircraftto the communica-with the correspondingdescent rate or flightpathangle for thetions manager.The communicationsmanagergathers data fromlatter cases. Althougha pilot would not actuallyfly a constantall sources (pseudo-aircraftand simulatedaircraft) and mergesinertialflightpathangle, the combinationof the three casesthe data into a commonformat for use by the air traffic con-allows for the greatestflexibilityin modelingautomaticandtroller'sdisplaysoftware.The air traffic controliers'objectivemanual descent procedures.in this evaluationwas to meet a precise spacing requirementof10 n.HorizontalPathThe horizontalpath is modeledas a series of straight-linesegments connectedby circular arcs, as illustratedin Figure 2.The line segmentsarc defined by waypoints(e.g., WP1 andWP2), and the turns arc defined by turn points (e.g., TP1 andTP2). The final waypoint used in generatinga 4D trajectoryisthe feeder fix. The radius of curvature, R, for each arc is deter-mined as a functionof the predictedaveragegroundspeedthroughoutthe turn. It is assumedthat the turn is performedatan average altitudeand true airspeed,which are approximatedby the altitude and true airspeed of the aircraft when it is abeamof the turn intersection(e.g., WP2). The average true airspeedand effectivewindspeedare combinedto predictthe ground-speed throughoutthe turn.Once the turn radius fixes the horizontalpath for a particu-lar set of conditions(e.g., speed profile and winds), the altitudeprofile is found by integratingequations (1) and (2) along thepath. The approximationsused above greatly simplify the cou-pled relationshipbetweenthe horizontalpath and the altitudeprofile, while still allowing the size of the turn to be determinedas a function of airspeed,altitude, winds aloft, and course.SIMULATIONDESCRIPTIONA piloted simulationwas conductedat the NASA AmesResearchCenter'sMan-VehicleSystemsResearchFacilityFor each aircraft handled, the DA stores data detailing the synthesized 4D trajectory for later comparison with the aircraft's actual trajectory.This information is used to track time error (defined as the difference between the actual and desired schedules) which may grow during the descent.An example of a synthesized trajectory is given in reference (4).mi.intrail at a feeder fix.The DA was used to assist the
+air- craft were predicted to be at 17,500 ft, with descent speed andscheduledtime dependingon the wind conditionsand thecontrollerinputs. A total of 96 advisor-assisteddescentswereused for the statisticaldata base and can be broken into threecategoriesfor the purposesof comparison:fast (Mach 0.84/350 KIAS),nominal(Mach0.81320KIAS),and slow(230 KIAS).In addition,12 baselinedescentswere flown inwhich the pilotsexecutedtheir current standard-airline-operating-procedureprofile descent (Mach 0.8/280 KIAS).Descentsflown in the simulation at speeds other than these arenot consideredhere since these three categoriesrepresent mostdescents,span the entire 727 descent-speedenvelope,andprovidea large enoughstatisticalbase from whichto drawconclusions.of actual aircraft states with the predicted 4D trajectories were the primary measures of performance.These states included altitude, position, time, CAS, Mach number, vertical speed, EPR, and total thrust.At the feeder fix, the
+Table 11lists the total variabilityin arrival time, defined asthe differencebetweenthe earliest and latest arrival,at thefeeder fix for the three wind conditions,and Table 2 lists theone-sigmastandarddeviationof arrivaltimes. The nominaladvisor-assisteddescentin calm wind conditionsmost closelyresemblesthe baselinedescentandshowsconsiderableimprovementin both arrival-timevariabilityand standarddeviation.The fast and slow advisor-assisteddescentsshowmore error than the nominalin arrival-timeaccuracy,but thiswas to be expectedsince most pilots are not accustomedtoflying profile descentsat these speeds. In addition,the fast pro-file requires a high descentrate, which makes precisespeedcontrol more difficulL
+Table 1 .1Total variability of arrival time in secondspath-stretching,extendedlevel flight at low altitudes,andexcessivedescent rates. Most of the advisor-assisteddescents(and all of the descentsused for statisticalpurposes)wereWind conditionBaselineAdvisor-assistedissued in a conventionalmanner (i.e., top-of-descentpoint anddescentspeed). By the end of the 4-hr simulationsessioninNominalFastSlowwhich the pilots executedseveral advisor-assisteddescents plustheir own baselinedescent in the ATC environment,nearly allTailwind404769pilots expressedenthusiasticsupport for the DA concept. TheyHeadwind487933liked the DA becausethey were able to fly the descentswithCalm87377254little or no training, and they were impressedwith the accuracyin time, altitude,and speed at which they arrived at the feederfix. The pilots also made several suggestionsfor improvement.Table 2. One-sigmastandard deviationof arrival time inThey recommended(1) that the fast descentsbe restrictedtosecondsgood weatherconditions,because of problemswith turbulenceand weatherpenetration;and (2) that provisionsfor cruisespeed variationsbe incorporated,becausemany pilots wouldWind conditionBaselineAdvisor-assistedbecomefrustratedwith the slow descents.The algorithmhasbeen modifiedto address these issues.NominalFastSlowCONCLUDINGREMARKSTailwind111221Headwind142110A ground-based,4D, DA algorithmhas been developedandCalm25122018tested in a simulatedATC environment.The concept showsconsiderablepromisefor assistingair trafficcontrollersinmeetingprecisetime schedulesand spacingrequirements.There are importantparametersother than arrival time atSimulationresults showedthat most pilots executingadvisor-the feeder fix that affect the success of a time-basedtraffic-assisteddescentsarrivedat the feeder fix within :t.20 sec ofmanagementsystem. Aircraft should arrive at the feeder fix nottheir scheduledarrival time, which is necessaryif a time-basedonly at their scheduledtime, but at their scheduledaltitude andtraffic managementsystemis to be effective.The simulationspeed. If aircraft arrived at the feeder fix on time, but with largealso showeda late bias in arrivaltime for advisor-assistedaltitude and speed errors, time errors and spacingerrors woulddescents,which may be attributedin large part to the method ineventuallybecomeevidentand propagateinto the finalwhich turns are modeled by the DA algorithm.This bias can beapproach area. The errors in the altitude and speed at the feedereliminatedby changingthe turn model to match more closelyfix were computedfor the two most similar descents,the base-the turns executedby airline pilots. It is also critical in a time-line descentand the advisor-assistednominaldescent in calmbased trafficmanagementsystemthat aircraftarriveat thewinds. For the baselinedescents,the mean altitude error wasfeeder fix at the predictedaltitude and speed. The simulation-131 ft and the standarddeviationwas 389 ft. The advisor-showedthat the altitude error at the feeder fix was acceptableassisteddescenthad a mean altitudeerror of -155 ft and abothfor the advisor-assisteddescentsand for unassistedstandarddeviationof 292 ft. Thus, the baselineand advisor-descents,but airspeederror was too large for the baselineassisted descentshave essentiallythe same altitude error, anddescents.Advisor-assisteddescentsreducedthe errortoboth are consideredacceptable.This is not the case with air-acceptablelevels.Pilots were able to executethe advisor-speed, where the baselinedescents resultedin a mean airspeedassisteddescentswithout prior trainingand were enthusiasticerror of 6 knots and a standarddeviationof 16 knots.Theabout the DA. Controllerevaluationsof the DA conceptandadvisor-assistednominal descentshad a mean airspeederror ofinterfacewere alsopositive,and indicatedconsiderable-5 knots and a standard deviationof 5 knots. The 16-knot stan-promise for this tool. Current plans call for additionalsimula-dard deviationin airspeedfor the baselinedescentsis nottion evaluationsof enhancementsto the DA algorithmandacceptable time errors as the aircraft passes the current time control point because it will rapidly propagate into significantcontroller tional evaluation interface. of the DA at an enroute traffic control center If these tests are successful, a live opera-and enters the TerminalRadar ApproachControl(TRACON)is anticipated.airspace.Improvementto a 5-knot standarddeviationin theadvisor-assisteddescentsis more conduciveto the preciseREFERENCESschedulingrequiredfor time-basedtrafficmanagementextendinginto the TRACON.(1) Erzberger,H. and Nedell,W., "Designof AutomationToolsfor Managementof DescentTraffic,"NASAIn the early stages of a simulationrun, several pilots wereTM-101078,Dec. 1988.skepticalabout the utility of the DA tool. These pilots felt thatthey could achievescheduledarrivaltimes withoutadvisor(2) Green,S.M., Davis, T.J., and Erzberger,H., "A Pilotedassistance.Therefore,some pilots were issued a time-onlySimulatorEvaluationof a Ground-Based4D DescentAdvisorAlgorithm,"Proceedingsof the 1985 AIAA Coli-ference on Guidance,Navigationand Control,Monterey,CA, pp. 1173-1180,Aug. 1987.descent instruction (e.g., "Arrive at Drako at XX minutes and YY seconds past the hour").Although they were able to meet the times accurately, the methods they used included extensive (4) Erzberger, H. and Chapel, J., "Ground
+Based Concept for Time Control of Air, raft Entering the Terminal Area," Pro- ceedings of the 1985 AIAA Conference on Guidance, Navigation and Control, Snowraass, CO, pp. 301-306, Aug. 1985.
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+A concept for aiding air traffic controllers in efficiently spacing Izaffic and meeting scheduled arrival times at a metering fix has been developed and tested in a real-time simulation.The automation aid, referred to as the ground-based four-dimensional descent advisor (DA), is based on accurate models of aircraft performance and weather conditions.The DA generates suggested clearances, including both topof-descent-point and speed-profile data, for one or more aircraft in order to achieve specific time or distance separation objectives.The DA algorithm is used by the air traffic controller to resolve conflicts and issue advisories to arrival aircraft.A joint simulation was conducwal using a piloted simulator and an advanced-concept air traffic control simulation to study the acceptability and accuracy of the DA automation aid from both the pilot's and the air traffic controller's perspectives.This paper focuses on the results of the piloted simulation.In the piloted simulation, airline crews executed controller-issued descent advisories along standard curved-path arrival routes, and were able to achieve an arrival-time precision of +20 sec at the metering fix.An analysis of errors generated in turns resulted in further enhancements of the algorithm to improve the predictive accuracy.Evaluations by pilots indicate general support for the concept and provide specific recommendations for improvement.Operational issues concerning how the DA was used for prediction, intrail spacing, and metering in a multiaircraft environment are described in a companion paper (Tobias; see block 15).			
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+		Recipient States in an Asymmetric System
+		10.1057/9781137442970.0007
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+		Recipient States in Global Health Politics
+		
+			Palgrave Macmillan
+			null
+		
+	
+	Recipient's Catalog No.
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+
+	
+		Date of Report
+		10.32388/itizhp
+		
+			
+			Qeios
+		
+	
+	Report Date
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+				
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+SUMMARYThis paper describes the design and simulator evaluation of an automation tool for assisting terminal radar approach controllers in sequencing and spacing traffic onto the final approach course.The automation tool, referred to as the Final Approach Spacing Tool (FAST), displays speed and heading advisories for arrivals as well as sequencing information on the controller's radar display.The main functional elements of FAST are a scheduler that schedules and sequences the traffic, a 4D trajectory synthesizer that generates the advisories, and a graphical interface that displays the information to the controller.(45 min) from the airport and continuing to the final approach fix.During the last two years, the three elements of this system have been evaluated by Center and TRACON controllers in several real-time simulations.This paperbeginswith anoverviewof the Center-TRACON AutomationSystemtools(i.e.,the TMA, DA, andFAST).Thenthepaperfocuseson thedesignandevaluationof FAST,themainfunction of which is to provide speedandturn advisoriesthathelp controllersachieveanaccuratelyspaced flow of traffic on final approach.Thepaperconcludes with a descriptionof resultsfrom a recentreal-time simulationwhich evaluatedthe acceptabilityof FAST to TRACON controllersandits effecton landing rate.
+OVERVIEW OF AUTOMATION SYSTEM CONCEPTThe Theoperationof the Scheduler, described in references 3 and5 is briefly reviewedhere.The primary inputsto the Scheduler areperiodicallyupdatedestimatedtimesof arrival (ETAs)for all aircraftthatare beingtrackedby the terminalarearadarsystems.WhentheETA of a newarrival first falls within the SchedulingWindow, which is definedasthetime interval betweenthe SchedulingandFreezeHorizons, the Scheduler beginsgeneratingscheduled timesof arrival (STAs).The Scheduler first attemptstoplace a newarrival at a time identicalto its ETA on therunway.If sucha choiceof STA createsa spacingviolation with previouslyscheduled aircraft,the Scheduler assignstheclosestavailabletime thatmeetsthe minimumallowed spacingdistanceon final approach.The minimum time separations usedby the Scheduler arederivedfrom minimum separation distances specifiedby FAA regulations.The minimum spacingdistances dependon the weightclasses of the aircraftin the landingsequence andcanberepresentedin a matrix of separation distances (n.mi.)asgivenin table1.As explainedin reference6, this matrix of distances is convertedto a corresponding matrix of time separations by incorporatingknowledgeof final approachspeeds.Furthermore, bufferson theorderof 10to 20 secareaddedto theseminimum time separations in orderto protectagainstunavoidable errorsin the ability to controllanding timesusingthe FAST advisories.The magnitudeof thedifferencesbetweenthe STAsandthe initial ETAs generated by the Scheduler depends both on the orderlinessof thearrival streamandon theexcessof the total arrival flow over the maximumlandingrate.If thearrivalsinto theTRACON airspacearecontrolledby theDA andTMA, theywill arriveat thegateswith only smalltime errorsandthe flow rate will matchthe runwayacceptancerate.In thatcasethe Scheduler in FAST will makeonly minorchangesin the STAsoriginally calculatedby the CenterTMA.Thesechanges will correctthe smalltime erroraccumulated during the descent andthe transitionfrom theCenterinto the TRACON.Mostof the time,therefore, the Scheduler will be ableto preservethe optimallandingsequence originally calculatedby the centerTMA.If the Centerautomationtools,DA andTMA, arenot in operation, theflow into theTRACON duringrushperiodswill be stronglybunchedandmay exceedthemaximumrunwayacceptance ratefor a periodof time.Because of maneuver airspace restrictionsandotherfactors,a TRACON Scheduler has lessfreedomto optimizethe arrival sequence thanthe CenterScheduler, andthereforecannotbeas effectivein reducingdelays.However,the FAST scheduler is designedto handlesuchdifficult flow conditionsin the bestpossibleway.It will generate landingsequences andSTAsthatminimizedelays subjectto operational constraints.Underexcess traffic load,the STAsgenerated by the Scheduler will absorbdelaysin theTRACON by holdingor pathstretching.An importantfunctionbuilt into the Scheduler is thecapabilityfor handlingmissedapproaches and popuptraffic.With thesefunctions,theScheduler opensup a time slot wheresuchaircraftcanbereinsertedinto the arrival sequence.Undersaturated traffic conditionstheinsertionof anextraslot will, inevitably,introducedelaysfor aircraft thatfollow the insertedaircraft.Thereschedulingfunction assists the controllerin finding a slot in the arrival sequence thatwill leastdisrupttheoveralltraffic flow.
+Four-DimensionalTrajectory GeneratorThe FAST descent trajectory synthesis algorithm is a modified version of the Center DA algorithm.A detailed description of the algorithm is given in reference 3. Similar to the Center DA, it employs a second-order Runge-Kuttaforward integrationschemeto synthesize a pathto therunwaybasedon stan-dardTRACON operations, aircraft stateandtype,andwind speedanddirection.Upon arrival into TRACON airspace, theFAST 4D TrajectoryGenerator predictsthearrival time of an aircraftat the final approachfix (outermarker)basedon its currentposition,altitude,speed, and heading.The predictionis basedon a setof standard arrival routes,air speeddecelerationschedules, and altitudeprofiles thatconformto standard operations at a givenTRACON.Thecurrentimplementationof FAST is basedonDenverTRACON operationsfor arrivalsto StapletonInternational Airport.Next,the FAST 4D TrajectoryGenerator computesa rangeof arrivaltimesbasedon theaircraft speedenvelope andallowablepathextension. Thesepredictedtrajectoriesareupdatedevery5 sec.If the STA andETA arethe same, the aircraft is maintainedon its present nominalpath,altitude,andspeed profile to the runway.If the ETA showsthe aircraftto beearly,theFAST 4D TrajectoryGenerator will synthesize a descent trajectorythatattemptsto eliminatethe time errorby first decreasing the aircraftairspeedand then,if necessary, extendingthe pathdistanceto the runway.If the ETA showsthe aircraftto be late,the controlleris advisedto havethe aircraftmaintainhigherspeeds or shortenits pathto therunwayby utilizing the HorizontalGuidanceModesthatwill bedescribednext.Constructionof thehorizontalroutealwaysbeginsat the currentpositionandheadingof the aircraft andterminatesat thefinal approachfix.The currentpositionneednot beon a standard path.The controller mayvector the aircraft anywherein theTRACON arrival airspaceanda horizontalroutewill be synthesized basedon eithera route-intercept (RI) procedureor a waypointcapture(WC) procedure (refs.3, 4).Routeinterceptoperates in conjunctionwith a setof standard or nominalarrival routesconverging onthe final approachcourseto therunway.Theroutescomprisingthe nominalarrival pathfrom the noah to Rwy 26L atDenver'sStapleton International Airport are the final approach course extending 15 n.mi.beyond the outer marker (Altur), a base leg positioned 5.5 n.mi.from the outer marker and extending 15 n.mi.noah from and perpendicular to the final approach course, and a downwind leg positioned 5 n.mi.noah of and parallel to the final approach course (fig.1).Each route has a corridor width of +1 n.mi.relative to its center line.As an aircraft enters the TRACON airspace from one of the feeder gates (Drako or Keann) the FAST trajectory synthesis algorithm puts the aircraft into a free vector mode.In this mode, the algorithm seeks an interception of one of the defined route segments by extending the instantaneous heading vector.From the first point of interception, the algorithm completes the path by following along the nominal route to the final approach fix.After the aircraft has captured the downwind leg, the horizontal synthesis computes a new RI of the base leg.Similarly, once the aircraft has intercepted the base leg, a new RI of the final approach course is computed.The path to the runway is recomputed approximately every 5 sec based on the current position and heading.This free-vector mode with RI logic allows the controller the freedom to vector aircraft anywhere in the arrival airspace and still maintain a highly accurate estimate of arrival time as long as the aircraft is heading for a standard route segment.The horizontal path synthesized by the waypoint capture (WC) mode consists of an initial circular arc starting at the current position and course followed by a straight-line segment leading directly to a designated capture waypoint, and ending with a circular arc turn intercepting the route containing the capture waypoint.The geometry of this construction is illustrated in figure 2. The algorithm determines theradiusof theturn from the airspeed, wind speed, andmaximumallowablebankangle.Furthermore, thedirectionof the turn towardthecapturewaypointis chosensothatthe total lengthof the pathis minimized.In orderto compensate for computational delaysandto allow for controllerresponse time, the algorithmalsomovesthe startof the turn ateachcomputational cycle a distanceequivalentto 10sec of flight time aheadof thecurrentaircraftposition.As in othertrajectorysynthesis modes, the predictive algorithmrefreshesthe WC profile in a 5-seccycleusing updatedaircraftstateinformation.TheWC modecanbe manuallyselected by the controllerfor specialsituationssuchasmissedapproachguidance.It is alsoselected automaticallyby FAST if the RI modefails to generatea4D trajectoryunder certaincircumstances.
+Graphical Advisory InterfaceSimilar to the Center DA, a vertical time line is used to display the current STA and ETA for all aircraft in, or expected to arrive in, the TRACON airspace.The right side of the time line displays the current ETA for each aircraft in green.The left side of the time line displays the current STA for each aircraft in blue if arriving from the West and white if arriving from the East.This increases the speed with which the controller can correlate an aircraft's location on the time line with its location on the plan view display (PVD).If the STA and ETA are different during the aircraft's flight in the TRACON, FAST will provide speed advisories and heading vectors required for the aircraft to meet the STA.As the advisories are displayed, the ETA on the time line will adjust itself to reflect the effect of each update.When FAST determines that a speed adjustment is necessary at a given point and the aircraft is within 5 n.mi. of that point, the advised Indicated Airspeed (IAS) is displayed on the aircraft data tag below the ground speed in orange.The use of color on the tag alerts the controller that an advisory is pending.Having the advised speed on the tag allows the controller to maintain his concentration on the aircraft position.In addition, the point along the current predicted path where the speed adjustment should be issued is highlighted with an orange marker to correlate with the orange speed advisory on the data tag.The 5-n.mi.advance notice and spatial display of the position at which the speed adjustment should occur allows the controller to plan ahead for its issuance.Another common technique used by TRACON controllers to delay or advance an aircraft is to extend or compress the downwind leg of the approach path or vary the intercept of the final approach course.Thus, when an aircraft arrives from the West to land on Rwy 26L and is within 5 n.mi. of its advised turn to base or turn to final, the data block is colored blue and a blue turn arc appears at the position where the instruction to turn should be issued.Once the aircraft has completed the base or final turn, the aircraft color reverts back to green, and the turn arc for that aircraft disappears.Similarly, aircraft arriving from the East are color-coded white for base and final turn advisories.The positions of the base and final turn advisories vary for each aircraft depending on its current time error relative to its STA and are displayed in the position that will eliminate the error.In addition to its display on the time line, time error is also displayed below the altitude slot on the third line of each aircraft's data tag.The arrival time error, in seconds, is preceded either by an "E" for early or an "L" for late.The controller may use this "Time Error" mode alone or in combination with the Speed/Vector and Time line advisory modes to improve time control accuracy.At the end of a simulation week, each controller was given a questionnaire and interviewed about the operational aspects of using the automation tools.Detailed results of these interviews, the pilot evaluations, and the accuracy data for the trajectory prediction algorithm will be presented in a later report.
+SIMULATION RESULTSSimulation results presented in this paper briefly address the issues of airspace utilization, interarrival spacings, capacity effects, and controller evaluations.
+Airspace UtilizationOne of the primary measures of an automation tool for final approach spacing is airspace utilization.The composite ground tracks of aircraft for the two types of runs discussed earlier, baseline and FAST+DA+TMA, areshownin figures3 and4.Thefiguressuperimpose thehorizontalplaneprojectionsof the flight pathsof all arrivalsrecordedduringa typical simulationrun.Thesefiguresshowtraffic arrivingfrom boththe northeast (Keann)andnorthwest(Drako)feedergates.Therunway is located in the southwest quadrant of thesefiguresandis markedwith an"X".The compositegroundtracksin bothof thesefiguresresultedfrom the samelist of input traffic coveringa time rangeof slightly more thanonehourof capacitylimited flow (40-46aircraftperhour).They arerepresentative of all otherruns madeby the other controllers.In all runs, traffic was controlled by a single controller.In the baseline run (fig.3), the controller used considerably more airspace to merge and sequence traffic.By the end of the run, traffic had backed up such that he was turning the aircraft onto the final approach course 18 n.mi.from the runway instead of the nominal 10 n.mi.The length of the final approach allowed at Denver without having to coordinate with other controllers is approximately 20 n.mi.from the runway.In the automation run (fig.4), almost all aircraft were turned to final at the nominal point between 10 and 11 n.mi.from the runway.There were a few aircraft turned to base and final further out; however, this occurred at the advice of FAST in order to precisely alleviate potential conflicts and to build slots for aircraft which arrived in the TRACON off schedule.Although these aircraft were turned to base and final further from the runway, this did not cause a buildup in delay of trailing aircraft as would be the case in a manual system.Rather it served to alleviate a buildup in delay, and kept each trailing aircraft on its nominal and shortest turn to base and final paths.The ability of the automation tools to precisely expand and contract the base and turn to final points provides considerable advantages to the controller.Assisting the controllers in keeping most aircraft on a short final allows them plenty of airspace to expand in case of an overload of traffic.In the baseline run, if an overload of traffic were to arrive, the controller would soon be forced to use alternative procedures to control the traffic, such as holding, sending traffic upwind then downwind (i.e., from the northeast arrival stream to the downwind portion of the northwest arrival stream), or to shut off the Center traffic feed for several minutes.
+Interarrival SpacingsData were also recorded on interarrival spacing of aircraft for both the baseline and automation runs.Tables 2 and3 present the results of all runs with capacity limited flow rate for all controllers.These tables present the sequence of aircraft (L for large, H for heavy), mean interarrival distances at touchdown (d), one-sigma standard deviation of distance (Od), mean interarrival time at touchdown (t) and one-sigma standard deviation of time (_t).As a point of reference, the desired distance separation for the LL and LH case is 3 n.mi., and the scheduling interval for this case was 78 sec.For the HL case, the desired distance separation is 5 n.mi., and the scheduling interval was 125 sec.Although the controllers were instructed to adhere strictly to the FAST advisories, no data were deleted for the few cases when the controller missed or ignored the advisories.The questionnaire also showed that the speed and vector advisories were their favorite feature.When the advisories did not coincide with their own plan, they commented that the FAST generated plan was just as good and sometimes better.They did not find that additional vectoring was necessary beyond the FAST advisories, and they thought the tools were flexible and did not feel restricted in their own decision making.Several suggestions were made for improving the controller interface though none of the suggestions pointed to basic changes or major additional requirements in the interface design.Some controllers suggested a "distance-based time line" on which in-trail distance projected at the runway is displayed rather than time.Such a method has been used in the Center DA tool and could be adapted to the TRACON.FIGURES ............................................................................................................................................ 12
+Figure 4 .4Figure 4. Ground tracks for an automation run.
+Table 22contains values measuredfor the baseline case which are very similar to those measuredforthe manual system in reference1. The tables show a substantialdecrease in interarrivalspacing in bothdistance and time. The automationtool runs resulted in a decrease in mean distanceseparationof0.4 n.mi. and a decrease in mean time separationof 9.8 sec for the LL and LH case. Most significantisthe decrease in the standard deviationsof both distanceand time separationsseen in the tables. Similarresults are seen for the HL case.
+Table 2 . Interarrival Data for Baseline Runs2Another suggestionwas to give the controlleran option to position the nominal downwindand base legat his or her discretion,and to incorporatecertain controllerpreferencesin the advisory logic. These andether suggestionsare being consideredfor incorporationinto FAST.Finally, all of the controllersexpressedstrong support for the integratedterminal automation systemconcept composedo: _._,nter DA and TMA and TRACONFAST. In particular,the Denver TRACONconTdlerswere es0e_:mlly enthusiasticin their support of FAST and were eager to participatein furthere::._i, _ _n_ ev_lt_afior _;o
+Table 3 . Interarrival Data for Automation Runs3AircraftNumber ofOdtcYtSequenceOccurrences(n.mi.)(n.mi.)(sec)(sec)LL and LH1253.40.783.017.0HL305.40.9124.516.7Figure 1.Arrival procedure for Denver TRACON to Rwy 26L.
+		
+		
+			
+Na_AwonmJ_¢S lindSpace Admir_adcrL I,Report No.NASA TM-102807			
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+		Evaluation of the terminal area precision scheduling and spacing system for near-term NAS application
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+		10.1201/b12321-12
+		Rep. No. NA-68-25 (RD-68-16
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+		Advances in Human Aspects of Aviation
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+	Martin, D. A.; and Willett, F. M.: Development and Application of a Terminal Spacing System. Rep. No. NA-68-25 (RD-68-16), Federal Aviation Administration, Aug. 1968.
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+		Simulation Evaluation of TIMER, a Time-Based, Terminal Air Traffic, Flow Management Concept. NASA TP-2870
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+			WRCapron
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+			Feb. 1989
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+	Credeur, L.; and Capron, W. R." Simulation Evaluation of TIMER, a Time-Based, Terminal Air Traffic, Flow Management Concept. NASA TP-2870, Feb. 1989.
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+		Design of Automated System for Management of Arrival Traffic
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+			HErzberger
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+			WNedell
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+	Erzberger, H.; and Nedell, W.: Design of Automated System for Management of Arrival Traffic.
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+		Thrust chamber thermal barrier coating techniquesQuentmeyer, R.J. NASA Tech. Memo. TM-100933 1988
+		10.1016/0142-1123(89)90460-x
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+		International Journal of Fatigue
+		International Journal of Fatigue
+		0142-1123
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+			June 1989
+			Elsevier BV
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+	NASA TM-102201, June 1989.
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+		Design and evaluation of an air traffic control Final Approach Spacing Tool
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+			ThomasJDavis
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+	Davis, T. J.; Erzberger, H.; and Bergeron, H.: Design of a Final Approach Spacing Tool for TRACON Air Traffic Control. NASA TM-102229, Sept. 1989.
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+		Analysis of Sequencing and Scheduling Methods for Arrival Traffic
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+			FNeuman
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+	Neuman, F.; and Erzberger, H.: Analysis of Sequencing and Scheduling Methods for Arrival Traffic.
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+		Fatigue crack growth study of SCS6/Ti-15-3 composite. (Report)Kantzos, P. and Telesman, J. NASA Lewis Research Center Report No NASA Tm-102332 1989, 18 pp
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+		International Journal of Fatigue
+		International Journal of Fatigue
+		0142-1123
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+			April 1990
+			Elsevier BV
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+	NASA TM-102795, April 1990.
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+		Time scheduling of a mix of 4D equipped and unequipped aircraft
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+			LeonardTobias
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+		10.1109/cdc.1983.269889
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+		The 22nd IEEE Conference on Decision and Control
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+	Tobias, L.: Time Scheduling of a Mix of 4D Equipped and Unequipped Aircraft. Proceedings of 22nd IEEE Conference on Decision and Control, San Antonio, TX, Dec. 1983, pp. 483-488.
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+INTRODUCTIONThe development of decision support tools for aiding air traffic controllers in managing and controlling air traffic has long been the subject of extensive research.The continued growth of air traffic throughout the world has caused increases in air traffic delays and has put considerable stress on both existing air traffic control (ATC) systems and on the air traffic controllers.Early work in the automation of terminal air traffic control was presented in the late 1960's (Martin and Willet, 1968).Martin and Willet described a system that provided speed and heading advisories to controllers to help increase spacing efficiency on final approach.Although tests of the system showed an increase in landing rate, controllers found that their workload was increased and rejected the system.An examination of the concept suggests that while some aspects of the design were sound, its acceptance was limited by the technology of the time period, especially the lack of an adequate controller interface.More recently, several automation systems have found their way into operational use in Europe due in large part to the introduction of modern computer processing and interfaces, and because of more careful design approaches (Volckers, 1990;Garcia, 1990).While these systems provide significant decision support functions for the overall management of arrival air traffic, they do not contain detailed modeling of complex terminal area arrival procedures and runway operations.A decision support system for the management and control of terminal area traffic that combines detailed models of aircraft performance, ATC procedures, and controller reasoning, has been under development by the NASA Ames Research Center.The system, referred to as the Center/TRACON Automation System (CTAS) (Erzberger et al, 1993), is comprised of the Traffic Management Advisor (TMA), the Descent Advisor (DA) (Green and Vivona, 1996), and the Final Approach Spacing Tool (FAST) (Davis et al, 1994, Lee andDavis, 1996).The advisories generated by these tools assist controllers in handling arrival aircraft starting at about 200 n.mi.from the airport and continuing to the final approach fix.These elements of the CTAS system have been evaluated in a series of operational tests during the past year at facilities serving the Denver, Colorado and Dallas/Fort Worth, Texas areas.This paper focuses on the operational testing of the terminal area portion of CTAS referred to as FAST.The main function of FAST is to provide advisories for landing sequence, landing runway, speed, and heading that assist controllers in managing arrival traffic and achieving an accurately spaced flow of traffic on final approach (Davis et al, 1994).The recent operational tests of FAST were limited to the sequence and runway advisory functions.This subset of FAST functionalities is referred to as Passive FAST or P-FAST.The paper will describe the objectives, conduct, and results from the operational tests of P-FAST.These tests, which were conducted at the Dallas/Fort Worth (DFW) International Airport, provided a unique opportunity to test a prototype decision support tool at a major, high volume hub airport at a phase in the development which allows for further refinement before the operational system is specified and built.The tests validated P-FAST as the first-ever advisory tool for TRACON air traffic controllers to be successfully demonstrated in a live operational environment.
+OPERATIONAL TESTOver the course of several years of development, more than two thousand hours of real-time, controller-in-theloop simulations of DFW traffic were conducted with P-FAST at the NASA Ames Research Center.These realtime simulations, along with analytical studies, demonstrated a significant potential for improvements in capacity and controller workload for TRACON air traffic controllers with the introduction of the P-FAST system.Based on these results, it was determined that an operational test of the system was necessary in order to validate these potential savings.Dallas/Fort Worth was chosen by the FAA because of its high capacity, complex airspace, many runway configurations, and high user demands.It was felt by the Federal Aviation Administration (FAA) that if the system could demonstrate benefits while achieving controller acceptance at the second busiest airport in the world, the risk of deployment to other sites would be substantially mitigated.
+Air Traffic Procedures at Dallas/Fort WorthThe DFW TRACON is the fourth busiest terminal area facility in the world, serving the second busiest airport in the world (DFW International Airport) along with several other major airports (e.g.Love Field, Alliance).During 1996, DFW TRACON averaged 3,320 aircraft operations per day.DFW TRACON is responsible for control of arrival, departure, and overflight traffic below 17,000 ft. and within 35 n.mi. of the DFW Airport.Fig. 1 shows a layout of the nominal DFW TRACON arrival flight paths as well as the runway layout for a South Flow configuration (aircraft landing and departing to the south).Because of the high volume of traffic, DFW Airport has six runways and typically operates with three arrival runways and three departure runways (a fourth arrival runway was added shortly after the tests, in late 1996).As shown in Fig. 1, arrival traffic lands primarily on runways 13R, 18R, and 17L.Departure traffic departs primarily from runways 18L, 17R, and 13L.During certain periods of the day, arrival and departure runways are used interchangeably.DFW uses a four-corner-post system in which arrivals transition from enroute, or Center, airspace to the TRACON airspace over an arrival meter or feeder fix, approximately 35 n.mi.from the airport.These four feeder fixes are labeled in Fig. 1 as Bridgeport and Blue Ridge to the North and Acton and Scurry to the South.Generally, aircraft will descend and be vectored along the flight paths shown in Fig. 1 to the runways depicted (Bridgeport arrivals to 13R, Blue Ridge and Scurry arrivals to 17L, Acton arrivals to 18R).During low volume traffic periods, controllers will attempt to vector aircraft to the runway closest to their parking terminal.During high volume traffic periods, controllers will attempt to balance the arrival traffic across the three arrival runways by vectoring aircraft to runways not necessarily closest to their arrival feeder fix so as to meet capacity on each runway and therefore maximize capacity for the airport.2.2 Role of Passive FAST at Dallas/Fort Worth Controllers from DFW were involved in the definition and development of the entire FAST concept (Lee and Davis, 1996).Through controller involvement, combined with many months of observation of terminal area operations, it was theorized that combining advanced trajectory synthesis technology with detailed models of the controller's reasoning process would allow one to build an accurate, real-time prediction of near-future traffic situations (near-future is defined as 10-20 minutes from the present time).Accurate predictions would then allow a decision support tool such as FAST to advise controllers on a more strategic plan which, if followed, would increase capacity and maintain acceptable workload levels.Each traffic rush into DFW arrives predominantly from the east or west.Because of the North/South runway directions, a significant issue arises with vectoring traffic to the opposite side of the airport such that all runways are equally utilized.By utilizing all runways on a nearequal basis, the full airport capacity can be realized.However, because of the volume of traffic and its associated high workload at DFW, the control of the arrival traffic to DFW Airport is split into as many as five sectors; two feeder sectors and three final approach sectors.Controllers working in any of these sectors find it difficult during high volume traffic rushes to have knowledge of traffic load or available landing slots in any other sector not adjacent to their own.As a result, controllers resort to making highly tactical decisions on runways and sequences late in the arrival process, thus adding to the overall workload and decreasing the efficiency of the operation.The runway advisories in P-FAST, which are displayed to the controller as the aircraft arrives over the feeder fix, provide the data necessary to balance the runways and the workload between controllers at the entry point into the TRACON arrival process.The P-FAST sequence advisories, which are updated and displayed to the controller on a continuous basis from entry into the TRACON until landing, provide the information on how to efficiently merge separate traffic streams and where to build arrival slots for aircraft, not yet seen, arriving from other sectors.With the P-FAST advisories, controllers gain a situational awareness of the entire traffic flow and a strategy for efficiently controlling it.Both the runway and sequence advisories are non-binding and continuously adjust to the actual traffic flow and the controllers' actions including disagreement with the advisories.
+Objective and Conduct of th e TestThe objectives of the P-FAST operational tests were to: 1) validate the capacity and throughput benefits of the P-FAST system in a live traffic environment, 2) confirm the controller acceptance observed in real-time simulations of the system, and 3) complete the definition of the functionality of P-FAST for the national operational system.The DFW facility provided a test team that included members of the FAST System Design Team (SDT) (Lee and Davis, 1996), a controller assessment team, and traffic management personnel.The test team operated the relevant traffic management, supervisory, and controller positions for the majority of the operational test.The operational tests were conducted intermittently over a period from January through July, 1996.Because only a small group of controllers (the assessment team) was trained to use P-FAST, the tests could only be conducted during periods when the majority of the assessment team was available.Typically, test periods spanned three days during the mid-week and were conducted for two weeks per month.The tests spanned all major arrival rush periods at DFW, included both Instrument Flight Rules (IFR) and Visual Flight Rules (VFR) operations, North and South Flow runway configurations, two and three arrival runway operations, and a variety of inclement weather conditions including severe thunderstorm activity.
+RESULTSThe air traffic system is highly dynamic and sensitive to a wide range of conditions.The system can change dramatically due to weather, airline schedules, and controller staffing on a daily basis.Because of these dynamics, it is difficult to objectively assess the impact of a system such as P-FAST.This difficulty leads to long searches for matching traffic samples and ultimately a limited set of data with which to compare operations with and without the tool.This section focuses on several overall aircraft/ATC performance metrics.Following the categorization of performance metrics as described by Den Braven (1995), the metrics cover throughput, safety, and control performance.For throughput, the metrics considered are airport throughput and excess in-trail separation on final approach.For safety, the metric is in-trail separations on final approach.Workload and controller acceptance are the metrics for control performance.In addition to these performance metrics, two engineering metrics are also analyzed: sequence advisory adherence and runway advisory adherence.It is essential to look at system performance from different, complementary viewpoints, to ensure that an improvement in one area is not negated by a decline in another area.For example, an increase in throughput may result in increased controller workload.Safety, on the other hand, should not be diminished under any circumstance.It is therefore important to remember that while the desired result may be that improvements are demonstrated in all areas, a practical result may well be that some metrics are held constant relative to current operations while others are improved.The following three subsections on Airport Throughput, In-trail Separation, and Safety will focus on an analysis of the 11:15 am "noon balloon" rush at DFW.The two subsections on Advisory Adherence and Workload and Controller Acceptance present data from the entire test.It should be noted that all rush periods were included in the test matrix during the DFW tests and that results for the other rushes are currently under analysis and will be presented in a later report.This rush was chosen because it is generally considered to be one of the longest, busiest, and most difficult rush periods at DFW, with complex operations beginning as a primarily east arrival rush and shifting to a west arrival rush after approximately thirty minutes.The 11:15 am rush is characterized by high arrival rates, high controller workload, and often results in the use of a fourth inboard runway (normally a departure runway) being opened for arrivals to accommodate the high volume.The results presented here appear to be indicative of the trends seen in the other rush periods at DFW.However, because of the small number of samples for each rush at DFW, due in part to the large number of variables across the various test cases (e.g.four samples with P-FAST for this particular rush), the results presented here can only be considered an indicator of probable trends when the system is installed on a permanent basis.
+Airport ThroughputAirport throughput is a measure of the rate at which aircraft arrive or depart during a specified period of time.Throughput is typically reported as either the rate of arrival or departure aircraft per hour.For the P-FAST tests at DFW, the arrival aircraft throughput is the most relevant measurement since the advisories were intended to improve the arrival traffic flow.Fig. 2 shows the mean throughput for arrival aircraft during the peak portion of the 11:15 am rushes at DFW.The peak is defined as the period of the rush in which the arrival rate rose and held above 96 aircraft/hour.Short dips in the arrival rate which fall below 96 aircraft/hour are still considered to be in the peak period.A typical rush is generally characterized by a rise from a steadystate arrival rate of 50 aircraft/hour to a peak period lasting 20-40 minutes.Before the operational test, there was some concern that P-FAST might increase surface congestion and departure delay because of the increased throughput.Because of this concern, observers were stationed in the DFW Tower to collect data on tower operations and to obtain feedback from the tower controllers (Crown, 1996).These observations netted several important results.First, a manual count of traffic over entire baseline and P-FAST rushes was conducted which resulted in an observed average increased landing rate of 15 aircraft/hour.Second, the same observations resulted in an observed average increased departure rate of 13 aircraft/hour and an average departure queue backlog reduction of 9% during P-FAST operations.Tower controllers indicated that these improvements were due to the improved runway balancing and the fact that the use of inboard departure runways for arrivals was not necessary during P-FAST operations.Finally, data from one of the major hub operators at DFW collected during baseline and P-FAST operations indicated no increase in taxi-in or taxi-out times despite the increases in arrival and departure traffic rates.These results suggest that the concern over increased surface and departure congestion appears to be negated.
+Excess In-trail SeparationExcess in-trail separation on final approach is a measure of the efficiency of runway utilization.While controllers currently perform the task of in-trail separation well, they provide a buffer of excess separation to account for uncertainties in weather, pilot response, and other factors.Their performance in separating aircraft is a function of the volume of traffic, their own skill, and the complexity of other decisions, such as sequencing and runway assignments, that they must perform at the same time.In the absence of established visual separation between two aircraft, FAA regulations require that aircraft flying below 18,000 ft.shall be separated in altitude by either 1,000 ft. or that they be horizontally separated by a distance based on their weight class.Fig. 3 shows a statistical comparison between the baseline and P-FAST excess in-trail separations above the required in-trail separation at the outer marker during the peak arrival periods of the 11:15 am rushes at DFW.The graph shows the mean and standard deviation for both IFR and VFR rushes.The graph shows a decrease in both mean and standard deviation of excess in-trail separation for both IFR and VFR during use of the P-FAST system.For IFR operations, the mean excess intrail separation is decreased by 0.48 n.mi.during P-FAST operations and the standard deviation is decreased by 0.60 n.mi.For VFR operations, the mean excess intrail separation is decreased by 0.33 n.mi.during P-FAST operations and the standard deviation is decreased by 0.96 n.mi.This data supports and is consistent with the earlier finding of an increase in airport throughput during P-FAST operations.It is interesting that while P-FAST does not issue advisories that directly affect in-trail separations, there still appears to be such a significant improvement.Controller debriefings indicated a possible reason for this trend: P-FAST advisories give controllers more time to focus on their primary task of separating aircraft.The reasons for this appear to be the improved runway and resulting workload balancing between controllers, as well as a rebalancing of the decision-making tasks, i.e. less time spent on runway and sequencing decisions and more time spent on separating aircraft.
+SafetyDuring VFR operations, it is legal and a common practice for controllers to permit visual separation between two aircraft as they are intercepting final approach.Once the controller has gained acknowledgment from the pilots, responsibility for maintaining "safe" separation with the other aircraft resides with the pilots.This process allows for a pilot to fly closer to adjacent aircraft with which he/she has visual contact than allowed by the regulations for IFR operations.A primary benefit that results from this practice is an increase in capacity, as seen in the earlier throughput results, due to a reduction in in-trail separations between aircraft.It is commonly accepted that the current practice of gaining visual separation between aircraft during VFR operations and the resulting reduction below IFR separation standards is safe; in addition, it could be argued that if a system increased throughput, decreased the variation in inter-arrival separation distances, and either maintained or decreased the number of in-trail separations below IFR standards during VFR conditions, the system was providing an enhancement in safety.Note that the standard deviation of the excess in-trail separation presented in Fig. 3 indicates that for the baseline data, there are more aircraft which are being separated below IFR minimums during VFR operations.Fig. 4 shows the mean total in-trail separations per rush below IFR standards during the peak period of the 11:15 am VFR rushes.Each peak rush period contains 85-95 aircraft.Note that the P-FAST data shows a reduction in in-trail separations below IFR standards by a factor of more than five.This result is representative for that particular rush period at DFW in which visual separation and inboard runway landings are common for the baseline cases.Other rushes at DFW in which inboard landings are not as common do not show as dramatic of a difference.Controllers commented that the increased organization in the traffic flow provided by the runway and sequence advisories in P-FAST more evenly balanced the arrival runways, resulting in more landings without using inboard runways for landing, without increased workload, and with more time to spend on the task of separating aircraft.
+Advisory AdherenceAn important element in confirming that the P-FAST advisories played a critical role in improving the airport throughput, in-trail separation, and safety is the degree to which the controllers followed the advisories.The system was designed to work with the controllers and to adapt to their actions when they differed from the advised plan.Without the system recognizing and adapting to the controllers' implicit changes, the system would become ineffective.Fig. 5 shows the percentage of aircraft that were vectored to the P-FAST advised sequence for each runway at the five and ten minute flight time location from the runway.The five minute point corresponds approximately to the point where the downwind and base aircraft are merged.The ten minute point corresponds approximately to the location where the aircraft are turning to the downwind and base legs.In all but one case (runway 17L, ten minutes flight time from the runway) the adherence to the sequence advisories is between 83-93%.Controller disagreement with the sequence advisories was primarily centered in the downwind-base merge area.While this was initially considered to be a high adherence, controller surveys and debriefings indicated that the sequence advisories should be improved to always be above 90% before a permanent installation of the P-FAST system.This result led to a refinement of the sequencing algorithm following the completion of the test.The algorithm was modified to more accurately reflect the controllers' use of altitude and speed differences in determining sequence.Subsequent simulations and shadow observations have shown that the refined sequencing algorithm will have an acceptable sequence adherence of over 95% (Robinson, et al, 1997).Fig. 6 shows the percentage of aircraft that were vectored to the P-FAST advised runway for IFR and VFR traffic scenarios (Isaacson, et al, 1997).Fig. 6 shows a high adherence of 94.8% during IFR operations and 97.1% during VFR operations.Overall, for the entire test, 96.4% of the runway advisories were accepted and utilized by the controllers.Nearly all controller disagreements with the advised runways were related to personal preferences and styles of the various controllers.In some cases, the disagreement related to either a perceived conflict with other aircraft if the advisory was followed while in other cases, disagreement centered on a desire to land aircraft on a runway closest to their parking terminal.In many cases, controllers commented during the debriefing sessions that the advised runway would have been more efficient than the runway that was chosen by the controller.Based on these results, no further modifications of the runway advisory logic are planned.
+Workload and Controller AcceptanceHuman factors data was collected to provide a qualitative measure of controller workload that is not available by assessing the engineering data alone.These ratings provide a measure of the benefits of increased throughput and runway balancing from the controllers' perspective.Questionnaires were administered to the controllers following each rush in which the P-FAST advisories were used.Each controller rated workload using a modified NASA Task Load Index (TLX) and acceptance using the Controller Acceptance Rating Scale (Lee & Davis, 1996).Workload : The modified NASA-TLX scale used to collect workload data included questions regarding mental demand, time pressure, performance support (provided by the P-FAST advisories), overall effort, and the satisfaction vs. frustration experienced.All workload ratings were on a 0 to 10 point scale, with 0 representing the lowest score (lowest workload, most favorable rating) and 10 representing the highest score (highest workload, least favorable rating).Fig. 7 depicts the mean workload ratings.As can be seen from the graph, all of the responses are clustered around the middle of the scale.This data shows that despite the added throughput, the controllers did not experience any significant increase in mental demand, time pressure, or overall effort.P-FAST was not rated as increasing their workload or reducing their job satisfaction.Perceived workload remained at about the level to which controllers have been accustomed.
+Controller Ac ceptance :After each test rush, the controllers provided Controller Acceptance Rating Scale (CARS) ratings to indicate acceptance.The CARS is a scale adopted from, and based upon, the Cooper-Harper Scale for pilot evaluation of aircraft handling qualities (Cooper & Harper, 1969).In addition to changes in content, the CARS is modified from the original Cooper-Harper by reversing the order of the anchors, such that a rating of "1" reflects a lower, more undesirable rating, and a rating of "10" reflects a higher, more desirable rating.The CARS has been consistently used in simulation testing of P-FAST prior to the beginning of the field test (Lee & Davis, 1996).The mean CARS rating across the entire field test was 7.82 (std dev=1.10).This rating is associated with the numerical rating of 8, with the following description: "System is acceptable and minimal compensation is needed to meet desired performance."Controller debriefings indicated that with the exception of the sequence advisory adherence, the system was acceptable as configured for the tests.
+CONCLUSIONSA decision support tool for terminal area air traffic controllers has been developed and successfully tested with live traffic at the Dallas/Fort Worth TRACON.The tool, referred to as the Passive Final Approach Spacing Tool (P-FAST), issues sequence and runway advisories to the controllers on a continuous basis, via the controller's radar display.The operational tests of P-FAST represented the first successful demonstrations of an advisory tool for TRACON controllers.The tests included periods encompassing a wide range of weather, airport configuration, and rush periods.The P-FAST performed well during the test with the controllers accepting and utilizing over 83% of the sequence advisories and over 96% of the runway advisories.P-FAST supported the controllers in increasing the airport landing rate, or throughput, and decreasing the mean in-trail separation between aircraft on final approach.In addition, P-FAST appears to have provided a safety benefit by decreasing the number of intrail separations below IFR standards that occurred during VFR operations.Departure rates at the DFW Airport also increased during P-FAST operations due to the more efficient and organized arrival traffic flow.Controllers provided workload ratings that indicate neither an increase nor decrease in workload with the P-FAST system despite the significantly increased airport throughput.Overall, this was considered a positive result and the controllers rated the system as "acceptable with minimal compensation needed to meet desired performance."The test results are based on a relatively small sample size for each individual case, however, they do point towards a significant trend of improvement over current operations without P-FAST.Ultimately, the true enhancement that the system will provide will not be fully measured until the system has been installed and operating continuously for several months.The P-FAST is currently being re-adapted for the DFW airspace with an additional runway which was added in October, 1996.The FAA plans to re-install the system at DFW on a permanent basis in mid-1997.Fig. 1 .1Fig. 1.Dallas/Fort Worth TRACON arrival procedures.
+Fig. 2 .2Fig. 2. Comparison of mean airport throughput during peak portion of 11:15 am rushes.Fig. 2 indicates that the average peak arrival rate rose 10 aircraft/hour from baseline (or 9.3%) during IFR operations with P-FAST and 12 aircraft/hour (or 10%) during VFR operations with P-FAST.In the cases of the VFR rushes, all baseline rushes included inboard runway landings of 3-5 aircraft/hour.Inboard landings are a result of unbalanced runway loads and are needed when the volume of traffic on a runway's final approach course exceeds that runway's capacity.No inboard runway landings were required during any of the P-FAST advised rushes.When the inboard landings are removed from the baseline throughput data (i.e.correcting for the number of runways used), the average peak arrival throughput rises by 16 aircraft/hour (or 13.3%).
+Fig. 3 .3Fig. 3. Comparison of excess in-trail separations at the outer marker during 11:15 am rushes.
+Fig. 4 .4Fig. 4. Mean negative in-trail separation per rush sample at the outer marker during VFR operations.
+Fig. 5 .5Fig. 5. Adherence to P-FAST sequence advisories.
+Fig. 6 .6Fig. 6.Adherence to P-FAST runway advisories.
+Fig. 7 .7Fig. 7. Modified NASA-TLX workload ratings.
+		
+		
+			
+
+				
+
+
+	
+		The use of pilot rating in the evaluation of aircraft handling qualities
+		
+			GECooper
+		
+		
+			RPHarper
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+		10.2514/5.9781600866630.0585.0598
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+		THE FINAL APPROACH SPACING TOOL
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+INTRODUCTIONThe development of automated systems for the control of air traffic has long been the objective of researchers and engineers.The continued growth of air traffic nationwide has caused increases in air traffic delays and has put considerable stress on both existing air traffic control systems and on the air traffic controllers.This paper describes the design of an automation system for assisting controllers in the management and control of arrival traffic in the terminal area.Early work in the automation of terminal air traffic control was presented in the late 1960's (Martin and Willet, 1968).This system provided speed and heading advisories to controllers to help increase spacing efficiency on final approach.Although traffic tests of the system showed an increase in landing rate, controllers found that their work load was increased and rejected the system.An examination of the concept suggests that while some aspects of the design were sound, its acceptance was limited by the technology of the time period, especially the lack of an adequate controller interface.More recently, several automation systems have found their way into operational use in Europe due in large part to the introduction of modern computer processing and interfaces, and because of more careful design approaches (Volckers, 1990;Garcia, 1990).However, these systems do not contain detailed modeling of complex runway operations.In addition, recent fast time simulation studies have confirmed the potential for increasing landing rates with the assistance of active advisories for controllers in the terminal area (Credeur and Capron, 1989).A candidate system for the automated management and control of terminal area traffic, referred to as the Center TRACON Automation System (CTAS), is under development at NASA Ames Research Center in collaboration with the FAA's Terminal Air Traffic Control Automation Program Office.The elements comprising the CTAS are the Traffic Management Advisor (TMA), the Descent Advisor (DA), and the Final Approach Spacing Tool (FAST) (Erzberger et al, 1993).The advisories generated by these tools assist controllers in handling aircraft arrivals starting at about 200 n.mi.from the airport and continuing to the final approach fix.Recently, the elements of the CTAS system have been evaluated in a series of real-time simulations at NASA Ames Research Center and in field testing at facilities serving the Denver and Dallas/Fort Worth areas.This paper focuses on the design and implementation of the terminal area portion of CTAS referred to as FAST.The main function of FAST is to provide landing sequence, landing runway assignments, speed, and heading advisories that help controllers manage arrival traffic and achieve an accurately spaced flow of traffic on final approach.The paper concludes with a description of current laboratory and field testing results.
+FINAL APPROACH SPACING TOOLFAST consists of several major software components: a route analyzer and trajectory synthesizer, a sequencer and scheduler, a conflict resolver, a runway allocator, and a controller interface.Each of these components is described below along with examples illustrating their operation.
+Route Analysis and Trajectory SynthesisThe FAST system is dependent on the accurate estimation of arrival times for all aircraft.These arrival times are used by FAST for sequencing and scheduling aircraft to the runway threshold.The process within CTAS that is responsible for the rapid update and accurate calculation of estimated times of arrival (ETA's) based on radar track or flight plan data is referred to as the Route Analysis and Trajectory Synthesis (RA/TS) program.The set of ETA's that the RA/TS computes represents ranges of possible arrival times given an aircraft's predicted route of flight combined with possible variations in degrees of freedom along those routes.Typical degrees of freedom include speed, horizontal and vertical maneuvers.Upon receipt of a flight plan or radar track data update (x, y, altitude, ground speed), the RA portion of RA/TS 'categorizes' each aircraft's situation for each potential landing runway in terms of destination airport, airport configuration, geographical sections of airspace, engine type (jet, turbo-prop, piston), approach segment (downwind, final, base, etc...), and aircraft states (x, y, altitude, heading, speed).Each situation category has a name and a complete description of route/degree of freedom combinations that are possible for the aircraft.The RA uses this siteadaptable data for each situational category to build a series of one or more routes for each aircraft, apply degrees of freedom to those routes, and finally to request the TS portion of RA/TS to compute ETA's for each route/degree of freedom combination.The trajectory synthesis (TS) portion of RA/TS is a modified version of an algorithm originally designed for computing descent-from cruise trajectories (Erzberger and Tobias, 1986).The inputs to the TS program are the aircraft state, winds aloft, temperature and pressure profiles, a series of waypoints depicting the expected route of flight for an aircraft, and vertical and speed constraints on the predicted route.The outputs from the TS include a complete time-based (4D) trajectory along the expected path including all pertinent data for resolving conflicts and estimating times of arrival at points along the path.As the aircraft flies through the arrival airspace and descends to the runway, it will change situation categories as it transitions from one flight segment to the next, producing stable sets of ETA's.These sets of ETA's, form the basis for the sequencing and scheduling process.Once the sequencing and scheduling process is completed, the same set of RA/TS trajectories will be used for conflict resolution.Finally, they will be used as a reference in computing expected delay for aircraft in the runway allocation process.
+Sequencer and SchedulerHuman controllers have the ability to construct a plan about how aircraft will merge together and land safely.Though we do not understand the controller's cognitive process in making such plans, a working model hypothesizes that they do this by comparing an aircraft's projected position to the projected position of other relevant aircraft.An automation aid will have to make the same comparisons.The method of breaking an aircraft's predicted trajectory into trajectory segments and grouping them with similar trajectory segments of other aircraft allows the comparisons to be limited to only the aircraft that have an effect on each other.A trajectory is made up of a set of time steps at defined intervals.Each time step contains a predicted x, y, altitude, speed, and heading of an aircraft at a future time.A trajectory segment is a grouping of time steps that fall within a predefined segment of flight.Figure 1 shows an aircraft and its trajectory broken into four trajectory segments called LONG_LEFT, DOWNWIND_LEFT, BASE_LEFT, and FINAL.With trajectories broken into common trajectory segments, the FAST planning process consists of comparing only the like trajectory segments.For example, all DOWNWIND_LEFT segments for different aircraft will be compared.This method eliminates the need to compare all aircraft trajectory segments, and entire aircraft trajectories.The first step in producing a plan or sequence is to determine an order in which to land the aircraft.It was learned, through extensive real-time simulations, that to produce an acceptable sequence for controllers to follow, it was necessary to consider all merges within the airspace, not just the merge on the final approach course.Ordering is the process of both creating a relative sequence within each trajectory segment and combining those sequences into a consistent global ordering for each runway.The tree branches represent merging possibilities that can take place within the TRACON, and the leaves on each branch are in the relative order of the aircraft on their current trajectory segment.FINAL #1 D #2 C #3 F #4 B #5 E #6 A Only FINAL #1 D Only DOWNWIND_LEFT #1 C DOWNWIND_LEFT #1 C #2 B #3 A BASE_LEFT #1 F #2 E #1 F #2 E Only BASE_LEFT LONG_LEFT #1 B #2 A Only LONG_LEFT #1 B #2 A
+Fig. 3. Trajectory Segment treeThe sequencing starts at the last leaf of each branch in Fig. 3. Once the aircraft in the leaves have been ordered, FAST blends these relative sequences of the branches into the final sequence.The relative order of the aircraft is maintained throughout the tree.At the beginning of each sequencing cycle, FAST builds new trajectories for updated aircraft positions.The sequencing algorithm uses both these new trajectories and the previous sequence to calculate a new sequence.There are two ordering algorithms which are utilized, one which orders the leaves of the tree up to the final merge, and one which accomplishes the final merge.The first algorithm makes use of a general sorting function that accepts as input an unordered list, and a function which compares two members of that list, returning their relative order.The heart of the algorithm is a sequence order function which makes the comparison for two aircraft.The function searches the list of time steps associated with the trajectory segment it is currently ordering, to find the earliest instant within the segment that two aircraft have time steps.These two time steps are called the First Common Time Steps (FCTS).The primary input to the logic is a measure of how far ahead/behind one aircraft is to another.A distance is calculated from the FCTS to the end point of a trajectory segment for each aircraft .The difference in the distances, divided by the required separation for any two aircraft gives a Normalized Separation Distance (NSD).
+NSD = (distance B -distance A)Required Separation(1)In Eqn.The remaining inputs to the logic are: the distance from each aircraft's current location to the specific trajectory segment being ordered, the speed difference between aircraft at the FCTS, and the last updated or previous relative order.If there is no previous order, FAST bases its ordering decision on the sign of the NSD.However, if the value of the NSD is small (less than 0.5), and one aircraft is flying significantly faster than the other, the faster aircraft will be ordered ahead.When a previous order exists, the system no longer bases the sequencing decision on the sign of the NSD.In order for the sequence to change, the aircraft that was previously sequenced behind must pull ahead of the lead aircraft by a specified NSD.The amount it must pull ahead is a function of the inputs to the logic.Figure 4 displays a family of curves that describe the decision region for determining if the previous order should be changed.The shape of the curve was experimentally derived from real-time simulations with air traffic controllers.Reading the graph from right to left, the curve climbs from an NSD of almost zero to one between a distance from the trajectory segment of forty to twenty miles.This allows FAST to be more responsive to sequence reversals when the aircraft are farther away from a trajectory segment and less responsive closer to a segment.As the aircraft approaches and joins the trajectory segment, FAST will quickly respond to changes in sequence.This is reflected in the curve, which drops back to nearly zero again in the last few miles.The Consequently, as aircraft are added to the output list, they may need to absorb delay in order to be sequenced behind aircraft already in the list.The algorithm computes a delay for each aircraft by subtracting the STA from the aircraft's nominal time.It compares two aircraft, A and B, by summing the delay that A and B will incur when they are ordered behind the last aircraft in the output list .Both possible sequences are checked: A followed by B and the reverse order, B followed by A. The primary input to the logic is the difference in these sums.The algorithm chooses the order based on a compromise between reducing total delay and disturbing the established order.Figures 5 and6 display the decision region for the final merge algorithm.Figure 5 describes the part of the decision region when at least one of the two aircraft has enough delay capability to fit behind the other.Figure 6 describes the part of the decision region when both aircraft are almost out of delay capability.As mentioned earlier, these curves were experimentally derived in real-time simulation.In Fig. 5, the y-axis denotes the delay savings when choosing the reverse order.The x-axis denotes the path distance of the trailing aircraft to the FINAL.The lines on the graph represent the loci of constant percentage of the delay required in the previous order for the trailing aircraft to fit behind the lead aircraft.As described before, if the updated input results in a point plotted above its delay line, the previous order is reversed.If the update results in a point plotted below the line, the previous order is maintained.As the percentage of delay the trailing aircraft is able to produce in order to remain behind the lead aircraft decreases, FAST is more likely to reverse the order.In fact, if it is unable to produce better than 105% of the delay required, then the order will change without any time savings.Figure 6 represents the decision space when neither aircraft can absorb all of the necessary delay.Recall that the aircraft being compared by this section of the decision space are competing for a slot behind aircraft that have already been ordered on the output list .The y-axis is delay savings by choosing the reverse order instead of the previous updated order.The x-axis is the percent of required delay for a given sequence that a lead aircraft in the previous order is capable of producing if the previous order were reversed.The lines on the graph are loci of constant percentage of the required delay that a trailing aircraft is capable of producing for the previous order.As described before, if the updated input results in a point plotted above its delay curve, the previous order is reversed.If the update results in a point plotted below the curve, the previous order is maintained.As the aircraft in front runs out of delay capability to fit behind the aircraft that is previously sequenced behind it (moving left along the x-axis) FAST becomes less likely to reverse the order.In fact, if the trailing aircraft has 100% or more of the delay required to stay behind, it will become impossible for the sequence to change as the aircraft ahead runs out of delay capability.
+Conflict ResolutionAll trajectory segments for an aircraft are checked for conflicts with other aircraft within the same segments.If there are no conflicts for an aircraft, it will be assigned its nominal trajectory.When a conflict is predicted, one or both aircraft trajectories must be manipulated to resolve the conflict.Because the aircraft are already ordered by FAST, the system knows which aircraft is ahead and which aircraft is behind.The algorithm will add delay to the trailing aircraft in order to resolve the conflict.The system accomplishes this by searching the trajectory for degrees of freedom which will help to resolve the conflict.The magnitude of the conflict is measured and translated into a required delay for the aircraft before it reaches the conflict point.Because the system knows which degrees of freedom will help to resolve the conflict, it can combine this knowledge with the route/degree of freedom/ETA values it received from the RA/TS to bound and then begin the iterative process for resolving the conflict.The process of resolving conflicts contains a number of complicated situations.It may seem that for each aircraft added to the sequence, FAST only has to resolve violations that are with an aircraft ahead in the final sequence.Unfortunately, there are a number of cases where the situation is more complicated.For example, Fig. 7 shows an aircraft A that will be merging with another aircraft B on DOWNWIND_LEFT and then another C on FINAL.Assume that the final ordering is B, C, then A. The idea of resolving only the conflicts A had with C could leave a DOWNWIND_LEFT conflict unresolved.The problem with checking for conflicts only with the aircraft ahead on FINAL is that a merge could be missed.FAST will search for and resolve conflicts on all trajectory segments between a given aircraft and the aircraft sequenced ahead of it.
+Runway AllocationAn algorithm for allocating runways based on a cost function to minimize delay was described by Brinton (1992).However, the problem of achieving a both procedurally acceptable and efficient runway allocation solution is beyond the reach of real-time optimization and instead requires detailed knowledge of a facility's procedures.Before the algorithm employed in FAST is described, a brief description will be given of how a controller selects a default or preferred runway, and how controllers select aircraft to be switched to secondary runways.In general, aircraft are vectored from Center airspace into a TRACON over a feeder gate or metering fix.Aircraft engine types (e.g.turbo-jets, turbo-props, piston) and feeder gate assignment map to a preferred runway which is typically the closest runway to that feeder gate.Depending on the procedures at a given TRACON, an aircraft may be eligible to change to secondary or alternate runways.Situations which would influence a controller-initiated runway change include excessive or unbalanced delay buildup on the preferred runway, controller workload for a given runway, and airline or control tower preferences for ease of ground traffic movement.A controller will select which aircraft to change to an alternate runway based on a number of considerations such as separating aircraft of a dissimilar engine type or weight class from the other aircraft in a busy stream of traffic, avoiding potential conflicts in current streams of traffic, or avoiding potential conflicts in merging streams of traffic.Ideally, a controller would like to change the runway early in the traffic flow (i.e.near the feeder gates), but because of the uncertainties of making such a decision early in the flow, changes are commonly held off until the last possible moment.This can cause an undesirable increase in workload for the pilots of arriving aircraft because of the late changes in selecting navigation frequencies and configuring the aircraft for an approach.The strength of an automation system such as FAST is its ability to assign runways based on accurate estimations of delay savings and workload benefits at an early stage of the arrival process.The runway allocation algorithm employed in FAST attempts to meet three primary objectives: 1) making an early and accurate decision, 2) reducing overall system delay and 3) maintaining controller acceptability.The algorithm is heuristically-based and site-adaptable.The approach is to define the preferred runway for all aircraft in the landing sequence and then to select the set of all aircraft which are eligible for reassignment, apply criteria to narrow this set to a most likely aircraft to be reassigned, to test the aircraft's new runway in a full sequencing and conflict resolution cycle with all other aircraft, and finally to apply detailed criteria to this solution set for all aircraft.Global Delay Reduction > 0.0 min.?Do
+Fig. 8. Example of decision tree for selecting aircraft for runway allocationThe set of aircraft eligible for runway reassignment is defined by a runway allocation time window for each runway.The time window begins with a "start testing runway allocation time horizon" measured in expected flight minutes from a given runway and ends with a "freeze runway allocation time horizon" also measured in expected flight minutes from the runway.Any aircraft with an estimated time of arrival for a runway which falls within the runway allocation time window are deemed eligible for allocation to that runway.Once the set of eligible aircraft are determined for all arrival runways, the system builds an estimated schedule and its associated delays for each aircraft to their currently assigned runway and to any available alternate runways.The system then selects those eligible aircraft which pass a set of runway allocation heuristics.This selection process is based on a site adaptable decision tree file which incorporates facility procedures, delay reduction, and controller heuristics.A simplified example of a runway allocation decision tree is shown in Fig. 8.In this example, only one thread through a series of branches on the decision tree is shown.The tree first branches on runway pair, followed by arrival feeder gate, followed by a criterion labeled "Odd Aircraft Type."This criterion examines the aircraft together with all aircraft meeting the previous criteria (runway pair and feeder gate), and determines if the aircraft currently traversing the decision tree is an odd type (e.g. the only turbo prop in a stream of jet traffic).If this is true, then the system examines a system-wide or global delay reduction criterion.Because the aircraft in this example is an odd engine type in its stream, the delay reduction criterion is small (0 minutes).If we examined the branch on the "No" answer for "Odd Aircraft Type," we would find that the global delay reduction criterion would require a larger value (typically 2-4 minutes).The reason for the difference in delay reduction requirements on these two branches is to force FAST to favor pulling a dissimilar engine or weight class aircraft out of the traffic stream.This serves to reduce workload for the controller.After all eligible aircraft have passed through this decision tree and thus narrowing the list of all eligible aircraft to a smaller set, FAST then selects a single aircraft which appears to have the greatest delay benefits to the overall arrival system.In some cases, there may not be any aircraft which pass these criteria and in this case, the runway allocation algorithm will not consider any aircraft for that update cycle.Once an aircraft is selected, it is then placed in an alternate runway sequencing and conflict resolution cycle.The entire arrival airspace sequencing problem is solved with this aircraft placed on its alternate runway.This allows the software to evaluate all aspects of the particular runway allocation.Full trajectory solutions are obtained for each aircraft which in turn give accurate sequences, expected delay, and conflict detection for the entire airspace.At this point, a new and more detailed set of criteria are applied.These criteria examine trajectory based issues such as potential conflict resolution problems and exhaustion of critical degree of freedom limits.They are applied to the alternate solution set in order to make the final determination as to whether or not to change the aircraft to the alternate runway.Once an aircraft has been switched away from a given runway, that runway is blocked off from further consideration for that aircraft.A more optimal solution would be to allow allocation of this aircraft back to its original runway if a situation warrants, but this was found to be unacceptable to controllers.Controllers always have the final authority in the runway assignment, and if a controller directs FAST to assign a given aircraft to a runway through a keyboard entry, the system will freeze that decision and no longer consider that aircraft for any other runway.Finally, once an aircraft's ETA falls below a runway's freeze time horizon, that runway will be blocked off from further consideration.After all but one runway has been blocked off, the runway assignment advisory is frozen for the remainder of the flight.In nearly all cases, the aircraft has a frozen runway assignment before twelve minutes of flight time from the runway.
+Controller InterfaceThe development of the controller interface has focused on implementing FAST on two different controller interface platforms.The first interface platform is the current controller interface in operation at Dallas/Fort Worth called the Full Digital ARTS Display (FDAD).The FDAD's employ a monochrome digital display with trackball, keyboard and analog input devices.The FDAD's will be used as the controller interface in the initial field implementation of FAST.The second interface platform is a Sun workstation color monitor with mouse/trackball and keyboard input devices.This color workstation was the initial development platform for FAST and was used primarily before the FDAD's became available for testing at NASA.To assure an effective controller interface, the FAST development team used active air traffic controllers throughout the interface design and four key guidelines evolved: 1) minimize screen clutter, 2) associate advisories with aircraft, 3) minimize keyboard entries, and 4) use graphical advisories where possible.The output of the previously described algorithmic components produce a set of advisories which must be transferred to the controller interface.There are two primary methods for displaying this information to the controller.The first method is to add information to the aircraft's flight data block.Figure 9 shows a typical flight data block for an aircraft currently displayed in TRACONs.The first line indicates the aircraft identification or call sign.The second line contains two data fields.The first data field contains the current reported altitude (in hundreds of feet) time-shared with a facility scratch pad (typically containing the current runway assignment), and the second data field contains the aircraft's current ground speed (in tens of knots) time-shared with the aircraft type (e.g.Boeing 727 is displayed as "B727").The third line shown in Fig. 9 is the FAST information data line and is not currently displayed operationally in TRACONs.This line also contains two data fields.The first data field contains the FAST relative sequence number to the runway time-shared with the FAST runway assignment advisory.The second data field can contain both indicated airspeed and heading advisory information.If the data is indicated airspeed information, it is shown in tens of knots, and if it is an advised heading, it is show in tens of degrees (magnetic North) preceded by an "H" for heading.The second method of advisory display in FAST is graphical and applies to speed and heading advisories.Speed advisories are typically displayed as a marker on the display and an advised indicated airspeed in the third line of the data block as described above (see Fig. 10).These speed advisories are displayed as orange markers along with an orange alphanumeric for the speed value in the data block.Heading advisories are displayed as a location to issue the turn (shown by a graphical marker), a magnetic heading in degrees next to the marker, and a turn arc depicting the projected aircraft path taking into account its speed, heading and the winds aloft (see Fig. 10).The graphical data is color coded based on arrival feeder gate for the aircraft.The aircraft flight data block changes color to match the graphical advisory.When the aircraft executes or passes the advisory, the flight data block reverts back to its nominal color.Fig. 10.FAST speed and turn advisory graphics
+SIMULATION AND FIELD TESTINGThe planning and development for field testing and implementation has been ongoing for several years.Recently, the simulations have been conducted almost exclusively with controllers from the Dallas/Fort Worth TRACON in preparation for field testing at that facility.The simulations have focused on a number of issues ranging from validation of the algorithms to an evaluation of human factors issues.They have assisted both in the development of the system and the planning of the field deployment.The simulations had the following objectives: 1) to assess the potential benefits of FAST, 2) to evaluate controller acceptability, and 3) to develop the system for operational testing.Initial information on the potential benefits was obtained in a simulation evaluation of FAST operating on a color display in a single runway, Instrument Flight Rules (IFR) configuration (Davis et al, 1991).This simulation demonstrated efficient use of airspace, increased landing rates, and controller acceptance of the system.Similar results were obtained in an independent study (Credeur et al, 1993).More recently, simulations of FAST designed for the Dallas/Fort Worth TRACON have demonstrated the system's performance in more complex operations with multiple runways in both IFR and Visual Flight Rules (VFR).These simulations have included parallel simultaneous and staggered approaches, as well as converging approaches.The simulations have been conducted on the FDAD's with traffic scenarios based on live traffic samples from the Dallas/Fort Worth TRACON.The primary results of the tests are discussed below.First, the controllers have reported that detection of the speed and heading advisories on a monochrome display is difficult and requires additional workload.This is largely because of screen clutter from non-arrival air traffic.In addition, controllers sometimes have difficulties associating advisories with the correct aircraft on the monochrome display.The controllers stated that the color display mitigates these problems substantially.Second, the controllers feel that sequence numbers and runway assignment advisories will provide substantial benefits even on the monochrome displays.The controllers report that these advisories often improve on their own decisions.The best use of the sequence numbers is in sectors where the controllers are merging streams of traffic.Runway assignment advisories have been found to match or improve the controller decisions in most cases.Occasionally, controllers have a tendency to doubt runway allocation advisories because FAST can "see" aircraft that are out of the controller's view and thus make an accurate assessment at an earlier stage.However, the "doubtful" runway assignment advisories are nearly always shown to be correct.One can assume that controllers will pass through an adjustment period in order to gain confidence or trust in the system.
+CONCLUDING REMARKSAn automation system for assisting terminal area air traffic controllers in efficiently managing and controlling arrival traffic has been developed and tested in simulation.The automation system, referred to as the Final Approach Spacing Tool (FAST),was developed through thousands of hours of real-time simulations with active air traffic controllers.Results from the simulations show potential benefits in efficient airspace utilization, reduced controller workload, and increased runway capacity.Some potential risks still remain, primarily in gaining acceptance of the monochrome version of the human interface and for controllers to reach a comfort level with an unfamiliar system.The system is currently undergoing its final phase of development in preparation for field testing at the Dallas/Fort Worth TRACON.The field testing is scheduled to begin in mid-1994 and will include a phased deployment schedule which delivers subsets of the full FAST functionality in increments.The field testing will include periods of shadow control and observation, simulation activities in the Dallas/Fort Worth TRACON training room, and limited operational testing with live traffic.The FAST system is designed to operate either independently or in direct coordination with the other CTAS tools.However, simulation results indicate that the arrival air traffic management process will receive the most benefits by utilizing all tools sets in CTAS.Integrated tests of all CTAS tools are planned for the Dallas/Fort Worth and Denver airports within the next two years.Fig. 1 .1Fig. 1.Trajectory Segments for an aircraft on long left
+Figure 22Fig. 2. Sequencing exampleFigure2depicts a situation where six aircraft merge together to land on the same runway.The network of trajectory segments can be thought of as a tree, Fig.3.The tree branches represent merging possibilities that can take place within the TRACON, and the leaves on
+Fig. 5 .Fig. 6 .56Fig. 5. Sequence order function when at least one aircraft has enough delay to fit behind the otherFAST is more responsive when the aircraft are far away from or nearly on the trajectory segment, and less responsive as the aircraft are approaching the trajectory segment.
+Fig. 7. Conflict on DOWNWIND_LEFT
+Fig. 9 .9Fig. 9. ARTS flight data block with FAST enhancements
+Table 1 .1Required separation (n.mi.)Leading AircraftTrailing Aircraft TypeTypeHeavyLargeSmallHeavy456Large Small3 33 34 31, if the NSD is positive, aircraft A would be ahead of aircraft B; a negative value would indicate that aircraft A is behind aircraft B. The exact value measures how much ahead/behind A is, relative to B. The required separation is defined by the aircraft weight classes and is shown in Table 1.
+above their speed difference line, the previous relative order is reversed.If the update results in a point plotted below the line, the previous sequence is maintained.1.4Aircraft A is1.210 kts FasterNormalized SeparationDistance (NSD)0.6 0.8 1Aircraft A is 30 kts Faster Aircraft A is 50 kts Faster0.40.20510152025303540Distance from Aircraft to TrajectorySegment Being Ordered (n.mi.)Fig. 4. Sequencing order function for merges not onfinal approach.The graph defines two decision regions, parameterizedby speed difference, for the case where aircraft B wassequenced ahead of aircraft A during the last updatecycle. A positive NSD is a measure of how muchaircraft A is ahead of aircraft B for this new update cycle, and a positive speed difference is a measure of how much faster aircraft A is moving than aircraft B. The curves are loci of constant speed difference in knots.If the updated input for the two aircraft results in a point plotted
+final merge algorithm merges streams of aircraft onto the FINAL trajectory segment in a similar manner by taking advantage of the additional time information that the RA/TS provides about the runway threshold arrival times (time range).The input to the algorithm is a set of ordered lists of aircraft.The output is a single ordered list that contains all the aircraft.The final merge algorithm compares the first aircraft on each list, two atSTA of Trail Aircraft=Arg Max(STA of lead aircraft + requited separation): Aircraft's Nominal ETA { } (2)a time, and determines which aircraft is first.It then removes this aircraft from its input list, adds it to the output list, and starts the process all over again.Part of the comparison is a calculation of a Scheduled Time of Arrival (STA).The STA for the number one aircraft is set to the aircraft's nominal arrival time.STA's for the remaining aircraft are calculated by Equation (2).
+NOT Consider for Runway Allocation Consider for Runway AllocationWhich Runway Pair?from Rwy A to Bfrom Rwy B to AWhich Feeder Gate?NorthSouthEastWestOdd Aircraft Engine Type?YesNoYesNo
+		
+		
+			
+
+				
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+IntroductionAir traffic flow management (ATFM) aims to compensate capacity shortfalls and/or demand peaks, either at an airport or in an air traffic control (ATC) sector; and impose traffic management initiatives that delay aircraft in such a manner that airborne traffic flows do not exceed what can be handled with available resources.In the ATFM community, it is widely accepted that ground delay at origin airports is preferable than airborne delay near the congested sector/airport, from a fuel consumption (and environmental) point of view (Carlier et al., 2007).This statement assumes that airborne delays are in the form of re-routings, air holding stacks or path stretching in terminal manoeuvring areas (TMA).Airborne delay, however, can also be absorbed by slowing an aircraft from its nominal cruise speed, thus intentionally increasing the trip time.This speed reduction strategy, aimed at partially absorbing ATFM delays while airborne, was presented by the authors in previous publications.Prats and Hansen (2011) proposed that ground delayed aircraft could fly at the minimum fuel speed (the maximum range cruise speed).In this way, the fuel consumption (and gaseous emissions) of these flights were reduced at the same time as some ATFM delay was absorbed in the air.The impact of this strategy was quantified by analysing the historical data of all delayed flights to San Francisco International Airport (SFO) over one year.Results showed values ranging from 5% to 15% of the initially assigned delay that could have been absorbed in the air, leading to fuel savings in the order of 4-7% for each individual flight, if compared with the nominal situation.A different strategy was proposed by Delgado and Prats (2012), where aircraft were allowed to fly at the lowest possible speed in such a way that the specific range (SR) (NM/kg fuel) remained the same as initially planned.In this case, the aircraft speed being slower than the maximum range cruise speed, higher values of delay absorbed in the air were obtained while exactly the same fuel as initially planned in the nominal situation was consumed.This strategy is interesting if we consider the fact that ATFM initiatives (as ground delay programs) can be cancelled before their initially planned ending time, as is often the case (Ball et al., 2009;Mukherjee et al., 2012;Inniss and Ball, 2002).Thus, if a GDP is cancelled, the aircraft that are already airborne can change their speed to the initially planned one and recover part of the delay at no extra fuel consumption, as shown by Delgado and Prats (2011).If weather clears (and the ground delay program is cancelled), the aircraft that are already airborne, and flying slower, can change their speed to the initially planned one and recover part of the delay at no extra fuel consumption.Thus, the suggested strategy, which can be used in conjunction with the current practise of distance-based exemptions, gives a new alternative that can be used by airlines when dealing with imposed delay due to ATFM.In this paper, all GDP initiatives that occurred in San Francisco International Airport during 2006 are studied and characterised by a K-means algorithm into three different clusters.The centroids for these three clusters have been used to simulate three different GDPs at the airport by using a realistic set of inbound traffic and the Future Air Traffic Management Concepts Evaluation Tool (FACET).The amount of delay that can be recovered using this cruise speed reduction technique, as a function of the GDP cancellation time, has been computed and compared with the delay recovered with the current concept of operations.The paper is structured as follows: section 2 discusses the required background for the paper with special focus given to explaining the ground delay program initiatives and the cruise speed reduction concept.Section 3 is devoted to explaining the different simulations that have been conducted.The different assumptions, data and architecture of the simulations is explained.Section 4 shows the application of the ground delay programs to the traffic and the results of the simulation of the GDPs with the speed reduction strategy.The introduction of abnormally slow traffic might have an impact on the air traffic control and air traffic management.Therefore, a brief assessment of the impact on the air traffic management is also performed.Finally the paper concludes with section 5, where the conclusions are summarised and further research highlighted.
+BackgroundSpeed control for air traffic management (ATM) purposes has been the subject of several research studies and projects.The majority of the applications focus on a tactical level, where speed adjustments are used to resolve (or mitigate) aircraft conflicts (Chaloulos et al., 2010).Some other works also propose speed control as a mechanism to enable traffic synchronisation strategies (Lowther et al., 2008).In this context, Günther and Fricke (2006) proposed en-route speed reductions to prevent aircraft from performing airborne holding patterns when arriving in the congested airspace.A similar rationale is behind the ATM long-range optimal flow tool developed by Airservices Australia (Airservices Australia, 2008), where aircraft within a 1,000 NM radius of Sydney Airport are proposed to reduce their flight speed in order to prevent them from arriving before the airport is open, and thereby reducing unnecessary holdings.More recently, a joint Federal Aviation Administration (FAA) and Eurocontrol study, estimated that half of the terminal area inefficiency in the system today could be recovered through speed control in the cruise phase of flight, without reducing throughput efficiency (Knorr et al., 2011).At a pre-tactical level, some research has also been conducted considering speed control as an additional decision variable (in addition to the amount of time of ground holding) to solve the Ground Holding Problem (Bertsimas andPatterson, 1998, 2000).However, the economic impact (or solely the impact on fuel consumption) caused by these speed variations is seldom investigated.In previous publications (Prats and Hansen, 2011;Delgado and Prats, 2011, 2012, 2013), the authors have studied the impact that changes in speed may have on the fuel consumption and how different speed reduction strategies may be used to enhance ATFM initiatives.This section gives a brief overview of ground delay programs and the principal concepts that are behind the speed reduction strategy described in this paper.
+Ground delay programsIn the US, a ground delay program (GDP) is implemented when an airport is expected to have insufficient arrival capacity to accommodate forecast arrival demand.The FAA, acting in its role as traffic flow manager, proposes a program in which flights are assigned to slots.Some flights are exempted from the FAA assigned delay.A first set of exempted flights are those airborne at the time the GDP is implemented and international non-Canadian flights.The second set is GDP dependent and exempts flights originating outside a certain radius from the affected airport (Ball and Lulli, 2004).One of the main reasons for applying this exemption policy is the uncertainty when estimating the arrival capacity of the airport.Predicted capacity reductions are often caused by adverse weather conditions which in turn, are sometimes forecast several hours before.Thus, too pessimistic forecasts can lead to excessive ground delays.Since flights originating further from the airport must execute their ground delay well in advance of their arrival, most of the delay is usually assigned to shorter-haul flights by exempting flights originating outside the above mentioned radius.The actual value of this radius is fixed at the GDP implementation and depends mainly on the forecast severity of the capacity reduction.In this paper, the use of a radius of exemption is not considered.By doing so, the distance of the aircraft serving delay is maximised, thus the maximum benefits of this strategy are analysed.However, notice that the suggested strategy could be used in conjunction with the current practice of distance-based exemptions.A detailed description of the different parameters defining a GDP can be found in (Vossen et al., 2011).For each non-exempt (or controlled) flight in a GDP, a controlled time of arrival (CTA) or arrival slot is assigned at the destination airport.Based on filed flight plans and weather forecasts, trip times can be estimated with a reasonable accuracy and consequently, the CTA is translated to a controlled time of departure (CTD) at the origin airport.Thus, the CTD is the CTA minus the trip time, and the ground delay is the CTD minus the estimated (scheduled) time of departure (ETD).The assignment of the slots are done in a first-schedule first-served rationing.This is known as Ration by Schedule (RBS) (Richetta and Odoni, 1994;Kotnyek and Richetta, 2006).After this assignment, individual airlines are given an opportunity to reassign and cancel flights based on updated flight status information and their internal business objectives.Besides ground delay, other strategies can also be initiated in order to solve capacity-demand imbalance problems, such as rerouting or air holding, all of them less desirable because of higher operating costs if compared with ground delays (mainly due to fuel consumption).For this reason, an equivalent slot allocation initiative is implemented when a capacity demand imbalance is detected in an airspace sector: the airspace flow program (AFP).In Europe, a similar process is implemented in order to deal with airspace or airport capacity constraints.Eurocontrol, through the central flow management unit (CFMU), manages the slot allocation system based also on a RBS basis (Eurocontrol Central Flow Management Unit, 2011).The main difference with the north-american GDP or AFP is that all CFMU controlled flights are affected regardless their origin airport (no delay exception if the flight originates outside a certain radius).Moreover, in Europe, a finite number of administrative slots are also given to airlines to schedule flights in the majority of European airports, aiming to keep demand below maximum capacities while unforeseen situations (such as severe weather affecting a particular airport) are absorbed via the CFMU.However, with very few exceptions, administrative slots are not imposed in the United States, where GDP initiatives can be a frequent issue in some airports where capacity can be highly reduced if weather degrades.For instance, in San Francisco Airport, in California, when low ceiling clouds are present, capacity drops from sixty planes per hour to only thirty.Due to restrictions on independent parallel runway configurations under instrumental meteorological conditions (IMC) (Janic, 2008).One of the problems that is faced when a GDP must be implemented is the estimation of the capacity shortfall and therefore, the duration of the GDP initiative.For example, for GDPs caused by degraded weather, if weather clears before forecast it will lead to under use of capacity at the airport and result in unnecessary delays.Conversely, if reduced capacity conditions last longer than expected the GDP will have to be extended and/or inefficient air holdings will be necessary near the destination airport.Since the predicted capacity at the airport is often subject to uncertainties, airspace managers are typically conservative with these scenarios and the GDP is usually planned to last longer than actually needed.Essentially, it is preferred to have planes waiting on ground, even if not necessary, and cancel the GDP earlier rather than having too many flights arriving at the concerned TMA when the available capacity cannot yet accommodate all of them.As can be seen in Table 1, on average at SFO the GDPs are cancelled almost two hours before the initial planned duration.This leads to a under use of capacity at the airport and to unnecessary ground delays (Cook and Wood, 2010).Although this paper focuses on some GDPs that were implemented in SFO, the speed reduction strategy would be also valid for AFP (or CFMU) initiatives.All these ATFM initiatives assign ground delays to a subset of flights and therefore, by flying slower, part of the assigned delay could be performed airborne at no extra fuel consumption.
+The cruise speed reduction conceptFor a given flight, three types of costs are present: fuel costs, time-dependent costs and fixed costs, which are independent of the time or fuel consumption (such as landing fees or aircraft ground handling).As shown in 1(a), fuel and time-dependent costs depend on the flight cruise speed.The optimal speed that gives the minimum fuel consumption for a given flight distance is the Maximum Range Cruise (MRC) speed.On the other hand, time-related costs decrease as speed increases, since trip times become shorter.Depending on the importance given by the operator to time related costs, the optimal speed for a given flight will change.To help the operator in assessing this trade-off, the Flight Management System (FMS) of the aircraft allows the pilot to enter a cost index (CI) parameter (Airbus, 1998).The CI expresses the ratio between the cost of the flight time and the cost of fuel.Thus, a CI set to zero means that the cost of fuel is infinitely more important than the cost of the time, and the aircraft will fly at the MRC speed.On the other hand, the maximum value of the CI gives all the importance to flight time1 .In this case, the aircraft will fly at the maximum operating speed with, in general, some safety margins.By choosing the CI the pilot is changing the ratio of cost between fuel and time and therefore, is determining the speed which minimises the total cost.This speed is usually called the ECONomic speed and will be denoted as V 0 in this paper (see Figure 1(a)).It should be noted that the CI value not only affects the cruise speed but also determines the whole flight trajectory.This means that the optimal flight level may change and that the climb and descending profiles might also be different for different CI settings.
+The equivalent speedGiven a flight distance, a payload weight and a cost index, the optimal flight level, the optimal cruise speed (V 0 ) and consequently, the fuel needed for that particular flight (block fuel), are fixed.Figure 1(b) shows the relationship of the specific range (SR) with the cruise speed.The specific range is defined as the distance that can be flown per unit of fuel burnt, and it is usually measured in NM/kg or NM/lb.The maximum SR is achieved when flying at the MRC speed which is the same as minimising the fuel consumption per unit of distance flown.Since typical operating speeds (ECON speeds) are higher than the MRC speed, the actual specific range will be lower than the maximum one.The equivalent speed (V eq ) is defined as the minimum speed that produces the same specific range (SR 0 ) as flying at the nominal speed V 0 (see figure 1(b) ).The margin between V 0 and V eq depends on the shape of the specific range curve which is aircraft, flight level and weight dependent.As the aircraft flies, fuel is burned and therefore its weight changes leading to changes in the V eq speed.It is worth mentioning that V eq might be limited by the minimum speed of the aircraft at that given flight level and weight with some safety margins.In this paper, a typical minimum margin against stalling at a load factor2 of 1.3g has been considered when computing the minimum operational (V min ) speed for a given weight and altitude3 .The goal of the speed reduction strategy proposed in this paper is to maximise the airborne delay but without incurring extra fuel consumption with respect the initially planed flight plan.Yet, only a few minutes of delay can be generated in the air by flying at V eq .For example, in a typical Frankfurt International Airport -Madrid Barajas flight (769 NM), 7 min of airborne delay can be realised without using extra fuel consumption (see in (Delgado and Prats, 2012) other example flights).Therefore, the airborne delay will be typically lower than the total assigned delay due to an ATFM regulation (such as GDP).Thus, the total assigned delay will be divided between some ground delay, at the origin airport, plus airborne delay while flying slower.In the presence of wind, the equivalent speed can be computed considering the specific range with respect to the ground speed defining the SR as ground NM/kg or ground NM/lb.However, the effect of wind is out of scope of this paper.Results of the amount of airborne delay in the presence of wind and its effects are presented in (Delgado and Prats, 2013).
+Speed reduction applicabilityFigure 2 compares the speed reduction strategy with the current ground delay strategy (baseline scenario).GDP controlled flights are expected to arrive at a given CTA at the destination airport, with a given time window or slot.With the current GDP implementation, this requires delaying the flight at the origin airport by D minutes.After this delay, the nominal flight plan is executed with a total flight time of T Vo minutes, as depicted in Figure 2(a).With the en-route speed reduction strategy, the aircraft incurs a ground delay of d minutes (with d ≤ D), takes off at a new departure time (CTD') and flies slower than initially planned, as shown in Figure 2(b).In this way, it will take T Veq minutes to reach the destination airport, in such a way that d + T Veq = D + T V0 (i.e. the aircraft is arriving at the same CTA as in the baseline scenario).It should be noted that the aircraft will still experience the imposed GDP delay at the arrival airport, since this delay has been distributed by waiting on ground at the origin airport and by flying slower during the route.Therefore, the fairness aspects due to the assignment of the delay, regarding different aircraft of different companies, are not affected by this speed reduction strategy.For a particular flight, if the GDP is not cancelled before the aircraft arrives at the destination airport the same amount of delay occurs in the baseline and in the speed reduction scenarios.Moreover, the same amount of fuel is burned in both cases (according to the V eq definition).If the GDP is cancelled while the aircraft is still flying, however, some benefits arise.Since flying at V 0 has the same fuel consumption per distance flown than flying at V eq , at the moment the GDP is cancelled, airborne controlled aircraft can accelerate to V 0 and recover part of the delay without incurring extra fuel costs over the initially planned fuel cost (see Fig. 2(c)).In the research presented in this paper, it is considered that the application of this airborne delay strategy is decided at a pre-tactical phase, and it is assumed that the airline maintains its initial computation of block fuel as its target fuel for the flight.The fact that a CTA has been issued and that some delay can be potentially recovered, might vary the amount of fuel the airline considers adequate for the flight.However, by maintaining the fuel consumption as initially planned this paper presents a conservative approach on the amount of delay that can be recovered.Other strategies, out of the scope of this paper, include the realisation of airborne delay at other speeds rather than V eq to save fuel or perform even more airborne delay and the recovery of delay by increasing the cruise speed above this nominal speed at the expense of more fuel consumption than initially planned (as studied, for instance in (Cook et al., 2009) ).With the current concept of operations (baseline scenario), where an aircraft absorbs the total amount of assigned delay on ground, delay recovery can only be done by speeding up over V 0 leading to more fuel consumption for that trip than initially planned.It is worth mentioning that speed reduction strategies are difficult to implement with the current concept of operations since CTA are still not enforced.Thus, even with the current baseline scenario, some companies may decide to accelerate their delayed aircraft, trying to recover part of the delay previously performed on ground (incurring higher fuel costs) and not meeting the assigned arrival slot (Knorr et al., 2011).Nevertheless, in the near future, more accurate control of the trajectory will be available to aircraft operators in the context of SESAR4 and NextGen5 projects.Thus, it is expected that CTAs will be effectively enforced on aircraft and the speed reduction strategy at a pre-tactical level as proposed in this paper will be useful.
+SimulationsWe have simulated the 24th-25th August 2005 inbound flights to SFO, subject to different GDP scenarios, by using the Future ATM Concept Evaluation Tool (FACET) developed by NASA-Ames (Bilimoria et al., 2000) and the Airbus Performance Engineer's Program (PEP) suite.This section gives more details on the data used and assumptions made for the different simulations.
+Simulated trafficThe August 24th-25th, 2005 Enhanced Traffic Management System (ETMS) data was used to generate traffic information required to perform the simulations.A total of 1,011 flights were simulated to generate the demand.Accurate cruise performances have been obtained by using the Airbus aircraft databases from the PEP suite.As only the Airbus family performances were available, aircraft were grouped into six different families, corresponding to six different Airbus aircraft models: A300, A320, A321, A330 and A340.The families of aircraft types were created based on the performances of the aircraft, in such a way that all aircraft in the same family had similar performances.Table 2 shows this grouping: 725 flights are simulated with Airbus performance, representing 71.7% of the total traffic.The 28.3% remaining aircraft are not considered for the speed reduction strategy either because they were already flying when the simulation started, or because they are notably different from any of the Airbus models available (i.e.small business jets, turboprops and propeller driven aircraft).All these aircraft, however, are simulated to correctly represent the demand at the airport, but are excluded from the speed reduction strategy.If any of those flights has some assigned GDP delay it will be done completely on the ground, as in the current concept of operations.During the simulation of the ground delay programs, only international (non-Canadian) and aircraft already flying are exempt from serving delay.No radius of exemption has been considered to study the effect of the V eq strategy.Aircraft that are already flying when the simulation starts are kept on their original aircraft type when simulated with FACET as it is not necessary to know their accurate cruise performance, since they are exempt from the GDP.As stated in section 2.2, the equivalent speed depends on the chosen Cost Index and the payload mass of the aircraft.A Cost Index of 60 kg/min has been used for all the flights except for the A330 and A340 families where a Cost Index of 120 kg/min has been selected.These values are representative for common operations (Airbus, 1998).Finally, to estimate the payload, an 80% of passenger load factor has been assumed for A320 and A319 flights, while for long haul flights (A300, A330 and A340) 80% of the total payload has been assumed (including also freight) (ELFAA -European Low Fares Association Members, 2008).
+Simulated ground delay programsDifferent scenarios in airport arrival capacity, or arrival acceptance rate (AAR), reflect in most cases well-identified weather patterns in the regions where the airports are located (Liu et al., 2008).In the case of SFO, it is common to have marine stratus which usually burn-off around the middle of the day.There are days, however, where the capacity remains at reduced values throughout the day.Finally, some reductions on the airport arrival acceptance rate are produced due to the rainy periods in the winter season.In this paper, we have analysed all 130 GDPs that occurred in SFO during 2006 (CDM archival database).By using a K-means clustering algorithm (Macqueen, 1967), the GDPs have been grouped into three different categories.To cluster the GDPs, the Euclidean distance has been used and they have been characterised by their filed time, starting time, planned end time and actual cancellation time.The number of clusters has been determined by the silhouette coefficient with an iterative process from two to eight clusters.The best clustering has been obtained with three clusters.The centroids of the three resulting clusters are shown in Table 3.The first cluster contains the majority of the year's GDPs (91) corresponding to Morning GDPs caused by low ceilings.These GDPs are typically declared early morning and cancelled when the weather clears, which on average, is around 3h25 before initially planned.The second group are All-day GDPs that also defined early morning, but expand during the whole day because the meteorological conditions do not improve.Finally, the third category of GDPs, correspond to Afternoon GDPs.GDPs of the first category are found during the whole year, whilst the GDPs of the second and third category are mainly declared only during the winter season.The duration and cancellation time that have been obtained for the centroids of the clusters of the GDPs are consistent with the values from (Cook and Wood, 2010).Moreover, this clustering is in line with the results presented in (Liu et al., 2008), where airports were characterised by their AAR during the day.It should be noted, however, that in our clustering we have not used AAR data but times related to the GDP definition and cancellation.The centroids of the three GDP clusters are considered representative of their category and were used to create the three different GDPs simulated in this paper.In these simulations, it has been considered that in nominal conditions (and when the GDP is cancelled), the AAR is 60 aircraft per hour, while during the GDP the capacity is reduced to 30 aircraft per hour; as the two parallel arrival runways of SFO cannot be independently operated when the visibility is reduced (Janic, 2008).It is assumed that once the GDP is cancelled, the capacity at the airport is unconstrained.Actually, this is not always true since the GDP has shifted the demand and, at the cancellation time, the forecast arrival demand at the airport might occasionally exceed the airport new arrival capacity.Moreover, the maximum delay that could be recovered has been computed assuming that the aircraft that are, at the cancellation time, delayed on ground can immediately take-off and that the airborne aircraft, which are flying at V eq , can speed up immediately to V 0 .
+ArchitectureAs explained in section 3.1, ETMS traffic has been analysed and modified for our simulations.Figure 3 shows the process followed to compute the initial traffic and the nominal parameters of the flights.In order to compute the initial traffic, the aircraft types have been replaced by Airbus aircraft when applicable.For these flights, the trip distances from their origin airport to SFO have been determined.For this purpose, the flight plan of each flight, as defined in the original traffic file, has been considered.Therefore, the distance between two airports might be different for two different flights depending on the actual route flown.Then, by using the Airbus PEP suite, and the assumed Cost Indexes and payloads, the nominal parameters for each flight have been computed: initial cruise weight, cruise flight level(s) and speed(s) with the required cruise steps if needed6 .The initial traffic has been simulated twice, as depicted in the diagram of figure 4. In the first simulation the speed and flight levels of the aircraft have been kept to their nominal values.The result of this simulation is the initial arrival demand at the airport.In the second simulation, the aircraft reduce the cruise speed to V eq .The second simulation represents the demand at the airport if all the aircraft fly at their equivalent speed.By doing a comparison of the arrival times, it is possible to compute the maximum airborne delay that each aircraft contributes without incurring in extra fuel consumption.FACET uses the Base of Aircraft Data (BADA) database (Eurocontrol Experimetnal Centre, 2011) to compute the performances of the different aircraft.However, it was necessary to accurately control the speed of the aircraft during the cruise.As the diagram of figure 5 level (F L 0 ), speed (V 0 ) and weight (W 0 ) are initialised with the parameters from the PEP computations.These values will be kept constant during the cruise and updated only when a change of cruise altitude is needed according to the nominal flight plan.At each iteration of the simulation the fuel flow is computed according to the Airbus performances of the aircraft.Recalling to section 2.2.1, the equivalent speed varies with the weight, therefore in the simulation of the reduced speed at each simulation step (one minute) the equivalent speed is recomputed for all the airborne flights considering their current weight (see figure 5(b)).If a particular aircraft had a change in cruise altitude in the nominal flight, it will also be performed in the second simulation.The application of the GDP to the initial arrival demand, in order to keep the demand below the airport capacity, will result in the amount of delay assigned to each aircraft.This delay assignment is done following a ration by schedule policy.The time when the capacity of the airport is changed from 30 to 60 aircraft per hour is computed in order to finish the regulation according to the centroids.Having previously computed the maximum airborne delay that each flight can perform by flying at V eq , the assigned delay is divided into ground delay and airborne delay.In the case that a particular flight has been assigned a delay smaller than the maximum airborne delay it can do by flying at V eq , a new speed (between V eq and V 0 ) is selected.With this new speed, the CTA is fulfilled and consequently, all the assigned delay is done in the air while saving some fuel with respect to the nominal situation.This process is represented in figure 6.Finally, if a GDP is cancelled before its defined ending time some delay might be recovered.In this paper the authors have computed the amount of delay that could be saved by assuming that when the GDP is cancelled the aircraft which are doing ground delay can take off at that moment and the aircraft which are flying at V eq can speed up to their nominal speed (V 0 ).
+ResultsThe results of the application of the three simulated GDPs are presented in Table 4, showing the total, the maximum and average delays assigned.As commented previously, in the speed reduction scenario, part of this total assigned delay can be realised airborne.Table 5 presents these division of the delay, between airborne and ground, in absolute and relative values.The amount of delay that can be airborne varies between 15.7% (Morning GDP) and 47.9% (Afternoon GDP) with respect the whole assigned delay.In the Afternoon GDP the average assigned delay is usually smaller than in the other GDPs, and since the amount of airborne delay that an aircraft can realise is usually small, the percentage of air delay assigned is larger than in the other scenarios.More than 71% of all the aircraft with assigned delay can realise part of it airborne.However, only 9.8% of the total traffic in the Morning GDP can perform all the delay assigned airborne, while 38.5% of the traffic in the Afternoon is in this situation.The main reason for the difference, is that Afternoon GDPs have smaller average delays for each flight (as seen in Table 4) and maximum airborne delays can reach up to 20 min in the best case as was reported in previous publications (Delgado andPrats, 2011, 2012).According to Table 3, the Morning GDPs (group 1) and the Afternoon GDPs (group 3) have a similar duration.As a consequence, the amount of airborne delay that can be realised is also similar (see Table 5).However, the total amount of delay is higher in the Morning GDP due to the fact that the arrival demand is greater.Thus, in the Afternoon GDPs almost half of the delay can be realised airborne, while in the Morning GDPs only 15.7% of the delay can be absorbed during the cruise.Notice that all the air delay is realised with the same amount of fuel consumption as in the baseline scenario since aircraft are flying at V eq (see section 2).
+GDP cancellationThe delay that could be saved if the GDP is cancelled has been computed for the three different scenarios.Figure 7 shows the results of these computations.Notice the two different scales in the y-axis.As a function of the time, it represents the accrued delay realised by all the aircraft.The recovered delay achieved if the GDP is cancelled at each time is also presented for both scenarios.In the baseline scenario, where all the delay is performed on ground, the recovered delay can only be the delay that has not been accrued yet.For a given flight, see figure 2(a), the delay recovered will be all the initially assigned delay if the cancellation time is before the flight's ETD.It will be the difference between the cancellation time and the CTD if the GDP cancels between the ETD and the CTD.If the flight has already taken off, however, no delay will be recovered for that flight since we are assuming that the flight will cruise at V 0 .With the speed reduction strategy, the recovered delay is increased by the time that can be gained by speeding up to V 0 (i.e.not using extra fuel) for the aircraft that are already flying at V eq when the GDP is cancelled.The extra delay being recovered due to the speed reduction strategy is the difference between these recovered delays and is presented in figure 7.At the beginning of the GDP, none of the delay has been accrued and therefore, if the GDP is cancelled all the delay can be recovered.As time advances, more delay has already been realised and therefore less delay can be saved if the GDP is cancelled.The benefit of the speed reduction strategy applied to the GDP programs depends on when the GDP is actually cancelled.If the GDP is finally not cancelled before initially planned, this strategy will lead to a change on where all the assigned delay is realised.In addition, the plots in figure 7 show the filed, start, ending and actual cancellation times of each GDP according to Table 3.The amount of extra delay that will be recovered with respect the baseline scenario depends on the number of aircraft that are at that time in the air realizing airborne delay.Figure 8 shows this dependency.There is a correlation between the number of aircraft in the air flying at V eq and the extra savings of delay if the GDP is cancelled.The curve showing the number of aircraft is indeed shifted to the right: when the aircraft are flying at V eq , the later the GDP is cancelled the more delay is already realised and the smaller is the distance available to recover delay.For this reason, close to the GDPs ending time, there are aircraft in the air that are doing airborne delay but there is no extra delay recovered, in this case those aircraft are already on their descending phase and have already realised the whole assigned delay.Table 6 shows the maximum number of aircraft that are at the same time in the air doing the speed reduction strategy and the maximum extra delay recovered with respect the baseline scenario for the three GDPs.Even if the aggregated total amount of airborne delay that can be realised in the three GDPs is very different, the maximum extra delay recovered with the speed reduction strategy is very similar for the cases, around 430 min.The extra time that can be recovered depends on the number of aircraft that are flying at V eq at the cancellation time, which it is very similar among the three GDPs.For the Morning and the Afternoon GDPs there is a maximum value of the extra delay that can be recovered (around 9h00 and 18h30 respectively) and then, it decreases until the end of the GDP.Conversely, for the All-day GDP, we observe two peaks of extra delay recovered: one in the morning and the other in the afternoon (see figures 7 and 8).This result shows the dependency between the amount of extra delay that is recovered and the actual demand at the airport.The maximum extra delay recovered is achieved  before the demand at the airport attains its maxima which is around 10h00 in the morning and 21h00 in the afternoon.As stated before, the simulations are computed without considering a radius of exemption, by doing so, the benefits of the strategy suggested in this paper are maximised.Figure 9 presents the average delay assigned for each flight as a function of its flight plan distance for all the GDPs studied grouped by 500 NM.It also presents the average ground and airborne delay per aircraft.It is possible to observe that the delay assigned per aircraft is independent on the flight plan distance as it is based only on the schedule of arrival.The maximum airborne delay realisable increases with the distance available for the flight, as more airborne delay can be realised for longer flights, and consequently the ground delay realised decreases as the flight plan distance increases.With a radius of exemption the aircraft that are further from the airport would be excluded.However, this strategy might be still interesting as the airborne delay recovered depends on the number of aircraft in the air flying at V eq at the cancellation time.
+Results at the actual cancellation timeAs presented in (Mukherjee et al., 2012;Cook and Wood, 2010), a probability distribution function of fog clearance time can be computed for SFO.This function could be used to compute the average extra delay recovered from the results presented in the previous section.However, in this paper, as the GDPs have  3) is used in order to compute the delay that could be extra saved.This is interesting as not all the GDPs defined in SFO in 2006 where due to low ceiling clouds, the clustering represents this variety in GDP causes.Table 7 shows the extra delay that is recovered with respect the baseline scenario at the actual cancellation time.In the Morning GDP, 155 min of extra delay are recovered, representing 27.6% of the total delay that can be saved in the baseline case.This percentage is increased for the GDPs that are forecast for all the day to 52.5% (208 min) and becomes very significant for the Afternoon GDP, with 172.1% of extra delay recovery (105 min).This extra delay has been saved by the aircraft that were at that moment flying at V eq .Therefore by dividing the extra amount of delay saved by the number of aircraft that were at that time doing airborne delay it is possible to obtain an average recovered delay per aircraft.In the Morning and All-day GDP the average delay recovered by aircraft flying at the cancellation time at V eq is around 4.5 min while in the Afternoon GDP this value is reduced to 2.5 min.This recovered delay would be done at no extra fuel consumption as the flights only use V eq and V 0 speeds (see section 2).At aggregate level, if we multiply the extra delay recovered with the speed reduction strategy for each GDP centroid by the number of GDPs present in each cluster, we obtain a total of 20,672 min of extra delay  recovery in a year, with contributions of: 14,105 min for the Morning GDPs, 4,992 min for the All-day long GDPs and 1,575 min for the Afternoon GDPs.
+ATM effectIn order to do a first assessment of the impact of this strategy on the air traffic system, the number of extra aircraft in the air with the speed reduction technique has been computed.The use of the speed reduction strategy will lead to more aircraft in the air because the aircraft will be flying slower to realise part of the delay in the air.Figure 8 shows the number of aircraft that are flying at V eq at every moment during the simulations and the number of extra take-offs, if compared with the baseline scenario.As shown in these figures (and also in Table 6), the number of aircraft that are in the air flying at V eq varies with the GDP and can be at its maximum value between 45 and 60 aircraft.However, this does not mean that there are 60 extra aircraft in the air that otherwise (in the baseline scenario) would be on the ground.A flight facing airborne delay and the same flight incurring all the assigned delay as ground delay have the same CTA at the destination airport.The distance between the position of the flight doing airborne delay and the position of the flight at the same time if flying at nominal speed gets reduced as the flight progresses.At the beginning of the descent, the aircraft doing airborne delay will be at the same position as where it would be if flying in the baseline scenario, therefore the impact on this strategy on the TMAs is small.There will be an overlap between the time when both aircraft are in the air.And almost all the time both flights will be airborne at the same time.This leads to the fact that the number of extra take-offs, i.e. * By aircraft recovering part of its delay by speeding up to V 0 , considering that all the aircraft are still in their cruise phase.the number of aircraft that are in the air with the speed reduction technique that would be on ground in the baseline scenario, is very small (as shown in Figure 8 and Table 6).During the entire simulations there are less than 10 aircraft in the air that otherwise would be on the ground.The amount of airborne delay is high enough to make this strategy interesting but small enough to not increase significantly the number of aircraft in the air and therefore not resulting in a significant increase in the number of aircraft controlled by the ATC.If more than one GDP is defined at the same time, the total number of flight applying this technique might be higher, but due to the natural spread of the airports and the limited impact on the ATM of an aircraft realising this strategy the effect is limited.If instead of cancelling earlier a GDP, it is extended, the number of aircraft excluded because they are airborne, when in the baseline scenario would be still on ground, is also very small.Therefore, this strategy does not have a negative impact on the replanning of the GDP initiatives.Finally, it is worth mentioning that the slow aircraft might interfere with the rest of the traffic due to their abnormally slow cruise speed.This might require some training from the ATC or the need of use offset tracks to avoid the saturation of the nominal tracks.
+ConclusionThe en-route speed reduction strategy presented in this paper has proven useful to recover delay if an air traffic flow management (ATFM) initiative is cancelled before planned (as is usually the case).Simulations were performed using the Future Air Traffic Management Concepts Evaluation Tool (FACET), along with performance data from the Airbus Performance Engineer's Program suite which allowed us to obtain accurate data of specific range and fuel consumption.In order to simulate representative and realistic ATFM scenarios, all the ground delay programs (GDPs) from 2006 at San Francisco International Airport (SFO) have been characterised by using a K-means algorithm, obtaining three different types of GDPs: Morning GDPs, All-day long GDPs and Afternoon GDPs.The Morning GDPs have an average planned duration of 4h56 and are cancelled in average 2h25 before planned, the All-day GDPs are planned for 13h34 and cancelled 2h24 before scheduled and finally, the Afternoon GDPs have an average duration of 5h43 and are cancelled around 1h45 before planned.As reported earlier by the authors, the amount of airborne delay that can be performed for an individual flight using the suggested strategy is not very high.Supposing a scenario where operators have enhanced control of their 4-D trajectories, this strategy is feasible and can complement and make more efficient current ATFM initiatives and do not increase the complexity of the airspace management.Only few aircraft are in the air that would be otherwise still on ground (less than 10 in the presented study).Simulation results show values of extra delay recovery (specially at aggregate level) that encourage further research on the proposed strategy.Around 20,000 min of extra delay could have been saved in 2006 only for SFO GDP initiatives.An average of 39 aircraft get benefit of this strategy by recovering around 4 min of extra delay on each GDP, and without using more fuel than the initially planned for the flight.It is worth noticing that this values have been calculated assuming no radius of exemption is applied.Thus, the effects of unrecoverable delay should be studied in further research.However, regardless of the radius of exemption, the suggested strategy will add some extra delay recovering to all the aircraft flying at V eq at the cancellation time of the GDP.Logically, if a GDP is cancelled as predicted when filed, the baseline scenario and the speed reduction scenario will lead to the same amount of delay and fuel burned.The only difference is that in the baseline scenario all the delay will be on the ground, at the origin airports, while in the other scenario some of it will be realised airborne, although costs other than fuel should also be considered.Moreover, even if the GDP cancels before planned, it is very unlikely that it will be cancelled a short time after its implementation.Therefore, it is very likely that the initial demand will have to perform all the assigned delay, and no benefits may arise from the cruise speed reduction strategy for these flights.A possible idea, for further research, is to start the GDP applying the classical ground delay strategy and, from a certain moment, change to the speed reduction strategy as long as the GDP cancellation time becomes more probable.It also seems interesting to formulate a ration by schedule policy at the beginning of the GDP (minimising the amount of delay assigned when the GDP is very unlikely to be cancelled) and transition to another policy, which maximise the potentially recovered delay if the GDP finally cancels.In this case, the effect on the total delay assigned should be analysed, as a trade-off will exists between the delay that can be potentially extra recovered and the total delay assigned.With the simulations, accurate results have been obtained and presented in this paper.However, it might be possible to analytically relate the flight plan distance with the maximum airborne delay realisable.As further research, this could be used to analyse the GDPs, minimising the number of simulations needed.Figure 1: Aircraft operating costs and specific range (SR) as a function of the cruise speed
+Figure 2: Schematic representation of the delays in the baseline and speed reduction scenarios and the delay recovery in case of GDP cancellation
+Figure 3 :3Figure 3: Nominal traffic generation
+Figure 4 :4Figure 4: Generation of the demand at SFO
+Figure 5 :5Figure 5: Diagram of the simulations
+Figure 6: Delay generation
+Figure 7 :7Figure 7: Delay incurred and recovered for the baseline and speed reduction scenarios
+Figure 8 :8Figure 8: Extra delay recovered, aircraft flying at Veq and extra take offs
+Figure 9 :9Figure 9: Average delay assigned, ground delay and airborne delay realised as a function of the flight plan distance for all the GDPs studied
+Table 1 :1(Cook and Wood, 2009)r SFO(2005)(2006)(2007).(Cook and Wood, 2009)Initial average affected flights
+Table 2 :2Number of aircraft simulated and grouping according to equivalent Airbus typesAircraft Family Aircraft TypesAbsolute number Relative numberof flightsof flightsA300A300, A31020.2 %A319, B727, B737-200,A319B737-300, B737-500, DC-9, MD-90, E-145, CRJ-200,28928.6 %CRJ-700, CRJ-900A320A320, B737-400, B737-800, B737-900, MD-8019319.1 %A321A321, B75714714.5 %A330A330, B767, B777, DC-10777.6 %A340A340, B747171.7 %Total Airbus-like aircraft simulated72571.7 %Aircraft without equivalence or already flying28628.3 %Total of simulated aircraft1,011100 %
+Table 3 :3Cluster centroids for the 2006 SFO ground delay programs (hours in local time)ID GDP groupNumberFiledStartingPlannedCancellationof GDPstimetimeending timetime1Morning GDPs916h318h5913h5511h302All-day GDPs246h128h5822h3220h083Afternoon GDPs1515h4217h0822h5121h06
+Table 4 :4Results of the application of the simulated GDPsNumber of Total assigned Maximum delay Average delayIDGDP groupaffecteddelayassignedassignedaircraft(min)(min)(min)1Morning GDPs1174,7986041.02All-day GDPs3479,3636527.03Afternoon GDPs1161,6152913.9
+Table 5 :5Division between ground and airborne delay for the simulated GDPs Percentage over the the total number of aircraft.Percentage over the total number of aircraft doing airborne delay.Total ground Total airborne AirborneAircraftAircraft realizingIDGDP groupdelaydelaydelayrealizingonly airborne(min)(min)airborne delay  †delay *1Morning GDPs4,04675215.7%71.3%9.8%2All-day GDPs6,9972,36625.3%76.8%26.4%3Afternoon GDPs84277347.9%82.0%38.5%†*
+Table 6 :6Maximum number of aircraft flying and doing airborne delay at the same time, number of extra take-offs and extra delay recoveredMaximum number ofMaximum extraIDGDP groupaircraft realizingMaximum number delay recovered  †airborne delayof extra take offs(min)at same time1Morning GDPs4573792All-day GDPs60104373Afternoon GDPs5110387† With respect the baseline scenario.
+Table 7 :7Results of the simulated GDPs at the actual cancellation time With respect the delay saved in the baseline scenario.AircraftDelay savedDelay savedExtra% ExtraAverage extraIDGDP groupflying at in the baseline V eq scenarioin the speed reduction scenario recovered recovered  † delay delaydelay recovered * (min)(min)(min)(min)1Morning3756271715527.6%4.22All-day4539660420852.5%4.63Afternoon4261166105172.1%2.5†
+			Strictly speaking, CI is defined as the cost of time divided by the cost of fuel and multiplied by a scalar.Depending on the FMS vendor, this scalar might be different and, therefore, the actual value of the maximum CI too.Typical CI maximum values are 99 kg/min or 999 kg/min
+			The load factor is defined as the ratio of the lift of an aircraft to its weight.
+			In order to ensure good aircraft manoeuvrability, while preventing the aircraft from stalling, the minimum operational speed is set to the stall speed at a given load factor.This load factor is typically chosen at 1.3g.(EuropeanAviation Safety Agency,  2011)
+			http://www.sesar.eu
+			http://www.faa.gov/nextgen
+			As long as the aircraft burns fuel and loses weight, the optimal flight altitude increases.Therefore, as function of the aircraft type and payload, there exist a certain distance where it becomes optimal to perform a step climb and change the cruise altitude.
+		
+		
+
+			
+AcknowledgmentsThe authors thank Airbus for the use of PEP program, which allowed us to undertake realistic aircraft performances simulations.We would also like to thank the Aviation System Division from NASA Ames, specially Dr. Hok Kwan Ng and Mr. Alex Morando and Dr. Avijit Mukherjee.Finally, we would like to acknowledge Dr. Avijit Mukherjee, Dr. Tasos Nikoleris and Dr. Mark Hansen for their comments on the paper and P. Slocombe for helping with English corrections.
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diff --git a/file187.txt b/file187.txt
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+
+	
+	
+		
+IntroductionThe primary research effort within CTAS has been the design of a set of automation tools that make use of this trajectory prediction capability to assist the controller in overall management of traffic.The two criteria upon which success is judged are controller acceptance and improvement in traffic flow as measured by reduced delays and improved aircraft operating efficiencies.Because of the complexity of the air space system, the approach taken has been to adopt a "design a little, test a lot" philosophy with real-time simulation and field testing included as an integral part of the design process.Analysis of real-time data and fast-time simulation methods are used to extrapolate the results of the field tests.The purpose of this paper is to review the process used in the development of CTAS and provide examples of the role of real-time simulation, field testing, and fast-time simulation.The paper will first discuss the overall technical approach.To illustrate the approach, the FAST development will be reviewed.The DA tool is somewhat different from FAST in that it allows more strategic control.This has led to some differences in the DA development approach that will be discussed.
+Technical ApproachThe overall technical approach is shown in figure 1.Instead of following the more traditional sequentialapproach, the requirements, design, simulation, and operational tests are conducted concurrently with a high level of interaction.Analysis of real-time simulation and live traffic data are used with fast-time simulation to quantify and extrapolate the performance of the system.A primary advantage of this approach is the involvement of controllers and pilots throughout the development.The research facility established to support this approach is illustrated in figure 2. The primary ATC simulation was developed at Ames.It includes an air traffic simulation using pseudo-pilots and an ATC facility simulation.Both are hosted on a network of workstations.
+Analysis of Real-Time DataSo far, we have been discussing the development process.To understand whether the concept will provide benefit,techniques
+DA DescriptionThe Descent Advisor is a set of automation tools to assist  Distance along predicted path, n.mi.  the better than 20 second delivery accuracy shown above to be achievable with DA, together with the benefits derivable with FAST and TMA are estimated to be in the order of $33M per year at the DFW airport.These data are being used by the FAA to develop a comprehensive assessment of the benefits achievable with CTAS.
+Concluding RemarksBecause of the complexity of air traffic control, CTAS has been developed using a "design a little, test a lot" philosophy.Controllers and the piloting community have been involved in the design throughout the program.In the case of FAST, most operational issues could be adequately addressed through a combination of real-time simulation and shadow-mode testing.Operational tests are scheduled to begin this fall to validate the concept in real operations in anticipation of national deployment.In the case of DA, the total system performance is highly dependent on the compatibility between aircraft or pilot and controller procedures.Issues that will affect system performance include the adequacy of the aircraft and wind modeling, and the ability and willingness of the crew to follow DA advisories.This difference has led to a greater involvement by pilots throughout the design and the initiation of early and non-intrusive field evaluations.Asystem for the automated management and control of terminal area traffic to improve productivity, referred to as the Center-TRACON Automation System (CTAS), is being developed at NASA Ames Research Center under a joint program with the FAA (ref.I).CTAS consists of three types of integrated tools that provide computergenerated advisories for both en-route and terminal area controllers to manage and control arrival traffic efficiently.The first tool, the Traffic Management Advisor (TMA), generates runway assignments, landing sequences, and landing times for all arriving aircraft, including those originating from nearby feeder airports (ref.2).TMA also assists in runway configuration control and flow management.The second tool, the Descent Advisor (DA), generates clearances for the en-route controllers handling arrival flows to metering gates (ref.3).The DA's clearances ensure fuel-efficient and conflict free descents to the metering gates at specified crossing times.The third tool, the Final Approach Spacing Tool (FAST) provides terminal area controllers with heading and speed advisories to help produce an accurately spaced flow of aircraft onto the final approach course (ref.4).The underlying premise behind the design of CTAS has been that successful planning of traffic in capacity constrained airspace requires the ability to accurately predict future traffic situations.The technology for accurate prediction of trajectories was developed in the early 1970s and has been incorporated in modern flight management systems.Data bases consisting of several hundred aircraft performance models, airline preferred operational procedures and a three dimensional wind model support the trajectory prediction capabilities within CTAS.(This is discussed in ref. 7.)
+Figure 4 .Figure 5 .Figure 6 .456Figure 4. FAST sequencing and scheduling.
+for analyzing real-time data are required to assure that the system will perform as expected in the real-world and to assist in quantifying potential benefits through use with fast-time simulation.The real-time analysis conducted in support of CTAS is to be published this fall in an article by M. Ballin and H. Erzherger (ref. 1 I).Two examples of this analysis are included here.First is the method used to calculate the arrival time errors at the feeder-fix into the terminal area.Based on fast-time simulation, Erzberger and Neuman have shown that the magnitude of these errors directly affect the portion of total delay that should be absorbed in the terminal area or TRACON (refs.12 and 13).The second is the method used to measure inter-arrival spacing at the threshold for different aircraft combinations, i.e. heavy followed by heavy, large followed by small, etc.These data are necessary to understand the delay reduction potential of improved sequencing and spacing and runway assignment.
+Figure 88Figure 8 shows a composite plot of flights into DFW taken over a 140 minute interval involving a major rush.A program has been developed to assist developers in analyzing these data (ref.14).The analysis program is constructed so that the CTAS estimated time of arrival (ETA) at the feeder fix, computed at any point along the trajectory, can be compared with the actual crossing time.The program is further refined so that a researcher can call up a specific trajectory to identify possible causes of any major error in the ETA.This tool has been invaluable in improving the overall robustness of the trajectory prediction algorithms.An example of the use of this tool for obtaining statistical data on ETA errors is shown in figure 9.It should be noted that the curve appears to be the superposition of two error sources, one with a Gaussian distribution and one with a Poisson distribution.If the Gaussian portion is attributed to errors in the ETA calculations where the flight is not affected by controller-induced delays and the Poisson portion is attributed to delays inserted to coordinate traffic flow, we can make a first order estimate of ETA accuracy achievable with an effective traffic management tool.
+Figure l0 shows aFigurel0shows a composite plot of flights into the terminal area.Here it is much more difficult to automatically sort through the data to achieve meaningful statistical results regarding ETA's at the threshold or estimates of the inter-arrival spacing.The tool must ignore all aircraft that are not landing, and it must identify the most likely runway for each landing aircraft.The greater the number of mistakes, the less valid the analysis.Shown in figure I i is an example histogram of interarrival spacing for aircraft having a legal separation of 2.5 n. mi.The few cases where separations were less than 2.5 n. mi.do not imply violations.Under current rules, as soon as the pilot has the runway in view, the pilot can declare VFR.Again, the curve seems to be a superposition of a Gaussian and Poisson distribution.In this case, it is assumed that the Gaussian portion represents the controller precision in spacing aircraft onto the final approach path given a steady stream of traffic and the Poisson portion represents those pairs where there were natural gaps.From these data, we can infer the controller target point, the errors that can be expected about the target point, and the buffer that can be used to model the controller's behavior.The potential for improvement
+Figure 9 .Figure 11 .911Figure 9. Histogram of ETA errors at meter fix.
+figure 13.A DA test station was set up in the Traffic Management Unit of the Denver en-route center.The existing CTAS system that supports TMA at Denver was used.The DA advisories were transmitted to a test engineer located at the sector controller position.The test engineer passed the advisory to the sector controller in a written script.The sector controller then issued the advisory to the participating flight.An example of a DA advisory for an unequipped aircraft would be: "UAL 123, begin descent 70 miles from the Meeker VORTAC; descend at 280 knots; if unable advise."An example DA advisory for a FMS equipped aircraft would be: "UAL 123, descend at pilot's discretion, descend at 280 knots; if unable advise."The exact phraseology and procedures were carefully coordinated between the facility and United Airlines.Examples of the data collected are shown in figure14.Both horizontal and vertical profile data as well as ETA errors were recorded.The data shown are for an aircraft with an FMS and for an aircraft without an FMS.A summary of the accuracy achieved at the meter fix is shown in table1in the form of mean and root mean square (rms).In all cases the CTAS prediction was within 20 seconds.The FMS in the TSRV predicted crossing time is also shown for comparison.It should be noted that these data are based on a single DA clearance and a prediction approximately 15 minutes before the meter-fix crossing.
+120Figure 14 .14Figure 14.Trajectory data collection.
+Fast-time simulationsand analysis of real-time data are used to quantify the performance of the system and to provide a basis for extrapolating limited results from realtime simulation, shadow-mode testing, and limited field tests to a variety of cases in a statistically significant manner.Results to date indicate a tremendous operational benefit through the introduction of CTAS type automation tools.9.Brinton, C.R.:AnImplicit Enumeration Algorithm for Arrival Aircraft Scheduling.Proceedings of the 1 lth Digital Avionics Systems Conference, Seattle, Wash., Oct. 1992.10.Cooper, G. E.; and Harper, R. P.: The Use of Pilot Rating in the Evaluation of Aircraft Handling Qualities.NASA TN D-5153, 1969.11.Ballin, M. G.; and Erzberger, H.: An Analysis of Aircraft Landing Rates and Separations at Dallas/Ft.Worth Airport.To be published as a NASA TM, Fall 1995.12. Neuman, F.; and Erzberger, H.: Fast-Time Statistical Evaluation of Sequencing and Scheduling Algorithms for Two Runways.To be published as a NASA TM, Fall 1995.13.Erzberger, H.: Integrating Physical Models and Expert Knowledge in the Design of Automated Air Traffic Management Systems.AGARD Lecture Series No. 200, Knowledge Based Functions in Aerospace Systems, Nov. 1995.14.Neuman, F.; Erzberger, H.; and Schuellar, M. S.: CTAS Data Analysis Program.NASA TM-108842, Ames Research Center, 1994.15. Green S. M.; Vivona, R. A.; and Sanford, B.., Aug. 7-9, 1995.
+APPROACH Each phase is viewed as a validation of previous stageDevelop DetailRequirementsI Conduct Detail Designl I Evaluate in SimulationI 'c°n uc' D tional Test Develop Detailed L Specifications for o Operational Build y M E I' NTTo study controller display integration issues, two terminal area radar displays (Fully Digital ARTS Display System, Time TRADITIONAL
+CTASAPPROACH Take reduced capability system to the field as early as possible -design for continuous improvementDevelop RequirementsIIConduct DesignIEvaluate in SimulationConduct Operational TestDevelop Specificationsfor Operational SystemDEPLOYMENTDEPLOYMENTDEPLOYMENTDEPLOYMENTBUILD 1BUILD 2BUILD 3BUILD 4Figure 1. Programmatic approach.FDADS) are integrated into the network. To investigatespecific air-ground communicationand traffic manage-ment issues, links were establishedwith existing fullpiloted simulators located at the Ames and LangleyResearch Centers. To understand actual traffic situationsand to support shadowing evaluations,live radar con-nections were established,first with Denver Center andthen expanded to include the Fort Worth Center and theDallas/Fort Worth terminal area (TRACON). To under-stand weather and evaluate its effect on the trajectoryprediction capability of CTAS, connections were estab-lished to receive weather informationfor both the Denverand Dallas/Fort Worth areas. We are currently receiving"rapid update cycle" weather data. Field tests are underway at Denver and Dallas.Applicationto Developmentof FASTThe steps taken in the FAST development are illustratedin figure 3. Fast-time simulation,real-time simulation,and live traffic testing in shadow-modehave been usedthroughout the development (ref. 5).
+Operational testing has been maintained as a target but has been delayed until the system design issues identified in simulation and,I\ involved throughout the process. Initial studies considered a generic airspace designed to evaluate basic con cepts. .......... I)As the program progressed, the effort addressed morerealistic environmentsbased on the Denver andAI If_.l.Dallas/Fort Worth areas.FAST DescriptionFAST is a tool for aiding the terminal area controller in S.LS31 9.LV CI:alVlAIOJLnV VV..-I£,I/CjVNsetting up the optimal landing sequence, selecting themost appropriate runway and providing the controllerwith turn and speed advisories to produce an accuratelyspaced flow of aircraft onto the final approach course(ref. 4). The sequence and runway advisors are referredto as "passive FAST." The turn and speed advisories arereferred to as "active FAST." Both passive and activeFAST advisories are based on trajectories that havebeen computed to be conflict free for the duration of theflight path. These trajectories and advisories are con-tinually updated based on new radar track data (every4.7 seconds) and on inference of controller intent. Moredetails on FAST are contained in references 4 and 6.The trajectory prediction computationsare reviewed inreference 7.shadow-modetesting are resolved. Controllers have been
+Operational Development Progress Test of PreparationEnvironment_for OperationalEvaluation_-TestAdaptationAssessment TeamJfor TargetfSiteSystemInitialDevelopmentTeam (
+SDT) Software On-site Controllers Analysis Shadowing Concept Definition Real-time Simulation v Engineers, Fast-time Slmulahon ID Human Factors, I_ Local Controllers T Phases of Controller Involvement Figure 3. FAST development process.As an exampleof the developmentprocess,we willreviewthe developmentsof the sequenceand schedulinglogic and the runwayallocationlogic.Sequencingand SchedulingAlgorithmThe sequencingand schedulingproblemaddressedwithin
+FAST are illustrated in figure 4. In the initial design, the sequence and schedule were optimized to assure mini- mum delays based on separations at the threshold. The speed and turn advisories were computed to assure efficient and conflict free flight (ref. 8). To achieve minimum delays, the system would allow overtakes upstream in the traffic flow. As the simulation was adapted to be more representative of Denver and Dallas/ Fort Worth, it became apparent that additional sequence constraints would be required to allow the controller to maintain a coherent view of the traffic situation. This led to the development of a trajectoryis shownin figure 5. Withoutgoing into the details,thelogic for determiningwhetherto allow an overtakedependson the relativepositionof two aircraftscheduledfor the same segmentin the TRACON(i.e., downwind,final, etc.), their speed differences,and the potentialdelaysavings.If the trailingaircraftfalls above the curve infigure 5, it is rescheduled.Subsequentanalysisandfast-timesimulationhave shown that these additionalconstraintsimposea negligiblepenaltyon overallperformance.RunwayAllocationThe runwayallocationalgorithmhas evolvedfrom aninitial algorithmthat was designedto optimizea singlefunctional(ref. 9), to an algorithmthat is more consistentwith currentprocedures,providesimprovedcontrollerawareness,and allowsconsiderationof multipleperfor-segmentbasedmancemetrics(refs. 4 and 6). The currentmethodbeginsorderinglogic that under certain conditionswouldwith a nominalrunwayassignmentbased on publishedmaintainsequencesestablishedprior to merging on finalproceduresat the particularairport.A decisiontree is(refs. 4 and 6). The segmentbased ordering methodenteredwhich branchesthroughalternativerunways,allows the overtakeof one aircraft by another if there is aentry gate to the TRACON,aircrafttype, and finallyendssufficientreductionin delay but it restricts the conditionswith a minimumglobal delay reductionrequiredfor aunder which this reorderingmay occur. The logic for therunwaychange.The overall benefitdue to a runwayreorderingwas derived from over 2000 hours of real-timechangeis computedand comparedwith the predeter-simulationsinvolvingcontrollersfrom Dallas/FortWorth.mined minimumdelay reduction.If the delay reductionexceedsthe minimumdelay, the changeis made.It is imbeddedin the CTAS code in the form of fuzzy logic.An example of the resulting logic for a reordering
+.......... Initial cruise........... _............. _._............ -....... /Vleter ...... i..................... i..................... !.................... !.................... ,. fix ES_TUS i ...L .......... i....... 350"'1" ........ ?.................... i ..................... i'"";'_'"'"i .................... i ............ "_"i ..................... ...... : .................. I,.{...._ ..................... i............................. condition ........... .........I................ ESTUS............ i.... .......................... I.i.................... i.................... i.................... i.........................................Conventional Example..2430. 45OTraffic Management CTAS Station UnitESTUS /4oo..I]!{,i'_{!i InitialcruiseA Zcondition_.......,_,[3ool ..]!f.."l_i::1'Radar[410320340360380400420\ 440'0......20i'''l'''l'''l''' 406080100120East, n.mi.Distance along predicted path, n.mi.DA test FMS Example I_"450DA test engineer__Radar Sector ESTUScommuncationsMeter fiiiiInitial cruiseS A @ Z43o.__Y _ UAL _ .,, I condition ..f:"/ aircraft <->ATC Normal VHF __i /_ ;redicted ] ,_!!_Or/ DRAK0 5 _[ ],a ,.=\ 35o..I 300..i :,: ,i ii i i:: i ......... i i :i '410320340360380400420440_/,". __ 2oo-_........ .,,_,.ey_---i ,, t,,,l,''l'''I.......... ................... _ ---, -.;..---; .... ' Radar..I ....... I'East, n.mi.020406080100ControllerD/_, sector oDserverFigure 13. DA test configuration."_ 2so4........ =t ............ _ .................... }.................. I--Predicted I............. 2ooJ........ __ ...................... i.................. t , ............. ......... Initial cru'se ............ :...................... ;.._ .............. /_! ................... i......... condition .......... ........... , .......... ii iiiiiiiiiiill .........
+Table 1 .1Meter fix crossing time accuracy (seconds)TSRV aircraftUALaircraftGuidanceFMSCTASCTASmodepredictionpredictionpredictionAll8.8 mean,-2.3 mean,2.4 mean,10.5 rms12.5 rms13.1 rmsnon-FMS16.8 mean,1.7 mean,7.4 mean,9.4 rms10.0 rms14.3 rmsFMS4.9 mean,-6.3 mean,-2.5 mean,9.4 rms12.4 rms10.0 rmsAs previouslynoted, fast time analysis has indicated astrong relation between operationalbenefits and the accu-racy with which aircraft are delivered across the meter-fix. Based on a preliminaryextrapolationof this analysis,
+		
+		
+			
+August 1995Technical Memorandum
+TITLE AND SUBTITLEThe Center-TRACON Automation System: Simulation and Field Testing
+AUTHOR(S)Dallas G. Denery and Heinz Erzberger A new concept for air traffic management in the terminal area, implemented as the Center-TRACON Automation System, has been under development at NASA Ames in a cooperative program with the FAA since 1991.The development has been strongly influenced by concurrent simulation and field site evaluations.The role of simulation and field activities in the development process will be discussed.Results of recent simulation and field tests will be presented.
+SUBJECT TERMS			
+			
+
+				
+
+
+	
+		Design of Center-TRACON Automation System. Proceedings of the AGARD Guidance and Control Panel 56th Symposium on Machine Intelligence in Air Traffic management
+		
+			HErzberger
+		
+		
+			TJDavis
+		
+		
+			SMGreen
+		
+		
+			1993
+			
+			Berlin, Germany
+		
+	
+	Erzberger, H.; Davis, T. J.; and Green, S. M.: Design of Center-TRACON Automation System. Pro- ceedings of the AGARD Guidance and Control Panel 56th Symposium on Machine Intelligence in Air Traffic management, Berlin, Germany, 1993, pp. 52-1-52-14.
+
+
+
+	
+		The Traffic Management Advisor
+		
+			WilliamNedell
+		
+		
+			HeinzErzberger
+		
+		
+			FrankNeuman
+		
+		10.23919/acc.1990.4790788
+	
+	
+		1990 American Control Conference
+		San Diego, Calif
+		
+			IEEE
+			May 1990
+		
+	
+	Nedell, W.; and Erzberger, H.: The Traffic Manage- ment Advisor. Proceedings of the American Control Conference, San Diego, Calif., May 1990.
+
+
+
+	
+		
+			SMGreen
+		
+		Time-Based Operations in an Advanced ATC Environment
+		
+	
+	Green, S. M.: Time-Based Operations in an Advanced ATC Environment.
+
+
+
+	
+		NASA CP-3090
+		
+			Conference
+		
+		
+			Oct. 1989
+			
+			Virginia Beach, Va
+		
+	
+	Conference, NASA CP-3090, Virginia Beach, Va., Oct. 1989, pp. 249-260.
+
+
+
+	
+		THE FINAL APPROACH SPACING TOOL
+		
+			TJDavis
+		
+		
+			KJKrzeczowski
+		
+		
+			CCBergh
+		
+		10.1016/b978-0-08-042238-1.50015-x
+	
+	
+		Automatic Control in Aerospace 1994 (Aerospace Control '94)
+		Palo Alto, Calif
+		
+			Elsevier
+			Sept. 1994
+			
+		
+	
+	Davis, T. J.; Krzeczowski, K. J.; and Bergh, C. C.: The Final Approach Spacing Tool. Proceedings of the 13th IFAC Symposium on Automatic Control in Aerospace, Palo Alto, Calif., Sept. 1994.
+
+
+
+	
+		The Development of the Final Approach Spacing Tool (FAST): A Cooperative Controller-Engineer Design Approach
+		
+			KatharineKLee
+		
+		
+			ThomasJDavis
+		
+		10.1016/s1474-6670(17)46729-2
+	
+	
+		IFAC Proceedings Volumes
+		IFAC Proceedings Volumes
+		1474-6670
+		
+			28
+			21
+			
+			Sept. 1995
+			Elsevier BV
+			Berlin, Germany
+		
+	
+	Lee, K. K.; and Davis, T. J.: The Development of the Final Approach Spacing Tool (FAST): A Coop- erative Controller-Engineer Design Approach. Proceedings of the 14th IFAC Symposium on Automatic Control in Aerospace, Berlin, Germany, Sept. 1995.
+
+
+
+	
+		Knowledge-based scheduling of arrival aircraft in the terminal area
+		
+			KJKrzeczowski
+		
+		
+			ThomasDavis
+		
+		
+			HeinzErzberger
+		
+		
+			IsraelLev-Ram
+		
+		
+			ChristopherBergh
+		
+		10.2514/6.1995-3366
+	
+	
+		Guidance, Navigation, and Control Conference
+		Baltimore, Md
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 1995
+		
+	
+	Krzeczowski, K. J.; Davis, T. J.; Erzberger, H.; Lev-Ram, I.; and Bergh, C. P.: Knowledge- Based Scheduling of Arrival Aircraft in the Terminal Area. Proceedings of the AIAA Guidance, Navigation, and Control Conference, Baltimore, Md., Aug. 1995.
+
+
+
+	
+		Terminal area trajectory synthesis for air traffic control automation
+		
+			RASlattery
+		
+		10.1109/acc.1995.520941
+	
+	
+		Proceedings of 1995 American Control Conference - ACC'95
+		1995 American Control Conference - ACC'95
+		
+			American Autom Control Council
+			June 1995
+		
+	
+	Slattery, R. A.: Terminal Area Trajectory Synthesis for Air Traffic Control Automation. Conference Proceedings of the American Control Confer- ence, June 1995.
+
+
+
+	
+		Design and evaluation of an air traffic control Final Approach Spacing Tool
+		
+			ThomasJDavis
+		
+		
+			HeinzErzberger
+		
+		
+			StevenMGreen
+		
+		
+			WilliamNedell
+		
+		10.2514/3.20721
+	
+	
+		Journal of Guidance, Control, and Dynamics
+		Journal of Guidance, Control, and Dynamics
+		0731-5090
+		1533-3884
+		
+			14
+			4
+			
+			July-Aug. 1991
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Davis, T. J.; Erzberger, H.; Green, S. M.; and Nedell, W.: Design and Evaluation of an Air Traffic Control Final Approach Spacing Tool. Journal of Guidance, Control, and Dynamics, vol. 14, no. 4, July-Aug. 1991, pp. 848-854.
+
+
+
+	
+		The effectiveness of visual aids in teaching foreign languages
+		
+			MUsmonov
+		
+		10.47689/linguistic-research-vol-iss1-pp233-235
+		OMBNo. 0704-0188
+	
+	
+		Zamonaviy lingvistik tadqiqotlar: xorijiy tajribalar, istiqbolli izlanishlar va tillarni o‘qitishning innovatsion usullari
+		ZLTXTII
+		2249-5959
+		
+			1
+			
+			20503
+			inScience LLC
+			Washington, DC
+		
+	
+	Paperwork Reduction Project (0704-0188
+	OMBNo. 0704-0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collectionof information. Send comments regardingthis burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.
+
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+	
+		Information for CME Credit—Child Influenza Vaccination and Adult Work Loss: Reduced Sick Leave Use Only in Adults With Paid Sick Leave
+		
+			Agency
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+		
+			Only
+		
+		10.1016/j.amepre.2018.12.001
+	
+	
+		American Journal of Preventive Medicine
+		American Journal of Preventive Medicine
+		0749-3797
+		
+			56
+			2
+			A3
+			
+			Elsevier BV
+		
+	
+	Leave blank
+	AGENCY USE ONLY (Leave blank)
+
+
+
+	
+		Final Technical Report High Energy Physics at Belle and Belle II PI John Yelton, University of Florida Dates covered 06/01/2015 to 03/31/2016
+		
+			JohnYelton
+		
+		10.2172/1332870
+	
+	
+		REPORT DATE 3. REPORT TYPE AND DATES COVERED
+		
+			Office of Scientific and Technical Information (OSTI)
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+	REPORT DATE 3. REPORT TYPE AND DATES COVERED
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+				
+			
+		
+	
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+LIST OF FIGURES
+INTRODUCTIONNASA is committed to supporting the Next Generation Air Transportation System (NextGen) through research and development in select areas.One such area, referred to as Super-Density Operations, is conducting research to develop technologies and procedures that will safely increase throughput in busy terminal area environments that involve multiple airports.As part of this effort there is a requirement to develop a Concept of Operations for Super-Density Operations that is consistent with the Joint Planning and Development Office (JPDO) NextGen Concept of Operations (ref.1).This document develops a Concept of Operations for the Tactical Separation Assurance function, one of the functions included in Super-Density Operations.The Tactical Separation Assurance function detects and resolves conflicts that are predicted to occur within a two-to three-minute time horizon.Several conflict-detection and -resolution algorithms for the Tactical Separation Assurance function have already been devised and evaluated (refs. 2 and 3).The document is organized to first provide an overview of Super-Density Operations.A functional architecture along with a general description of each function and a discussion of the role of automation in the near-term, mid-term, and end-state implementations are presented.This overview of Super-Density Operations is expanded in reference 4. The overview is followed by a more detailed description of the Tactical Separation Assurance function.The next section presents several story boards to illustrate how the Tactical Separation Assurance function is envisioned to be used operationally.The last section presents a suggested pathway to implementation.
+SUPER-DENSITY OPERATIONSA functional architecture for Super-Density Operations is provided in figure 1.The architecture is based on a new concept for airspace operations, referred to as the Advanced Airspace Concept (AAC) (ref.5).The AAC architecture achieves a high level of safety by including a two-layered architecture for separation assurance, a strategic layer and a tactical layer.The Merging and Spacing and Off-Nominal Recovery functions shown in the figure represent the strategic layer, while the Tactical Separation Assurance function represents the tactical layer.A third layer of protection is provided by an independent collision-avoidance system similar to the Traffic Alert and Collision Avoidance System (TCAS) that is located on the aircraft.The Extended Terminal Area Routing and Scheduling functions have been added to complete the description of capabilities required for Super-Density Operations.A brief description of each function is provided in the following sections.The descriptions are followed by a brief discussion of the role of automation as applied to separation assurance in the near-term, mid-term, and end-state implementations.Extended Terminal Area Routing: This function provides optimized routes for efficiently routing en-route traffic to the desired airport and runway and for routing surface traffic to en-route airspace.The routing is based on many factors, including weather and airport configuration.Scheduling: This function provides a schedule for achieving a smooth flow of arrivals and departures along the defined terminal area routes.The scheduling of flights along the routes is coordinated with the user (i.e., Airline Operations Center, or AOC), the airport, and en-route operations.Merging and Spacing: This function provides minor adjustments to the schedule and routing to maximize the flow rate along the routes while meeting the separation requirements.This function may be implemented on the ground or in the air.Tactical Separation Assurance: This function provides an independent prediction of a conflict (loss of separation) and, if a conflict is predicted, defines a short-term conflict-free path that resolves the conflict.In doing this task, it provides a layer of safety to protect against an operational error (blunder) or trajectory error introduced by any of the other functions.Off-Nominal Recovery: This function provides options for reinserting an aircraft into the stream of traffic if the aircraft is sufficiently out of conformance that the original sequencing and scheduling cannot be maintained by minor path perturbations or speed control.An aircraft that responds to a resolution issued by the Tactical Separation Assurance function is an example of an aircraft that would have to be reinserted into the sequence.Controller Interface: This function provides the means by which the controller can interact with the automation and surveillance systems to conduct Super-Density Operations.In the near-term concept (2015), the controller and pilot procedures are essentially the same as today.The traffic management coordinator uses Traffic Management Advisor and other information sources to schedule the flights along the nominal routes.The controller is responsible for the Merging and Spacing, Off-Nominal Recovery, and Tactical Separation Assurance functions without the benefit of computer-generated advisories.The major difference from today's operations is an improved capability for predicting an impending loss of separation.In the mid-term concept (2025), the Merging and Spacing, Off-Nominal Recovery, and Tactical Separation Assurance functions provide the controller with computer-generated advisories to assist in the performance of these functions.It is also expected that the controller may delegate merging and spacing to the flight deck under some conditions.However, the controller is responsible for the control of traffic and identifies the function that is appropriate to the situation.In the end-state concept (2035), the system is largely automated.Under routine conditions, the Merging and Spacing and Tactical Separation Assurance functions are performed without controller intervention.The Off-Nominal Recovery function is highly automated but is likely to require some controller interaction.It is assumed that a controller is responsible for monitoring the health of the system and is able to engage in the control of traffic if required or in cases such as when an aircraft is unable to receive data-link.
+TACTICAL SEPARATION ASSURANCE FUNCTIONThis section contains a more complete description of the Tactical Separation Assurance function and its operational use in the near-term, mid-term, and end-state concepts.Near-term concept (2015): In the near-term concept, the Tactical Separation Assurance function is the responsibility of the controller.A conflict-detection capability is included to assist the controller in identifying potential conflicts, but the controller is responsible for resolving the conflicts without the benefit of computer-generated resolution advisories.The controller procedures, interfaces, and responsibilities are similar to those used with Conflict Alert (CA) (ref.6), and those being planned for Automated Terminal Proximity Alert (ATPA) (ref.7).Although the displays for ATPA have not yet been completely defined, a notional depiction of the CA and the planned ATPA displays is provided in figure 2. The figure shows a sequence of in-trail traffic designated by the characters "H" moving from left to right along a defined course.The sideways V represents a site-specific landmark.A Conflict Alert results in the characters "CA" being added to the upper line of the data block where they are blinked.A CA also results in an audible warning.ATPA provides a "Cautionary Alert" and an "Immediate Attention Alert" based on time to predicted loss of separation.A "Cautionary Alert" causes the display to show a cone (see fig. 2) in yellow.The apex of the cone begins at the aircraft track and extends along the direction of flight.The length of the cone is related to the separation requirements.An "Immediate Attention Alert" turns the ATPA display orange.In the figure, the orange cone indicates an "Immediate Attention Alert" for EFG650 and EFG423.The "Immediate Attention Alert" is activated when the predicted loss of separation is less than a predefined time, T IA .The "Cautionary Alert" is activated when the time to a predicted loss of separation is greater than T IA but less than a predefined time, T C , where T C >T IA .Depending on the phase of flight, it is expected that the values for T IA and T C will range between 22 and 60 seconds and between 45 and 120 seconds, respectively.Because the intended purpose of ATPA is to allow the safe control of traffic as near to the separation minima as possible while Conflict Alert is to protect against a potential collision by generating an alert of a potential or actual infringement of separation minima, the Conflict Alert is activated when the predicted loss of separation is less than a predefined time, T CA , where T CA ≤ T IA .The controller may decide that a tactical resolution is necessary at any time, independent of the alert status.The displays and alerts are provided to improve the controller's awareness of the situation, but they do not require a controller response.If the controller decides that a resolution is required, the controller determines the resolution and transmits it to the flight deck via voice.The major difference between the near-term Tactical Separation Assurance function and that used today for CA and proposed for ATPA is the use of improved trajectory predictions and a strict use of Federal Aviation Administration (FAA) separation standards to provide the controller with earlier alerts and reduced false-alert rates.The improved conflict-detection algorithm drives both the CA warnings and the ATPA displays.The improved trajectory-prediction algorithm, referred to as the Flight Intent Algorithm, uses knowledge of area navigation system (RNAV) routes and site-specific terminal area procedures to infer the intended flightpath of the aircraft.The Flight Intent Algorithm combines this limited-intent information with dead-reckoning trajectory predictions to define the trajectories that are examined for a conflict.A comparison with other approaches to conflict detection using fast-time simulation of recent arrival and departure operations, some of which included operational errors, demonstrated that this algorithm achieves a significantly reduced false-alert rate with slightly improved alert lead times (ref.3).Mid-term concept (2025): The major differences from the near-term operation are in the improvement of the conflict-detection algorithm and in the addition of automation to assist the controller in resolving a conflict involving one or more aircraft pairs.The conflict-detection algorithm differs in that it is modified to take advantage of improved surveillance data and knowledge of specific terminal routes to provide a better understanding of the intended flightpath of an aircraft, thereby potentially allowing earlier detection of an impending loss of separation and a reduced false-alert rate (ref.2).The conflict-resolution automation assists the controller by identifying a clearance that results in a 2-minute conflict-free flightpath for each aircraft that must be maneuvered to avoid the conflict (ref.3).It is expected that 2 minutes is adequate for the Off-Nominal Recovery function to intervene.The algorithm for resolving a conflict results in a set of speed, altitude, and/or vector changes.Since there are usually many possible resolution clearances, they are prioritized.Each item in the prioritized list is a set of maneuvers for the aircraft involved.Depending on the location and the encounter geometry of the aircraft involved, the priorities of the clearances may be different.For example, during sequential final approaches, the relative speeds of the aircraft are small and speed maneuvers may be preferred over vector or altitude clearances.Also altitude clearances may be more efficient and less interrupting than vector clearances and so are preferred.On the other hand, in the downwind and base turn area, altitude and vector clearances may be preferred over speed maneuvers.To minimize interference with the Traffic Collision Avoidance System (TCAS), three additional rules are used in developing a prioritized list of clearances.First, because a TCAS Resolution Advisory (RA) is restricted to "climb" or "descend," the Tactical Separation Assurance function resolutions are restricted to the horizontal plane when there is concern that a TCAS RA may also be activated.Second, horizontal resolutions are prioritized so as to reduce the likelihood of inducing a TCAS RA.Third, when possible, altitude resolutions are restricted to those that do not cause the two aircraft to cross each other's altitude.A trial maneuver set is then selected from the prioritized list and validated by computing the trajectories that would result from the trial maneuver set using realistic aircraft performance parameters and determining if the resulting separations meet the separation standards.If the resolution does not provide a 2-minute conflict-free path, the process is repeated using the next trial set of maneuvers within the prioritized list.A detailed description of the algorithm is contained in reference 3.As in the near-term implementation, the conflict-detection algorithm generates a "Cautionary Alert," an "Immediate Attention Alert," or a "Conflict Alert," depending on the time to a predicted loss of separation.In a "Cautionary Alert" situation, the ATPA displays are shown in yellow.In an "Immediate Attention Alert" situation, the ATPA displays change to orange.For a "Conflict Alert" the characters "CA" are added to the upper line of the data block.How and under what conditions the resolution is presented is subject to debate and will be a matter of research.Two options are considered here.In the first option the controller can call on the Tactical Separation Assurance function to provide a suggested conflict resolution for a "Cautionary Alert," an "Immediate Attention Alert," or a "Conflict Alert" by a simple manual entry.The conflict-detection alerts are used only to increase the controller's awareness of a potential loss of separation but not to directly cause the resolution to be displayed.The concern with this approach is that it requires a manual entry in a high-workload situation.Another option being considered is similar to the first option with the exception that in a "Conflict Alert" situation the resolution is automatically displayed in a simplified format.The concern with this approach is that it may replace an existing advisory that the controller still believes will resolve the problem or result in conflicting advisories if both are retained.With either option, upon requesting a conflict resolution, the Tactical Separation Assurance function generates a clearance that results in a 2-minute conflict-free flightpath for each aircraft that requires maneuvering to resolve the conflict.If the controller accepts the resolution advisory, the controller provides the resolution to the flight deck via voice and data-link and upon concurrence by the flight deck is used to automatically update the trajectory information for that flight.If the controller issues a clearance that is not generated by the Tactical Separation Assurance function, the controller must enter it manually into the system or the flight will be treated as a nonconforming flight that is managed by the Off-Nominal recovery function.End-state concept (2035): In the end-state concept the Tactical Separation Assurance function is similar to that for the mid-term concept except that the controller is no longer responsible for routine separation of traffic.The conflict-detection algorithm still generates a "Cautionary Alert," an "Immediate Attention Alert," or a "Conflict Alert."However, since the controller is no longer responsible for separation assurance, these alerts control which function is responsible for the aircraft involved in the conflict at a given instant in time.For a "Cautionary Alert" or an "Immediate Attention Alert," the functions responsible for managing the involved flights at the time of the alert receive notice of the alert but continue to manage the flights (i.e., Merging and Spacing or Off-Nominal Recovery function).For a "Conflict Alert," there is a transition of control of the involved flights from the functions in control at the time of the alert to the Tactical Separation Assurance function.The Tactical Separation Assurance function generates the necessary 2-minute conflict-free path that is automatically transmitted to the appropriate aircraft via voice and data-link and upon concurrence by the flight deck is used to automatically update the trajectory information for that flight.The "Cautionary Alert," "Immediate Attention Alert," and "Conflict Alert" for aircraft in conflict are also shown on a plan-view display so that a controller in charge can monitor the overall health of the system.The controller in charge can request an automatically generated 2-minute conflict-free maneuver for an aircraft in conflict or intervene in the management of a specific flight at any time.The controller is also required to handle special cases such as those in which an aircraft is not equipped to receive data via data-link.
+STORY BOARDS FOR TACTICAL SEPARATION ASSURANCEThe purpose of the story boards is to assure that the concept of operations for the Tactical Separation Assurance function leads to a similar set of procedures for a wide range of possible scenarios.To illustrate the similarity in operation in different situations, complete story boards are presented for two scenarios: 1) both aircraft are in three-dimensional (3-D) conformance and on the same route and 2) the aircraft are on different routes with one of the aircraft being out of 3-D conformance.An aircraft is said to be in 3-D conformance if it is on the assigned horizontal route at the correct altitude.Mid-term operations together with the first option where the controller must make a simple manual entry to be provided with an automatically generated resolution are used as the basis for developing the primary story board for each scenario.Differences in near-term and end-state operations are then discussed.Aircraft A1 and A2 are in 3-D conformance on the same route: Situation: Aircraft A1 and A2 are on downwind.A1 is flying at 210 knots ground speed.A2 is assigned an airspeed based on altitude and winds that would position A2 behind A1 with the correct spacing.Because of errors, the clearance leads to A2's closing on A1 faster than expected, causing a potential loss of separation.The hypothesized sequence of actions in case of such an error follows:Mid-term implementation follows:• A loss of separation is predicted to occur at a time less than T C but more than T IA , thereby activating a "Cautionary Alert."-A yellow ATPA cone, which extends from the trailing aircraft, A2, in the direction of flight, is displayed on the controller's screen.-The "Cautionary Alert" can be transmitted to the automation within the Merging and Spacing function for use if required.-The "Cautionary Alert" can be transmitted to the aircraft for use via voice and/or datalink if required.-The controller continues to use the Merging and Spacing function to provide speed and path adjustments to maintain spacing.• A loss of separation is predicted to be less than T IA seconds, thereby activating an "Immediate Attention Alert."-The ATPA cone on the trailing aircraft is changed to orange on the controller's screen.-The controller reviews the situation, determines that a resolution is required, and requests a tactical resolution advisory from the Tactical Separation Assurance function.-The Tactical Separation Assurance function generates a suggested 2-minute conflict-free path for A2 that resolves the predicted conflict.-The controller accepts the automatically generated 2-minute conflict-free path and transmits the resolution in the form of a clearance to the flight deck of A2 via voice and datalink.-Upon concurrence by the flight deck the resolution is used to automatically update the trajectory information for that flight.Near-term implementation follows:In the near-term implementation, the scenario is the same except that the controller is required to define a resolution without the benefit of a computer-generated advisory.End-state implementation follows:In the end-state implementation, the system is largely automated.The Merging and Spacing function maintains control over the involved flights until a "Conflict Alert" is issued.For a "Conflict Alert," the Tactical Separation Assurance function automatically generates a 2-minute conflict-free path that is transmitted to the appropriate aircraft via data-link and computer-generated voice without requiring controller intervention.Aircraft A1 and/or A2 are out of 3-D conformance:Situation: A1 is following a nominal terminal area arrival route.A2 is a departure from a nearby airport along an RNAV departure route.A2 incorrectly believes that it was given a clearance to do a "direct to."In executing the "direct to," A2 is on a path that will lead to a loss of separation with A1.A1 is under responsibility of the Merging and Spacing function.It is recognized that A2 is not following the assigned clearance and is being managed by the Off-Nominal Recovery function.Both aircraft are in radio contact with the terminal radar approach control facilities (TRACON).The hypothesized sequence of actions in case of such an error follows:Mid-term implementation follows:• A loss of separation is predicted to occur at a time less than T C but more than T IA , thereby activating a "Cautionary Alert."-ATPA displays are presented in yellow on the controller's screen.(The actual displays to be used in this situation have not yet been defined.)-The "Cautionary Alert" can be transmitted to the Merging and Spacing and Off-Nominal Recovery functions for use if required.-The "Cautionary Alert" can be transmitted to the aircraft via voice and data-link for use if required.-The controller or controllers continue to use the Merging and Spacing and Off-Nominal Recovery functions to provide strategic resolutions to A1 and A2, respectively.• A loss of separation is predicted to be less than T IA seconds, thereby activating an "Immediate Attention Alert."-A1 and A2 ATPA displays are changed to orange on the controller's screen.-The controller reviews the situation, determines that a tactical resolution is required, and requests a tactical resolution advisory from the Tactical Separation Assurance function.-The Tactical Separation Assurance function generates clearances that will provide 2minute conflict-free paths for both A1 and A2 that resolve the predicted conflict (a resolution involving only one of the aircraft was not found).-The controller accepts the automatically generated clearances and transmits the clearances to the two aircraft via voice and data-link.-Upon concurrence by the flight deck the resolution is used to automatically update the trajectory information for that flight.Near-term implementation follows:• In the near-term implementation the scenario is the same except that the controller is required to determine a resolution without the benefit of computer-generated advisories.End-state implementation follows:• In the end-state implementation the system is largely automated.The Merging and Spacing and Off-Nominal Recovery functions maintain control over aircraft A1 and A2, respectively, until the "Conflict Alert" is issued.Upon activation of the "Conflict Alert," the Tactical Separation Assurance function generates 2-minute conflict-free paths for A1 and A2 that are transmitted to the appropriate aircraft via data-link and computer-generated voice without requiring controller intervention.The resolution is automatically added to the trajectory information for the involved flights.
+PATHWAY TO IMPLEMENTATIONThe pathway to implementation is highly uncertain and will depend on further research.Nevertheless, it is advantageous to at least envision how this capability may eventually be implemented into the National Airspace System (NAS).In preparation for the near-term time frame, NASA will engage with the FAA to determine the feasibility and benefits that would be derived by modifying the ATPA and CA to include an improved conflict-detection capability based on some of the concepts developed as part of the NASA Airspace Systems Program.Although a direct comparison has not yet been conducted, fast-time simulation of recorded arrivals and departures in a busy terminal area has demonstrated that the Flight Intent Algorithm with a strict use of FAA minimum separation requirements may provide earlier detection of a conflict with a lower false-alert rate than those currently being used with CA and ATPA.A next step would be to conduct a more thorough evaluation of the different approaches to conflict detection and take the best of each approach to develop the conflict-detection algorithms that would form the basis of the automation included in the near-term Tactical Separation Assurance function.Similarly, in the mid-term time frame, it is proposed to work with the FAA in the development and evaluation of the algorithms for conflict resolution.Although an initial conflict-resolution algorithm has been developed under the NASA Airspace Systems Program, it has not been tested in simulation with FAA controllers.The next step will be to further refine the algorithm through controller-in-theloop simulations.In parallel, the conflict-resolution algorithm will be tested against live traffic data to determine its ability to successfully resolve a conflict.Upon FAA concurrence, the algorithms will be evaluated by active controllers in a shadow mode at an FAA-selected facility.The shadowmode evaluation will be followed by a field evaluation.Upon successful completion of the field evaluation, the technology will be transferred to the FAA for implementation into the terminal area automation system.In the end-state time frame, the Tactical Separation Assurance Function is meant to automatically detect and resolve conflicts without controller intervention.The clearances would be automatically transmitted to the flight deck via data-link and upon concurrence by the flight deck they are uploaded into the flight management system (FMS) or flight control system for execution.This scenario clearly represents a major departure from the mid-term concept where the automation provides the controller with resolution advisories but the controller is still responsible for separation.The transition will depend on confidence that the automation will lead to safe and acceptable operations.To gain the necessary level of confidence, the resolution advisories provided in the mid-term concept will be monitored and evaluated for accuracy, robustness, and acceptability and a comprehensive risk analysis will be conducted.In parallel, the end-state version of the Tactical Separation Assurance function will be tested in fast-time simulations to assure statistically significant results and in human-in-the-loop simulations to understand the feasibility of controller intervention in emergency situations.Initial use of the Tactical Separations Assurance function for controlling traffic will be in low-density traffic conditions with controller oversight.As confidence improves (based on use in low-density conditions and safety analysis), the end-state Tactical Separation Assurance function will be used with increasing levels of traffic until it is accepted for routine operations.INTRODUCTION ................................................................................................................................ SUPER-DENSITY OPERATIONS ...................................................................................................... TACTICAL SEPARATION ASSURANCE FUNCTION ................................................................... STORY BOARDS FOR TACTICAL SEPARATION ASSURANCE ................................................ PATHWAY TO IMPLEMENTATION ............................................................................................... REFERENCES ...................................................................................................................................
+Figure 1 .1Figure 1.Major functions required for Super-Density Operations.......................................................
+Figure 2 .2Figure 2. Notional depiction of displayed information.........................................................................
+Figure 1 .1Figure 1.Major functions required for Super-Density Operations.
+Figure 2 .2Figure 2. Notional depiction of displayed information.
+TABLE OF CONTENTSOF
+			UARC, University of California at Santa Cruz, Ames Research Center, Moffett Field, CA 94035-1000.
+		
+		
+			
+
+				
+
+
+	
+		The Next-Generation Air Transportation System's Joint Planning Environment: A Decision Support System
+		
+			EdgarWaggoner
+		
+		
+			ScottGoldsmith
+		
+		
+			JoshElliot
+		
+		10.2514/6.2009-7011
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		
+			American Institute of Aeronautics and Astronautics
+			June 2007
+		
+	
+	Concept of Operations for the Next Generation Air Transportation System, Joint Planning and Development Office, version 2.0, June 2007.
+
+
+
+	
+		Tactical Conflict Detection in Terminal Airspace
+		
+			HuabinTang
+		
+		
+			JohnRobinson
+		
+		
+			DallasDenery
+		
+		10.2514/6.2010-9294
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 2010
+		
+	
+	Tang, H.; Robinson, J.; and Denery, D.: Tactical Conflict Detection in Terminal Airspace. AIAA Aviation, Technology, Integration, and Operations conference, Fort Worth, TX, Sept. 2010.
+
+
+
+	
+		Tactical Separation Algorithms and Their Interaction with Conflict Avoidance Systems
+		
+			HuabinTang
+		
+		
+			DallasDenery
+		
+		
+			HeinzErzberger
+		
+		
+			RussellPaielli
+		
+		10.2514/6.2008-6973
+		AIAA-2008-6973
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		
+			American Institute of Aeronautics and Astronautics
+			Aug. 2008
+		
+	
+	Tang, H.; Denery, D.; Erzberger, H.; and Paielli, R.A.: Tactical Separation Algorithms and Their Interaction with Collision Avoidance Systems. AIAA, GNC conference, AIAA-2008-6973, Aug. 2008.
+
+
+
+	
+		A Concept for Robust, High Density Terminal Air Traffic Operations
+		
+			DougIsaacson
+		
+		
+			JohnRobinson
+		
+		
+			HarrySwenson
+		
+		
+			DallasDenery
+		
+		10.2514/6.2010-9292
+	
+	
+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, TX
+		
+			American Institute of Aeronautics and Astronautics
+			Sept. 2010
+		
+	
+	Isaacson, D.; Swenson, H.; and Robinson, J.E.: A Concept for Robust, High-Density Terminal Air Traffic Operations. AIAA Aviation, Technology, Integration, and Operations Conference, Fort Worth, TX, Sept. 2010.
+
+
+
+	
+		Concept for Next Generation Air Traffic Control System
+		
+			HeinzErzberger
+		
+		
+			RussellAPaielli
+		
+		10.2514/atcq.10.4.355
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			10
+			4
+			
+			Aug. 30, 2004
+			American Institute of Aeronautics and Astronautics (AIAA)
+			Yokohama, Japan
+		
+	
+	Erzberger, Heinz: Transforming the NAS: The Next Generation Air Traffic Control System. 24th International Congress of the Aeronautical Sciences (ICAS), Yokohama, Japan, Aug. 30, 2004.
+
+
+
+	
+		Federal Aviation Administration Commercial Space Transportation Research and Development Program
+		
+			ChuckLarsen
+		
+		10.2514/6.2007-6282
+	
+	
+		AIAA SPACE 2007 Conference & Exposition
+		
+			American Institute of Aeronautics and Astronautics
+			April 2007
+		
+		
+			U.S. Department of Transportation, Federal Aviation Administration
+		
+	
+	Common ARTS Computer Program Functional Specification (CPFS) Conflict Alert, NAS-MD- 632, U.S. Department of Transportation, Federal Aviation Administration, April 2007.
+
+
+
+	
+		Human-in-the-Loop Investigation of Interoperability Between Terminal Sequencing and Spacing, Automated Terminal Proximity Alert, and Wake-Separation Recategorization
+		
+			ToddJCallantine
+		
+		
+			ThomasPrevot
+		
+		
+			NancyBienert
+		
+		
+			AbhayBorade
+		
+		
+			ConradGabriel
+		
+		
+			VimmyGujral
+		
+		
+			KimJobe
+		
+		
+			JoeyMercer
+		
+		
+			JoshuaKraut
+		
+		
+			LynneMartin
+		
+		
+			FaisalOmar
+		
+		10.2514/6.2016-3300
+		ATO0T-CARTS-1055
+	
+	
+		16th AIAA Aviation Technology, Integration, and Operations Conference
+		
+			American Institute of Aeronautics and Astronautics
+			Oct. 2008
+			20
+		
+	
+	Functional Description Narrative, N32422 Automated Terminal Proximity Alert (ATPA)-Final Approach Course, ATO0T-CARTS-1055, Version 20, Federal Aviation Administration, Oct. 2008.
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+				
+			
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+IntroductionThe Federal Aviation Administration (FAA)'s plan to modernize the National Airspace System (NAS) in order to increase its capacity by 2025 is known as Next-Generation Air Transportation System (NextGen) [1].One of the goals of NextGen is to accommodate the expected traffic-demand increase in the already congested terminal airspace.In the near term, this goal calls for tools to predict separation violations in order to plan conflict resolution strategies.Overall, there are two categories of tools for the final approach: tools that maximize throughput, such as the Traffic Management Advisor (TMA) [2] or the Terminal Area Precision Scheduling and Spacing System (TAPSS) [3], and those that predict separation violations such as the Automated Terminal Proximity Alert (ATPA) [4] or the Tactical Separation-Assisted Flight Environment (TSAFE) [5].Both categories of tools, however, require accurate landing-speed estimates to provide accurate space between successive arriving aircraft because all the tools rely on predictions of the flightpath of the aircraft.The air traffic controllers' practices and procedures dictate aircraft speed throughout most of the airspace, but the flight crew (and aircraft procedures) decides the speed on approach in preparation for landing.Therefore, accurate landing-speed predictions are required for tools intended for use on final approach.Uncertainty in landing speed is manifested as either an increase in separation violations (i.e. a safety risk) or as increased separation buffers, a reduction in throughput.There exist many different approaches to establish the landing speed of an aircraft to be used in the decision support tools.However, most of the options would require an upgrade to avionics equipment aboard the aircraft, increasing the airline's expenses, or verbal reports from the flight crew on landing speed, increasing the workload for both the flight crew and the controllers.Thus, to fulfill the goal of this work for near-term implementation with no additional equipment required, the focus of this research is to develop an accurate landing-speed model based on information that is readily available in today's air traffic control system.In a previous study, the multivariable regression technique based on the response surface equation (RSE) is used to develop a statistical model of aircraft approach speed [6].While the model yields an acceptable level of accuracy, the high number of variables it requires as inputs is unavailable at most airports.With the reduced number of input parameters, the RSE technique fails to provide an acceptable prediction model.To address this limitation, the alternative of the non-linear regression of the Neural Network modeling approach is used.The regression model is restricted to input variables that are more likely to be available (airlines internal data are usually difficult to obtain).The regression technique employed data for descent and approach phases of flight, as well as environmental and airport characteristics data.
+3D2-2
+BackgroundThe forecasted increase in the traffic demand beyond the current capacity of the NAS may, in turn, lead to an increase in air traffic controllers' workload.This issue cannot be resolved by simply expanding the air traffic control (ATC) staff and facilities.The complexity of ATC, which central task is to ensure separation for all pairs of aircraft, grows quadratically with the number of aircraft [7].Consequently, there is a need to create tools that can facilitate and strengthen the air traffic controllers' decision process, while still meeting the safety requirement.This paper focuses on such tools for the terminal environment, specifically those that address separation between successive arrivals.The specific challenge of such separation assurance lies in pursuing two competing goals: to maintain runway arrival throughput, and to ensure there are no separation violations between any pair of aircraft.The latter goal is set because separation violations carry the risk wake vortex hazards if too little separation is prescribed [8].The task of separating aircraft becomes increasingly complex in highly congested terminal airspace [9].Among the requirements for a model that addresses this challenge is the capability to predict an aircraft landing speed.The role of knowing landing speed is as follows.As a pair of successive arriving aircraft approaches the landing runway, there is a natural process of compression of the distance between the pair when the leading aircraft is slowed ahead of the trailing aircraft [10].To determine the approach-speed profile of the trailing aircraft, one must know the landing speed of the leading aircraft.Since the air traffic controller does not prescribe a landing speed, those data must either come from the aircraft or be estimated.Landing-speed estimation must be sufficiently accurate for effective use by the different ATC tools.Currently, to mitigate the impact of inaccurate landing-speed estimates and to account for uncertainty in trajectory predictions, air traffic controllers often add excess separation beyond the required standards between pairs of aircraft.This practice leads directly to loss of runway throughput.
+MotivationThe main objective of this work is to build a landing-speed model based on variables readily available in today's air traffic automation systems.To make this model useful, the required predictions of landing speed must be computed in real-time operations, efficiently and with sufficient accuracy.There are three options to consider when determining the landing speed of an aircraft:1.The flight crew communicates the intended landing speed to controllers electronically (e.g., via ADS-B message data).2. The controller requests the landing speed from the flight crew by voice communication and feeds it into an interactive tool.3. The landing speed of aircraft is predicted using a mathematical model.Of these three options, the first requires installations of costly avionics aboard the aircraft, while the second increases workload for the controllers and the flight crew.Also, these two options require changes to the ground equipment and to controllers' procedures, significant undertakings that could face resistance from various parties involved.For these reasons, the third option is the approach of choice in this paper.It is geared toward near-term implementation, using readily available information to estimate the landing speed.The modeling methodology in this paper uses the neural network regression technique and can be found in section 2. The currently known and used methods of landing speed prediction are summarized in section 3. A set of recorded data is used to build a predictive landing speed model using the model in section and the results are analyzed in section 4. Finally, a summary of the findings and a future direction of the research are laid out in section 5.
+Modeling MethodologyAll data-driven modeling frameworks for building predictive models rely on the quality of the raw data and on the processing methods used.To be useful, raw data typically need thorough preprocessing steps.
+Data Selection and ProcessingThe approach taken in this paper to predicting landing speeds is multi-variables nonlinear regression 3D2-3 that fits contributing inputs to the corresponding historical landing speeds as outputs.From among the different approaches to collect and process historical data, the following methodical three-step approach was chosen because it leverages previous work [11].The three steps are: data collection, screening, and parameters selection decision.
+Data Collection step:The first step of a model development consists of identifying and collecting as many variables as possible to make sure no impactful variables are overlooked.Thus, for this landing-speed modeling, a model is built for a specific aircraft type at a given airport because each airport has its specific characteristics.The raw data are composed of all airline monitored flight parameters (e.g., fuel consumption, aircraft weight, etc.), radar recorded data (e.g., ground speed, etc.) and airport environmental conditions (e.g., wind, visibility, ceiling, etc.).Screening step: This step centers the analysis on variables that directly impact the modeling task.It serves to detect the parameters that contribute the most to the landing-speed behavior.The screening is necessary to get rid of duplicates and redundancies in the input information for the model.Also, obvious parameters with no impact (e.g., departure airport, etc.) on the model are eliminated.Other variables with no or little perceivable effect on the overall model accuracy such as city-pair, route information such as domestic or foreign (i.e.landing weight may vary due to requirement of reserve fuel), are discarded.
+Parameters selection decision step:In this step, final parameters to be including in the model are decided based on the result of the screening as well as engineering judgment.Hence, it is important to have a good understanding of the system to be modeled.For instance, it is common practice by airlines to track both the planned and actual variables for many key parameters.However, in this work, only the actual values of the variables are used.The use of actual variables would allow achieving more realistic models because planned variables are typically based on the predicted actual variables augmented by a margin value to account for the uncertainty of flight parameters (e.g., fuel burn, time, etc.).
+Neural Network Mathematical ModelingThe neural network (NN) is a mathematical modeling technique based on regression.Intended to mimic the cognitive and inferential functions of the human brain, a NN is capable of modeling highly non-linear behavior.In general NNs can be an alternative modeling methodology when linear modeling approaches perform poorly [12].One of the easiest types of neural network to build and implement is the feed-forward back propagation (FFBP).Usually, FFBP is a good starting point for building a model because a feed-forward neural network can in theory fit any nonlinear relationship between inputs and target.However, its main disadvantages for this type of study is that it works like a black box for the end user, providing no qualitative insight on the impact or the variance of each input variable on the landing speed.
+Neural Network BackgroundArchitecturally a FFBP NN is composed of an input layer, hidden layers, and an output layer, as illustrated in Figure 1.
+Figure 1. Feed-Forward Neural NetworkWhile the hidden layer is the data processing center of the network, the output layer is the response to the simulation.A layer is composed of neurons.In a layer, each input element is connected to each neuron through a weight (w), and a bias (b) is added to the weighted input.Mathematically, the value of the hidden node is computed by composing the logistic function (1) With a linear function that fits the design variables X i 's.The hidden node has the form 3D2-4 (2) where: d j : intercept term for the j th hidden node c ij : coefficient for the i th design variable X i : value of the i th design variable H j : value of the j th hidden node N: number of input variables The logistic sigmoid function S defined by equation ( 1) is used to "squish" the inputs so that its output is a value between 0 and 1.Typically, there are three main steps to building a neural network: training, validation and testing.Consequently, a dataset composed of an input set and corresponding target output are divided into those three groups.Usually there is a pre-processing step to get the data into a useful form.Then the training step consists of tuning the values of weights and biases of the network to optimize network performance [13].The validation step consists of using the second (validation) set of the data to adjust the quality of the regression.Finally, the test set serves to check the network on a set different from the one used for its construction.Among the many existing NN training algorithms, the one by Levenberg-Marquardt [14] is the most commonly used for feed-forward networks when rapid training is desired.Based on the goal of this research, to model a regression of the input variables to predict the corresponding landing speed, the network is trained for function approximation as opposed to pattern recognition.A NN training requires a training set based on known inputs and corresponding target outputs.It is also customary for neural networks to undergo the training more than once (a technique known as retraining) to improve the quality of the fit.Then to stop the training process, the Mean Square Error (MSE) performance function defined in equation ( 3) is used as the stopping criteria.(3) where: MSE: Mean Square Error, the average squared error between the network outputs and target outputs e i : i th error between the network outputs and target outputs t i : i th target or actual output a i : i th network predicted output
+Model EvaluationAfter the model is built, it needs to be evaluated to assess its effectiveness.The model evaluation consists of determining the predicted landing speed errors for the neural network model and the baseline (i.e.recommended target landing speed).To make this comparison the following two metrics are defined:Error Vtarget : defined as the absolute difference between the actual landing speed and the baseline speed (V Target ):(4)Error Model : defined as the absolute difference between the actual landing speed and the landing speed predicted by the neural network model: (5) With:V Actual : Actual speed or true speed is defined as the sum ground speed (obtained from the terminal radar approach control facilities (TRACON) radar data) and the reported wind at the airport (from METAR), V Target : Target speed or flight manualrecommended landing speed is defined as the flight manual-recommended approach speed based on aircraft type and aircraft weight.This speed is used as the baseline landing speed for comparison.V Model : Landing speed of the model is defined as the neural network model predicted landing speed.
+3D2-5
+Flight Operating Manual -BaselineThere exists a flight-operating manual for each aircraft type.In this work, the flight operating manual recommendation for landing-speed prediction based on the landing-aircraft weight represents the baseline.To assess the quality of the proposed landingspeed model, its predictions are compared to the state-of-the art prediction or baseline (i.e. the flightoperating manual recommendations).The example of an aircraft type used here is the MD80, also known to as the Super 80, one of the most common narrow-body air-transport aircraft.For this aircraft type, the flight operating manual has two recommendations: one for low-and-no-gust condition defined as gust wind below 10 knots, and another for high-gust condition for gust wind greater than 10 knots.
+Landing with Low-and-no-Gust ConditionIn the low-and-no-gust condition on the final approach, pilots are recommended to target an approach speed of: (6) Where:: Final approach target speed (knotsindicated airspeed (IAS)): Reference speed (knots IAS)The reference speed is provided in the flight manual as a function of aircraft weight and aircraft type.
+Landing with High-Gust ConditionIn the high-gust condition, the operations manual recommends that on the final approach the pilot target the speed of: (7) Where:: Final approach target speed (knots IAS) : Reference speed (knots IAS) : Steady wind defined as the headwind component of the reported winds : Gust wind defined as the headwind differential between the reported winds and the reported gusts With the restriction that the total wind additive to the V ref should not exceed 20 knots.
+Proof of Concept and Results
+Background on the Choice of Data UsedPrevious studies [10,15] suggested that airportspecific parameters (airport elevation, runway length, etc.) contribute to the final approach performance of a flight.Also, each aircraft type (e.g., B737-800 or MD80) has specific performance parameters that would directly impact its landing speed.To account for all these variations that may affect the fidelity of any landing-speed model, an airport-specific and aircraft-specific landing model is created using historical data.
+Data Processing: Variables Used in the StudyAs a proof of the concept, the proposed neural network is applied to the MD80 aircraft type operations at the Dallas/Fort Worth International Airport (DFW).DFW was chosen as a test case for the availability of data and because the SP80 is the highest number of a single aircraft type in service at this airport.
+Step 1: Data collectionThe raw dataset used for this study contained over 300 variables.These data consisted of airport topological (e.g., runway length and configuration), environmental information (e.g., visibility, wind condition, ceiling), flight-specific parameters (e.g., city pair flown, fuel burn rate, landing weight, etc.), and aircraft-specific parameters (e.g., empty gross weight, aircraft maximum payload, etc.).For the remainder of this paper the collected data are considered as the true value or actual value.Therefore these variables are assumed known exactly.
+Step 2: ScreeningThe term screening is used in this paper to mean identification of the predictive values of the parameters.Screening is done here in two steps: elimination of irrelevancies and elimination of duplicate information to the model.Variables identified at the outset as irrelevant are automatically 3D2-6 eliminated (e.g., aircraft tail number, city of departure, etc.).The elimination of redundancies is based on the statistical correlation between pairs of variables.If two or more variables provide the same information, only one of them is used for the modeling.For example, as shown in Figure 2, the variables pax (number of passengers) and LF_pax (Load Factor for passengers) have a correlation coefficient of 1.For modeling purposes these two variables provide the same information, so the use of one of them is sufficient.Consequently, after dropping the linear dependent parameters, LF_pax, FBR (Fuel Burn Rate), and ESAD_nm (Equivalent Still Air Distance) are among the retained variables for modeling, while FGFH (Flight Gallons/Flight Hours) and GCD_sm (Great Circle Distance (statute mile)) are dropped.
+Figure 2. Example of Correlated Variables Matrix Step 3: Selection decisionThe result of the screening and physical relevance steps, based on the principle of using the minimum number of variables possible, yields a reduced set of thirteen parameters as the most relevant to aircraft landing speed.Then, In order to broaden the modeling approach to other major US airports, variables that are deemed difficult to obtain (i.e.airline proprietary data) are eliminated from the consideration.Finally, the result of the data processing is a reduced set of parameter of five variables shown in Table 1.These data are typically available to individuals through normal acquisition means (e.g., Federal Aviation Administration (FAA) websites).Only the selected five variables are used as inputs variables to build the neural network model, where the output is the landing speed.
+Neural Network ModelingBecause the baseline landing speed prediction is divided according to the landing gust condition, the data are divided into two groups as well: low-and-nogust in and high-gust conditions.Then, the five inputs variables shown in Table 1 are used as inputs to build a feed-forward neural network for each gust condition using the Matlab software.The neural network schematic illustrated in Figure 3 has three hidden layers and one output layer.
+Figure 3. Neural Network IllustrationThe dataset is randomly divided into three groups as follows: 70% used for training, 20% for validation, and the remaining 10% for testing.The rational for this percentage split of the dataset is to allow a large enough training set size, given the limited number of data point available.Then, the training process consists of tuning the weights and biases until the validation dataset converges as illustrated in Figure 4 for the high gust condition.
+3D2-7
+Figure 4. MSE Plot for High Gust ConditionIn the example shown in Figure 4 for high gust case, converge occurs after the sixth iteration.
+Neural Network Models EvaluationAfter the models are built, and the landing speeds predicted, the quality of the predictions is evaluated.Figure 5 and Figure 6 represent the graph of actual landing speed as a function of the predicted landing speed for the low-and-no gust and high gust respectively.If the models were perfect predictors of the actual recorded landing speeds, all the red stars would have fallen on the blue lines (also called the 45-degree line).The quality of a fit can be assessed using the goodness-of-fit metrics of the coefficients of determination R 2 and the root mean square (RMS) error.For the above dataset, the R 2 and RMS error turn out to be 0.43 and 3.5 for the low-and-no gust, and 0.56 and 3.5 for high gust conditions, respectively.Also, it can be inferred from Figure 5 and Figure 6 that there is sizable dispersion in the landing-speed prediction for the low-and-no-gust as well as the high gust conditions, despite the use of the same dataset for modeling and testing.This large dispersion is reflected in the lower R 2 values (ideal value is 1.0).Despite the lower number of data points (e.g.426), the goodness-of-fit metrics show that overall the model for the high-gust condition is a better predictor than the model obtained for the low-and-nogust wind condition (e.g.1373 data points).
+Results DiscussionBased on the clustered nature of the data points shown on Figure 5 and Figure 6, intuition suggests that the actual and predicted landing speeds present normal distribution characteristics.Consequently, the models errors distributions are more likely to have normal distribution characteristics as well.As illustrated by the normal probability plot shown on Figure 7, the assumption of a normal distribution is reasonable for the low-and-no gust 3D2-8 condition because all the data point fall near the line.For the high gust condition, a similar trend is observed.Therefore normal distributions statistics can be used to compare the models to their corresponding baselines.Table 2 is the summary of the calculated statistical parameters.Table 2 indicates that the mean values of the percentage error of the models for both low-and-nogust and high-gust conditions are an order of magnitude smaller than those of the baseline values.Furthermore, the standard deviation values are smaller for the models (4.2 and 4.0 respectively) than for the baseline (4.9 and 4.4 respectively).These statistics demonstrate that the neural network landing-speed prediction models outperform the baseline predictions of the actual landing speed for both the low-and-no-gust and high-gust conditions.For both gust conditions, the error distributions for the models and for the baseline approximate a normal distribution as shown in Figure 8 (low-and-no gust case) and Figure 9 (high gust condition).A direct comparison between the distributions shows lower variance for the neural network models.For low-and-no gust condition the model error has a spread from the twenty-fifth percentile to seventyfifth percentile of 5.3% (-3% to 2.3%) while the baseline has a spread of 6.6% (0.8% to 7.4%).Similarly, for the high gust condition, the spread from the twenty-fifth percentile to seventy-fifth percentile of model error is 5.3% (-3% to 2.3%) and the baseline spread is 5.7% (-9.9% to -4.2).Another indication of the error distributions is that for the low-and-no gust case condition while the peak of the model is 0, the baseline's probability 3D2-9 density function (pdf) attains a maximum at approximately 5%, i.e. the baseline exhibits a systematic error, with the distribution shifted to the right.By contrast, for the high gust case the baseline error distribution is shifted to the left with a pdf maximum at approximately 8%.
+Results InterpretationA quantitative analysis of the low-and-no-gust condition suggests that the neural network modeling approach provides a better landing-speed predictor than the baseline.First, because the mean values of the neural network models' error distributions are around 0%, it implies that they have superior accuracies than the baseline.Then, because the neural network models errors have a standard deviation of 4.2 and 4.0 for low-and-no gust and high gust conditions respectively compared to 4.9 and 4.4 for the baseline.In essence, the error distributions of the neural network models represent a reduction of 18% and 9.5% from the baseline error distributions standard deviation for the low-and-no gust and the high respectively.In other words, the neural network models reduce the landing speed errors by 18% and 9.5% for the low-and-no gust and for the high gust respectively from the baseline landing speed prediction errors.In turn, these reductions in dispersion (standard deviation) can be directly translated in uncertainty reduction.Based on the fact that the distributions approximate normal distributions as illustrated on Figure 7, other interesting deductions can be made.For instance, since the standard deviation values are known, it can be said that the neural network models predict accurately 95% of the landing speed within an error margin of 12.6% and 12% (i.e. three sigma) for low-and-no gust and the high gust conditions respectively.Another observation from Figure 8 is that for the low-and-no-gust condition, the flight-operating manual tends to underestimate the landing speed; that is, it under-predicts the landing speed (see equation ( 4)).In other words, at the low-and-no-gust condition, pilots overshoot (i.e.actual landing speed is higher than recommended) the landing speed compared to the flight manual recommendation.By contrast, in the high-gust condition error distribution shown in Figure 9, the flight-operating manual overestimates the landing speed.Thus, the predicted landing speed is over predicted compared to pilots' achieved landing speed.Overall, for the normal landing-speed range, neural network modeling approach is capable of predicting the landing speed within a few knots for most data points.Figure 5 .Figure 6 .56Figure 5. Actual vs. Predicted Plot for Low-and-No Gust
+Figure 7 .7Figure 7. Normal Probability Plot for Low-and-No Gust Condition
+Figure 8 .Figure 9 .89Figure 8. Error Distributions for Low-and-No Gust
+Table 1 . Variables Used for Modeling1ParameterDescriptionHead windHead WindGust windGust WindCeiling_ftForecast CeilingVis_ftForecast VisibilityAct_Land_WgtActual Landing Weight
+Table 2 . Summary of Landing Speed Errors Statistics Statistical Parameters Low-and-No Gust Error High Gust Error Model Baseline Model Baseline2Mean-0.14.2-0.2-6.9std Dev4.24.94.04.4Min-13.5-14.5-12.5-19.2Median-0.14.3-0.4-7.3Max14.219.613.47.625%-3.00.8-3.0-9.975%2.37.42.3-4.2
+		
+		
+
+			
+AcknowledgementsThe author thanks Alex Sadovsky and Confesor Santiago for providing great recommendations that made this paper so much clearer.
+			
+
+			
+Concluding Remarks
+SummaryThe quality of fits of the neural network developed for the variables listed in Table 1 are assessed with coefficient of determination R 2 of 0.43 for the low-and-no-gust model and 0.56 for the highgust model.Based on the normal distribution nature of the error distributions, the neural network models are found to predict the landing speed in 95% of cases with an error margin of 12.6% for the low-andno gust condition, and with an error margin of 12% for high gust condition.The landing speed modeling technique presented in this paper is a promising research direction toward addressing the challenges in the prediction of final approach speed.The obtained predictions of landing speed achieve a higher accuracy (zero mean error) and higher precision (smaller variance) than do the current state-of-the art predictions of the flight operating procedure recommendations.Specifically, in the case of the narrow-body aircraft type used as test-bed, neural network models reduced the uncertainty of the landing speed prediction by 18% for the low-and-no gust and by 9.5% high gust conditions, respectively.Furthermore, the study indicates that pilots tend to overcompensate landing speed in the low-and-nogust condition and to under-shoot landing speed under high-gust conditions.These findings are very important in the modeling of pilots' behavior to improve existing and future terminal area tools.
+Directions for Future ResearchAmong the reasonable next steps of this research is the application of these modeling approaches to other airports and aircraft types.Though a new network needs to be trained for each aircraft type and airport configuration.
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+	Robinson, J., M. Kamgarpour, 2010, "Benefits of Continuous Descent Operations in High-Density Terminal Airspace Under Scheduling Constraints", ATIO Conference, Fort Worth, Texas
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+		Comparison of Trajectory Synthesis Algorithms for Monitoring Final Approach Compression
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+			JohnRobinson Iii
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+			OusmaneDiallo
+		
+		
+			RonaldReisman
+		
+		10.2514/6.2011-6900
+	
+	
+		11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Virginia Beach, Virginia
+		
+			American Institute of Aeronautics and Astronautics
+			2011
+		
+	
+	Robinson, J., O. Diallo, R. Reisman, 2011, "Comparison of Trajectory Synthesis Algorithms for Monitoring Final Approach Compression", ATIO Conference, Virginia Beach, Virginia
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+		Atlanta law firm teams up with Georgia State University on data analytics
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+			2010
+			Institute for Operations Research and the Management Sciences (INFORMS)
+			Atlanta, Georgia
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+			Georgia Institute of Technology, Thesis office
+		
+	
+	Ph.D. Thesis
+	Diallo, O. N., 2010, "A Data Analytics Approach to Gas Turbine Prognostics and Health Management", Ph.D. Thesis, Georgia Institute of Technology, Thesis office, Atlanta, Georgia
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+			Atlanta, Georgia
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+		Neighborhood based Levenberg-Marquardt algorithm for neural network training
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+			ChesterGong
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+			AlexanderSadovsky
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+		10.2514/6.2010-9117
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+		10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference
+		Fort Worth, Texas
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+	Gong, C., A. Sadovsky, 2010, "A Final Approach Trajectory Model for Current Operations", ATIO Conference, Fort Worth, Texas
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+COMPARISON OF PREDICTIVE MODELING METHODS OF AIRCRAFT LANDING SPEEDOusmane N. Diallo, Ph.D
+Ames Research Center
+SUMMARYExpected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports.Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations.There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors.This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway.As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE).Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed.The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport.However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods.Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach.For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation.Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.
+INTRODUCTIONOne of the goals of our nation's Next-Generation Air Transportation System (NextGen) is the accommodation of an expected traffic-demand increase in the already congested terminal airspace (ref.1).To meet that objective in the near term, there is a need to create tools to predict separation violations in order to plan conflict-resolution strategies.Overall, there are two categories of tools for the final approach: tools that maximize throughput such as the Traffic Management Advisor (TMA) (ref.Independent of the decision support tool used, accurate landing-speed estimates are needed to provide accurate space between successive arriving aircraft because all the tools rely on predictions of the flight-path of the aircraft.The air traffic controllers' practice and procedures dictate aircraft speed throughout most of the airspace, but the flight crew (and aircraft procedures) dictates the speed on approach in preparation for landing.Therefore, accurate landing-speed prediction will be required for tools intended for use on final approach.Uncertainty in landing speed is manifested as either an increase in separation violations or as increased separation buffers (leading to reduced throughput).There exist many different approaches to establish the landing speed of an aircraft to be used in the decision support tools.However, most of the options would require an upgrade to avionics equipment or a flight crew to verbally report landing speed, thereby increasing the workload for both the flight crew and the controllers.Thus, to fulfill the goal of this work for near-term implementation with no additional equipment requirement, the focus of this research is to develop an accurate landing-speed model based on information that is readily available in today's air traffic control system.As a first choice, the multivariable regression technique based on the response surface equation (RSE) is used to develop a statistical model of aircraft approach speed.The regression technique employed data for descent and approach phases of flight, as well as environmental and airport characteristics data.The MD80 (SP80) aircraft characteristics and the physical characteristics data of the Dallas/Fort Worth International Airport (DFW) provide a test bed to implement the proposed statistical modeling approach.There is a trade-off between the amount of information needed to build the model and its accuracy.Then, as a need to expand the study to other major airports arises, the modeling approach is restricted to input variables that are more likely to be available (airlines internal data are usually difficult to obtain).However, because with the reduced number of variables the RSE performs poorly, an alternative approach, the feed-forward neural network, is developed.The remainder of the paper is organized as follows: Section 2 sets the context, background, and motivation for the problem addressed.Section 3 presents the modeling methodologies.Then section 4 summarizes the current state-of-the-art or baseline for the landing-speed prediction.In section 5 the modeling methodologies are applied to actual recorded data, and the results are presented and discussed.Finally, section 6 summarizes the findings.
+BACKGROUND AND MOTIVATION
+BackgroundNextGen is the Federal Aviation Administration (FAA)'s plan to modernize the National Airspace System (NAS) in order to increase its capacity by 2025.While the plan projects an increase in the traffic demand, which would exceed the current capacity of the NAS, there are concerns about the increase in air traffic controllers' workload.The air traffic controllers' function is to guide aircraft from departure to destination safely and efficiently.There is a need to create tools that can facilitate and strengthen the air traffic controllers' decision process, while not compromising the safety requirement.This paper focuses on tools in the terminal environment, specifically those that address separation between successive arrivals.To maintain runway arrival throughput, controllers sequence aircraft as they approach the airport for landing and try to ensure there are no separation violations between any pair of aircraft.The task of separating aircraft becomes increasingly complex in highly congested terminal airspace (ref.6).Research in the field of air traffic management (ATM) investigates ways to automate separation assurance, to predict potential future violations, and to develop procedures to avoid such violations.Conflict prediction and resolution tools require the estimation of the landing speed of an aircraft to construct an accurate trajectory from its current position to the runway threshold.As a pair of successive arriving aircraft approaches the landing runway, there is a natural process of compression of the distance between the pair when the leading aircraft is slowed ahead of the trailing aircraft (ref.7).Knowledge of the landing speed of the leading aircraft is required for determination of the approach-speed profile of the trailing aircraft.Since the air traffic controller does not prescribe a landing speed, those data must either come from the aircraft or be estimated.Landing-speed estimation must be accurate to be used effectively by the different air traffic control (ATC) tools.
+MotivationThe main objective of this work is to build a landing-speed model based on variables readily available in today's air traffic automation systems.A landing-speed model is necessary because for a given pair of aircraft on final approach, the leading aircraft landing-speed must be predicted with sufficient lead time to allow tools to prescribe trajectory changes to be communicated to the trailing aircraft to avoid separation violations, with a potential risk of wake vortex hazards if too little separation is prescribed.To mitigate the impact of inaccurate landing-speed estimates and to account for uncertainty of trajectory prediction, it is common for air traffic controllers to add excess separation between pairs of aircraft, leading directly to loss of runway throughput.There are three viable options to consider when determining the landing speed of an aircraft:1.The intended landing speed can be electronically communicated (e.g., via ADS-B message data) to controllers by the flight crew.This approach would require special avionics.2. Controllers could verbally request the landing speed from the flight crew and input it in an interactive tool.This approach could increase the controller's and/or flight crew's workload.3. A model can be built to estimate the landing speed of the aircraft.The first two options require changes to the ground equipment and/or controller procedures: significant undertakings that could face resistance in their implementation.This paper focuses on the third option, which concentrates on near-term implementation, using readily available information to estimate the landing speed.
+MODELING METHODOLOGIESWhile there are many data-driven modeling techniques that can be used to build predictive models, all of them rely on the quality of the raw data and processing approach utilized.To be useful, raw data typically need to go through a thorough cleaning-up process.
+Data Selection and ProcessingThis paper proposes to predict a landing-speed estimate using regression of multi-variables to fit contributing inputs to corresponding historical landing speed as output.The first step of any such data-driven technique is data collection and processing.Although there are many different ways to collect and process historical data, a methodical approach leveraging previous work (ref.8) is used and summarized as follows:
+Data CollectionThe first step of a model development consists of identifying and collecting as many variables as possible to make sure no impactful variables are overlooked.Thus, for this landing-speed modeling, a model is built for a specific aircraft type at a given airport because each airport has its specific characteristics.The raw data are composed of all airline monitored flight parameters (e.g., fuel consumption, aircraft weight, etc.), radar recorded data (e.g., ground speed, etc.) and airport environmental conditions (e.g., wind, visibility, ceiling, etc.).
+ScreeningThe screening step is important to center the analysis on variables that directly impact the modeling task.Thus, the screening is necessary to get rid of duplicative and unnecessary information for the model.This step serves to detect the parameters that contribute the most to the landing-speed behavior.Also, during the screening step obvious parameters with no impact (e.g., departure airport, etc.) on the model are eliminated.
+Physically Relevant ParametersIt is common practice by airlines to track both the planned and actual variables for many key parameters.However, in this work, only the actual values (as opposed to planned values) of the variables are used.The use of actual variables would allow achieving more realistic models because planned variables are typically based on the predicted actual variables augmented by a margin value to account for the uncertainty of flight parameters (e.g., fuel burn, time, etc.).Also, the variables irrelevant to these modeling methods such as city-pair, route information (domestic or foreign), etc. are discarded.
+Mathematical Modeling: Multi-variable Regression ModelingThe mathematical modeling is focused on two regression approaches.The first choice is the linear regression technique because is easier to build and implement.However, when the linear approach fails to provide an acceptable predictive model, the nonlinear feed-forward neural network is used.
+Response Surface EquationThe linear regression used in this study is the multivariate regression technique of the Response Surface Methodology (RSM) (ref.9).The RSM consists of building a response surface equation (RSE), which constitutes simplified polynomial equations used to model behavior of complex systems.RSE is used to relate the factors (or predictors) to the measured responses (landing speed) over some specified region of interest.Use of the RSE can be justified by its ability to combine parameters of different natures, and their cross-effects, in a single equation.The most popularly used RSE is the second-order Taylor series approximation because it requires a minimal computational investment: (1) where: R: the dependent parameter (response) of interest : the intercept term : regression coefficients for the first-order terms : coefficients for the pure quadratic terms : the coefficients for the cross-product terms : the independent variables : the number of factors : the error associated with neglecting higher-order effects The RSE assumes that for any model the error, ε, should be normally distributed as N(0,1).
+Neural Network ModelingIn general neural networks are an alternative to Response Surface Methods in the creation of regression models for problems where the polynomial representation of the RSE does not perform well (ref.10).Among the neural network types, the feed-forward back propagation (FFBP) is one of the easiest to build and implement.Usually, it is a good starting point to build a model because a feed-forward neural network can theoretically fit any nonlinear relationship between inputs and target.R = b 0 + b i x i i=1 k  + b ii x i 2 i=1 k  + b ij x i x j + ε j =i+1 k  i=1 k -1  o b i b ii b ij b j i x x ; k ε Figure 1. Feed-forward neural network illustration.
+Neural Network BackgroundArchitecturally a neural network is composed of an input layer, hidden layers, and an output layer as illustrated in Figure 1.The hidden layer is the data processing center of the network.The output layer is the response to the simulation.A layer is composed of neurons.In a layer, each input element is connected to each neuron through a weight (w), and a bias (b) is added to the weighted input.Typically, there are three main steps to build a neural network: training, validation and testing.Consequently, a dataset composed of an input set and corresponding target output are divided into those three groups.Usually there is a pre-processing step to get the data into a useful form.Then the training step consists of tuning the values of weights and biases of the network to optimize network performance (ref. 11).The validation step consists of using the second (validation) set of the data to adjust the quality of the regression.Finally, the test set serves to check the network on a set different from the one used for its construction.Many training algorithms exist.The Levenberg-Marquardt is the most commonly used algorithm for feed-forward networks when rapid training is desired.Based on the nature of this research to model a regression of the input variables to predict the corresponding landing speed, the network is trained for function approximation as opposed to the pattern recognition.A proper training requires a training set based on known inputs and corresponding target outputs.It is also customary for neural networks to undergo the training process (retraining) a couple of times to improve the quality of the fit.Then, the performance function used as the stopping criteria is the Mean Square Error (MSE) defined in equation (2) as:∑ ∑ (2)
+PROOF OF CONCEPTPrevious studies (refs. 7, 12) suggested that airport-specific parameters (airport elevation, runway length, etc.) contribute to the final approach performance of a flight.More importantly, it is clear that each aircraft type (e.g., B737-800 or MD80) has specific performance parameters that would directly impact its landing speed.Consequently, to account for all these variations that may affect the fidelity of any landing-speed model, an airport-specific and aircraft-specific landing model was created using historical data.As a proof of the concept, the proposed methodologies to predict aircraft landing speed are applied to the MD80 aircraft type also referred to as the Super 80 (SP80) operations at the Dallas/Fort Worth International Airport.DFW was chosen because data for detailed analysis were available and the SP80 is highest number of a single aircraft type in service at this airport.
+RSE Application to Thirteen Input VariablesThe different steps of the proposed methodology are carried out using DFW and the SP80 aircraft type.Step 1: Data collection A dataset of over 300 variables is recorded over time.Collected data are composed of airport topological (e.g., runway length and configuration), environmental information (e.g., visibility, wind condition, ceiling), flight-specific parameters (e.g., city pair flown, fuel burn rate, landing weight, etc.), and aircraft-specific parameters (e.g., empty gross weight, aircraft maximum payload, etc.).For the remainder of this paper the collected data are considered as the true value or actual value.Step 2: ScreeningThe main goal of this section is the identification of the predictive values of the parameters.First, variables that are known to be irrelevant are automatically eliminated (e.g., aircraft tail number, city of departure, etc.).Also, if two or more variables provide the same information, only one of them is used for the modeling.For example, as shown in Figure 2, the variables pax (number of passengers) and LF_pax (load factor for passengers) have a correlation coefficient of 1.For modeling purposes these two variables provide the same information, so one of them is sufficient.Consequently, after dropping the linear dependent parameters, LF_pax, FBR (fuel burn rate), and ESAD_nm (equivalent still air distance) are among the retained variables for modeling.Step 3: Physically irrelevant parameters to the model are eliminated.The result of the screening and physical relevance steps, based on the principle of using the minimum possible number of variables, yields a reduced set of thirteen parameters as the most relevant to aircraft landing speed (shown in Table 1).Only the selected thirteen variables are used as inputs to fit the multivariable regression model defined in equation ( 1), where the response is the landing speed.It should be noted, however, that not all of these parameters have equal importance.The MD80 flight manual provides a set of recommendations for low-and-no-gust condition and another set for high-gust condition (ref.13).Under the normal operating conditions, the MD80 flight manual defines the low-and-no-gust condition for reported wind between 0 and 10 knots, whereas it defines the high-gust condition for reported wind over 10 knots.A landing-speed model will be constructed for each of the two scenarios set forth by the MD80 flight manual.The model that predicted landing speed will be compared to the one recommended by the airlines' flight manual to highlight the benefits of the proposed modeling approach.
+Low-and-No-Gust RSE ModelUsing the 13 retained parameters shown in Table 1, the coefficients of the first-order terms, the second-order terms, the cross-term, and the intercept are all calculated by fitting the variables into the response.The statistical package JMP is used to compute the different RSE coefficients (see appendix A).The equation obtained is the multivariable model.To illustrate the contribution of each variable and cross-term to the landing speed model for the low-and-no-gust condition, a Pareto chart shown in Figure 3 is generated.The Pareto chart provides much information to the system modelers.It shows the orthogonal estimate, which is the orthogonal projection of the model along the RSE terms.For example, the model projection along the APAY axis has a value of 3.62, which represents the norm (or weight) of the model vector along the APAY axis.Furthermore, it can be inferred from the Pareto chart that the variable APAY contributes the greatest variability to the landing-speed model with about 13.5% contribution, followed in decreasing order by the headwind (~8.2% contribution), the actual landing weight (~5.2% contribution), the ceiling (~4.3%), the cross-term of headwind-landing weight (~3% contribution), and so on.Also, the Pareto chart allows us to assess the relative importance of each term in the RSE; for example, the relative contribution of the headwind variable (with a value of 2.23) weights about 1.6 times the relative contribution of the actual landing weight (Act_Land_Wgt) variable (with a value of 1.42) to the landing-speed model for the low-and-no-gust condition.Other important information gained from the Pareto chart is the visualization of the cumulative effect of the RSE terms.Using the cumulative contribution curve, systems modelers can make a decision about the appropriate level of fidelity necessary.As illustrated in Figure 3, the cumulative contribution of the truncated Pareto chart showing the first 19 out of 104 terms represent 57% of the 13-variables RSE model for the lowand-no-gust condition.Other information obtained from the Pareto chart is that even though the flight manual proposes that headwind and gust wind play important roles in predicting the landing speed (see equations ( 2) and ( 3)), their significance is not as important as suggested, weighting only 8.2% and 2%, respectively, of the landing-speed model.After the model is obtained, the quality of the model is assessed using several goodness-of-fit metrics.Figure 4 illustrates the predicted landing-speed model compared to the recorded actual landing speed.The coefficient of determination R 2 and the root-mean-square (RMS) error are computed to be 0.43 and 6.16, respectively.The blue line represents the actual landing speed (also called the 45-degree line); a perfect predictor would have values along the blue line.As Figure 4 shows, there still is some sizable landing-speed prediction error for the low-and-no-gust condition despite the use of the same dataset modeling and testing.The error on baseline speed and the error on the model are calculated for the low-and-no-gust condition using equations ( 5) and ( 6), respectively.Figure 5
+High-Gust RSE ModelFor the high-gust condition, the same thirteen input variables in Table 1 are used to construct a RSE model.A new set of RSE coefficients are calculated (see appendix A) and the Pareto chart is obtained and shown on Figure 6.Similar to the low-and-no-gust condition, the APAY variable is the biggest contributor to the landing speed for the high-gust condition, with 11%.However, the second contributor is the visibility (~8% contribution), contrary to the low-and-no-gust condition.Also, the relative contribution parameters are different from the low-and-no-gust case; thus, the Pareto chart for the high-gust condition is different from the low-and-no-gust one.Correspondingly, the error on baseline speed and the error on the model are calculated for the high-gust condition using the same equations, ( 5) and ( 6), respectively.Figure 8 illustrates the calculated error metrics for high-gust conditions.The red stars represent the absolute value of the model error, and the blue open circles, the absolute value of the target-speed error.As is evidence in Figure 5 (low-and-no-gust condition) and Figure 8 (high-gust condition), the value of the model error is consistently smaller than that of the target speed error.Although the model error is better than the target-speed error for both wind conditions, Figure 8 shows that model error values for the high-gust case are relatively smaller than the error values of the lowand-no-gust case, while Figure 5 shows larger errors for the low-and-no-gust case.
+Summary and Discussion of Results for RSE Models with Thirteen Input VariablesTable 2 summarizes the statistical characteristics of the RSE model versus the baseline landingspeed error.It appears that the RSE model with thirteen input variables is better than the airline's flight manual-recommended target landing speed based on the statistical parameters metrics.The mean value of the error of both the low-and-no-gust and high-gust wind conditions is very close to 0 (i.e., -1.80e-4 and 5.07e-5, respectively), whereas the mean values for the target error landing speed are 4.25 for the low-and-no-gust wind condition and -6.92 for the high-gust wind condition.The fact that the mean of the model error is nearly 0 illustrates the closeness of the model to the actual landing speed.The standard deviations of the model error are also smaller (4.36 and 3.65, respectively, for low-and-no-gust and high-gust conditions) compared to the standard deviations of the target landing-speed error (5.18 and 4.38, respectively, for low-andno-gust and high-gust conditions), illustrating less dispersion of the model-predicted landing speed from the actual landing speed than the target landing speed.Overall, the standard deviation of the RSE model error reduced the standard deviation of the baseline error by 18% for the low-and-no-gust condition and 22% for the high-gust case.Another way to show the accuracy and precision of the RSE models compared to the baseline is by examining the error distributions.Figure 9 shows the absolute value of the model error (blue bar) and the baseline error (red bar) distributions for the low-and-no-gust condition.Figure 9 shows that out of the 1387 data points considered, around 950 of them have predicted landingspeed error within 0 to 4%, while there are only about 600 data points within the same error interval for the baseline error.On the opposite side, for a larger percentage error range of 8 to 12%, there are about 340 data points for the model error and about 500 data points for the baseline error.In summary, for the thirteen input variables case, the comparison metrics used all show that the RSE model is better than the current state-of-the-art (baseline).That is, for both the low-and-nogust and high-gust conditions, there is 18% less dispersion for the low-and-no-gust case and 22% less dispersion for the model error compared to the baseline error.This finding implies the model is more precise in predicting the landing speed.Also, for both gust conditions, there are more instances of data-point percentage error concentrated within the 0% to 4% range for the model than the baseline, indicating that the model is more accurate.However, the coefficients of determination R 2 (0.43 and 0.62, respectively) are smaller relative to the ideal value of 1.That lack of fit of the model may be explained in part by the model uncertainty and the lack of knowledge of the pilots' decision-making process to achieve landing.
+Application to Reduced Number of Input Variables: Five InputsIt is important to note that the thirteen variables used in the previous example would not be available at most airports because airlines typically do not provide the data to the public.Therefore, the study is repeated for the more realistically obtainable variables composed of the In a similar approach, the data are divided into two groups: low-and-no-gust condition in one group and high-gust condition in another.As a first attempt, the multivariable linear regression modeling method of RSE is reproduced.
+RSE ModelingThe RSE is built following the steps underlined in the modeling methodologies section using the same data points as in the case of thirteen input variables.However, only the five input parameters listed in Table 3 are fitted into a RSE with the recorded actual landing speed as target output for each of the gust conditions.
+RSE Model for Landing with Low-and-No-Gust ConditionFigure 11 shows that for a study with only five variables, the main contributor is the aircraft landing weight, representing about 32% toward the model, followed by the headwind (18%), ceiling (9.5%), and visibility (5%), respectively.The computed coefficients of the second-order regression equation can be seen in appendix B for the low-and-no-gust condition.Then the crossterms of headwind-visibility, ceiling-landing weight, and the gust wind followed.In other words, the gust wind contributes to the model the least.The quality of the fit is assessed by calculating the values of the coefficient of determination R 2 of 0.37 and the RMS error (RMSE) is 6.29.These values show that the RSE approach performs worse for the five input variables case than the thirteen.
+RSE Model for High-Gust ConditionLike in the low-and-no-gust case, as illustrated in Figure 12, the aircraft weight is main contributor to the landing speed for the reduced number of variables (five), representing over 28% of the high-gust model.The computed coefficients of the second-order regression equation can be seen in appendix B for the high-gust case.Contrary to the low-and-no-gust condition, the visibility is the second-highest contributor with a negative impact representing around 19%, followed by headwind, which contributes around 9%.The ceiling (2.5%) and the gust wind (0.4%) contribute marginally to the overall model.The quality of the fit is assessed by calculating the values of the coefficient of determination R 2 of 0.50, and the RMS error is 5.95.Similar to the low-and-no-gust case, it is clear that the RSE approach performs worse for the five input variables than for the thirteen.Because of the poor fit, other options such as the neural network are investigated.
+Neural Network ModelingThe neural network model handles nonlinear relationships between inputs and target better than linear regression techniques.Because the linear regression technique of RSE did not provide high enough quality of fit, it is worth exploring a nonlinear technique such as the neural network.Like in the RSE modeling approach, a neural network model is built for the low-and-no-gust condition and another one for the high-gust condition.
+Neural Network Model for Low-and-No-Gust ConditionThe five variables from Table 3 are used as inputs to build a feed-forward network with a schematic shown in Figure 13 for the low-and-no-gust condition.The dataset is randomly divided into three groups as follows: 70% used for training, 20% for validation, and the remaining 10% for testing.Then, the training process consists of tuning the weights and biases until the validation dataset converges as illustrated in Figure 14.After the training, validation, and testing steps, the model output is plotted versus the target for the dataset as shown in Figure 15.The graphs of all three datasets are shown for the sake of transparency.Also from the Figure 15, the regression values of the network output versus the target are 0.62, 0.58, and 0.56 for the training set, the validation, and the testing set, respectively.The fact that the correlation values of all three datasets are within the same range shows a consistency between the training sets and the testing set.The error distribution in Figure 16 shows the dispersion of the training, validation, and testing set errors between the network prediction and the target.The error distributions approximate a normal distribution for each dataset.The reduced standard deviation of the testing set may be explained by the smaller number of its data points.Additionally, Figure 16 stresses the fact that all three datasets have similar statistical characteristics.The remaining neural network model characteristics can be found in appendix C for both the low-and-no-gust and high-gust conditions.
+Neural Network Model for Landing with High-Gust ConditionIn a similar fashion, a neural network model is built for the high-gust condition as illustrated in Figure 13.For the sake of consistency, the high-gust dataset is randomly divided into three groups: 70% used for training, 20% for validation and 10% for testing.Figure 17 illustrates the convergence of the network after twelve iterations, with the best validation performance reached at the sixth iteration.Figure 18 shows how well the model output for the training, validation, and testing datasets did versus the target values after 6 iterations.Also, it shows the regression fit in each case.The regression values of the network output versus the target are better for the high-gust condition than for the low-and-no-gust condition with 0.70, 0.65, and 0.62 for the training set, the validation, and the testing set, respectively.Similar to the low-and-no-gust condition, the correlation values of the three datasets are within the same range, showing a consistency among the datasets used to model the high-gust case.The error distribution in Figure 19 shows the dispersion of the training, validation, and testing set errors between the network prediction and the target.For the high-gust condition, the training and validation sets have similar error distributions, but they are different from the testing set.This lack of well-defined error distributions may be explained by the reduced number of data points for the high-gust condition at DFW.
+Models Evaluation for Five Input VariablesThe two different modeling methodologies are evaluated and compared to the baseline landing speed using the two error metrics defined earlier in equations ( 5) and (6).Thus, the two models (RSE and neural network) are evaluated for both the low-and-no-gust and high-gust conditions using the error metrics defined earlier.For the low-and-no-gust condition, Figure 20 shows that the absolute value of the predicted landing-speed errors of the models are concentrated mostly between 0 and 6% for most of the 1387 data points used, while the flight-operating manual-predicted landing speed has most of the absolute value error within the 8% to 20% range for the same dataset.As for the high-gust condition, a visual inspection of Figure 21 shows that the magnitude of the absolute value of the predicted landing-speed errors of the model are mostly concentrated between 0 and 6% for most of the 426 data points used.However, the most of the absolute values of the flight-operating manual predicted landing-speed errors are within the 4% to 16% range for the same dataset.As illustrated in Figure 20 (low-and-no-gust condition) and Figure 21 (high-gust condition), the absolute values of the predicted landing-speed errors of the model (neural network (red stars) and RSE (black plus signs) are consistently lower than those of the current state-of-the-art landingspeed prediction (blue circles).
+Results and Discussions for Five Input VariablesBecause the RSE model with the reduced number of input parameters poorly predicts the landing speed, the neural network models were built for both the low-and-no-gust and high-gust conditions.Then, the neural network models were assessed and compared to the RSE model and to the recommended landing speed provided by the operating flight manual.Despite the reduced number of input variables from thirteen to five, the neural network models provide better landing-speed predictions for both low-and-no-gust and high-gust conditions than the airlines' operating flight manual.Table 4 summarizes statistical parameters, comparing the errors between the neural network models and the flight manual.
+Low an d No Gust Error High Gust E rrorAn analysis of Table 4 indicates that the mean values of the percentage error of the models are about 0 for both low-and-no-gust and high-gust conditions (-0.1% and -0.2%, respectively) compared to existing flight manual-recommended values (4.2% and -6.9%, respectively).Furthermore, the standard deviation is smaller for the models than the existing flight operating manual recommendations.These two statistical parameters demonstrate that the neural network landing-speed prediction models are closer to the actual landing speed for both the low-and-nogust and high-gust conditions.It is also worth comparing the neural network and the RSE models to see how each of them fared compared to the baseline.Figure 22 and Figure 23 show the comparative error distributions of the neural network model (blue bars), the RSE (light green bars) and the baseline (red bars) for the low-and-no-gust and the high-gust conditions, respectively.For both gust conditions, the error distributions of the models approximate a normal distribution, implying that the errors of the models are random, with peak around 0% error.A direct comparison between the two types of models shows that the neural network model performs better than the RSE model, because there are more instances of 0% to 2% error for the neural network than for the RSE.The number of instances of RSE error becomes larger for higher error values.There are also some important differences between the individual distributions.An analysis of the error distribution for the low-and-no-gust condition (Figure 22) shows that the neural network has the highest number of cases around 0%.As the error value increases, the number of instances of neural network model error decreases faster than the RSE, meaning there are fewer data points with larger predicted landing-speed errors for the neural network model than for the RSE.It is also clearly apparent in Figure 22 that both models (with peak value at 0%) perform better than the baseline (with peak at 5%).Another interesting observation from Figure 22 is that for the low-and-no-gust condition, the flight operating manual tends to underestimate the landing speed; that is, it under-predicts the landing speed (see equation ( 6)).In other words, at the low-and-no-gust condition, pilots overshoot the landing speed compared to the flight manual recommendation.Similarly, the error distribution of the high-gust condition shown in Figure 23 demonstrates that both the neural network model and the RSE model having their highest number of cases of error around 0% (with peak value at 0%) predict the landing speed with less error than the baseline (with peak at -10%) does.As the error value increases, the number of instances of neural network model error decreases faster than the RSE, whereas the error values get larger in the negative direction, so it is not clear which model performs better.However, an analytical analysis of the high-gust condition statistics shows that the neural network model predicts landing speed more precisely than the RSE model because while both models have mean values of error at 0 (i.e.similar accuracy), the neural network model error has a standard deviation 5.5% lower (less dispersion) than the RSE model.Moreover, while the neural network model represents a sizeable 9.5% landing-speed prediction precision improvement over the baseline prediction, the RSE is a mere 3.8% improvement over the baseline.Contrary to the low-and-no-gust condition, in the high-gust case shown in Figure 23 the flightoperating manual overestimates the landing speed.Thus, the predicted landing speed is over predicted compared to pilots' achieved landing speed.Overall, both models predict landing speed more accurately (e.g., 0 mean value) and more precisely (e.g.lower error standard deviation) than baseline under both gust conditions.For the normal landing-speed range, both models are capable of predicting the landing speed within a few knots for most data points.However, for a reduced number of input variables, the neural network models for both gust conditions yield a better landing-speed prediction than does the RSE modeling approach.More illustrative figures showing the advantages of the neural network model over the RSE model can be found in appendix D. The ability of neural networks to handle the nonlinear regression better is to be credited for the improved precision capability.One of the main disadvantages of using the neural network modeling approach for this type of study is that it works like a black box for the end user, providing no insight on the impact or the variance of each input variable on the landing speed.Therefore, valuable information provided to researchers about the contribution of each parameter to the RSE model shown on Pareto chart types would be lost in the neural network modeling approach.
+CONCLUSIONSThe principal goal of this study is to create aircraft landing-speed models that are unbiased (accurate) with minimum variance (precise), and demonstrate potential for near-term implementation.An accurate predicted landing-speed is required for the achievement of NextGen goals, so that a trailing aircraft trajectory (speed and altitude) can be adjusted to avoid any predicted separation violations.Among the immediate potential benefits to having an accurate landing speed model are: an increase in airport throughput by decreasing the excess separation buffer between successive arrival aircraft, a potential increase in overall safety in lowvisibility conditions, and a possible decrease in fuel burn as a consequence of decreased travel time and landing procedures.It is worth noting that the inability to reduce or eliminate the excess separation buffer due to trajectory uncertainty at the nation's busiest airports is one of the key hurdles to meeting the anticipated demand of the NextGen NAS.As a proof of concept to the proposed multivariable regression technique of RSE, the thirteen most impactful variables for MD80 landing speed at DFW for the case of thirteen input variables are used.The qualities of fit of the models are assessed with a coefficient of determination R 2 of 0.43 for the low-and-no-gust model and R 2 of 0.62 for the high-gust RSE model.In order to expand the landing-speed prediction modeling approach to other major airports, the RSE modeling is repeated with the five likely readily available variables.The quality of fit for the RSE model deteriorates with coefficients of determination R 2 of 0.37 and 0.5 for low-and-nogust and high-gust conditions respectively.These results reveal that more work on the model is required to enhance its predictive ability.In an attempt to improve on the predictive performance of the RSE model with five input variables, feed-forward neural network models are developed for each gust condition.While both models have similar accuracy, the neural network models approximate the actual landing speed much better than the RSE models because the error standard deviations of the neural network models were reduced by 5.6% and 5.5% (better precision) for the low-and-no-gust and high-gust conditions, respectively.More importantly, the prediction of the landing speed of the neural network is an 18.8% and 9.5% precision improvement over the existing state-of-the-art that is the flight manual recommendation for low-and-no-gust and high-gust conditions respectively.Comparatively, the RSE models are 12.5% and a mere 3.8% improvement of the landing-speed prediction precision over the flight operating manual recommendation for low-and-no-gust and high-gust conditions, respectively.Ultimately, the proposed data-based modeling approach shows great potential for predicting actual landing speed far better than the existing operating flight manual recommendation for final approach speed commonly prescribed in commercial aircraft flight manuals.The two different regression techniques presented in this paper begin to address the research challenges in the prediction of final approach speed modeling.The models achieved a more accurate (mean value of error of 0) and precise (less dispersion) prediction of the landing speed for the narrowbody air transport used as a test-bed than the current flight operating procedure recommendation can.Thus under normal landing conditions, each of the constructed models predicted the landing speed within a couple of knots of the actual achieved speed.The next steps of this research should be applying these modeling approaches to other airports and aircraft types.Finally, the proposed modeling approach can be improved by identifying more relevant parameters and finding a way to quantify pilots' decision-making processes for the landing procedure.In the meantime, the study allows us to conclude that pilots tend to overcompensate landing speed in the low-and-no-gust condition and under-shoot landing speed under high-gust conditions.This finding is very important in trying to model pilots' behavior to improve existing and future terminal area tools.
+APPENDIX A. RSE COEFFICIENTS FOR THIRTEEN VARIABLES
+APPENDIX B. RSE MODELS FOR FIVE INPUT VARIABLESFigure 1 .1Figure 1.Feed-forward neural network illustration.................................................................. Figure 2. Example of correlated variables matrix................................................................... Figure 3. Truncated Pareto chart for low-and-no-gust condition............................................ Figure 4. Predicted vs. Actual speed for low-and-no-gust condition...................................... Figure 5. Model error vs. Target speed error for low-and-no-gust condition........................ Figure 6.Truncated Pareto chart for high-gust condition....................................................... Figure 7. Predicted vs. Actual speed for high-gust condition................................................. Figure 8. Model error vs. Recommended landing speed error for high-gust condition......... Figure 9. Low-and-no-gust error comparison......................................................................... Figure 10.High-gust error distribution..................................................................................... Figure 11.Pareto chart for low-and-no-gust condition for the reduced number of variables (5).............................................................................................................Figure 12. Pareto chart for high-gust for reduced number of variables (5)............................... Figure 13.Network for five input variables.............................................................................. Figure 14.Mean square error for low-and-no-gust condition................................................... Figure 15.Output vs. Target for training, validation and testing for low-and-no-gust dataset...................................................................................................................... Figure 16.Error distribution of training, validation and testing for low-and-no-gust condition.................................................................................................................. Figure 17.Mean square error for high-gust condition.............................................................. Figure 18.Output vs. Target for training, validation and testing for high-gust dataset............ Figure 19.Error distributions of training, validation and testing for high-gust condition........ Figure 20.Absolute value of percentage error for neural network, RSE, and baseline for low-and-no-gust condition....................................................................................... Figure 21.Absolute values of percentage error for neural network, RSE, and baseline for high-gust condition.................................................................................................. Figure 22.Percentage error distributions for low-and-no-gust condition................................. Figure 23.Percentage error distributions for high-gust condition............................................
+Figure 4 .4Figure 1.Feed-forward neural network illustration.................................................................. Figure 2. Example of correlated variables matrix................................................................... Figure 3. Truncated Pareto chart for low-and-no-gust condition............................................ Figure 4. Predicted vs. Actual speed for low-and-no-gust condition...................................... Figure 5. Model error vs. Target speed error for low-and-no-gust condition........................ Figure 6.Truncated Pareto chart for high-gust condition....................................................... Figure 7. Predicted vs. Actual speed for high-gust condition................................................. Figure 8. Model error vs. Recommended landing speed error for high-gust condition......... Figure 9. Low-and-no-gust error comparison......................................................................... Figure 10.High-gust error distribution..................................................................................... Figure 11.Pareto chart for low-and-no-gust condition for the reduced number of variables (5).............................................................................................................Figure 12. Pareto chart for high-gust for reduced number of variables (5)............................... Figure 13.Network for five input variables.............................................................................. Figure 14.Mean square error for low-and-no-gust condition................................................... Figure 15.Output vs. Target for training, validation and testing for low-and-no-gust dataset...................................................................................................................... Figure 16.Error distribution of training, validation and testing for low-and-no-gust condition.................................................................................................................. Figure 17.Mean square error for high-gust condition.............................................................. Figure 18.Output vs. Target for training, validation and testing for high-gust dataset............ Figure 19.Error distributions of training, validation and testing for high-gust condition........ Figure 20.Absolute value of percentage error for neural network, RSE, and baseline for low-and-no-gust condition....................................................................................... Figure 21.Absolute values of percentage error for neural network, RSE, and baseline for high-gust condition.................................................................................................. Figure 22.Percentage error distributions for low-and-no-gust condition................................. Figure 23.Percentage error distributions for high-gust condition............................................
+Figure A- 1 .1Figure A-1.Absolute values of percentage error distribution for low-and-no-gust condition for thirteen input variables ....................................................................................... Figure A-2.Absolute values of percentage error distribution for high-gust condition for thirteen input variables .............................................................................................
+Figure C- 1 .Figure C- 3 .13Figure C-1.Training characteristics for low-and-no-gust condition........................................... Figure C-2.Regression plot for all the values (training, validation, and testing) together.........
+Figure D- 1 .1Figure D-1.Absolute values of percentage error for low-and-no-gust condition for five input variables......................................................................................................... Figure D-2.Percentage of error distribution for low-and-no-gust condition for five input variables................................................................................................................... Figure D-3.Absolute values of percentage error for high-gust condition for five input variables................................................................................................................... Figure D-4.Percentages of error distribution for high-gust condition for five input variables. .
+Figure 2 .2Figure 2. Example of correlated variables matrix.
+Figure 3 .3Figure 3. Truncated Pareto chart for low-and-no-gust condition.
+Figure 4 .4Figure 4. Predicted vs. Actual speed for low-and-no-gust condition.
+Figure 5 .5Figure 5. Model error vs. Target speed error for low-and-no-gust condition.
+represents a visual representation of the calculated error metrics for the low-and-no-gust condition.The red stars represent the absolute value of the model error, and the blue open circles, the absolute value of the targetspeed error.The error metric clearly shows that the baseline error is larger than the RSE model error for the low-and-no-gust condition.
+Figure 77Figure7shows the predicted versus actual landing speed.The goodness of fit of the model is assessed with the calculation of the coefficient of determination R 2 of 0.62 and the RMS error of 5.79.The blue line on Figure7represents the actual landing speed (also called the 45-degree line).It also corresponds to the ideal fit of a coefficient of determination R 2 of 1.The goodnessof-fit metrics for the high-gust wind condition shows that overall the model for the high-gust condition is a better than the model obtained for the low-and-no-gust wind condition.It is worth
+Figure 6 .6Figure 6.Truncated Pareto chart for high-gust condition.
+Figure 7 .7Figure 7. Predicted vs. Actual speed for high-gust condition.
+Figure 8 .8Figure 8. Model error vs. Recommended landing speed error for high-gust condition.
+Figure 9 .9Figure 9. Low-and-no-gust error comparison.
+Figure 10 .10Figure 10.High-gust error distribution.
+Figure 10 represents10Figure10represents the error distributions of the absolute value of the RSE model (blue bar) and the baseline (red bar) for the high-gust condition.A similar tendency is observed for the highgust case as well.For the lower error percentage range (0 to 4%), there are about 310 out of 426 data points for the model compared to only about 90 data points or so for the baseline error.At the higher error range (8% to 12%) there are only 15 data points for the model versus 140 instances of baseline errors.
+Figure 11 .11Figure 11.Pareto chart for low-and-no-gust condition for the reduced number of variables(5).
+Figure 12 .12Figure 12.Pareto chart for high-gust for reduced number of variables (5).
+Figure 13 .13Figure 13.Network for five input variables.
+Figure 14 .14Figure 14.Mean square error for low-and-no-gust condition.
+Figure 15 .15Figure 15.Output vs. Target for training, validation and testing for low-and-no-gust dataset.
+Figure 16 .16Figure 16.Error distribution of training, validation and testing for low-and-no-gust condition.
+Figure 17 .17Figure 17.Mean square error for high-gust condition.
+Figure 20 and20Figure 20 and Figure 21 are visual representations of the error metrics for the low-and-no-gust and high-gust conditions, respectively.The blue open circles represent the absolute value of the baseline error, the red stars represents the absolute value of the neural network model error, and the black plus sign represent the absolute value of the RSE model error.
+Figure 18 .18Figure 18.Output vs. Target for training, validation and testing for high-gust dataset.
+Figure 19 .19Figure 19.Error distributions of training, validation and testing for high-gust condition.
+Figure 20 .20Figure 20.Absolute value of percentage error for neural network, RSE, and baseline for low-and-no-gust condition.
+Figure 21 .21Figure 21.Absolute values of percentage error for neural network, RSE, and baseline for high-gust condition.
+Figure 22 .22Figure 22.Percentage error distributions for low-and-no-gust condition.
+Figure 23 .23Figure 23.Percentage error distributions for high-gust condition.
+Figure A- 11Figure A-1 and Figure A-2 show the absolute values of the percentage error distributions for the low-and-no-gust and high-gust conditions, respectively, for thirteen input variables example.
+Figure A- 2 .2Figure A-2.Absolute values of percentage error distribution for high-gust condition for thirteen input variables.
+Figure A- 1 .1Figure A-1.Absolute values of percentage error distribution for low-and-no-gust condition for thirteen input variables.
+Figure C- 33Figure C-3 shows the error distribution for the 1387 (training, validation, and testing) data points.
+Figure C- 44Figure C-4 shows the neural network model characteristics for high-gust condition
+Figure C- 4 .4Figure C-4.Training characteristics for high-gust condition.
+Figure C- 55Figure C-5 shows the regression plot for all the data (training, validation, and testing) together for the high-gust condition.
+Figure C- 66Figure C-6 shows the error distribution for all 426 (training, validation, and testing) data point used to model the high-gust condition together.
+Figure C- 5 .5Figure C-5.Regression plot for data together.
+Figure C- 6 .6Figure C-6.Error histogram for all 426 data points together.
+Figure C- 77Figure C-7 shows the absolute values of percentage error distribution for the high-gust neural network model for five input variables.It is clear that the error is more spread for the recommended (baseline) value for the high-gust case.As the error values increase, there are fewer instances of the model and more of the recommended.
+Figure C- 88Figure C-8 also illustrates a larger spread of the recommended (baseline) error compared to the model for high-gust case.
+Figure C- 7 .7Figure C-7.Absolute values of percentage error distribution for high-gust neural network model for five input variables.
+Figure C- 8 .8Figure C-8.Regression plots comparison of neural network model error versus the recommendation error for high-gust condition.
+Figure D- 33Figure D-3 gives the absolute values of percentage error, and Figure D-4 gives the percentages of error distribution for the high-gust condition for five input variables.
+Figure D- 3 .3Figure D-3.Absolute values of percentage error for high-gust condition for five input variables.
+Figure D- 4 .4Figure D-4.Percentages of error distribution for high-gust condition for five input variables.
+
+
+
+
+
+Table 1 .1Variables Used for Modeling ...................................................................................
+Table 2 .2Summary of Recommended Error vs. RSE Model Error for Landing Speed ..........
+Table 3 .3Five Input Variables .................................................................................................
+Table 4 .4Summary Landing Speed Models Error vs. Baseline Error for Five Input Variables ..................................................................................................................Table A-1.RSE Coefficients for Thirteen Input Variables ........................................................ Table B-1.RSE Coefficients for Five Input Variables .............................................................. Table B-2.RSE Terms Contribution for Low-and-No-Gust Case for Five Input Variables ..... Table B-3.RSE Terms Contribution for High-Gust Case for Five Input Variables ..................viii
+TABLE 1 .1VARIABLES USED FOR MODELINGParameterDescriptionHead windHead WindGust wind Ceiling_ft Vis_ft asmsGust Wind Forecast Ceiling Forecast Visibility Available Seat MilesAPAY Act_Land_Wgt FUEL_LOADActual Payload Actual Landing Weight Fuel LoadAFUBO FBR RSVActual fuel burn Fuel burn rate Reserve fuelESAD_nm LF_paxEquivalent still air distance Load factor for passenger
+TABLE 2 .2SUMMARY OF RECOMMENDED ERROR VS.RSE MODEL ERROR FOR LANDING SPEED
+TABLE 3 .3FIVE INPUT VARIABLESParameterDescriptionHead windHead WindGust windGust WindCeiling_ftForecast CeilingVis_ftForecast VisibilityAct_Land_Wgt Actual Landing Weightfive parameters listed in
+Table 3 .3These data are typically available to individuals through normal acquisition means (e.g., Federal Aviation Administration (FAA) websites).
+TABLE 4 .4SUMMARY LANDING SPEED MODELS ERROR VS.BASELINE ERROR FOR FIVE INPUT VARIABLESSta tisticalP a ra me ter sNN_ Mo de lRS E _M odelB a selineNN_ModelR SE_M o de lB a selineMean-5.9E-02-1.4E-044.2-1.6E-011.3E-06-6.9std D e v4 . 44.65.24.04.24.4Min-13.5-14.9-14.5-12.5-13.3-19.2Me dia n-0.1-0.14.3-0.4-0.4-7.3Max29.532.840.913.415.07.625 Pe rcen tile-3.0-3.10.8-7.0-2.7-9.975 Pe rcen tile2.42.77.52.32.6-4.2
+Table A -A1 list the RSE coefficients for thirteen input variables.
+TABLE A -A1. RSE COEFFICIENTS FOR THIRTEEN INPUT VARIABLESTermLow Gust Coefficient EstimateHigh Gust Coefficient EstimateIntercept-575.351078.88headWind-8.76-6.74gustW ind-560.982.18Ceiling_ft-4.94E-035.48E -03Vis_ft5.06E -034.59E -03asms1.57E -030.03APAY6.53E -030.02Act_ Land_Wgt0.02-0.03FUEL_LOAD0.010.01AFUBO-0.01-0.01FBR1.191.66RSV-0.230.08ESAD_nm-0.02-4.83LF_pax-5.276.66headW i nd*headWind0.013.50E -03h e a dWind*gustW ind-0.294.23E -03gustWind*gustWind0.22-0.01h e a dWind*Ceiling_ft8.16E -061.02E -05gustW ind*Ceiling_ft1 . 5 6 E -0 5-2.60E-06Ceilin g_ft*Ceiling_ft7.55E -092.87E -08hea dWind*Vis_ft-5.98E-064.36E -05gustW ind*Vis_ft0.022.38E -05Ce i l i n g_ft*Vis_ft1 . 0 8 E -0 7-2.99E-08Vis_ft*Vis_ft-2.65E-08-5.69E-10hea dWind*asms-5.42E-063.28E -05gustWind*asms-4.98E-05-4.30E-05Cei l ing_ft*asms-6.83E-09-2.25E-08Vi s_ft*asms-4.13E-09-6.76E-08asms*asms-6.82E-092.36E -08hea dWind*APAY6.10E -06-4.92E-04gustWind*APAY-6.79E-053.77E -04Cei l ing_ft*APAY-2.34E-08-2.83E-08Vis_ft*APAY6.07E -093.73E -07a sms*APAY-6.08E-084.25E -07APAY*APAY8.68E -08-1.40E-07hea dWin d*Act_Land_Wgt1.17E -04-1.89E-05gustW ind*Act_Land_Wgt-6.66E-052.85E -05Ceili n g_f t*Act_Land_Wgt-3.33E-09-8.34E-08Vis_f t* Act_Land_Wgt-2.82E-089.67E -08a sms* Act_Land_Wgt-2.62E-08-3.15E-07AP AY* Act_Land_Wgt9 . 3 3 E -0 9-5.88E-07Act_ La nd_Wgt*Act_Land_Wgt-1.08E-071.43E -07hea dWin d*FUEL_LOAD-1.30E-043.87E -04gustWind*FUEL_LOAD-3.10E-05-2.92E-04Ceil i ng_ft*FUEL_LOAD3.38E -08-7.07E-09Vis_f t*FUEL_LOAD-1.02E-07-6.87E-08a sms*FUEL_LOAD9.76E -083.30E -07APAY *FUE L_LOAD1.49E -061.54E -06Act_ La nd_Wgt*FUEL_LOAD-5.65E-082.62E -09FUE L_LOAD*FUE L_LOAD-7.91E-082.20E -08h eadWind*AFUBO7.40E -06-2.80E-04gustW ind*AFUBO-1.50E-051.85E -04Cei ling_ft*AFUBO8.37E -092.76E -08Vis_ft*AFUBO1.24E -085.09E -08a sms*AFUBO7.16E -09-1.59E-08APAY*AFUBO-3.37E-07-5.91E-07Act_ La nd_Wgt*AFUBO8.27E -08-6.98E-08FUE L_LOAD*AFUBO-6.13E-077.54E -07AFUBO *AFUBO9.40E -09-4.59E-07headWind*FBR-7.51E-030.05gustWind*FBR-5.27E-03-0.02Cei l i ng_ft*FBR2.14E -066.13E -06Vis_ft*FBR-6.32E-06-2.87E-05a sms*FBR1.37E -05-9.66E-06APAY*FBR-1.84E-052.29E -04Act_ L a nd_Wgt*FBR-1.63E-053.37E -05FUE L_L OAD*FBR8.74E -055.08E -05AFUBO *FBR6.71E -061.38E -05FBR *FBR-2.95E-03-1.39E-03h ea dWind*RSV-2.57E-041.08E -03gustWind*RSV1.26E -04-1.13E-03Ceiling_ft*RSV3.56E -073.68E -07Vi s_ft*RSV-2.64E-07-3.20E-06a sms*RSV-1.62E-075.25E -07APAY*RSV-2.19E-06-3.05E-07Act_ L a nd_Wgt*RSV1.10E -06-7.24E-07FUE L_L OAD*R SV-2.86E-06-4.71E-06AFUBO *RSV1.36E -065.10E -06FBR *RSV3.27E -04-1.22E-03RSV*RSV1 . 5 3 E -0 53.52E -05h ea dWind*ESAD_nm9 . 6 0 E -0 4-1.51E-03gu stW ind*ESAD_nm7.04E -034.58E -03Ce i l i n g_ft*ESAD_nm9 . 2 9 E -0 73.19E -06Vi s_ f t*ESAD_nm7.07E -071.02E -05a sms*ESAD_nm1.88E -06-7.89E-06APAY *ESAD_nm1.26E -05-6.05E-05Act_ L a nd_Wgt*ESAD_nm2.84E -065.24E -05FUE L_LOAD*E SAD_nm-4.31E-06-6.40E-05AFUBO *ESAD_nm-2.31E-071.35E -05FBR *ESAD_nm-2.42E-031.47E -03RS V*ESAD_nm4 . 0 3 E -0 6-1.47E-04ESAD _ nm*ESAD_nm-1.49E-045.98E -04h eadWind*LF_pax-0.030.13gu stW ind*LF_pax0.05-0.11Cei ling_ft*LF_pax4.84E -063.11E -05Vi s_ft*LF_pax1.39E -05-1.20E-04a sms*LF_pax2.43E -05-3.18E-05AP AY*LF_pax-2.33E-052.81E -04Act_ L an d_Wgt*LF_pax4.87E -057.18E -05FUE L_LOAD*LF_pax-4.01E-04-4.35E-04AFUBO*LF_pax6.73E -051.69E -04FBR*LF_pax0.01-0.07R SV*LF_pax-5.87E-05-5.86E-04E SAD _nm*LF_pax0.002.46E -03L F _ pax*LF_pax-0.01-0.05
+Table B -B1 gives the response surface equation (RSE) coefficients for the low-and-no-gust and high-gust conditions for the five input variables.Table B-2 and TableB-3 give their contribution for the low-and-no-gust and high-gust conditions, respectively.
+TABLE B -B1. RSE COEFFICIENTS FOR FIVE INPUT VARIABLESTe r mLow Gust CoefficientHigh Gus t CoefficientEstimateEstimateI n ter cept37.543892301.49002h e a dWin d0.15337886.1802607gustWin d-613.7826-4.735832Ce ilin g_f t-0.001962-0.001217Vis_ ft0.0006099-0.00105A ct_L a n d_ W gt0.0011325-0.003061h e a dWin d*h e a dWind0.00251220.0379639h e a dWin d*gustWind-0.280288-0.069674gu stWin d*gustWind0.20229180.0209201h e a dWin d*Ce ilin g_ft9.08E-062.57E-05gustWin d* C e ilin g_ft8.01E-06-1.59E-05Ceilin g_ft* Ce ilin g_ft9.36E-092.19E-08h e a dWind* Vis_ ft-1.72E-05-7.32E-05gu stWin d*Vis_ f t0.01936260.0001126Ce ilin g_ft*Vis_ f t2.99E-089.66E-10V is_ ft*V is_ f t-1.90E-08-1.86E-08h e a dWin d*A ct_L a n d_Wgt5.12E-06-3.13E-05gu stWin d*A ct_L a n d_Wgt1.93E-061.45E-05C e ilin g_ ft* Act_ L an d_Wgt5.40E-094.84E-09V is_ ft* Act_ L an d_Wgt2.28E-094.22E-09A ct_L a n d_W gt*A ct_L a nd_Wgt-3.12E-091.61E-08
+		
+		
+			
+TABLE OF CONTENTS (cont.)Appendix: Mean Square Error, the average squared error between the network outputs and target outputs : i th error between the network outputs and target outputs : i th target or actual output : i th network predicted output : number of inputs
+BASELINING: FLIGHT OPERATING MANUAL RECOMMENDATIONAircraft operating manuals provide landing-speed recommendations for each aircraft type based on the landing-aircraft weight.In order to assess the quality of the proposed models, the current aircraft operating manual recommendations are used as a baseline.As a case study, one of the most common narrow-body air-transport aircraft is used.
+BaseliningThe MD80 super 80 version has two recommendations: one for low-and-no-gust condition defined as gust wind below 10 knots and a high-gust condition for gust wind greater than 10 knots.
+Landing with Low-and-No-Gust ConditionIn the low-and-no-gust condition on the final approach, pilots are recommended to aim for a target approach speed of: (3) where:: Final approach target speed (knots indicated airspeed (IAS))
+: Reference speed (knots IAS)The reference speed is provided in the flight manual as a function of aircraft weight and aircraft type.
+Landing with High-Gust ConditionIn the high-gust condition, it is recommended in the final approach that pilots use a target approach speed of: (4) where:: Final approach target speed (knots IAS) : Reference speed (knots IAS) : Steady wind defined as the headwind component of the reported winds : Gust wind defined as the headwind differential between the reported winds and the reported gusts However, for the high-gust condition there is also a restriction that the total wind additive to the V ref should not exceed 20 knots.
+Model EvaluationAfter building the landing-speed model, there is a need to compare the model with the recommended landing speed or target approach speed, which is the best available prediction of landing speed.To compare the model against the target speed, two metrics are defined:Error Vtarget : calculated as the error between the V Target (i.e.baseline speed) and the actual landing speed as illustrated in equation ( 5): (5) Error Model : calculated as the error between the model predicted landing speed and the actual landing speed using the formula in equation ( 6): (6) with:V Actual : Actual speed or true speed is defined as the sum ground speed (obtained from the terminal radar approach control facilities (TRACON) radar data) and the reported wind at the airport (from METAR), V Target : Target speed or flight manual-recommended landing speed is defined as the flight manual-recommended approach speed based on aircraft type and aircraft weight.This speed is used as the baseline landing speed for comparison.V Model : Landing speed of the model is defined as the model predicted landing speed.			
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+Modeling the LAX TowerFutureFlight Central is a virtual airport tower that replicates for air traffic controllers, any airport environment as close to reality as possible.From the stairs that emerge through the floor of the tower cab, to the 360-degree out-the-window scene and the surrounding urban area, the simulation of LAX created a credible work environment that helped accurately assess human factor implications of the ideas under study.Three main aspects of the LAX controller's job were emulated to create this reality: the out-thewindow view, the voice communications, and the tower cab interior.First, the out-the-window scene was created by using the facility's twelve projectors to display a continuous computer generated image of the three-dimensional (3D) model of LAX (Figure 1).
+SearchThe 3D model was based on computer aided design (CAD) data provided by the airport engineering department.It was overlaid with realistic details of the terminal area, landscape, and distant cityscape using high-resolution photo textures.(Figure 2).Moving vehicles such as aircraft and service trucks were modeled in 3D, and moved about the airport scene under the control of "pseudo-pilots" who occupied the downstairs stations in FutureFlight.Pseudopilots controlled pushback time, taxi route, speed, take-off and landing in coordination with ATC in the virtual tower.Engineers used daytime conditions for the scene, although other times of day are possible.They modeled both clear day visibility and fog.For voice communications, controllers operated a touch panel that allowed them to select radio frequencies, similar to actual FAA tower voice communication systems.Pseudo-pilots at the other end of the radio responded with standard phraseology for the aircraft under their control.Frequency congestion was a real part of the radio environment in FutureFlight, just like the real world (Figure 3).The tower cab interior provided a third aspect of realism.Controllers used stations accurately oriented to their positions relative to the stairs and airport scene.Flight strips and plug-compatible head set jacks with long base cords, made the virtual tower as familiar as possible.Center-of-the-room modular tables were configured to the rectangular shape in the LAX tower.Controllers used surface radar displays, which synchronized information with the activity going on in the out-the-window scene.Hanging BRITES displayed the ASR-9 radar (Figure 4).
+Case Study of LAX Runway Incursion AlternativesBackground Los Angeles International Airport has the fourth busiest airfield in the nation.Air traffic has grown rapidly over the past ten years.However, the airfield and airspace have the same capacity and configuration they did ten years ago.In both 1998 and 1999, LAX lead the list of the nationØs busiest airports for number of runway incursions.Despite numerous changes to pavement markings, operating procedures, taxiway lighting and air traffic control procedures, runway incursions and surface incidents year after year present a major concern at LAX. (Figure 5)The FAA and Los Angeles World Airports (LAWA), the operator of Los Angeles International Airport, determined that resolving the runway incursion problem requires an approach that will minimize possibility for human error in the cockpit and in the tower.LAWA, the FAA, United Airlines (UAL), and NASA entered into a joint agreement to use FutureFlight Central (FFC) at NASA Ames Research Center to study changes to the Los Angeles International Airport., A study Steering Group was assembled with participation of LAWA, FAA Tower Control, UAL, FAA Western Pacific Region, National Air Traffic controllers Association (NATCA) and the Air Transport Association (ATA).This group was comprised of people intimately familiar with runway incursion issues specific to LAX.
+South Complex Runways 25L and 25RPhase I Baseline FFC tower was configured to replicate layout of the LAX tower (Figure 6).Three operational conditions were used for validation: a VFR Arrival Rush, a VFR Departure Rush, and an IFR Arrival/Departure Rush.Air Traffic Control Specialists from LAX tower operated hour-long simulation exercises over a fourday period.Phase I data collected at FFC included controller workload, aircraft surface movement data, and controller communications.This data was compared to that obtained from the LAX airport.LAX officials, FAA Air Traffic Controllers, and FAA observers judged that the FFC simulation was sufficiently representative of LAX operations that FFC could be used to study the impact of the alternatives proposed in Phase II on operations at LAX.
+Phase I key findings:q Controllers rated their simulation workload as "about the same as LAX." q Controller rated the realism of the simulation as "about the same as LAX." q The simulation successfully tasked controllers with the highest sustained traffic arrival and departure rates experienced at LAX. q Outbound taxi times were accurate within 1-2 minutes of LAX times for aircraft originating in the North and South Complex gates, representing 82% of aircraft in the simulation.q Runway occupancy times were within three seconds of corresponding LAX times for the inner runways, 24L and 25R.For the outer runways, 24R and 25L, occupancy times were longer than LAX.q Controller voice communications closely modeled available recordings from the LAX tower.Duration of transmissions was on average 5-8% longer at FFC. Results indicated 10-15% more transmissions per hour at LAX, and the air time distribution (percentage of time controller, pilot or neither were transmitting) was approximately 3% less for both controllers and pilots in FFC.
+Phase II AlternativesThe purpose of Phase II was to evaluate "Úair traffic control techniques, pilot procedures, airfield pavement geometry, and traffic management solutions to help eliminate runway incursions at LAX." Alternatives were compared objectively against data collected during Phase I and subjectively by the controllers and observers on the workload, efficiency, and safety criteria.Each alternative was tested under visual flight conditions using two traffic scenarios: a peak arrival rush and a peak departure rush.Both the north and south sides of LAX were simulated, with a complement of 22 airlines and an aircraft mix representative of LAX in the summer of 2000, for which NASA obtained actual LAX operational statistics.To ensure a valid comparison of the data between Phase I and Phase II, alternative scenarios were built from the Baseline scenarios, using the same arrival and departure rates as well as the same mix of aircraft fleet.Following alternatives were proposed for testing:Alternative #1: Swapping Inboard and Outboard Runway Operations Aircraft arrive on the inboard runways, and depart on the outboard runways with some landings occurring on the outboards, and some departures occurring on the inboards, depending on traffic demands (Figure 7).Alternative #4: Utilizing a B-16 Extension at ATC Discretion Again, a B-16 extension will be used, but the controller has discretion over its use, with one basic rule to guide him.For arrivals on 25L, if the controller can issue an instruction to cross 25R without having to issue a hold-short command, he may exit the aircraft to the north (J, K, etc.).If the controller anticipates having to issue a hold-short command, he will exit the aircraft left onto Alfa (Figure 11).Alfa-Alfa will be controlled by GC-2, and the Bridge route will be available.For aircraft bound for the North Complex taxiing on the B-16 extension, GC-3 has the option of the West Route (Alfa-Alfa) or the North Route (Quebec).Traffic sent along the West Route must hold short of Alfa-Alfa and contact GC-2.
+Data CollectedIn FutureFlight Central, engineers collect video and audio data, subjective surveys, and surface metrics during the course of each simulation run.Remote cameras in the tower, make it possible to watch and record cross-cab coordination, flight strip passing, facial expression and heads-up vs. heads-down time.This video, synchronized with out-the-window scene and voice communication recordings, can provide data for analyzing potential workload problems.Measurements were takes of individual aircraft taxi times, and combined to assess arrival and departure rate and other measures of airport efficiency.Three types of test data were collected during this study:q Controller subjective measures q Airport operations statistical data q Controller voice communications data Controller Subjective Measures ATC participants contribute expert knowledge of the efficiency, safety, communication, coordination, and traffic complexity and manageability of each alternative through responses on questionnaires.Each controller completed a survey immediately following each run of a scenario.Because each controller was randomly reassigned to a different work position during each scenario, their individual differences (response biases, fatigue-related effects, etc.) should have distributed approximately randomly over all of their ratings and not add bias to any single test condition.procedural variations of the B-16 extension.
+Conclusionsq Based on controllersØ subjective judgements of safety, the following were the top three alternatives for addressing LAX runway incursions.Significantly, both simulations that included no change to the current airport geometry ranked relatively low for safety r B-16: Bridge Open (Alt.3a) r B-16: With 2 Locals (Alt.5) r B-16: ATC Discretion (Alt.4) q Based on the subjective judgements of the controllers, alternatives that included a B-16 taxiway extension were more easily managed than the current LAX airport plan.q Based on controllerØs subjective evaluations, all alternatives which include a B-16 taxiway were regarded as more easily managed than the alternatives which included no modification to the airport geometry (e.g.simply swapping the runways use or adding another Local controller to South operations).q Alternative 1 (Current Plan: Swapping Runways), while offering improved arrival taxi times and requiring less coordination by controllers, was not subjectively judged as safe as other alternatives.It was also regarded as having about the same potential for runway incursions as the current mode of operations.q Alternative 2 (Current Plan: Two South Locals), resulted in lower departure rate and was judged by controllers as having a higher potential for a runway incursion than current operations, mostly LAWA, the FAA and United airlines to save time and money in finding ways to improve runway safety.The key benefit of testing in a near real-life operational setting is that safety was maintained while alternatives were evaluated as closely to field implementation as possible.NASA determined that the B-16 extension under combined features of two proposed alternatives, provided the most desirable solution of those tested.It could reduce the possibility of human error by eliminating runway crossings altogether from the south side operations.Arrival taxi time, though negatively impacted, would be offset by improving the efficiency of departure operations.The LAWA team narrowed their options through virtual reality testing to ensure the expense of detailed designs and Environmental Impact Statement (EIS) would be spent on a plan that controllers considered workable and analysis indicated viable.LAX is now proceeding with the implementation phase, which includes conformance to TERPS regulations and other considerations.Figure 1 :1Figure 1: NASA FutureFlight Central Tower Cab
+Figure 2 :2Figure 2: Comparison of LAX Tower Views (above) with Simulated View (below)
+Figure 3 :3Figure 3: LAX Controller Wearing Headset in FutureFlight Central
+Figure 4 :4Figure 4: LAX Tower D-BRITE Display in FutureFlight Central
+Figure 5 :5Figure 5: LAX Runway Incursion Data 1997-2000.Incursions shown in white.a
+Figure 6 .6Figure 6.FutureFlight Central Tower Layout
+Figure 7 :7Figure 7: Alternative 1. Swapping Inboards and Outboards Operations
+Figure 8 :8Figure 8: Alternative 2. Two Local Controllers on the South Side
+Figure 9 :9Figure 9: Alternative 3. Utilizing B-16 Extension, AA Is One Way
+Figure 10 :10Figure 10: Alternative 3a.Utilizing B-16 Extension, Bridge Route Open
+Figure 11 :11Figure 11: Alternative 4. B-16 Extension, ATC Discretion
+Figure 13 :13Figure 13: Critical Issues of the Alternatives b
+Figure 14 :14Figure 14: Controller Subjective Ratings during Peak Departure Scenarios c
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+			http://ffc.arc.nasa.gov/about_us/technical_papers/nasa_lax.html(7 of 15) [6/5/2003 11:37:58 AM]
+			http://ffc.arc.nasa.gov/about_us/technical_papers/nasa_lax.html(15 of 15) [6/5/2003 11:37:58 AM]
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+The answers to questions used a scale from 1 to 5 where value '3' represents "about the same" as current LAX operations.A rating of 5 means "better than LAX today" and a rating of 1 means "worse than LAX today."For each question the Mean Rating and Standard Deviation was calculated by controller position.In a final question, controllers could select up to three criteria to indicate the most challenging aspects of each alternative.Every time a controller selected a criterion, it was counted as an "occurrence."The resulting value, "Frequency of Occurrence", indicated how frequently this operational criterion was marked as critical across all positions.Airport Operations Data During all six days of Phase II, FFC collected airport operations data in order to compare the Baseline with the alternative scenarios.Collected data enabled calculations of average departure rate, outbound taxi time by origination point, arrival rate, and inbound taxi time by destination point.Controller Voice Communications Recordings FFC created digital audio recordings of each simulation run.Voice data was recorded from each controller station on the South side.At each position, the controller's microphone provided an input signal to one channel and the pilot's transmissions received through the headphones were recorded on another channel.In addition the console microphone was recorded on a separate channel.This capability allowed assessment of the controller workload through analysis of their inter-position communication.
+Summary of ResultsThe results were based on analysis of the above data as compared with the baseline for each alternative.The following figure represents the subjective rankings of the alternatives by LAX controllers: rankings included questions on safety and efficiency.These results indicate that LAX controllers regard the B-16 extension under procedures of Alternative 3a as the safest and most efficient.For rating critical issues, alternatives 3a, 4, and 5 had the least number of occurrences.There were all because of the increased coordination required between Locals."
+DiscussionThe virtual reality based evaluation of LAX safety alternatives provided a key benefit that would be impossible with any other type of assessment, short of testing changes in a live tower.It enabled real-time operational testing by the very people who would be most impacted by the changes, namely, the controllers.By showing that they could make a variation of the B-16 extension work, without negatively impacting their workload, efficiency, or judgement of safety, a way to reduce runway incursions was validated beyond the theoretical or simulation analysis.In general, airport planners and designers have failed in the past to adequately utilize the insight air traffic controllers can lend to airport planning.In the virtual reality evaluation in FutureFlight Central, LAX controllers suggested a variation that had not been considered in the study design, namely, to combine the features of the best B-16 Alternative 3a with the additional south side local controller Alternative 2. By giving the controllers the flexibility to fine tune procedures, they optimized a "hybrid" alternative.This alternative not only rated the highest subjectively, but also performed the best under analysis of departure metrics.Cultivating the input of those affected by decisions can only lead to greater consensus for the ultimate solution.By including airline and air traffic control involvement, LAWA, the FAA and United Airlines built support and momentum toward an eventual solution.
+Next Steps for LAXThe B-16 extension that emerged as the most favored alternative, was assessed in FutureFlight Central primarily for controller human factors, and made some assumptions about conformance to implementation regulations.In particular, while runway safety improvements need to be treated with a sense of urgency, LAX also needs to consider how they will integrate into long-term plans and the current operational scheme.Furthermore, Terminal Instrument Procedures (TERPS) regulations and Code of Federal Regulations part 121, Operating Requirements: Domestic, Flag, and Supplemental Operations, must be considered specifying obstacle avoidance clearance distance under instrument flight conditions and engineout conditions, respectively.LAX is working through these next steps with implementation issues by submitting an application to the FAA Airspace Branch for review.LAWAØs proactive and urgent approach to the runway incursion problem has moved them from number one for two years running at 10 incursions in 1999 to 7 incursions in 2001.The addition of the B-16 extension, assuming it passes FAA scrutiny, will further improve safety at LAX by reducing the conditions under which human errors can be made.
+SummaryThorough testing and refinement by LAX tower controllers in NASAØs FutureFlight Central, enabled			
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+		The Local Area Augmentation System: an airport surface guidance application supporting the NASA runway incursion prevention system demonstration at the Dallas/Fort Worth International Airport
+		
+			RThomas
+		
+		
+			MDibenedetto
+		
+		10.1109/dasc.2001.963340
+		FFC-LAX-R001
+	
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+		20th DASC. 20th Digital Avionics Systems Conference (Cat. No.01CH37219)
+		
+			IEEE
+			May 2001
+		
+	
+	NASA FutureFlight Central
+	Los Angeles International Airport Runway Incursion Studies Phase I Baseline Simulation, NASA FutureFlight Central, FFC-LAX-R001, May 2001
+
+
+
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+		The Local Area Augmentation System: an airport surface guidance application supporting the NASA runway incursion prevention system demonstration at the Dallas/Fort Worth International Airport
+		
+			RThomas
+		
+		
+			MDibenedetto
+		
+		10.1109/dasc.2001.963340
+		FFC-LAX-R002
+	
+	
+		20th DASC. 20th Digital Avionics Systems Conference (Cat. No.01CH37219)
+		
+			IEEE
+			August 2001
+		
+	
+	Los Angeles International Airport Runway Incursion Studies Phase II Alternatives Simulation, NASA FutureFlight Central, FFC-LAX-R002, August 2001
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+		Official US atomic clock about to be updated
+		10.1063/pt.5.027823
+		Updated:12/09/02 Curator
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+		Physics Today
+		Phys. Today
+		1945-0699
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+INTRODUCTIONIn 1994, Federal Aviation Administration (FAA) and NASA visionaries partnered with the goal of applying Ames Research Center's expertise in information technology toward more efficient airport surface operations.A rapid prototype development project, called Surface Movement Advisor (SMA), was completed in 18 months to coincide with the 1996 summer Olympics in Atlanta, Georgia 1 .SMA is an information system innovation, which provides projected flight and trend information to multiple recipients at the airport.At Atlanta's Hartsfield International Airport, it was installed in the FAA Air Traffic Control Tower (ATCT), ramp towers, airport management areas, Air Route Traffic Control Center (ARTCC), Terminal Radar Approach Control (TRACON), and Delta Air Lines' strategic Operational Control Center.Developers identified difficulties during the rapid prototyping cycles.Obtaining feedback from end users in a laboratory environment, while useful, is inherently limiting.The lab environment is not as complex, lacking, for example, the visual demands of the real tower environment and the distractions of a full crew operation.Furthermore, to evaluate how robust the tool is requires testing under off-nominal conditions, which can be impossible to recreate in the laboratory setting.Thus, SMA was moved to the real tower environment relatively early in a development cycle to obtain a full operational context for testing.However, a live traffic operation does not lend itself well to the disruptive nature of rapid prototyping.The controllers must adhere to their #1 priority: maintaining a safe and efficient flow of traffic on the airfield.Even a new display intended to augment situation awareness diverts attention from the primary task at hand, especially during the learning curve.With less than a minute often separating landings or takeoffs during peak times, engineers were initially forced to install SMA during the graveyard shift.In that quieter familiarization period, controllers learned how they might use the information on the new display, but during periods of high traffic volume, the tool was largely ignored.Ironically, this is when the most benefit might have been gained.Another disadvantage of testing on live traffic is the infrequent and unpredictable nature of off-nominal conditions.Developers might wait days, weeks or months for all the necessary conditions to occur for thorough testing: for example, weather induced visibility restrictions, unusual surface flow patterns, or emergency procedures.Live testing has one additional drawback.Since every ATCT and airport is different, one cannot assume a tool such as SMA would have equal benefit for all.Ideally, the tool should be tested at each airport, not only to assess potential benefit, but also to uncover any unique requirements of that installation.However, such an undertaking would be very costly, and a needless investment if the results indicated the benefit was insufficient to justify the deployment.Thus the interagency team spun off a parallel effort to develop a highly realistic ATCT simulator to mitigate the risk, and provide the flexibility and efficiency that was lacking in the SMA development experience.They coined a simple and descriptive name, the "SMA Development and Test Facility" (SDTF).A design team was formed spanning all aspects of airport operations that would potentially benefit from such a unique facility: air traffic control specialists, FAA supervisors, pilots, airlines, ground crews and airport operations personnel. 2All participants felt that the simulator needed to be as operationally realistic as the available technology and funding would allow, so that conclusions drawn from simulation tests would be as applicable as possible to real world conditions.The design team believed that a high fidelity human-inthe loop (HITL) simulation of an ATCT would provide a unique and unprecedented benefit to the FAA and NASA.It would serve as a safe platform in which controllers could more effectively evaluate SMA functionality and interface design without the concern for maintaining safety.The SDTF would provide a cost-effective way to quickly assess SMA's potential benefit for other airports.A more thorough evaluation would be possible by having the flexibility to alter test conditions such as traffic volume, fleet mix, flow patterns, visibility conditions, and various pilot deviations.SMA designs could be checked within the context of the entire tower operation.This last benefit is especially valuable due to the diverse tasks which comprise a tower controller's job: radio communications, within-tower coordination, visual airfield scanning, flight strip management, and monitoring radar and other displays.Finally, ATCT staff could be trained off-line on the new tool prior to deployment thus contributing to a successful acceptance and overall utilization.The SDTF design team realized very early in the process that a high fidelity ATCT simulator had potential beyond just the testing and deployment of SMA.Any changes envisioned at an airport could be similarly tested with the same benefits: new air-side construction, procedure changes or integration of other technologies for the tower.The SMA Development and Test Facility was renamed the Surface Development and Test Facility to reflect the broadened scope.Later, the facility was commissioned as FutureFlight Central (FFC).
+PHYSICAL DESCRIPTIONThe realism begins even before entering the tower cab.Just as real ATCTs are reached via stairs that wind from below, so too in FFC, stairs bring tower cab occupants from a lower level emerging through the floor.Though there is a wide variation in tower cab dimensions in the real world, FFC's 24 ft.diameter mimics the newer and larger hub airport towers.The lower level consists of rooms for real-time simulation participants, meeting rooms, computers and equipment.
+Visualization SystemThe out-the-window visualization system is one of the most important components of realism because visual identification is an integral part of a tower controller's situation awareness.A 360-degree field of view is accomplished using a dodecagon (twelve-sided) projection system (Figure 1).Twelve 10 x 7.5 ft.screens surround the tower cab.The screens consist of a Fresnel lenticular acrylic optical material, which provides rear projection at optimum brightness.Highbrightness projectors reflect the images off of "first surface" mirrors onto the back of each screen.The mirror system was chosen to reduce the required footprint.Glass windows with mullion dividers are located approximately two feet in front of the screens to enhance depth perception.
+Figure 1. FutureFlight Central Tower CabGraphics engineers build a 3-dimensional (3-D) airport databases in Open Flight format combining data from multiple sources: CAD layout of the airport, aerial photography, and photographs taken from the tower cab elevation.All non-stationary elements of the photographs must be removed before they are used to texture polygons in the airport scene.The run time software is Vega, which underlies the application software and provides visual effects such as fog, clouds, lights, articulation of vehicle models, rotation of propeller blades and blinking lights.An SGI image generator (IG) computer system outputs twelve simultaneous channels of video at 30 Hz and 1280 by 1024 resolution, rendered from the database of the airport.Position updates for aircraft and ground vehicles occur at 5 Hz and must be extrapolated.The images are drawn based on a viewpoint at the center of the tower cab.The IG has 16 processors, 2 gigabytes of random access memory (RAM) and 64 megabytes of texture memory.Photo texturing adds significant visual fidelity without burdening the drawing process.A typical airport scene is composed of a background of 9,000 to 12,000 polygons, 50 to 60 megabytes of photo textures, and 100 to 120 3-D moving models of aircraft and ground vehicles.In order to optimize the computational demands of rendering the scene, aircraft models are drawn at four levels of detail, depending on the distances of the model from the eye point.The distance thresholds are configurable and must be set based on the unique geometry of each airport.
+Tower CabThe tower interior was designed to be as flexible as possible to account for the variation in equipment that exists in real tower facilities (Figure 2).There are 9 perimeter positions for local and ground control and up to 3 positions at the center console.The center console is composed of modular sections which can be recombined to form different shapes to model the variation among real ATCTs.FFC's emulation of Airport Surface Detection Equipment (ASDE-3) provides a surface radar display.Similarly, Airport Surveillance Radar (ASR-9) provides radar imagery typically 10-30 miles from the airport.FFC can present
+Figure 2. FutureFlight Central Cab InteriorASR-9 both at the console level or on up to 6 hanging monitors, which replicate the Bright Radar Indicator Terminal Equipment (BRITE) displays.Voice communication equipment, which also varies widely amongst tower facilities, is represented at each position in FFC's tower cab as the more modern touch screen communication (comm) panel, with multiple frequencies, multiple pages, intercom and interphone connection to outlying facilities.The comm system emulates the VHF radio and is used to communicate with pseudo-pilots and pseudo-ramp controllers in the lower level of the facility.The comm panel is reconfigured for each airport's dedicated frequencies.Separate channels are also used for coordination by FFC's staff throughout the facility.The FFC team prepares nearly exact replicas of the Flight Progress Strips for each simulation exercise.These are the strips of paper controllers use to keep track of each departure flight.They can be loose, in strip holders, or in strip bays depending on the method employed in the airport tower being modeled.Pseudo-pilot Room Aircraft targets are controlled in real-time by pseudopilots who "fly" the planes using a graphical user interface (GUI), from the pseudo-pilot room on the first floor.Pseudo-pilots provide the cockpit communications with the tower, and control airplanes from typically 5-10 miles out, through landing, taxi-in, docking at the gate, pushback, taxi-out, and departure.As many as 25 pilots are needed to manage the high volume and pace of aircraft activity of a busy airport.Pseudo-pilots are required to have a background in aviation operations so that they are intimately familiar with phraseology and airport procedures.Extensive practice on the subject airport ensures they conduct the movement of airplanes and communicate in a way that makes the simulation as realistic as possible.
+Test Engineer's RoomThe nerve center of FFC is the Test Engineer's Room on the first floor, where the simulation is controlled and monitored.The test engineer is responsible for configuring, starting up and monitoring the simulation software called MaxSim, a commercial-off-the-shelf package from Adacel Inc.The test engineer launches traffic exercises called "scenarios" that have been prepared in advance.The software allows the test engineer to change the weather, lighting conditions, or insert new aircraft dynamically into the running simulation.The test engineer can cue a pilot to simulate an emergency or a deviation.Video monitors in the room display a portion of the tower cab's out-thewindow scene, radar displays, and views from four remote cameras located in the tower cab.A flexible audiovisual Integrated Control System (AVICS) enables recording and routing of various audio and video signals throughout the facility.Any out-the-window video signal, camera video or radar image can be recorded to VHS, Betacam or DVD.A digital audio system records pilot-controller voice communications for later analysis.Microphones located on the consoles and in the ceiling capture ambient communications between controllers.A suite of video editing equipment is used to create professional quality videos for FFC customers to share the simulation experience and results with airport stakeholders.
+RESEARCH CAPABILITIESThe research capabilities of FFC incorporate a variety of features.Custom traffic scenarios, a wide array of data collection, and the ability to integrate external software or simulators, provide experimenters with ready access to the tools they need for research studies.
+Traffic ScenariosAccurately representing an airport's traffic can be a challenging task.Data on actual flights including call sign, aircraft type, arrival or departure time, runway, and route waypoints are all needed to prepare a realistic scenario.Several sources of this data exist, although no one source contains all the information that is needed.ATC analysts must "fill in the blanks" for every flight.Simulation engineers program an arrival sequence that typically extends 45 minutes to an hour.At the same time, they place aircraft at appropriate gates and prepare a departure schedule for the scenario(s).If the study requires simulation of future traffic projections, simulation engineers can augment the scenarios to add additional flights and adjust the fleet mix accordingly.
+Data CollectionThe research value of a simulator is directly related to the data that can be collected during the runs.Studies of airport efficiency utilize FFC's airport surface metrics software that calculates data such as taxi times, holds or stops, runway occupancy time, and airport departure rate.In addition, subjective data is frequently gathered from controller and pilot questionnaires.Cumulative airport noise based on simulation data can be mapped upon completion of a run, using a custom interface to the FAA's Integrated Noise Model.Safety studies and technology development projects typically use data such as counts of runway crossings, measurements of controller task load and radio frequency congestion, and survey responses.Human factors research is especially interested in video and audio recordings to correlate activities in the tower cab with events on the airfield.Time stamped video recordings and quad-split video allows four sources to be correlated on the same video image.Researchers use audio recordings to measure transmission rate and distribution as one indicator of workload.They may also analyze them post-simulation, for subtle contextual insights.Live Web Casting Web-based video streaming allows remote viewing of the simulations in real-time over the Internet.Up to 200 remote viewers can watch and listen simultaneously to a particular portion of the airfield under study or activities within the tower cab.Login and password protection can control access for privacy.The only software required is QuickTime 5 or 6, free and downloadable to either Mac or PC platforms.Integration with External Software and Facilities One of the key features of FFC is it's ability to integrate and test new or emerging technologies such as decision support tools (DSTs) to help air traffic controllers better manage surface traffic, incoming approaches or departures.This unique capability enables FFC to link the tower simulator with other simulation components and/or facilities using the industry standard High Level Architecture (HLA) protocol.This was successfully demonstrated for the Surface Management System (SMS) Study conducted at FFC, in September 2001 and January 2002.SMS is an enhanced DST that enables controllers and Traffic Management Coordinators (TMCs) to better manage traffic by matching arrival and departure capacity with time-varying airport demands.SMS consists of a map display which gives controllers a bird's eye view of an airport depicting each of the runways, taxiways, terminals and gate locations, as well as all ground traffic.The traffic is identified with an aircraft symbol and optional flight specific information, including flight identification and aircraft type.SMS forecast runway demand 30 minutes ahead for the tower TMCs, who then entered this data into the Traffic Management Advisor (TMA).TMA adjusted the airport acceptance rate, rescheduled new arrival times for inbound flights and departure times for outbound flights.TMA is another DST developed in support of the Center TRACON Automation System (CTAS), and is currently in use at the Fort Worth Center.It is used to assist TRACON and Center TMCs in arrival flow management planning.For this study, FFC sent data to SMS via the HLA interface to emulate the radar feeds that SMS would receive in the field.This data provided the arrival and departure demands for the upcoming hour to the tower TMC who then used this data to better schedule arrival traffic.For these studies, the tower TMC was able to determine when runway usage could be changed to better meet traffic demands at the simulated airport, successfully demonstrating the viability of integrating the efficient use of DSTs such as SMS and TMA.In addition to integrating SMS, FFC has been linked to other flight simulation facilities at NASA Ames.These include the Crew Vehicle Systems Research Facility's (CVSRF) Boeing 747-400 Flight Simulator, and one of the cabs at the Vertical Motion Simulator (VMS) Complex.This interface is requisite for supporting future work such as the Virtual Airspace Modeling and Simulation (VAMS) Project and the Access 5 Remotely Operated Aircraft (ROA) Project.For these projects, NASA envisions a need to link various simulation facilities and/or components to better emulate operations in the national airspace system.These projects will require not only integrating various air traffic control facilities, but also the vehicles flying within the given airspace.Traffic traversal between simulation environments will support human factors research across both airborne and surface domains.For example, there will be a need to pass control of ownership between targets of the various Air Traffic Management Simulations tools such as the NASA Ames developed Pseudo Aircraft System (PAS), and FFC's target generation tool.PAS is an integral component of the CVSRF's ATC Simulation, the Airspace Operations Laboratory (AOL) and the CTAS ATC Laboratory at NASA Ames.As the target generation tool for each of these simulations, PAS provides commonality between each of the given simulations and it would be beneficial to be able to transfer PAS-generated targets along with other targets within the FFC simulation environment.This capability will enable FFC to participate in gate-to-gate simulations in which traffic can take-off from one airport environment, fly through the terminal airspace, en route and then to another terminal area and airport environment.FFC could act as one of the two chosen airports or, by reinitializing and rejoining the simulation, act as both airports if needed.
+APPLICATIONS
+LAX Safety StudiesDue to increasing traffic and airport congestion in the late 1990s, there was a growing trend in the number of runway incursions nationally each year, each with a potential for collision and fatalities.A runway incursion is a loss of safe separation of an arriving or departing aircraft with another aircraft, vehicle, person or object on the ground.Los Angeles World Airports (LAWA), along with the FAA and United Airlines conducted a series of studies at FFC to determine how to reduce the increasing number of runway incursions at Los Angeles International Airport (LAX).The studies evaluated proposed procedural changes as well as a taxiway redesign in an effort to improve surface operations and airport safety.Tested conditions concentrated on redistributing surface traffic on the congested south side of the airport, historically associated with runway incursion events by reducing the need for runway crossings, and potentially improving the manageability of the surface traffic.The studies were broken up into two phases.The first phase demonstrated that FFC could simulate the amount of traffic representative of LAX.The second phase simulated the proposed changes and assessed their potential to improve safety and alleviate surface congestion.During the simulations, researchers measured airport take-off and landing capacity, including runway occupancy time, inbound and outbound taxi times, hold times, and arrival and departure rates.Additionally, researchers measured controller-pilot communications, controller workload, delays and other factors.These data, along with video and audio recordings, allowed the project team to understand the impact of possible new runway procedures and construction on ground traffic flow and airport capacity.
+DFW Master PlanningIn an effort to deal with the problem of increasing capacity and congestion issues, Dallas/Fort Worth International Airport (DFW) in conjunction with the FAA conducted a study at FFC to demonstrate the viability of perimeter taxiways.Perimeter taxiways allow aircraft to move from the runways to the gates without crossing another runway.Under current operations, the departure demand causes delays in the arrival operation, and vice-versa.The local controller is especially impacted because he must conduct all runway crossings before the aircraft can be released to the ground controller.Radio frequency congestion increases as the controller works to balance all operations and meet airport demand.The problem is most evident during peak traffic periods.As a potential solution, airport planners designed taxiway extensions to circumnavigate the runways and enable arrival and departure traffic to operate independently of each other.To demonstrate the improvement and gain acceptance of this concept, FFC integrated NASA's 747-400 simulator as part of the test to provide both pilots and controllers with a perspective of what the proposed design changes would look like.The results of this effort are anticipated to reduce runway incursions and improve airport safety.
+Remote ScienceAnother unique application for using FFC was in support of the Haughton Remote Science Experiment, under the sponsorship of the Center for Mars Exploration at NASA Ames.For this study, FFC's viewing tower was used to evaluate the ability to conduct geological science research at remote locations (Figure 3).The objectives of this experiment were to display live panoramic and standard digital images from the Haughton site (a crater in the Arctic region) in Canada, evaluate the contents of the images, and to direct a camera-equipped all terrain vehicle
+FUTURE CAPABILITIES AND APPLICATIONSVoice Recognition Advances in voice recognition and synthesis over the past decade have made it a viable alternative to human pseudo-piloting for some ATC simulation applications. 3lthough some researchers may prefer the realism that only the human contributes in the voice realm, activities such as training may proceed equally well or better with voice recognition and synthesis.FFC is considering a voice recognition and synthesis system in a flexible mixed mode where both humans and the automated voice software could co-operate.The approach and departure phases of flight, for example, could then be voice automated without impacting study results of surface flow.Reduced operational cost for simulations is an additional anticipated benefit.
+Visualization of Live DataBesides being able to use it's own target generator tool or some other tool such as PAS, it is highly desirable to be able to externally feed traffic data either previously recorded or from a "live" feed for viewing purposes.This would be useful in visualizing real life scenarios as they would have occurred but through FFC's virtual environment.This could play a powerful role in studying human factors issues or in accident investigations based at a particular airport location.Another potential future use of live ATC data is to enable the facility to act as a remote tower in cases of emergencies.It is highly possible to feed "live" data into the facility to remotely monitor and control traffic during emergency situations such as in the event of an earthquake or some other disaster.Using live data, controllers would be able to virtually control traffic from FFC where it may not be possible at the airport in question.
+Command and Control for Military and SpaceAlthough FFC is primarily an ATC tower simulator, it would not be difficult to imagine the facility being used as a Command and Control Station for some type of military application, or quite easily as a remote viewing station for space exploration applications.Thanks to it's large field of view, FFC could easily be adapted to support military operations by establishing a remote battlefield command post in which military personnel could manage military campaigns from a centralized remote location.In support of space exploration initiatives, FFC could be used as a remote viewing observatory to monitor external spacecraft docking operations or for Crew Transfer Vehicle (CTV) operations for shuttling crews back and forth to space stations as envisioned in support of the Orbital Space Plane Program.NASA and the Department of the Navy have also had discussions about using FFC as a virtual aircraft carrier operations flight deck to research fleet mix studies for next generation aircraft carriers.In support of future command and control concepts, FFC could enable Navy personnel to monitor and control ship-based operations aboard future aircraft carriers from a remote location without requiring a view of the actual carrier deck.
+SUMMARYIn just three years of operation, NASA's FutureFlight Central has exceeded the vision of its designers in supporting a wide array of applications from airport expansion planning to proof of concept for remote exploration.The key value it offers across all uses is the inclusion of human performance in simulation studies.Human factors is increasingly recognized as a critical early consideration in science and technology development.A successful plan or tool is one in which the end users have contributed early to the design and tested the idea in a real life operational setting.When real life testing is not feasible, high fidelity simulation such as that offered by FutureFlight Central fills a critical need.As complex interactive simulation becomes increasingly robust, this national resource will play a major part in predicting the performance and benefit of new concepts for aeronautics and space, with minimal initial investment.Figure 3 .3Figure 3. Researchers in FFC view image transmitted from Canadian high arctic
+Figure 4 .4Figure 4. Researchers in FFC viewing potential space operations
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+I. IntroductionConvective weather is the leading cause of flight delays within the National Airspace System (NAS).In fact, the vast majority of flight delays greater than fifteen minutes are caused by weather. 1,2 hese delays result from actions such as ground delays, airborne holding, and miles-in-trail restrictions that are used to manage the reduced capacity of the weather-affected regions and airports.En-route air traffic can be negatively affected by convective weather due to reductions in sector capacity and to extra flight distance caused by rerouting.When a portion of a sector is blocked by weather, aircraft must be vectored around it and may be sent to neighboring sectors, possibly overloading them.The resulting traffic flow patterns do not match the static sector boundaries that were designed with normal good-weather traffic patterns in mind.In an effort to alleviate these negative effects and reduce controller workload, it is desirable to flexibly alter sector designs in response to blocking convective weather.Current work in systematic sector design has focused primarily on a clean-sheet approach using historical or simulated flight track data as its input.References 3-7 are just a sampling of the work that has recently been done in this area.The philosophy is that if high-performing sectors can be systematically designed based on traffic flow patterns, then changes in those patterns (including changes due to weather) will result in appropriately designed sectors.Less work has been done in the area of explicitly designing sectors around convective weather constraints.In Ref. 3 the authors use a Mixed Integer Linear Program model to optimally design sectors based on changing traffic patterns due to weather.The sectors are designed based on historical weather-affected traffic patterns.In Ref. 8 the authors present an algorithm for changing the size and shape of existing sectors based on current sector loading conditions by trading sector building blocks known as Fix Posting Areas between neighboring sector boundaries.One problem with implementing these methods is that they need to identify new traffic patterns that arise from blocking weather on which to base the new sector designs.While it is possible to synthesize the weather-affected traffic with traffic modeling software, it would be convenient to avoid having to do so.An additional issue with dynamically redesigning sectors in real time is that abruptly changing sector boundaries during operations increases controllers' workload as they transition from one sector configuration to another.This has been demonstrated by recent humanin-the-loop experiments in which controllers were subjected to varying sector designs during normal traffic conditions in an operational setting. 9It might be preferable to redesign sectors incrementally and avoid major boundary shifts so that the sector configuration changes produce less work for controllers.In this paper, a new method of altering sector boundaries around blocking convective weather is presented.This method is based on the premise that the existing sector design exhibits high-performance in nominal traffic conditions from a controller's operational perspective.When blocking convective weather is present, it reshapes, or "stretches" sectors around blocking weather in order to better handle rerouted traffic.Thus, instead of radically changing sectors in response to weather conditions, smooth and intuitive changes are made gradually.This reduces the amount of controller workload involved with transitioning from one sector design to another.It also eliminates the necessity of using historical or synthesized weather-affected traffic patterns to design the new sectors.This paper is arranged as follows: Section II describes the theory on which the new sector design algorithm is based, and Section III details the application of the theory to the new sector design method.Section IV presents the results of the method applied to some notional blocking convective weather.Comparisons are made to another sector design method that uses synthesized traffic to design sectors around blocking weather.Finally, the performance of the new sectors is analyzed in simulation.Conclusions and directions of future work are discussed in Section V. When redesigning sectors to lessen the negative impact caused by weather, it is helpful to understand how weather impacts sector demands.This relation is discussed in this section, followed by a means of quantifying sector capacity reduction due to blocking weather.
+II. Background
+II.A. Effects of Blocking Weather on SectorsIn Figure 1, Sector ZFW42 in Fort Worth Center is directly affected by the notional blocking weather constraint.Traffic normally passing through this sector must be rerouted around this constraint with the possibility of overloading surrounding sectors.Thus, while Sector ZFW42 will be significantly affected by the weather, the neighboring sectors will also be heavily impacted-perhaps more so.This leads to severe workload inequity among all the sectors in the region.Furthermore, to prevent the capacities of these sectors from being exceeded due to traffic demand, Traffic Flow Management (TFM) actions like ground delays, airborne holding, and miles-in-trail restrictions may intervene that lead to greater systemwide delays.In this work, it is assumed that a polygon can be constructed around the severe weather and that air traffic will not be allowed to pass through that region.In actual operations, weather is not treated as a no-fly zone; often limited traffic is allowed to fly through gaps between severe weather cells.However, this assumption will be made as an approximation to existing operations and will serve as a basis for the sector design method described herein.It is the goal of this sector design method to alter the boundaries of the sectors in the region affected most by the weather in order to manage the rerouted traffic in a more equitable manner.In such situations, it is proposed that altered sector designs may actually dictate and help direct traffic routes instead of reacting to the resulting traffic.
+II.B. Estimating Sector Capacity ReductionIn order to redesign the sectors to improve the flow of weather-impacted traffic, it is helpful to have a means of quantifying the decrease in sector capacity caused by weather.This is a vital part of this sector design process.Attempts at estimating capacity reduction are described in Refs.10-12.In Ref. 10 the authors propose a method for estimating sector capacity reduction due to severe weather.Sector capacity is difficult to measure-even in perfect conditions.Often, a sector's Monitor Alert Parameter (MAP) is used as an estimate for sector capacity, but this is not a hard and fixed capacity limit.The work of Ref. 10 offers a means of estimating the reduction of this capacity based on the predicted traffic flows through the sector and an application of the Max-flow, min-cut Theorem.The basis of the Max-flow min-cut Theorem is that a network can only handle as much volume as the weakest link will allow.Applied to flights in a sector, it is proposed that the capacity for a sector to handle traffic between two of its neighboring sectors is proportional to the minimum cross-sectional distance (min-cut) across that flight path.For instance, the capacity of the sector shown in Figure 2 to handle traffic between sectors A and C without weather is proportional to the distance O AC .In the presence of weather represented by the pink polygon, this min-cut distance is reduced to W AC , and the percentage of capacity reduction for traffic between sectors A and C is simply the min-cut ratio WAC OAC .The total capacity reduction for the sector is the average of all the flight path min-cut ratios weighted according to the number, F i , of predicted flights for a given time span in each path where i ∈ {All neighbor pair flight paths}.Thus, the total sector capacity reduction due to blocking weather may be calculated asPercent Capacity Reduction = i F i • Wi Oi i F i .(1)Referring to Figure 2, where only two neighbor pair paths are shown, it can be seen that the min-cut ratio for path BD is less than that for path AC.However, if there is significantly more traffic volume between sectors A and C, the AC min-cut may have a more significant impact on the sector's capacity reductionhence, the use of a weighted average based on individual flight path volume in Eq. 1.If the existing sectors are to be re-shaped based on blocking convective weather to better serve the resulting traffic patterns, they should be altered commensurate with the magnitude of weather impact.To that end, one approach is to enlarge the weather-impacted sectors to make up for the volume lost due to the blocking weather.However, as demonstrated here, a sector's capacity reduction due to weather is largely dependent on traffic direction and volume as well as weather.Thus, it is reasonable to apply an intelligent bias to the direction of sector reshaping that best reduces the negative effect of the weather.This is the basis for the sector design algorithm described in the following section.
+III. MethodThis sector design method consists of two steps.The first step is to identify the weather-affected sectors and the directions in which they should be deformed to best increase their capacities.Based on Eq. 1, a metric is sought that identifies the flight path direction that is most significantly affected by the weather, contributing the most to the sector's reduction in capacity.Recognizing that the most critical flight path contains a large volume of flights relative to a small min-cut ratio (M CR), this metric is defined asFlow-Weather Impact = F i M CR i ,(2)and the most critical flight path is identified as that between the neighbor pair i such that this metric is the maximum among all neighbor pair flight paths.Therefore, using a dataset of nominal good-weather traffic, the flight volume parameters F i , which represent the number of flights between each neighboring sector pair over a given period of time, can be found and the critical flow direction is identified for every sector with an intersecting blocking weather polygon.Note that values for F i may be captured offline independent of the weather.To illustrate the validity of this metric, consider Figure 3, which shows the top three flight paths according to traffic volume through sector ZFW42 during 4 -5 p.m. Central Time (which is one of the highest volume one-hour time periods for sectors in this region).Here, flight volume data was accumulated from a multiple of days for this time period.Note that the flight volume for the sector pair ZME20/21-ZFW48 is by far the largest with F = 1129; however, the flight path between ZFW48 and ZFW90 is the most significantly affected by the blocking weather with a min-cut ratio of M CR = 0.21.Therefore, the metric from Eq. 2 for the ZFW48-ZFW90 path is 425/0.21= 2024 which is larger than 1129/0.63= 1792 from the ZME20/21-ZFW48 path.Thus, for this particular weather pattern, it makes the most sense to adjust the sector boundaries in a way that benefits the ZFW48-ZFW90 flight path.With the critical flight path directions established for all the weather-intersecting sectors, the second step is to deform the sector boundaries accordingly.The evolving sector algorithm models the edges and vertices of the sectors as if they are a flexible net, or "mesh."Each sector edge is modeled as a linear spring, and each vertex is anchored to its original location also via an imaginary linear spring.Displacement of the vertices from their original locations results in a force calculated by F = k • δx where δx is the deflection of a vertex, or stretch of an edge.The stiffness values of k can be set for both vertex anchor springs and boundary edges independently.Additionally, within each sector, an interior force F int pushes outward on every vertex.The magnitude and direction of this force is related to the way weather intersects the sectors.F Int F Int F Int F V ert F Edge F Edge F Edge
+Original Vertex LocationSe cto r Bo un da ry Ed ge For sectors that directly intersect a weather polygon, this force isF int = α mc (O * -W * ) O * ,(3)where O * is the non-weather min-cut distance associated with the critical flight path direction, and W * is the critical min-cut distance in the presence of weather.α mc is a multiplier factor used to tune the algorithm.The direction of F int for the weatherintersecting sectors is normal to the critical flight path.Thus, these sectors tend to enlarge with a bias in the direction that best increases the capacities of the most critical flight paths.The neighboring sectors that do not directly intersect weather resist deformation by their own internal forces.For these sectors, the internal forces on their vertices is related to their current area and is given byF int = α A (A 0 -A) A 0 ,(4)where A 0 is the original polygonal area of the sector, A is the current area due to boundary deformation, and α A is another weighting multiplier used to tune the algorithm.The direction of F int for these sectors is always outward from an interior point in a direction that bisects the interior angles of the polygon at each vertex-there is no bias for dominant flight paths taken into account.In this force/deflection model the sum of forces on each vertex is a function of the vertex positions.See Figure 4. To determine the final location of the vertices, an iterative algorithm is used to move each vertex in the direction of the net force until equilibrium is found ( F = 0).At every step in the iteration, new values of δx, W * , and A are computed in order to determine F Edge , F V ert , and F int at every vertex.The stiffness values k and the weighting multipliers α mc and α A are algorithm tuning parameters which are sized relative to each other in order to bias the results as desired.For instance, if it is desired that the sectors move very little in response to weather, the α constants would be set lower than the k constants.The algorithm proceeds until the maximum vertex movement in a single iteration falls below a predetermined threshold.The methodology and implementation of this algorithm is inspired by the mesh generation work of Ref. 14. Sometimes, given certain weather polygon shapes and sizes, the algorithm can produce radically altered and, occasionally, tangled sector boundaries.To prevent this, each vertex is limited by a maximum permissible translational distance.Indeed, the current algorithm is not robust to complex and varied weather patterns.For instance, multiple weather polygons covering an entire sector will cause problems for the algorithm.Some of these shortcomings will be covered in the next section following a discussion of the results.The overall result of this approach is that sectors directly intersecting weather will enlarge and bend based on the min-cut analysis in order to better accommodate the anticipated rerouted traffic.Meanwhile, the immediately adjacent neighboring sectors will absorb the deformations (albeit to a lesser degree) while maintaining their original size as much as possible.The amount of deformation tapers off for sectors farther away from the weather.In this way, the burden of the weather is spread about the region.Several experiments were performed to study the performance of this sector design method.For each of these, the nominal good-weather traffic patterns for the weather-intersecting sectors during the time span of interest was captured in advance.For this work, 57 high-volume low-delay days were identified using the Federal Aviation Administration's Air Traffic Operations Network (OPSNET) database.For more information on the selection of this dataset, see Ref. 15.
+IV. Results
+ZFW94
+IV.A. Large Weather PolygonThe first experiment involves studying the performance of the algorithm when one large weather polygon blocks the majority of a single sector.Given the notional weather polygon shown in Figure 1 occurring between 4 -5 p.m. Central Time, the critical flight path and the associated min-cut distances O * and W * are determined for sector ZFW42 (since this is the only weather-intersecting sector).The algorithm is run for all the high sectors in ZFW center with the center boundary vertices held fixed, and the resulting sectors are shown in blue in Figure 5 with the original sectors shown in green.Note the critical flight path direction and observe that the algorithm responded by expanding sector ZFW42 in a perpendicular direction to better accommodate this flight path without having to overload the adjacent sectors.The adjacent sectors ZFW48, ZFW50, and ZFW90 are slightly reduced in size, but remain similar to their original shape.Sectors like ZFW92 that are far away from the blocking weather exhibit almost no deformation.For this experiment, all the sectors within ZFW center were available for deformation, yet, only those in close proximity to the convective weather were altered significantly.This is consistent with how the traffic in the region would be affected; traffic in the western and southern portions of ZFW would be much less impacted than in the north-eastern side.As mentioned in the previous section, some weather patterns may cause the algorithm to produce drastic deformations or tangled overlapping edges.These results may be avoided by placing restrictions on vertex movement, but the algorithm may still produce sectors with bad characteristics.Some of the vertex movements may be undesirable like those of the western edge of sectors ZFW90 and ZFW71 in Figure 5 which translated westward into ZFW46.This could result in some extremely short sector transit times for aircraft flying within the region.Situations like this may be addressed with some post-processing smoothing, or with more advanced algorithm design.These shortcomings of the existing algorithm will need to be addressed in future work.As a comparison to an existing sector design method, the Mixed Integer Linear Programming (MILP) sector design method is used to design the sectors around this weather polygon.This method clusters small hex cells together to form sectors in an optimal manner such that the aircraft counts in the clusters are balanced and the dominant traffic flows are reflected in the final sector shapes.Like many other sector design methods, it requires traffic data for its input.Here, FACET 16 was used to synthesize weather-affected traffic by simulating traffic based on the same dataset of 57 high-volume low-delay days between 4 -5 p.m. Central Time.FACET's weather avoidance algorithm was used to fly aircraft around the weather polygon of Figure 1.Only a subset of sectors were redesigned using the MILP method and the results are shown in Figure 6.For more information on this sector design method, refer to Refs. 3, 4.  The results demonstrate that the MILP method does a good job of making sectors that capture the major rerouted traffic flow around the weather.Note, too, that sectors farther away from the weather (ZFW05X, for example) appear to be similar to their closest matching existing sectors shown in Figure 5.This validates the claim that the existing sectors are well matched to the existing traffic flow, and that sectors farther away from inclement weather do not need to be altered as much as those near the weather.Also, like the sectors altered by the evolving sector method, there are some undesirable geometric characteristics exhibited in the MILP sectors (the long pointed southern edge of ZFW02X, for example).However, unlike the MILP sectors, the evolved sectors have the advantage of more closely matching the original sectors, and therefore would require less time for controller and operational adjustment.Furthermore, the evolved sectors were produced without having to synthesize rerouted air traffic.
+IV.B. Response to Moving WeatherIn order to further test the algorithm, six notional blocking weather polygons were used to simulate the movement and expansion of weather through the region.The results are shown in Figure 7.All six polygons intersect sectors ZFW42 and ZFW90.Recall that none of these steps required simulating routes around the weather.Most of these results were consistent with expectations presuming that the critical flight paths stem from arriving and departing flights into and out of DFW airport.However, it is interesting to note that for Step A and Step D, the critical flight path for sector ZFW90 is found to be in the north-south direction.This results in deforming the western edge (instead of the southern edge) away from the weather.These results suggest that perhaps identifying multiple critical flight path directions makes more sense.These ideas can be explored in future work.
+IV.C. Sector Loading AnalysisFinally, a comparison of sector traffic loads can be made between the existing sectors and the altered sectors when weather is present.The aircraft count distributions for the unaltered sectors ZFW42, ZFW48, ZFW50, and ZFW90 resulting from the nominal (no weather) traffic paths in the region during this time window are shown in Figure 8.These results were produced by FACET using the same flight dataset as before.Then, using the notional blocking weather of Figure 1, FACET's weather avoidance algorithm is used to direct traffic around the blocking weather polygon.The resulting distributions produced by this simulation for the same set of sectors is shown in Figure 9(a).The weather avoidance algorithm generates a shortest-path trajectory around the blocking polygon for all flights that would intersect it.It does not maintain proper separation or miles-in-trail restrictions for the altered traffic flows; however, this is a sufficient simulation for demonstrating the effects of blocking weather on sector demands in the region.This is because it isolates the effect of the weather on the sectors without the associated reduction in traffic volume due to ground delays and miles-in-trail restrictions that would be present in historical weather-affected traffic data.Comparing the shapes of the distributions and the mean and max aircraft count values of Figure 8, to those of Figure 9(a), it is clear that traffic volume significantly drops in ZFW42 while increasing in the neighboring sectors.This is to be expected.
+V. ConclusionsA new strategy for moving sector boundaries based on convective weather is presented in this paper.Sectors that intersect blocking weather are stretched in a direction that increases capacity where it is needed the most, while the adjacent sectors absorb the deformations in a way that tends to preserve their original size and shape as much as possible.This lessens the workload required by controllers to adjust to the new sector shapes.The algorithm only requires nominal (historic or simulated) traffic patterns as input; weatheraffected traffic does not need to be simulated as with many other sector design methods.Thus, the response to changing weather conditions can be immediate.Also, since the new sector shapes are based inherently on the original sector shapes, the associated workload involved with incorporating the new sector shapes is minimized.Rather than predicating sector designs on a predetermined TFM response, it may make sense in this framework to base the TFM actions on the resulting sector changes.Preliminary results demonstrate that the algorithm is capable of producing sectors that absorb the anticipated rerouted traffic and improve the capacity of the weather-impacted sectors.Currently, the algorithm can produce some inconsistent and unstable results depending on the shape and location of the weather polygon.Future work must address these issues so the algorithm can handle varied and realistic weather patterns.Also, it is difficult to judge with certainty what advantage (if any) altered sector boundaries offer.This is in part due to the difficulty of trying to simulate TFM actions taken by controllers in response to weather.More research is necessary to make the evolving sector algorithm more robust and to further improve and validate its performance.Figure 1 .1Figure 1.The polygon containing the severe convective weather is removed from the intersecting sector(s) and treated as a no-fly zone.
+1313
+Figure 2 .2Figure 2. Computing sector capacity reduction due to blocking weather.
+MFigure 3 .3Figure 3. Top three traffic flow paths in ZFW42 with corresponding nominal (no weather) flow volume F and min-cut ratio M CR due to blocking weather shown in pink.
+Figure 4 .4Figure 4.The combination of forces on a sector boundary vertex.The equilibrium vertex location is determined by finding the location where the net force sums to zero.
+Figure 5 .5Figure 5. Original sectors shown in green, deformed sectors produced by the evolving sector algorithm shown in blue.
+Figure 6 .6Figure 6.Redesigned sectors in the local weather-affected region surrounded in black produced by the MILP method.
+Figure 7 .7Figure 7. Several notional blocking weather patterns with the evolved sectors shown in blue and the original sectors shown in green.
+		
+		
+			
+Next, the same traffic used in Figure 9(a) was run through the evolved sectors from Figure 5 produced by the algorithm.The histograms of the aircraft counts for the altered sectors are shown in Figure 9(b).Indeed, when compared to the results of 9(a), the expansion of sector ZFW42 reduces the mean and max loads of the surrounding sectors, and the flight counts are more evenly distributed among the four sectors.The max peak count of 27 in sector ZFW42 would not be permitted in current operations.However, 99.4% of the aircraft count data in this sector are below the nominal MAP value of 18.Furthermore, in reality, rerouted flights are more intelligently controlled, and the diverted aircraft would be rerouted with proper separation assurance and sector capacity taken into account.These results confirm that the most heavily affected sector (ZFW42) is capable of being enlarged using the algorithm to handle greater capacity without tremendously burdening the neighboring sectors, all the while maintaining a similarity to the original sector shapes.			
+			
+
+				
+
+
+	
+		Weather Index With Queuing Component For National Airspace System Performance Assessment
+		
+			AKlein
+		
+		
+			RJehlen
+		
+		
+			DLiang
+		
+		
+	
+	
+		7 th USA-Europe ATM R&D Seminar
+		Barcelona, Spain
+		
+			July 2007. 2
+		
+	
+	FAA OPSNET and ASPM data
+	Klein, A., Jehlen, R., and Liang, D., "Weather Index With Queuing Component For National Airspace System Perfor- mance Assessment," 7 th USA-Europe ATM R&D Seminar , Barcelona, Spain, July 2007. 2 FAA OPSNET and ASPM data. At: http:/www.apo.data.faa.gov/.
+
+
+
+	
+		Phase-I SBIR Final Report
+		
+			AYousefi
+		
+		
+			TLewis
+		
+		
+			HHackney
+		
+		
+			RHoffman
+		
+		No. NNX07CA10P
+	
+	
+		45300 Catalina Court, Suite
+		
+			101
+			20166. July 2007
+			Metron Aviation Inc
+			Dulles, VA
+		
+	
+	Tech. Rep. Contract
+	Yousefi, A., Lewis, T., Hackney, H., and Hoffman, R., "Phase-I SBIR Final Report," Tech. Rep. Contract No. NNX07CA10P, Metron Aviation Inc., 45300 Catalina Court, Suite 101, Dulles, VA 20166, July 2007.
+
+
+
+	
+		Analysis of an optimal sector design method
+		
+			MichaelDrew
+		
+		10.1109/dasc.2008.4702801
+	
+	
+		2008 IEEE/AIAA 27th Digital Avionics Systems Conference
+		St. Paul, MN
+		
+			IEEE
+			October 2008
+		
+	
+	Drew, M., "Analysis of an Optimal Sector Design Method," 27 th Digital Avionics Systems Conference, St. Paul, MN, October 2008.
+
+
+
+	
+		Airspace Sector Redesign Based on Voronoi Diagrams
+		
+			MinXue
+		
+		10.2514/1.41159
+	
+	
+		Journal of Aerospace Computing, Information, and Communication
+		Journal of Aerospace Computing, Information, and Communication
+		1542-9423
+		
+			6
+			12
+			
+			December 2009
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Xue, M., "Airspace Redesign Based on Voronoi Diagrams," Journal of Aerospace Computing, Information, and Com- munication, Vol. 6, December 2009.
+
+
+
+	
+		Airspace Sectorization by Dynamic Density
+		
+			ChrisBrinton
+		
+		
+			JimHinkey
+		
+		
+			KenLeiden
+		
+		10.2514/6.2009-7102
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, SC
+		
+			American Institute of Aeronautics and Astronautics
+			September 2009
+		
+	
+	Brinton, C. R., Leiden, K., and Hinkey, J., "Airspace Sectorization by Dynamic Density," 9 th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), Hilton Head, SC, September 2009.
+
+
+
+	
+		Geometric algorithms for optimal airspace design and air traffic controller workload balancing
+		
+			AmitabhBasu
+		
+		
+			JosephS BMitchell
+		
+		
+			GirishKumarSabhnani
+		
+		10.1145/1498698.1537598
+	
+	
+		ACM Journal of Experimental Algorithmics
+		ACM J. Exp. Algorithmics
+		1084-6654
+		
+			14
+			2.3
+			May 2009
+			Association for Computing Machinery (ACM)
+		
+	
+	Basu, A., Mitchell, J. S. B., and Sabhnani, G., "Geometric Algorithms for Optimal Airspace Design and Air Traffic Controller Workload Balancing," ACM Journal of Experimental Algorithmics, Vol. 14, No. 2.3, May 2009.
+
+
+
+	
+		Dynamic FPAs: A new method for dynamic airspace configuration
+		
+			AlexanderKlein
+		
+		
+			MarkDRodgers
+		
+		
+			Hong Kaing
+		
+		10.1109/icnsurv.2008.4559176
+	
+	
+		2008 Integrated Communications, Navigation and Surveillance Conference
+		Bethesda, MD
+		
+			IEEE
+			2008
+		
+	
+	Klein, A., Rodgers, M. D., and Kaing, H., "Dynamic FPAs: A New Method for Dynamic Airspace Configuration," Proceedings of the 2008 ICNS Conference, Bethesda, MD, 2008.
+
+
+
+	
+		Effect of Dynamic Sector Boundary Changes on Air Traffic Controllers
+		
+			JaewooJung
+		
+		
+			PaulLee
+		
+		
+			AngelaKessell
+		
+		
+			JeffreyHomola
+		
+		
+			ShannonZelinski
+		
+		10.2514/6.2010-8289
+	
+	
+		AIAA Guidance, Navigation, and Control Conference
+		Toronto, ON CA
+		
+			American Institute of Aeronautics and Astronautics
+			August 2010
+			
+		
+	
+	Jung, J., Lee, P., Kessell, A., Homola, J., and Zelinsky, S., "Effect of Dynamic Sector Boundary Changes on Air Traffic Controllers," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Toronto, ON CA, 2 -5 August 2010.
+
+
+
+	
+		Predicting Sector Capacity under Severe Weather Impact for Traffic Flow Management
+		
+			LixiaSong
+		
+		
+			CraigWanke
+		
+		
+			DanielGreenbaum
+		
+		
+			DavidCallner
+		
+		10.2514/6.2007-7887
+	
+	
+		7th AIAA ATIO Conf, 2nd CEIAT Int'l Conf on Innov and Integr in Aero Sciences,17th LTA Systems Tech Conf; followed by 2nd TEOS Forum
+		Belfast, Northern Ireland
+		
+			American Institute of Aeronautics and Astronautics
+			18 -20 September 2007
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+	
+	Song, L., Wanke, C., Greenbaum, D. P., and Callner, D. A., "Predicting Sector Capacity under Severe Weather Impact for Traffic Flow Management," Proceedings of the 7 th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), No. ATIO 2007-7887, Belfast, Northern Ireland, 18 -20 September 2007.
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+		Methodologies of Estimating the Impact of Severe Weather on Airspace Capacity
+		
+			LixiaSong
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+			CraigWanke
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+			StephenZobell
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+			DanielGreenbaum
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+			ClaudeJackson
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+		10.2514/6.2008-8917
+		No. ATIO 2008-8917
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+		The 26th Congress of ICAS and 8th AIAA ATIO
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+			American Institute of Aeronautics and Astronautics
+			14 -19 September 2008
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+	The 26 th Congress of International Council of the Aeronautical Sciences
+	Song, L., Wanke, C., Greenbaum, D., Zobell, S., and Jackson, C., "Methodologies for Estimating the Impact of Severe Weather on Airspace Capacity," The 26 th Congress of International Council of the Aeronautical Sciences (ICAS), No. ATIO 2008-8917, Anchorage, AK, 14 -19 September 2008.
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+		Directional Demand, Capacity and Queuing Delay in En-Route Airspace
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+		10.2514/6.2009-6963
+		AIAA 2009-6963
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+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		Hilton Head, SC
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+			American Institute of Aeronautics and Astronautics
+			September 2009
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+	Klein, A., Cook, L., and Wood, B., "Directional Demand, Capacity and Queuing Delay in En-Route Airspace," Pro- ceedings of the 9 th AIAA Aviation Technology, Integration, and Operations Conference (ATIO), No. AIAA 2009-6963, Hilton Head, SC, 21 -23 September 2009.
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+		10.21236/ada594171
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+		A Simple Mesh Generator in Matlab
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+			PPersson
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+			GStrang
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+			2005
+			Cambridge, MA 02139
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+			Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Ave.
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+	Persson, P. and Strang, G., "A Simple Mesh Generator in Matlab," Tech. rep., Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, 2005, See: http://www-math.mit.edu/ ~persson/mesh/.
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+		Air Traffic Sector Configuration Change Frequency
+		
+			GanoBrotoChatterji
+		
+		
+			MichaelDrew
+		
+		10.2514/6.2010-8291
+	
+	
+		AIAA Guidance, Navigation, and Control Conference
+		Toronto, ON CA
+		
+			American Institute of Aeronautics and Astronautics
+			2 -5 August 2010
+		
+	
+	Chatterji, G. and Drew, M., "Air Traffic Sector Configuration Change Frequency," Proceedings of the AIAA Guidance, Navigation, and Control Conference, Toronto, ON CA, 2 -5 August 2010.
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+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
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+			BanavarSridhar
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+			ShonRGrabbe
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+		10.2514/atcq.9.1.1
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+		Air Traffic Control Quarterly
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+			American Institute of Aeronautics and Astronautics (AIAA)
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+I. IntroductionD espite predictions of increased future air traffic congestion, many portions of the national airspace are, and will continue to be under-utilized.In fact, as shown in Ref. 1, 75% of the sectors in Cleveland center operate near or below half their capacity even during the busiest times of the day.When sectors with less traffic volume are combined into larger sector clusters for some length of time, fewer controller teams are required to manage the same airspace.By flexibly increasing or decreasing the number of sector clusters in response to air traffic volume during the course of a 24-hour period, resources at the Air Route Traffic Control Center can be allocated more efficiently.Sector combining within centers is currently done today based on historical experience and operational policy.Refs. 1 and 2 present a method of performing the same operation on a systematic basis using a greedy algorithm that combines sectors into sector clusters on an hourly basis.The algorithm has the benefit of being computationally fast and flexible and is shown in simulation to produce fewer open sectors (or clusters of sectors) when compared with historical operational data of the same time period.However, this greedy algorithm may miss the optimal solution (the solution with the fewest number of sector clusters), especially as the number of sectors considered for combining grows.In Ref. 3 a hybrid branch-and-bound/neural network trained on a metric of controller workload is used to determine optimal sector combinations.However, it is presented with the intent of forecasting airspace configurations and traffic congestion-not as a method of tactical decision support in the vein of Refs. 1 and 2.This paper presents a Mixed Integer Linear Programming model that finds an optimal solution of combined sectors resulting in the fewest number of sector clusters given current demand and capacity constraints.While the objective and constraints are nearly identical to that of Refs. 1 and 2, it is shown that this model provides a minor performance improvement over the greedy algorithm.Assuming sector combining is calculated for every hour in an operational setting, over the course of a day this minor improvement translates into a significant reduction in the number and duration of open sector clusters (measured as sector-hours).The improvement is greater as the number of sectors considered for combining increases.For instance, if a larger set of sectors is considered for combining than what is presently permitted, the greedy algorithm is more likely to miss the optimal cluster combination that produces the fewest number of clusters.This paper is arranged as follows: Section II provides some background on present-day operations with additional details on the sector combining problem.Section III presents the approach used in developing the optimization model including all the necessary mathematical constraints.Section IV discusses how this system is implemented in software and the assumptions used in the simulation.Section V presents the results and compares the performance of the optimal Mixed Integer Linear Programming model to that of the greedy algorithm.Conclusions and directions of future work are discussed in Section VI.
+II. BackgroundThe National Airspace System (NAS) over the continental U.S. is partitioned into 20 Air Route Traffic Control Centers (ARTCCs, or centers for short).Each center is further subdivided into several smaller partitions known as sectors.Sectors are partitions in altitude as well as latitude and longitude, and they are typically classified as either low, high, or super-high according to their altitude range.Figure 1 shows the top view of Cleveland center.There are 27 high sectors, but only 11 are directly visible from above.Each sector in the center is usually monitored by 1-3 air traffic controllers who communicate with the pilots of the aircraft in the sector to ensure safe and efficient traffic flow therein.However, during periods of low traffic volume, it is not always necessary to have a dedicated controller for every sector in the center.It often makes sense to combine two or more sectors together to increase the operational efficiency of the control center.In present-day operations, sectors are only permitted to combine with others in the same group of sectors known as an area of specialization.Each center contains several areas of specialization, which are contiguous subsets of sectors, and controllers are trained and certified to manage each of the sectors in their area.Thus, a single controller is capable of handling a cluster of combined sectors within his or her area of specialization when traffic permits.A simplified notion of three areas of specialization are represented by the three color shades in figure 1.Current research in sector design and the generic sectors 4 concept may, in the future, yield areas of specialization with more sectors, or eliminate their need all together.In current operations, the traffic demand and complexity within a sector must be within the controller team's ability to manage it.While various metrics of traffic complexity and sector capacity are currently being researched, the sector traffic complexity metric in operational use today is the maximum instantaneous aircraft count over a 15-minute interval. 1This metric is forecast in multiple 15-minute intervals for some time horizon (typically one hour) and is available to controllers and their supervisors via ETMS a so they can anticipate future workload and make adjustments as necessary.The capacity of a sector is estimated by the Monitor Alert Parameter (MAP), which is the maximum instantaneous aircraft count suggested for a sector.Typically, controllers and traffic managers strive to keep the maximum instantaneous aircraft count of a sector less than the sector's MAP value, but violations of this constraint are not uncommon depending on traffic characteristics.Because these two metrics of sector demand and capacity are used in current air traffic operations, they are used in Ref. 1, as well as in this work for systematically combining sectors.
+III. ApproachSimilar to the approach in Refs. 1 and 2, the objective is to reduce the number of open sector clusters within a center with the condition that the capacity of each cluster cannot be exceeded.A cluster's demand over a future 15-minute interval is estimated by summing over all the sectors in the cluster the maximum instantaneous aircraft count of each sector.For a future interval of one hour, the largest of these values over the four future 15-minute intervals is taken to be the cluster's future one hour demand.The cluster capacity is conservatively estimated to be the maximum MAP value of all the sectors in the cluster.Finally, because the future demand for a cluster is based on sums of predicted peak aircraft counts, the estimated capacity a Enhanced Traffic Management System: A software tool used by controllers to predict future traffic loads in sectors.of the cluster is reduced by a safety cushion known as the gap parameter which accounts for errors due to traffic prediction uncertainties.
+III.A. The Mixed Integer Linear Programming ModelWhile both the objective and constraints of this problem are simple, implementing a technique to find the optimal solution is not.One difficulty lies in the size of the solution space.For instance, with as few as ten sectors, the number of possible cluster arrangements is nearly 116,000. 5Although in reality, spatial contiguity would preclude many of these sector combinations from consideration, it is clear that solving the problem is not trivial due to the combinatorial size of the solution space which grows exponentially with the number of sectors.The greedy heuristic approach described in Ref. 1 is very efficient, but, as demonstrated herein, occasionally misses the optimal solution.On the other hand, the Mixed Integer Linear Program method promises optimality, but has its own complications.Posing the clustering problem as a Mixed Integer Linear Programming (MILP) model that captures any feasible cluster's capacity and demand is not straightforward.The inspiration for this model comes from the optimal sector design method discussed in Refs.6 and 7.This method makes use of a network flow model in which the sectors (nodes) are clustered by a connection (flow) variable passed between them.Connection flow is an abstract variable that is used to cluster sectors as it captures and sums up an attribute within them.Each sector is considered either a flow source or sink.In this model, the connection flow variable accumulates the cluster's demand by capturing the maximum number of predicted aircraft in a sector over a discrete time step interval and passing it from one sector to another, forming a sector cluster.Exactly one sector in the cluster must become a sink sector to absorb and terminate the flow.In this way, clusters are formed from the contiguous collection of sectors through which the flow is made to traverse, and the total predicted aircraft in a cluster is captured by the quantity of flow absorbed by the cluster's sink sector.Sector traffic demands are predicted in terms of the maximum number of aircraft in a future 15-minute interval, so the maximum total demand of a given cluster of sectors must be computed accordingly.The predicted maximum peak aircraft count in a cluster over a 15-minute interval is the sum of the maximum peak counts of each sector in the cluster for this interval.Thus, if a sector cluster is to be operational for one hour, the greatest of these values over the four future 15-minute intervals yields the predicted maximum cluster demand over the next hour.Figure 2 depicts a simplified example where only one time step is considered and only a subset of the sectors of Cleveland Center are considered.In figure 2(a) the estimated demand and capacity (MAP) values are shown for each sector along with all the possible flow paths between them.A viable cluster solution is depicted in figure 2(b).The details of the individual flow variables f k ij will be discussed in the next section.Note that the flow leaving sector 2 is equal to the flow going into sector 2 plus the traffic demand of sector 2. This flow path is then terminated in sector 4, which was selected to be the sink of that 3-sector cluster.Sectors 1 and 2 are source sectors.Clusters are identified by the index of their sink sector.Here the cluster demand is determined as the sum of the flow going into sector 4 plus the demand of sector 4 itself.Note that sink sectors also behave like source sectors in that they also contribute to the flow variable, but sink sectors, unlike source sectors, terminate flow by not permitting outward flow.Finally, the cluster capacity is set to the maximum MAP value of the cluster's sectors.It must be stressed that there is no real-world meaning or implication to which sector in the cluster becomes a sink.Any sector is free to become a sink, because it must be possible to form "clusters" consisting of only one sector.The sink assignment is determined at run time not a priori.Furthermore, the concept of connection flow has no physical meaning in this formulation.That is, the path in which it is made to traverse has no relation to the direction traffic flows.In fact, in this setting it is fair to consider flow variables to be links that carry with them the predicted demand of each sector as they form an ordered set of sectors.The specific order of the sectors in a cluster as determined by these links is ultimately irrelevant.The primary advantage of this method is that contiguity of a sector cluster is guaranteed because it is easy to prevent flow (links) between non-adjacent sectors from being established.Contiguity is not a trivial constraint to enforce with other clustering methods.Here, it is inherent to the model structure.Also, by using the flow variable to sum up the traffic demand of the sectors in the cluster, the total cluster demand is easily captured, and circular flow path cycles are prevented because a conservation of flow in and out of each sector is enforced.The next section will detail how this model is implemented mathematically through MILP model decision variables and constraints.
+III.B. The MILP Model FormulationGiven N sectors considered for possible clustering, I = {1...N } is the set of sector indices, and T = {1, 2, 3, 4} is the set of discrete time steps for the four future 15-minute time intervals of the next hour, the MILP model parameters, decision variables, objective function, and constraints are now presented.Here, i, j, k ∈ I, and t ∈ T.Although indices i, j, and k all span the same set of sectors, index k is associated with the cluster index which is equal to the index of that cluster's sink sector.Recall that any sector is capable of becoming a sink for its cluster's connection flow, but many sectors will not become sinks.Thus, variables with k indices of non-sink sectors must be constrained to be zero-valued because they represent variables associated with non-existent clusters.
+III.B.1. Model ParametersThe model parameters that are known a priori are AC it -the number of aircraft predicted to be in sector i at time step t, M AP i -the Monitor Alert Parameter for sector i, n ij ∈ {0, 1} -if sector i is a spatial neighbor of sector j, n ij = 1, otherwise n ij = 0, and gap i ≥ 0 -the gap parameter, a safety cushion on the predicted demand of a sector.This is usually identical for all sectors i ∈ I, but may be individually altered for specific sector capacity violations.
+III.B.2. Model Decision VariablesDecision variables are those that are not known a priori and are solved by the optimizing software at run time.The basic set of decision variables isf k ij ∈ Z + -the flow of accumulated predicted aircraft count at time step t = 1 going from sector i to sector j and ultimately terminating at sink sector (cluster index) k,x ik ∈ {0, 1} -if sector i is assigned to cluster k, x ik = 1, otherwise x ik = 0, d kt ∈ Z + -the total air traffic demand of cluster k at time step t, D k ∈ Z + -the maximum demand of cluster k over all time steps t ∈ T, C k ∈ Z + -the capacity of sector cluster k, and y i ∈ {0, 1} -if sector i becomes a sink, y i = 1, otherwise y i = 0.
+III.B.3. Objective Function and ConstraintsThe objective is to minimize the number of sector clusters, or in model terms, minimize the number of sectors that become sinks.Thus, the objective function is simply minimizei∈I y i .(1)The primary constraint of preventing the traffic demand of each cluster from surpassing its capacity less the gap parameter is enforced by:D k ≤ C k -y k • gap k ∀k ∈ I(2)The difficulty lies in implementing constraints that capture values for D k and C k .Implicit in these values is the assignment of sectors to clusters which requires determining values for x ik .This, in turn, requires clustering the sectors according to the flow connection variables that are determined through the following constraints:j∈I f k ij = j∈I f k ji + x ik • AC i1 ∀i, k ∈ I| i = k(3)d k1 = j∈I f k jk + x kk • AC k1 ∀k ∈ I (4) t∈T d kt L ≤ y k ∀k ∈ I(5)Constraint (3) sets up the conservation of flow among all source (non-sink) sectors.Considering flow heading to sink sector k (or, cluster k), the total flow out of sector i into all other sectors j must equal the total flow into sector i from all other sectors plus the number of predicted aircraft in sector i at time step t = 1.Note that the connection flow variable is based on traffic flow predicted for the first time step horizon AC i1 .This is because the flow only needs to operate on one additive sector attribute; the choice is arbitrary.The traffic demand for the remaining time steps is taken into consideration using an additional relation discussed below.Also, note that the assignment variables x ik are used as a filter so that the flow variables for sectors i that are not assigned to cluster k are zero.Constraint ( 4) is similar to constraint (3), but is specific for sectors that become sinks.Each sink sector k absorbs the flow heading to itself-there is no out-going flow.Thus, the demand variable d k1 is equal to the total flow into sector k based on traffic predictions for time t = 1 plus the number of predicted aircraft in sector k itself at time t = 1.This is the mechanism by which the total number of predicted aircraft in a cluster of sectors is captured.Again, refer to figure 2 for a depiction of this.Because k spans all sectors I (since all sectors are possible sinks), d kt must be zero for all non-sink sectors.This is accomplished by constraint (5).Here, and throughout the remainder of this section, L represents an arbitrary number sufficiently large enough to prevent the quotient on the left from being greater than one.Constraints of this form force the non-binary variable on the left to be zero when the binary variable on the right is zero, but if the binary variable is equal to one, the non-binary variable can be any value greater or equal to zero.The next constraints define the relations between the connection flow variables f k ij , the sector-cluster assignment variables x ik , and the sector-sink variables y k .k∈I x ik = 1 ∀i ∈ I (6) j∈I f k ij L ≤ x ik ∀i, k ∈ I(7)x kk = y k ∀k ∈ IConstraint ( 6) forces every sector i to be assigned to exactly one cluster k.Constraint ( 7) is what relates the connection flow variable to an actual sector-cluster assignment.Each sector i is assigned to the same cluster k as the flow that leaves it.Because flow does not leave sink sectors, constraint ( 8) is necessary to define sectors assigned to themselves as sinks.One of the primary reasons for using the network connection flow technique is that the contiguity of the clusters is guaranteed through the following simple constraint:k∈I f k ij L ≤ n ij ∀i, j ∈ I(9)Constraint ( 9) restricts all flow from sector i from going into non-neighboring sectors as defined by the n ij parameters.Thus, only sectors that are spatially contiguous may be clustered.In addition to enforcing contiguity, the connection flow technique also yields cluster traffic demand for the first time step d k1 in constraint (4).Using the sector-cluster assignment variables x ik , the remainder of the cluster demand variables d kt for t > 1 can be related through the following equation:d kt = i∈I x ik • AC it ∀k ∈ I, t ∈ T| t > 1 (10)Here, the x ik variables serve as a filter in (10) so that the traffic demands of every cluster for the remaining time steps are the sum of predicted aircraft counts in only those sectors belonging to cluster k.Finally, the cluster demand and capacity variables are given by:D k = max t∈T {d kt } ∀k ∈ I (11) C k = max i {x ik • MAP i } ∀k ∈ I (12)Thus, constraint (2) can be realized.Recall that since k spans all sectors, for values of k involving non-sink sectors D k , C k , and y k are zero so constraint ( 2) is never violated.Since constraints ( 11) and (12) invoke the max operator which is nonlinear they cannot be directly implemented.Fortunately, this is overcome with some additional constraints and variables discussed in the following section.
+III.B.4. Linearizing Max ConstraintsThe technique for linearizing a max operator is well known, but will be presented here for thoroughness.Instead of implementing constraints (11) and ( 12) directly, each one is replaced by three linear constraints and the addition of a set of binary decision variables.Introducing a kt ∈ {0, 1}, constraint (11) is enforced with the following:D k ≥ d kt ∀k ∈ I, t ∈ T (11a) D k -d kt L ≤ 1 -a kt ∀k ∈ I, t ∈ T (11b) t∈T a kt = 1 ∀k ∈ I (11c)The strategy is that constraint (11a) bounds D k on the bottom by the largest value of d kt .Then constraint (11b) prevents D k from being any larger than max t {d kt } by ensuring that the difference between D k and d kt is zero at least once since, by constraint (11c), for every k, a kt = 1 exactly once across the t domain.Likewise, with b ik ∈ {0, 1}, nonlinear constraint (12) is implemented by the following linear constraints:C k ≥ x ik • MAP i ∀i, k ∈ I (12a) C k -x ik • MAP i L ≤ 1 -b ik ∀i, k ∈ I (12b) i∈I b ik = 1 ∀k ∈ I (12c)Once again, L is a number large enough to prevent the quotient from being greater than 1.
+III.C. Model DiscussionGiven the above mathematical formulation, some observations are made about this MILP model.Referring again to figure 2, it is clear that the same cluster solution could be produced from different sink assignments as well as different flow arrangements.There are three possible sink locations for the top cluster, but there are also three different possible flow path arrangements for every possible sink location.(For example, flow could go from sector 2 to sector 1, then into sector 4 with f 4 21 = 3, and f 4 14 = 8.) Coupled with the two possible sink choices for the lower cluster (with only one flow arrangement possible for each), there are exactly eighteen unique MILP model solutions that produce the exact same sector clustering arrangement shown.Furthermore, depending on traffic demand and sector capacity, there may be more than one feasible cluster arrangement that satisfies constraints and results in the fewest number of clusters.Even the greedy heuristic approach could, in theory, produce alternate clustering solutions having the same number of clusters.After all, it is only the number of clusters that matters to the objective function of both methods, not the specific arrangement.The optimal MILP model, however, can produce exponentially more internal variable specific solutions (distinguished only by flow and sink variables) than the greedy heuristic approach that result in the same number of clusters.Thus, the network flow model adds a significant level of complexity that can lead to lengthy computation times.This will be discussed in greater detail in Section V.The implementation of the model in simulation is presented next.
+IV. ImplementationBased on historical flight data, the Future ATM Concepts Evaluation Tool (FACET) 8 is used to record the sector traffic loading of a center.In practice only predicted traffic can be used to make decisions on sector combining, but for the simulations herein, actual traffic loads are used for the four 15-minute look-ahead time steps.Thus, given a specific center, data for AC it is recorded for each sector i at four time steps for each hour.For every sector i in the center, its MAP value M AP i , as well as its set of spatial neighbors n ij is determined.The default gap parameter is set at three, so gap i = 3 ∀i ∈ I, except for the case when traffic in an individual sector violates the capacity constraint.Thus, if for some sector i the peak traffic in the next hour AC it exceeds M AP i -3, the gap parameter for that sector is lowered so that constraint (2) can be met.This is necessary because without it, the MILP model is infeasible.By reducing gap i to the point that constraint (2) is just barely met means that the sector in question will not be combined with any other sector, and will become a cluster of one.Recall that the intent of the gap parameter is to be a cushion for traffic prediction error.Once the model parameters are captured from FACET, the model is solved using either the open-source GNU Linear Programming Kit (GLPK) 9 optimization package, or the commercial AMPL/CPLEX 10 package.
+V. ResultsFor these simulations, historical flight data from 8 February 2007 in Cleveland Center are used to compare the performance of the optimal MILP sector combining method to that of the greedy algorithm method.Only high sectors are considered for combining.In the first scenario, sectors are only permitted to combine with those in the same area of specialization.This is known as the restricted case, and the total number of resulting high-sector clusters are shown in figure 3(a) for each hour over a 24-hour period.There are 27 high-altitude sectors in this center grouped into eight areas of specialization.This means that at each time step eight separate optimal solutions are found-one for each area of specialization.The results demonstrate that the greedy algorithm produces near-optimal results.The MILP model provides, at most, an improvement of one less cluster for some time steps.However, given the relatively small size of each area, it is not surprising that improvements are modest.Nevertheless, over the course of this day, a savings of seven sector-hours is realized.(A sector-hour refers to one sector, or cluster of combined sectors operational for one hour.)Over several weeks, this savings adds up and becomes significant.As the number of sectors considered for combining increases, intuition suggests that because the solution space increases exponentially, the greedy algorithm is more likely to miss the optimal combined sector solution.This assumption is validated by testing the performance of both methods, while allowing all the  high sectors to combine without regard to their areas of specialization.This is known as the unrestricted case, and the results are shown in figure 3(b).The same traffic data from Cleveland Center is used and all 27 sectors are allowed to combine without restriction.Here, the difference between the two methods is much more dramatic with a total improvement of 34 sector-hours for the day.In figure 4 the MILP results for 0:00 -1:00 EST are shown.It is easy to see by the size and convoluted arrangement of the clusters why such unrestricted combinations are not performed in current operations.Nevertheless, the unrestricted scenario is a rigorous test of the performance of both methods' ability to handle greater numbers of sectors for combining.This will be important if the areas of specialization are enlarged, as would be possible among generic sectors.Also, while it is not investigated here, both methods could benefit by selecting solutions from a pool of optimal or near-optimal solutions according to additional criteria.The tendency of the greedy algorithm to not only miss the optimal solution, but to produce inconsistent results depending on the input data is best demonstrated by the results at 11:00 -12:00 EST.Here, note that in figure 3(a) the greedy algorithm produces a result of 21 sector clusters whereas in the unrestricted case, where the number of clusters should obviously be no greater than the restricted case, figure 3(b) shows it produces a solution of 22 sector clusters.Because the optimal MILP method is not subject to these types of inconsistencies, the unrestricted case always has equal or fewer sector clusters than the restricted one.These results focus on the resulting number of clusters that each method produces, not the cluster arrangement themselves.It should be noted, that when the two methods produce the same number of clusters at a given time step, the clusters may or may not be identical.As discussed above, there may be more than one possible cluster arrangement depending on traffic conditions.Some arrangements may be more operationally practical than others, but no attempt to analyze them has been made here.
+V.A. Computation TimeThough the MILP model is capable of finding the optimal solution to the sector combining problem, the computation times can be significant.For the restricted case when the size of the problem is limited to the number of sectors in an area of specialization (the largest of which contains six sectors), solution times range in the tens of seconds using the open-source GLPK package.When the number of sectors considered for clustering is increased to all 27 high sectors solution times for each time step are often measured in hours using the commercial (and computationally efficient) AMPL/CPLEX package.In practice, this would render the method useless, but fortunately the greedy algorithm (with computation times less than a minute) can be used to provide a starting solution for the optimal solver, thus dramatically reducing solution times.In fact, this technique was used for the unrestricted simulation case shown above.If time does not permit the exact optimal solution to be found, the solver will usually find a superior (if not optimal) solution within several minutes.
+VI. ConclusionsThis paper describes a method of optimally combining sectors into clusters according to traffic demand and capacity.The results demonstrate that the existing greedy algorithm is very efficient for quickly determining a near-optimal solution to the sector combining problem; as long as the problem size is small, it finds the optimal solution most of the time.With a small number of sectors, the improvement offered by the optimal MILP model is slight.However this improvement becomes significant over time.When dealing with a large number of sectors, the optimal MILP model demonstrates a significant and consistent improvement over the greedy algorithm.However, this comes with the cost of exponentially increasing computation times.This is addressed by using the greedy algorithm to provide an initial solution for the optimal model, which drastically reduces computation times.Doing so allows the optimization software to find improved solutions in matters of minutes as opposed to hours.While the optimal MILP model shows promise for improving a center's workload efficiency, unlike the greedy algorithm, it is not capable of handling nonlinear or non-additive sector workload metrics.An active area of research is investigating methods of more accurately estimating sector capacity and workload.Unlike the conservative methods used in this optimal MILP model and the existing greedy algorithm, many of the proposed methods (like Dynamic Density) involve nonlinear and/or non-additive metrics.Such metrics are difficult, if not impossible, to incorporate into the MILP domain.Research is needed to evaluate the accuracy of these alternative metrics, and to determine the extent of improvement they offer over the basic metrics of aircraft count and MAP when used in sector combining methods.While it may be impossible to directly apply these metrics to the MILP model, it is certainly possible to evaluate the many optimal or near-optimal solutions the MILP model produces according to any number of additional constraints.The desirable solution could then be selected accordingly.Nevertheless, this optimal MILP model may be a useful tool for suggesting sector combinations in both present and future operations.Also, if future concepts like generic sectors are adopted, it may be feasible to combine larger sets of sectors without area of specialization restrictions.In such case, the optimal MILP is especially useful for determining the minimum feasible number of clusters given traffic predictions and sector capacity.Figure 1 .1Figure 1.Top view of Cleveland Center.Colors represent a simplified notion of sector areas of specialization.
+Unsolved set of connection flow variables f k ij .Clustered solution of flow variables and sink locations.Only non-zero values shown.Sink sectors indicated with red dot.
+Figure 2 .2Figure 2. A notion of the network flow clustering model.Note the unsolved variables f k ij which when solved along with a sink location forms 2 sector clusters.
+11:00 -12:00 EST (a) Sector combinations are restricted to areas of specialization.11:00 -12:00 EST (b) Sector combinations are unrestricted by areas of specialization.
+Figure 3 .3Figure 3.Total number of high sector clusters produced by the optimal MILP method compared to the greedy algorithm method.Based on traffic data for 8 February 2007 in Cleveland Center.
+Figure 4 .4Figure 4.An example of unrestricted cluster results produced by the optimal MILP for 0:00 -1:00 EST consisting of 4 sector clusters.
+		
+		
+
+			
+AcknowledgmentsThe author would like to thank Michael Bloem and Pramod Gupta for providing details of their sector combining algorithm along with their simulation results.
+			
+
+			
+
+				
+
+
+	
+		Combining Airspace Sectors for the Efficient Use of Air Traffic Control Resources
+		
+			MichaelBloem
+		
+		
+			ParimalKopardekar
+		
+		10.2514/6.2008-7222
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, HI
+		
+			American Institute of Aeronautics and Astronautics
+			August 2008
+		
+	
+	Bloem, M. and Kopardekar, P., "Combining Airspace Sectors for the Efficient Use of Air Traffic Control Resources," Proceedings of AIAA Guidance Navigation and Control Conference, Honolulu, HI, August 2008.
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+		Algorithms for Combining Airspace Sectors
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+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
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+			17
+			3
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+			2009
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
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+	Submitted
+	Bloem, M., Gupta, P., and Kopardekar, P., "Algorithms for Combining Airspace Sectors," Air Traffic Control Quarterly, 2009, Submitted.
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+		GRIPS Plan
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+		Proc. of 8 th USA/Europe ATM Seminar
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+			Office of Scientific and Technical Information (OSTI)
+			June 29 -July 2 2009
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+	Gianazza, D., Allignol, C., and Saporito, N., "An Efficient Airspace Configuration Forecast," Proc. of 8 th USA/Europe ATM Seminar , Napa, CA, USA, June 29 -July 2 2009.
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+		Airspace Configuration Concepts for the Next Generation Air Transportation System
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+			ParimalKopardekar
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+			BanavarSridhar
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+		10.2514/atcq.16.4.313
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+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			16
+			4
+			
+			2008
+			American Institute of Aeronautics and Astronautics (AIAA)
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+	
+	Kopardekar, P., Bilimoria, K. D., and Sridhar, B., "Airspace Configuration Concepts for the Next Generation Air Transportation System," Air Traffic Control Quarterly, Vol. 16, No. 3, 2008.
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+		Clustering Algorithms I
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+			SergiosTheodoridis
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+			KonstantinosKoutroumbas
+		
+		10.1016/b978-012369531-4/50012-3
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+		Pattern Recognition
+		Amsterdam
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+		Trigger Metrics for Dynamic Airspace Configuration
+		
+			ArashYousefi
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+			RobertHoffman
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+			MarcusLowther
+		
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+			BabakKhorrami
+		
+		
+			HerbertHackney
+		
+		10.2514/6.2009-7103
+		No. 34N1207-001-R0
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		45300 Catalina Court, Suite
+		Dulles, VA
+		
+			American Institute of Aeronautics and Astronautics
+			20166. December 2007
+			101
+		
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+	Tech. Rep. Report
+	Yousefi, A., Khorrami, B., Hoffman, R., and B.Hackney, "Enhanced Dynamic Airspace Configuration Algorithms and Concepts," Tech. Rep. Report No. 34N1207-001-R0, Metron Aviation Inc., 45300 Catalina Court, Suite 101, Dulles, VA 20166, December 2007.
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+		Analysis of an optimal sector design method
+		
+			MichaelDrew
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+		10.1109/dasc.2008.4702801
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+		2008 IEEE/AIAA 27th Digital Avionics Systems Conference
+		St. Paul, MN
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+	Drew, M., "Analysis of an Optimal Sector Design Method," 27 th Digital Avionics Systems Conference, St. Paul, MN, October 2008.
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+		FACET: Future ATM Concepts Evaluation Tool
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+			KarlDBilimoria
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+			BanavarSridhar
+		
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+			ShonRGrabbe
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+			GanoBChatterji
+		
+		
+			KapilSSheth
+		
+		10.2514/atcq.9.1.1
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			9
+			1
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+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
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+	Bilimoria, K., Sridhar, B., Chatterji, G., Sheth, K. S., and Grabbe, S., "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, Vol. 9, No. 1, 2001, pp. 1-20.
+
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+		GLPK (GNU Linear Programming Kit)
+		10.1201/b12733-5
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+		Linear Programming and Algorithms for Communication Networks
+		
+			CRC Press
+			July 2009
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+	"GLPK (GNU Linear Programming Kit)," July 2009, http:/www.gnu.org/software/glpk/.
+
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+	
+		AMPL crossmark policy
+		
+			IncIlog
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+		10.32948/ampl
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+			July 2009
+			Asian Medical Press Limited
+		
+	
+	ILOG, Inc., "AMPL," July 2009, http://www.ilog.com/products/ampl/.
+
+
+				
+			
+		
+	
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+I. IntroductionThe current U.S. National Airspace System (NAS) is congested, and estimates suggest that traffic volume will increase by 2 to 3 times in the coming decades [1].One of the options being considered to address this problem and increase throughput while decreasing delay is to redesign the current airspace, which currently consists of 20 Air Route Traffic Control Centers (ARTCC) across the continental U.S. that are each subdivided into sectors.Today's sectors have evolved over the years in an ad hoc fashion according to current flight patterns and volume.Minor changes are occasionally made to accommodate variations in the airspace demand.While the current sector designs are adequate for today's traffic, the existing airborne congestion and predicted future demand is motivation for improving sector capacity using scientific methods.Thus, methods of analytically redesigning sectors according to certain geometric and flight pattern constraints are sought in order to improve the efficiency of the entire NAS.Many of the current sector design methods are either heuristic-based or optimization-based (e.g., Linear Programming (LP) or Mixed Linear Programming (MIP) methods).One advantage of heuristic methods is their ability to apply several complex design criteria based on sector geometry as well as air traffic flow patterns.In [2] and [3] the authors apply computational geometry techniques to recursively partition a 2D airspace region.They investigate different types of partition cutting methods to maintain equitable workload balance within the region.One attractive feature of this method is that workload metrics are not required to be additive as they are in current LP and MIP models.They are also able to partition the space with controls on the resulting sector convexity and aspect ratio.In [4] Xue uses Genetic Algorithms (GA) to select the location of Voronoi partitions within the region.Here, GA is used to judge the resulting partitions on workload estimates and dominant flow directions to increase the region's total sector capacity.The use of GA simplifies the algorithm design while maintaining the ability to use any number of complex sector evaluation criteria.In [5] a method of optimal ARTCC design is presented while in [6] a MIP model for sector design is described.This model is capable of forming sectors that are aligned with major traffic flows while keeping the workload within the center balanced among the sectors.It is a compelling method of sector design both for its ease of implementation and promise of an "optimal" solution.Yet, in practice, there are some behavioral inconsistencies in the model that are not well understood, and sometimes it produces poor sector designs that would not be feasible without additional processing.A deeper understanding of the MIP model is required before it can be put into practice.This paper focuses on the implementation, analysis, and improvement of the MIP sector design method introduced in [6].Section II provides the details of the MIP model and Section III describes the implementation of the model within a simulation framework.Section IV presents some of the resulting sector designs along with an analysis of the benefits and shortcomings of the model.This ©2008 IEEE.Personal use of this material is permitted.However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.is the primary thrust of the paper, since it focuses on the major problems with the MIP model performance.Section V presents the improvements made to the original model that address these issues.Finally, Section VI concludes with an overall assessment of this sector design method and its potential for future use.
+II. The Optimization ModelThe basis of the MIP model presented here is the discretization of the airspace into hexagonal cells.As simulated or historic flight data traverse the airspace, both a workload metric w i and connectivity metric c ij are recorded for each cell i.The workload metric can be the number of aircraft track counts, or a more complex metric like Dynamic Density (DD).However it is not clear how a workload metric like DD should be discretized in sub-sector increments since the MIP model structure currently restricts the workload value to be an additive quantity.The connectivity metric c ij is the total number of planes that travel from cell i into a neighboring cell j.
+Figure 1. Cell workload flow pathsReferring to Figure 1, the model works on the abstract quantity of workload flow (red arrows) given by the decision variables f ij in the model.Flow enters each cell from at least one of its neighbors then exits into exactly one neighboring cell.The workload of that cell is added to the flow, and certain cells called seeds (yellow cells) have the option of becoming sinks (green cells) that absorb the flow.The number of sinks is constrained to be equal to the number of desired sectors, which is known a priori.In this way a sector becomes the contiguous cluster of cells that feed into one sink, and the total workload of the sector is the amount of flow absorbed by that sink.This is topologically a network model, and although workload flow is an abstract quantity, solving for flow paths between cells allows the creation of sectors that are aligned according to dominant aircraft trajectories while maintaining an equitable distribution of workload among all the sectors.A major advantage of this method is the implicit contiguity of all the cells within a sector.Depending on the objective function and constraints, the workload distribution among the sectors can be controlled, and so can the dominant orientation of the resulting cell cluster.
+Model SpecificsYousefi's MIP optimization sector design method as adapted from [6] may be formally defined as follows:subject to the following constraints: Here f ij , D i , y s , and u ij are decision variables with f ij , D i ∈ ℜ + , and y s , u ij ∈ {0, 1}.f ij represents the workload flow from cell i into cell j .D i is the workload demand of cell i .y s is an assignment variable that determines when seed s is selected to be a sink.u ij is the assignment variable controlling the direction of flow exiting cell i .For n hexagonal cells, I = {1, … , n} is the set of all cell indices, and J = {1, … , 6} represents a cell's six adjacent neighbors starting with the southwestern neighbor proceeding counterclockwise around the cell.S ⊆ I is the set of all seed cell indices.Seed locations are determined ahead of time and are interspersed throughout the grid as evenly as possible.Thus, a sector consists of all the cells whose flows converge to one sink, and by minimizing equation 1, the clusters will tend to be oriented along the dominant traffic flows.Constraint 2 maintains the conservation of workload flow from one cell to the next.In words, flow-out minus flowin equals the workload addition of the cell minus the cell demand (which is zero for all non-seed cells according to constraint 3).This constraint sets up a network model between the cells, which forces cell contiguity within the resulting sector design.Here N ij is an n × 6 look-up table that returns the cell index of cell i 's j-th neighbor.P j = [4, 5, 6, 1, 2, 3] is another look-up table that returns the neighbor index of cell i relative to its neighboring cell j.The use of these look-up tables reduces the size of the f ij matrix from what would otherwise be a sparsely populated n × n sized matrix to a more compact n × 6 matrix.In constraint 4, y s = 1 if seed cell s becomes a sink, and the sector demand D s is bounded by the upper and lower bounds Uµ and Lµ.If y s = 0, seed cell s is not a sink and D s = 0, causing cell s to behave like any other source cell.This constraint is what distributes the resulting sector workload in an equitable fashion.By tuning L and U with L = (0, 1.0], and U = [1.0,∞) the resulting workload variation among the sectors in the center can be controlled.There must be exactly one flow path out of each non-sink cell; otherwise, the flow would be divided between more than one sink (sector) leading to erroneous sector workload values and improper workload distribution.This is enforced by the u ij decision variables and the last 3 constraints.Constraints 6 and 7 force ∑ j u ij to be 1 for all nonsink cells and 0 for those cells selected to be sinks.In conjunction with constraint 8, setting M > Uµ, forces all non-sink cells to have exactly one out-bound flow path and all sink cells to have no outbound paths.This MIP model is attractive for many reasons.For one, it is linear and can be solved in a reasonable amount of time depending on the number of cells, seeds, sinks (sectors), and tightness of bounds (U and L).Even flow constraints 6 -8, which dramatically increase computation time, do not make the model prohibitively unwieldy to solve.Furthermore, as previously mentioned, because it is a simple network flow model, contiguity of cells within a sector is an implicit constraint.This is not trivial to enforce in other cell-based clustering optimization models.There are some drawbacks to this model, perhaps the greatest of which is the fact that it has no inherent notion of a sector.That is, there is no cell-sector assignment variable that could be used to enforce additional geometric constraints.It is possible to add this variable with additional constraints, but this will drastically increase solution times.Geometric constraints like convexity and aspect ratio could be a useful addition to the model since, as the results in the next section demonstrate, it frequently produces geometrically undesirable sector shapes.Also, the shapes tend to be sensitive to model parameters and can vary dramatically from one near-optimal solution to the next.Fortunately, there are some simple methods to address these issues that will be presented herein.
+III. Model ImplementationThe Future ATM Concepts Evaluation Tool (FACET) is a software package developed, maintained, and used by NASA Ames Research Center.[7] It is capable of analyzing both historic and current air traffic and weather data across the entire NAS.It is commonly used as a research tool to evaluate "what if" scenarios of various strategic and tactical air traffic and air space design concepts.A hex grid application was added to FACET, which records aircraft counts (w i ) and connectivity data (c ij ) for each cell.Any historical data can be run through the grid in either "playback" or "simulation" mode.Playback mode uses actual recorded radar locations of the aircraft, whereas simulation mode uses either the filed flight plan or great circle paths for aircraft locations.Flight plan simulations were used for the results presented herein.Implementation data from certain times of day or from different weather conditions can be used as a basis for sector designs optimized (as defined by the model) for those conditions.Hex grids can be created of any size and at any altitude range, and can be placed over the entire NAS or over individual centers.Care must be taken, however, to consider the size of the cell verses the sampling rate of the data files.Most historic flight data are recorded at 60 second sampling intervals.Thus, hex cells smaller than about 8 nautical miles in height can be missed entirely during a simulation.This is addressed by interpolating flight data between samples, thereby up-sampling the data prior to use in FACET.Of course, flights passing through a cell corner may still be missed by that cell, but this is not a problem because there will be sample points in neighboring cells that are adjacent to each other, so the connectivity data from the flight will remain contiguous.In aggregate, these effects tend to become negligible.
+IV. Model Results and AnalysisOne of the nice features of the MIP model is the ability to capture the dominant flow directions in the shape of the sector while keeping the workload balanced.Figure 4 shows the sector design produced by the MIP model based on the data shown in Figure 2. Note that the dominant direction of most of the sectors is aligned with the dominant trajectories depicted in Figure 2. Sectors that are aligned with their dominant flows are desirable, because the average aircraft dwell time will be higher and likewise the capacity of the sector.As is obvious from Figure 4, many of the sector shapes are extremely non-convex and have rough geometric features (beyond those produced by the jagged hex cell edges alone) not desirable for sector boundaries.When implementing this model, experimentation revealed additional issues that will now be discussed in detail.
+Figure 4. Optimal sector design results
+Seed SensitivityThe number and location of the seed cells can dramatically affect sector design results.Recall that seeds are cells chosen to be candidate sinks that absorb the abstract quantity known as flow.Thus, for each center there must be at least as many seeds as there are sectors.Sinks have no inherent meaning in the real world and are only used as a device in the network model for collecting and measuring the total workload present in a sector.It seems reasonable that as long as a lot of seeds are evenly dispersed throughout the center, good solutions should be found.In practice, using 3 -5 times the number of sinks as there are sectors tends to produce desired results that solve in reasonable time.the more options the solver has for potential sinks, lower the best integer bound of the MIP problem goes, which results in a more optimal solution according to equation 1.Unfortunately, allowing all cells to be seeds (potential sinks) makes the problem too complex to solve consistently in tolerable time.Consider the results of Figure 5, which shows four sector designs based on identical track data using an increasing number of seeds.Some of the sector boundaries remain relatively stable (particularly the western side of the center between the 97 and 211 seeds examples), but many sectors change dramatically as the number of seeds is increased.Note, also, that the sector designs with the largest number of seeds are not necessarily any more geometrically desirable (in terms of convexity, aspect ratio, etc.) than those with fewer seeds-despite producing objective function solutions with lower values.This really comes as no surprise, since there are no geometric functions or constraints within this MIP model.
+Figure 5. Sector designs with increasing number of seeds Solution Space SensitivityAnother concern is the variation of the resulting sector designs within the feasible solution space itself.As a MIP model is solved, the objective function value at the current best integer feasible solution is compared with that of the best known lower node bound.This difference is known in CPLEX as the mip gap, and often the solver is permitted to terminate once this value drops below a pre-determined limit to prevent excessively long run times.For this model solutions with a mip gap of less than 2% were considered satisfactory.Experience has shown that widely varying sector geometries can result from minor differences in the objective function value-even as the mip gap approaches zero.This solution instability among near-optimal solutions may be more or less drastic depending on the track data, but it is always present to some extent.Figure 6 shows the relative normalized objective function values of several solutions along with a shape change metric.This is a simple method of quantifying the geometric difference between one sector design and another, and it is based on the relative change in cell-sector assignments.To compare two sector design solutions A and B, the shape change metric SC is defined as follows:Here K is the number of sectors, and n k is the number of cells assigned to sector k in solution A. α k is the number of cells assigned to sector k in solution A that are assigned to sector j in solution B where j is the dominant cell assignment of cells from solution A sector k in solution B.
+Figure 6. Difference in near-optimal solutionsFigure 6 demonstrates that as the objective function is minimized, there are still major geometric differences between subsequent solutions-even as the solution becomes close to optimal.Notice that at solution 12, there is a spike in the shape change metric even though the objective function values of solutions 12 and 13 are within 0.04% of each other.The sector boundaries for these solutions are shown above the plot.Note that several of the sectors the east and northwest regions of the center show drastic differences.The fact that the changes in sector geometry are not strongly correlated to changes in the objective function value is especially obvious here.Considering the objective function of equation 1 this effect is not surprising.It is apparent that small changes in the routing of flow f ij can produce drastic differences in the resulting sector geometry.Conversely, there is no unique mapping from a particular sector design geometry to a feasible set of decision variable solutions in the MIP model.While most of the feasible solutions near the final optimal solution appear relatively stable, major geometric changes can and do occur between solutions.In the next section, methods of improving these results will be discussed.
+V. MIP Model ImprovementsIt is clear that this sector design method has some issues in its original form.However, the promise of an elegant model that provides a reliable and tractable method of producing an optimal design is motivation for investigating ways of improving this model.Here, methods of improving the stability of the solutions and the geometry of the final designs are presented.
+Symmetric ConnectivityThe rough and extremely non-convex sectors of Figure 4 can partially be explained by considering that the optimization model will seek solutions that direct the abstract flow quantity along paths that minimize equation 1. Recalling that since c ij is the record of planes that flew from cell i to the neighboring cell j , if more planes flew from cell i to cell j than from cell j to i , the flow will be biased to go the same direction.Considering this bias, it is apparent that convoluted flow paths may arise leading to rough sector edges containing unwieldy spurs as shown in Figure 4. Since what is desired, however, is that cell i connect to cell j, it is ultimately irrelevant whether the flow goes from i to j or from j to i. Therefore, prior to running the optimization model, c ij is redefined to be equal to the number of planes that flew from cell i to cell j plus the number of planes that flew from cell j to cell i.In this way, the c ij data across a cell edge will be equal on both sides, and flow will be likely to go in either direction giving the optimization software more options to find a feasible solution with a lower objective function value.This method is referred to as the "symmetric connectivity method" and is depicted in Figures 7 and8.In Figure 7 a simple example of a grid segment is shown using the standard data structure and resulting sector shape.Figure 8 demonstrates the effects of applying the symmetric connectivity method which produces smoother sector shapes that better correspond to the dominant flows.The results of running the same data set through the optimization model using this method is shown in Figure 9.While there are still some undesirable shapes, this is a significant improvement over the original method's results in terms of sector boundary geometry.Furthermore, this minor change reduces the model's seed sensitivity and stabilizes the solution space results as shown in Figure 10.Compared to Figure 6, the symmetric connectivity method exhibits a stronger correlation between shape stability and objective function.Near-optimal solutions tend to be more similar to each other making the model less sensitive to the mip gap stopping criteria.
+Convexity ConstraintsOne of the most unfavorable behaviors of this MIP model is its tendency to produce sector designs with long spurs or thin regions."Boomerangshaped" or even "Y-shaped" sectors have been known to arise.These tendencies are reduced by the symmetric connectivity method, but not entirely.If a measure and control of convexity could be enforced the sector shapes could be improved (although non-convexity should probably not be prohibited entirely).This particular implementation cannot provide a means of controlling the resulting sector geometry, because it does not contain the notion of a sector or of cell-sector assignments.However, methods of extending the model to include a cellsector assignment variable are available.Yousefi's 3-D sector design method in [6] includes this extension.Also, the author has developed a model that includes some geometric convexity control, but it remains to be seen if it can be applied to representative problems.The additional complexity of the extended model exponentially increases solution time but efforts are underway to improve its efficiency.A more practical approach may be to sift through a pool of solutions produced by the solver and select those that meet desired criteria.Using Ampl CPLEX's solution pool feature, improvements in sector shape have been realized by selecting from many solutions those that have sector boundaries that are the most convex-thus eliminating some of the extreme spurs and branches of some sectors.Convexity is measured by counting the number of perimeter cells in each sector.Designs that have the least number of perimeter cells are considered the most convex.So far, improvements have only been minor and may not be worth the additional effort, since the nearoptimal solutions using the symmetric connectivity method are similar to each other and already more convex than before.
+Boundary SmoothingOne of the basic inconveniences of this sector design method is that hex cells will always produce jagged edges that are undesirable for real sector boundaries.It is therefore reasonable to consider ways of smoothing these boundaries to make them simpler and more manageable.Doing so may also improve some of the geometric inadequacies of the MIP model mentioned above.
+Figure 11. Four iterations of the Douglas-Peucker algorithmThe sector boundaries may be simplified using a common smoothing algorithm from the field of computational geometry known as the Douglas-Peucker (DP) algorithm.[8] This is a recursive algorithm that starts with the first and last vertices of the polyline.Then, the perpendicular distance between each intermediate vertex of the original line and the current simplified polyline segment is measured.The vertex with the largest perpendicular distance is added to the simplified polyline if the distance is greater than some distance tolerance ε.This process repeats as shown in Figure 11 until none of the vertices of the original polyline are greater than ε distance to the segments of the simplified line.The speed of this algorithm is output-dependent running in O(nm) worst case time where m is the number of vertices in the simplified polyline.Applying this method to the sector design process requires first identifying the shared boundaries of the sectors so they can be smoothed as individual edges.Vertices consisting of the intersection of multiple sector edges are preserved and are not moved during the smoothing process.Once smoothed, the edges are stitched back together to create sector polygons which can be imported into ATM software packages like FACET for further analysis and validation.Figure 12 shows the smoothed version of the sector designs from Figure 9. Values for ε are usually relative to the cell height h.Here a value of ε = 3h produces good results.
+Figure 12. Final smoothed sector designThe results of Figure 12 are promising since many of the resulting sector shapes are fairly reasonable in size and geometry.There are still some obvious problem areas, however, but when applied NAS-wide, the sectors produced by using the symmetric connectivity method in conjunction with this post-optimization smoothing algorithm have shown considerable capacity and performance gains in preliminary ACES (Airspace Concepts Evaluation System [9]) software simulations.Of course, depending on how ε is tuned, some of the optimality provided by the MIP model may be lost.This is usually not a concern with smaller values of ε.However, if necessary, the loss of optimality in terms of workload balance may be measured.Workload loss (or gain) can be estimated by the fraction of the area of each lost (or gained) cell multiplied by that cell's workload value.The red and green shaded areas of Figure 11 depict this.Although it is not demonstrated here, it is possible to apply the DP algorithm in an incremental fashion to maintain the workload balance within some bound.Such an algorithm may be applied to one edge at a time while keeping track of the change in workload.This is a greedy heuristic with the disadvantage that the last edges to be processed are usually less smooth than the first because there is less available workload deviation in the neighboring sectors.
+VI. ConclusionsThis paper presents an existing mixed integer programming model that provides an optimal sector design method, but in its implementation may tend to produce some geometrically undesirable sector boundaries.There are also problems that arise from its sensitivity to model parameters and to minor solution space variations within the neighborhood of the optimal objective function value.These issues have been investigated, and the sensitivity and sector geometry problems have been substantially reduced through a modification to the model's input data.Additional improvements were introduced with a post-optimization smoothing algorithm.There are some remaining disadvantages to this MIP method of sector design including the difficulty in applying geometric constraints and in using more complex workload metrics.It is not clear if any of these can be completely addressed within the MIP model framework.However, with the improvements made herein, this sector design procedure offers a quick method of synthesizing sector designs that can be easily applied to varying traffic patterns and demand.Supplemental heuristics that augment the optimal model results may be developed, but these might ultimately defeat the advantages the optimal design method provides.Figure 2 .2Figure 2. Hex grid aircraft counts from FACET After running a simulation in FACET, the results are processed by Matlab to convert the data into a form usable by the optimization solver.The grid cell output is shown in Figure 2 as plotted by Matlab.Here, 2205 hex cells 8 nmi high are used to record 24 hours of flight data from April 21, 2005 over ZFW center at flight level 240 -350.This figure shows the aircraft counts of each cell along with 82 seed cells shown in black.Next, Ilog's OPL Studio or AMPL CPLEX solves the optimization problem described in the previous section.Because the solution to the optimization problem consists of only work flow values and sink demands (f ij , D s ), it
+Figure 3 .3Figure 3. Sector design procedure
+Figure 7 .Figure 8 .78Figure 7. Example grid sector using standard connectivity
+Figure 9 .9Figure 9. Sector design using symm.conn.method
+		
+		
+
+			
+AcknowledgementsThe author wishes to acknowledge Arash Yousefi and Robert Hoffman of Metron Aviation for their contribution, collaboration, and support of this work.
+Email Addressesmichael.c.drew@nasa.gov
+			
+
+			
+
+				
+
+
+	
+		The Next-Generation Air Transportation System's Joint Planning Environment: A Decision Support System
+		
+			EdgarWaggoner
+		
+		
+			ScottGoldsmith
+		
+		
+			JoshElliot
+		
+		10.2514/6.2009-7011
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		
+			American Institute of Aeronautics and Astronautics
+			July 24, 2006
+		
+	
+	Version 2.0, Tech. rep., Joint Planning and Development Office
+	"Concept of Operations for the Next Generation Air Transportation System," Version 2.0, Tech. rep., Joint Planning and Development Office, July 24, 2006.
+
+
+
+	
+		Dynamic Airspace Configuration Management Based on Computational Geometry Techniques
+		
+			JoeMitchell
+		
+		
+			GirishkumarSabhnani
+		
+		
+			RobertHoffman
+		
+		
+			JimmyKrozel
+		
+		
+			ArashYousefi
+		
+		10.2514/6.2008-7225
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, Hawaii
+		
+			American Institute of Aeronautics and Astronautics
+			August 2008
+			
+		
+	
+	Mitchell, J.S.B., G. Sabhnani, J. Krozel, R. Hoffman, and A. Yousefi, "Dynamic Airspace Configuration Management Based on Computational Geometry Techniques," Proc. of AIAA Guidance, Navigation, and Control Conference and Exhibit, Honolulu, Hawaii, 18-20 August 2008.
+
+
+
+	
+		Geometric Algorithms for Optimal Airspace Design and Air Traffic Controller Workload Balancing
+		
+			AmitabhBasu
+		
+		
+			JosephS BMitchell
+		
+		
+			GirishkumarSabhnani
+		
+		10.1137/1.9781611972887.8
+	
+	
+		2008 Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments (ALENEX)
+		San Francisco, CA
+		
+			Society for Industrial and Applied Mathematics
+			January 2008
+			
+		
+	
+	Bau, A., Mitchell, J. S. B., and Sabhnani, G., "Geometric Algorithms for Optimal Airspace Design and Air Traffic Controller Workload Balancing," Proc. of Workshop on Algorithm Engineering and Experiments, San Francisco, CA, January 2008.
+
+
+
+	
+		Airspace Sector Redesign Based on Voronoi Diagrams
+		
+			MinXue
+		
+		10.2514/6.2008-7223
+	
+	
+		AIAA Guidance, Navigation and Control Conference and Exhibit
+		Honolulu, Hawaii
+		
+			American Institute of Aeronautics and Astronautics
+			August 2008
+			
+		
+	
+	Xue, M., "Airspace Sector Redesign Based on Voronoi Diagrams," Proc. of AIAA Guidance, Navigation, and Control Conference and Exhibit, Honolulu, Hawaii, 18-21 August 2008.
+
+
+
+	
+		Temporal and Spatial Distribution of Airspace Complexity for Air Traffic Controller Workload-Based Sectorization
+		
+			ArashYousefi
+		
+		
+			GeorgeDonohue
+		
+		10.2514/6.2004-6455
+	
+	
+		AIAA 4th Aviation Technology, Integration and Operations (ATIO) Forum
+		
+			American Institute of Aeronautics and Astronautics
+			2005
+		
+		
+			George Mason University
+		
+	
+	Ph.D. dissertation
+	Yousefi, A., "Optimum Airspace Design with Air Traffic Controller Workload-based Partitioning," Ph.D. dissertation, George Mason University, 2005.
+
+
+
+	
+		Trigger Metrics for Dynamic Airspace Configuration
+		
+			ArashYousefi
+		
+		
+			RobertHoffman
+		
+		
+			MarcusLowther
+		
+		
+			BabakKhorrami
+		
+		
+			HerbertHackney
+		
+		10.2514/6.2009-7103
+		No. 34N1207- 001-R0
+	
+	
+		9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO)
+		
+			American Institute of Aeronautics and Astronautics
+			December 2007
+		
+	
+	Tech. Rep. Report
+	Yousefi, A., B. Khorrami, R. Hoffman, and B. Hackney, "Enhanced Dynamic Airspace Configuration Algorithms and Concepts," Metron Aviation Inc., Tech. Rep. Report No. 34N1207- 001-R0, December 2007.
+
+
+
+	
+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
+		
+			BanavarSridhar
+		
+		
+			ShonRGrabbe
+		
+		
+			GanoBChatterji
+		
+		
+			KapilSSheth
+		
+		10.2514/atcq.9.1.1
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			9
+			1
+			
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Bilimoria, K., B. Sridhar, G. Chatterji, K.S. Sheth, and S. Grabbe, "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, vol. 9, no.1, pp. 1-20, 2001.
+
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+
+	
+		Analysis of Ramer-Douglas-Peucker algorithm as a discretization method
+		
+			FurkanGoz
+		
+		
+			AlevMutlu
+		
+		
+			OrhanAkbulut
+		
+		10.1109/siu.2018.8404748
+		
+	
+	
+		2018 26th Signal Processing and Communications Applications Conference (SIU)
+		
+			IEEE
+			
+		
+	
+	"Ramer Douglas Peucker algorithm," http://en.wikipedia.org/wiki/Ramer-Douglas- Peucker_algorithm.
+
+
+
+	
+		Build 4 of the Airspace Concept Evaluation System
+		
+			LarryMeyn
+		
+		
+			RobertWindhorst
+		
+		
+			KarlinRoth
+		
+		
+			DonaldVan Drei
+		
+		
+			GregKubat
+		
+		
+			VikramManikonda
+		
+		
+			SharleneRoney
+		
+		
+			GeorgeHunter
+		
+		
+			AlexHuang
+		
+		
+			GeorgeCouluris
+		
+		10.2514/6.2006-6110
+	
+	
+		AIAA Modeling and Simulation Technologies Conference and Exhibit
+		Keystone, CO
+		
+			American Institute of Aeronautics and Astronautics
+			August 21-24 2006. 2008
+		
+	
+	27 th Digital Avionics Systems Conference October 26-30
+	Meyn, L., R. Windhorst, K. Roth, D. Van Drei, G. Kubat, V. Manikonda, S. Roney, G. Hunter, and G. Couluris, "Build 4 of the Airspace Concept Evaluation System," Proc. of AIAA Modeling and Simulation Technologies Conference, Keystone, CO, August 21-24 2006. 27 th Digital Avionics Systems Conference October 26-30, 2008
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+I. IntroductionE n-route traffic in the U.S. national airspace system is handled by Air Route Traffic Control Centers (ARTCCs).Each ARTCC (or Center) is divided into a small number (typically fewer than 10) of Areas of Specialization (AoS) which are each subdivided into a small number (generally 5 to 10) of sectors.Each sector is operated by a team of one or two (occasionally three) air traffic controllers who are certified to work in an Area.Sectors are designed to distribute workload equitably and to conform with the local traffic flow. 1 Cleveland Center recently redesigned significant portions of their airspace in response to new traffic flow patterns that emerged over the past several years.The two primary factors responsible for these traffic pattern changes were the de-hubbing of Pittsburgh International Airport by US Airways in 2004, and the transition from turbo-prop to regional jet aircraft, which fly at higher altitudes.A team of Cleveland Center staff modified their airspace design to conform with the new traffic patterns.The process for determining this modified airspace design was primarily qualitative, based on the extensive operational knowledge and experience of the redesign team.In order to provide a balanced perspective, an independent quantitative analysis of the airspace design changes was conducted by researchers at the NASA Ames Research Center.This paper documents that effort.This paper is organized as follows: Section II highlights the most significant airspace design changes that were made and analyzed.Section III details the method used for the airspace analysis.This includes a description of the traffic data used, as well as the metrics and factors used to evaluate the performance of the new design.Section IV presents the major results of the analysis on both an individual sector basis, as well as on a larger regional basis.Section V concludes the analysis of Cleveland Centers airspace redesign effort.
+II. Cleveland Center Airspace ChangesChanges to the Cleveland Center airspace design were extensive.Example changes include combining two sectors into one larger sector, splitting sectors horizontally and vertically to form new sectors, and altering the floors and/or ceilings of sectors.Additionally, some Areas gained and/or lost sectors, and one Area was completely redesigned and constructed with entirely new boundaries.These changes were gradually implemented over several months from 2011 into 2012.While all the changes were simulated and analyzed, only the analysis of a subset consisting of the most major changes is presented in this paper.Listed according to AoS, the subset of changes analyzed in this paper consists of:• Area 4:-Sector 45 split horizontally into 45 and 46 (sub-section IV.A).-Sector 40 moved into Area 8 (sub-section IV.A).• Area 5:-Johnstown TRACON added below Area 5 (sub-section IV.B).-Combine sectors 50, 52, 53, 55, and 61 into three sectors (herein called 50, 53, 55) (sub-section IV.B).-Restratify and split sector 59 into sectors 59 and 58 (sub-section IV.C).• Area 6: Restratify high and super high sectors (sub-section IV.D).• Area 7: Decommission high sector 74; raise ceiling of lower sectors (sub-section IV.F).• Area 8 Low:-Combine sectors 01 and 02 into sector 02 (sub-section IV.E).-Combine sectors 05 and 06 into sector 06 (sub-section IV.E).
+III. ApproachThe method used to evaluate Cleveland Center's design changes involves comparing the former airspace design ("Old") to the planned ("New") one.For both sector designs, the Future ATM Concepts Evaluation Tool (FACET) 2 is used to record the number of aircraft in each sector (also referred to as the sector count ), as well as several other workload factors for each sector, using historical traffic from thirty-one days between May and July, 2010.For each day, only the traffic between 6 am and 10 pm EDT is used, which is when most of the traffic occurs.The selected days are all weekdays that were judged to be relatively free of major weather impacts throughout Cleveland Center and nearby airspace, such as that near the major airports along the northern east coast of the US.Sectors should be designed to be robust to weather, for example by being able to facilitate typical weather reroutes, but weather-related factors were not included in this analysis.
+A. Sector LoadingThe first metric used in the analysis is called sector loading.This metric relies on an assumed capacity for each sector for which the published (if available) or estimated sector Monitor Alert Parameter (MAP) is used. 1 The sector loading is found by dividing a sector's aircraft count by its MAP value.Thus, a value of 1 (or 100%) indicates that the sector is at full capacity.The raw traffic data consists of sector counts recorded every minute over a 16-hour period for 31 days, resulting in nearly 30,000 data points for every sector.Then, the maximum value of these sector count values during every 15-minute interval is recorded.This reduces the data set to roughly 2000 points per sector.Dividing by the sector's MAP value results in a corresponding set of 2000 sector loading values from the thirty-one selected days.A typical sector loading distribution plot is shown in Fig. 1.Note that the median value of this sector is shown as is the MAP threshold line at 100%.The orange bar indicates that in roughly 20% of the 2000 data points, this sector has a 15-minute peak loading of between 40 -50%.Also, for some small but not  insignificant part of the time, the sector is over-loaded in that it experiences sector counts exceeding its MAP value.
+Median MAP ThresholdBy comparing the sector loading distributions between the old and new designs (on both an individual basis and Area-wide bases), quantitative insight can be gained into the potential benefit of the new design.For example, sector load distributions that have values beyond 100% loading and/or median lines to the right of about 45% indicate the sector may be over-loaded.Likewise, load distributions with median lines to the left of about 25% suggest the sector is under-loaded.The majority of sectors within Cleveland Center that were subject to the greatest changes fell into these categories before being redesigned.Also, when sector loading distributions are plotted together from multiple sectors within a region (all sectors within an AoS, for example), similar sector loading distributions are preferred because they indicate that workload is well balanced among those sectors.
+B. Additional Workload FactorsFACET also computes and records four additional workload factors every minute for every sector.These factors are counts of:
+Vertical Sector Boundary CrossingsEach of these factors are evaluated between the two designs in a manner similar to the sector loading metric.Whereas over fifty factors have been suggested in the literature (see Ref. 3 for example), short-term conflicts, transitioning flights, and handoffs/point-outs were identified by subject matter experts (SMEs) at Cleveland Center as being among the most critical.The last two factors, flights near horizontal sector boundaries and vertical sector boundary crossings, are used as an approximation for handoffs/point-outs.As with sector loading, the maximum values of each factor within each 15-minute interval is used for the analysis.
+Short-term ConflictsShort-term conflicts are estimated by projecting each flight in a sector along its current 3D trajectory in 15-second intervals for 5 minutes into the future.A short-term conflict is counted for the sector if any pair of these dead-reckoning predictions originating from the sector results in a loss of separation: aircraft within 5 nmi horizontally and 1000 feet vertically at the same time.
+Transitioning FlightsThe number of transitioning flights within a sector is measured by counting the number of flights ascending or descending faster than 300 ft/min.
+±4 nmi
+Flights Near Horizontal Sector BoundariesFigure 2 shows eight flights within and one flight outside of a notional sector.Flights within 4 nmi on either side of the sector's boundaries are counted as boundary flights.In this case, there are four boundary flights at this instant in time.This factor, along with the Vertical Sector Boundary Crossings factor, was programmed as a simple proxy for true boundary crossings and point-outs.Note that this metric does not count flights near vertical boundaries, and that some flights counted as near horizontal boundaries may not actually be point-outs or boundary crossings.
+Vertical Sector Boundary CrossingsThis metric counts flights that have entered or exited each sector during the last minute via the floor or ceiling of the sector as a vertical boundary crossing for that sector.
+IV. ResultsA comprehensive analysis involving all the previously-discussed metrics was performed on all the design changes listed in Section II.This section highlights the airspace regions and metrics displaying the greatest contrast between the old and new sector designs.Several of the workload factors showed little to no appreciable difference, as the intent of the design change was likely not based on those factors.
+A. Area 4The changes involved with this area include splitting the ultra-high sector 45 into two sectors-45 and 46.Also, low sector 40 is moved out of Area 4 into Area 8.These changes are depicted in Fig. 3.The Sector Loading distributions for all Area 4 sectors are shown in Fig. 4. The old sector design is shown on top, and the new design is shown on the bottom.Note that sector 45 had been the most congested sector within the area (as demonstrated by its median and distribution being substantially to the right of the other sectors), and that sector 40 had been the least congested sector according to this metric.The bottom plot shows that the distributions in Area 4 match closely, indicating that there is a better balance of sector loading within the area.The resulting median values of sector loading between 30 and 40 percent suggest that the workload conditions for this area are neither too heavy nor too light.Furthermore, there are fewer instances of over-MAP conditions.Also, sector 40 is the only one in Area 4 classified as a low altitude sector.Its transfer out of the area results in a more consistent traffic type in the area as a whole.These reasons justify the decision to split 45 and transfer 40 out of Area 4.The other four workload factors were also evaluated for the Area 4 changes.Not surprisingly, splitting sector 45 into two sectors significantly reduced the number of short-term conflicts.The performance of sectors 45 and 46 of the new design is shown to be more consistent with the other sectors in the area (see  Fig. 5).Note that the median values of all short-term conflicts for sectors in the area are matched at one in the new design.The remaining three factors show minor improvements as well.In Fig. 6, for example, the average number of flights near horizontal boundaries is plotted in a stacked bar fashion for both the old and new designs.Comparing the combined height of the blue and green bars for sectors 45 and 46 in the new design of Fig. 6 to the height of the green bar for the old sector 45 shows that the same volume of airspace now experiences a greater average number of flights near horizontal boundaries.This is partly due to the splitting of sector 45 into two smaller sectors, which produces more horizontal boundaries (one boundary counted twice-once for each new sector).However, on a per-sector basis, the height of the individual bars shows that this factor has been reduced for sector 45, and for sector 46 it is less than the outgoing sector 40.In fact, in all of Area 4 combined, the average number of flights near horizontal sector boundaries has been slightly reduced.
+Old DesignNew Design 0
+B. Area 5 LowThe low sectors (FL230 and below) of Area 5 were changed in two major ways.The new Johnstown TRACON with a ceiling of 8000 feet was carved out below several of the Area 5 Low sectors.Then, the four low sectors from Area 5 and sector 61 from Area 6 were redesigned into three sectors.At the time of this analysis these three new sectors had not been given official designations, nor been commissioned for operations.Herein they will be denoted as sectors 50, 53, and 55.MAP values for the three new sectors are estimated based on input from SMEs familiar with Cleveland Center airspace.Boundaries of the old and new sectors in this region are shown in Fig. 7.Note that the boundaries shown in Fig. 7(b) have been slightly modified since this analysis was performed.The sector loading analysis for the old and new Area 5 Low sector design is shown in Fig. 8. Notice that in the old design, five sectors appear to be under-utilized based on their low median sector loading values.In the new design, the median loading values of the three new sectors are slightly higher, but there are still no over-MAP occurrences during the roughly 2000 15-minute intervals measured over the course of the thirty-one days.The workload factor analysis for the number of short-term conflicts, transitioning flights, and vertical sector boundary crossings show only minor increases, despite there being two fewer sectors in the new design.This is partly due to the nonlinear relation of this analysis method to airspace size.Recall that only the maximum value of each factor during a 15-minute interval is recorded.Thus, an airspace region partitioned into fewer sectors will not necessarily produce proportionally larger peak factor values.Furthermore, the volume of the three new sectors is smaller than the volume of the older five sectors due to the new Johnstown TRACON located within.Again, analyzing the number of flights near horizontal sector boundaries shows a contrast between the old and new designs.With fewer sectors covering the same volume of airspace (except for the new Johnstown TRACON), one would expect to see workload slightly increase on a per-sector basis.However, in Fig. 9 where the average number of flights near horizontal boundaries are plotted for the old and new designs, the height of the bars on the right indicate that on a per-sector basis, only sector 50 experiences a significant increase in this factor compared to other sectors.More importantly, with fewer horizontal boundaries in this region, cumulatively this factor has dropped significantly, and the new design shows a reduction of more than three.This translates into a reduction in the amount of handoff-and point-out-related workload.Seen another way, the distributions of the peak 15-minute counts of flights near horizontal boundaries shown in Fig. 10 demonstrate that the new design better balances this factor among the three new sectors.
+Old DesignNew Design
+C. Area 5 HighThe original high altitude sectors of Area 5 consisted of sector 57, ranging from FL240 -FL310, directly beneath super-high sector 59 spanning FL320 and above.In the new design the ceiling of sector 57 is raised to FL320, and sector 59 spans FL330 -FL370.A new sector (58) is added covering FL380 and above.The MAP value of sector 57 is held constant at 16, but since the volume of sector 59 is reduced, the MAP value is decreased from 19 to 17.The new sector 58 primarily handles high-altitude en-route flights that are simpler to control.Its MAP value is also set at 17. Figure 11 depicts the old and new Area 5 high sectors.Fig. 12 shows this design change's effect on sector loading.Note that new sector 58, with its high floor of FL380 and relatively high MAP value of 17, is shown to be very under-loaded compared to the other sectors.However, the traffic loading of sectors 57 and 59 is now more closely matched.Because the design change in this region involves vertically splitting an existing sector (as well as altering floor and ceiling altitudes), it is interesting to observe the effect on vertical sector boundary crossings.In Fig. 13, the average number of peak 15-minute vertical sector boundary crossings is plotted in stacked-bar form.The new sectors show an increase in this workload factor on both a per-sector and region-wide basis.This is primarily because a vertical boundary has been added inside what used to be one large sector (old sector 59).Obviously, the same traffic will cross more vertical boundaries in the new design.Even the new sector 57 shows a slight increase in this factor due to its ceiling being raised to FL320.
+Old DesignNew Design  The 15-minute peak sector loads for both designs are compared in Fig. 15, and the new design is shown to perform better in terms of balancing workload.Also, the median loading of sectors 66 and 67 is markedly reduced.The remaining workload factors do not show much contrast as this change was motivated primarily by balancing flight volume.From Fig. 17 it is apparent that the original Area 8 low sector design involved several small sectors that were under-utilized.In fact, several of the sectors display median loading values of only 15%.In the new design, the sector loading of the newly formed sectors 02 and 06, along with sector 40, is very closely matched with a median of 31%.As a whole, the low sectors are well balanced, yet despite containing fewer sectors, there are no over-MAP conditions.As expected, the distributions of the other workload factors are shifted to the right for the new larger sectors 02 and 06.However, none of the factors increase substantially on a per-sector basis, despite the new sectors being roughly double the size of the old ones.
+F. Peak Sector Loading AnalysisIn an effort to see all the changes made to Cleveland Center airspace from a broader perspective, a metric called Peak Sector Loading is plotted together for all the low, high, and super-high sectors in the center.Here, "peak" sector loading is measured as the top 99-percentile value from all the 15-minute sector loading data collected for each sector.The remaining 1% of data are considered to be outliers.As an example, the sector loading data for sector 73 shown in Fig. 18 shows a peak sector loading value of 78.6%.The difference between peak sector loading and 100% is indicative of the sector loading margin relative to the sector capacity (MAP).Figure 19 shows a comparison of peak sector loading for all the low sectors in Cleveland Center.Here, the changes to Area 5 are quite obvious.Combining the five older sectors (50, 52, 53, 55, 61) into three new ones (50, 53, 55) does not result in critically high peak loading values.Also, within the old design, Area 8 sectors 02, 05, and 06 were among the lowest peak loaded sectors, whereas in the new design, most of Area 8, which now includes sector 40, shows a consistently low peak loading value.Sector 02 is the exception with a peak loading value of 85%.This is consistent with the distributions plot of Fig. 17 that shows sector 02 having the highest median value and a distribution farther to the right than the other Area 8 low sectors.The peak loading also significantly increased in areas 3 and 7 (sectors 31, 33, 70, 73) because the ceiling of these sectors was raised from FL230 to FL270.This was partially done to fill the airspace formerly occupied by high sector 74.This change may result in occasional over-loading of sector 73, which remains the highest loaded low sector with a peak load of 93%.
+99-percentileA similar analysis is done for the high sectors (FL240 -approx.FL320) in Fig. 20.Most of the changes done to the high sectors involve those in the eastern portion of the center, and most resulted in lower peak loading values.The most notable change involves sectors 36 and 37, which show significantly lower peak loading value as a result of their floors being raised from FL240 to FL280.Sector 57 is the only sector showing a minor increase in peak loading because its ceiling was raised from FL310 to FL320.Again, note    that the vacant space in Fig. 20(b), which is the result of sector 74 being decommissioned, has been filled by raising the ceilings of low sectors 70 and 73, and lowering the floor of super-high sector 77.Lastly, the peak loading results for the old and new super-high sectors (approx.FL320 and above) are shown in Fig. 21.The difference between the old and the new design is stark.The peak loading of the old sector 45 was easily the highest with a value of 100%.After being split into 45 and 46, the peak loads are greatly reduced.Similarly, sector 59, which showed the second highest peak loading of 89% is split in the new design, and the new sector 58 has a peak load of only 33%.Other than sector 77, which exhibits a slightly higher peak loading due to having its floor lowered from FL290 to FL280, the other sectors remain unchanged.
+V. ConclusionsAn analysis of the Cleveland Air Route Traffic Control Center is detailed in this paper.A sector loading metric is defined and used to compare the new design to the old one.In order to analyze some of the more nuanced contributors to controller workload, selected workload factors are used to highlight the differences between the two designs.Also, a measure of peak sector loading is used to evaluate the performance of the sector design changes on a center-wide low, high, and super-high sector basis.The majority of the changes address imbalances among sectors that are either under-or over-loaded.Changes like those in areas 5 and 8, where multiple sectors are combined to reduce under-utilization, demonstrate a predictable increase in loading as well as other workload factors.However, in none of these situations does the data suggest that the new sectors would be over-loaded or unmanageable with current-day traffic patterns.Nevertheless, the changes made to sectors 02 and 73 resulted in a significant increase in loading,Figure 1 .1Figure 1.Example Sector Loading distribution.
+Figure 2 .2Figure 2. Flights near horizontal sector boundaries.
+Figure 3 .Figure 4 .34Figure 3. Area 4 Sectors.
+Figure 5 .5Figure 5. Peak 15-minute counts of short term conflicts in Area 4.
+Figure 6 .6Figure 6.Average number of flights near horizontal sector boundaries for Area 4. Each bar's height is the average of the the approx.2000 maximum values-one from each 15-minute interval.
+Figure 7 .7Figure 7. Area 5 Low Sectors FL230 and below.TRACON boundaries are not shown.
+Figure 8 .8Figure 8. Sector Loading distribution for Area 5 Low Sectors.Note that sector names and MAP values in the new design are estimated based on SME input.
+Figure 9 .Figure 10 .910Figure 9. Average number of flights near horizontal sector boundaries for Area 5 Low.Each bar's height is the average of the the approx.2000 maximum values-one from each 15-minute interval.
+Figure 11 .11Figure 11.Area 5 High Sectors.
+Figure 12 .12Figure 12.Sector Loading distribution for Area 5 High Sectors.
+Figure 13 .13Figure 13.Average number of vertical sector boundary crossings for Area 5 High.Each bar's height is the average of the the approx.2000 maximum values-one from each 15-minute interval.
+Figure 14 .14Figure 14.Area 6 Sectors.
+Figure 15 .15Figure 15.Sector Loading distribution for Area 6 High Sectors.
+Figure 16 .16Figure 16.Area 8 Low Sectors FL230 and below.
+Figure 17 .17Figure 17.Sector Loading distribution for Area 8 Low Sectors.
+Figure 18 .18Figure 18.Sector Loading distribution for sector 73 showing the 99-percentile "peak" sector loading value of 78.6%.
+Figure 19 .19Figure 19.Peak sector loading of all Cleveland Center Low Sectors (FL230 and below).
+Figure 20 .20Figure 20.Peak sector loading of all Cleveland Center High Sectors (FL240 -approx.FL320).
+Figure 21 .21Figure 21.Peak sector loading of all Cleveland Center Super-high Sectors (approx.FL320 and above).
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 18, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5538
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 18, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5538
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 18, 2013 | http://arc.aiaa.org| DOI:
+			10.2514/6.2012-5538
+			of 15 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 18, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5538
+			of
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 18, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5538
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTRE on April 18, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2012-5538
+		
+		
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+			
+AcknowledgmentsWe would like to thank the Cleveland Center airspace design team consisting of Connie Atlagovich, Eric Gaines, Brian Hanlon, Doug Odell, Craig Pass, Mike Ruples, and Ron Wood for providing us with the data required for this analysis and for answering our questions about airspace design.We are also grateful to Mark Evans (Dell) for his assistance with this analysis.
+			
+
+			
+which may result in future traffic congestion in those regions.Conversely, changes made to sectors 45 and 59 resulted in a welcomed reduction in traffic volume on a per-sector basis, as well as a better balance of workload within the area of specialization.			
+			
+
+				
+
+
+	
+		Federal Aviation Administration
+		10.4135/9781544377230.n127
+	
+	
+		Federal Regulatory Guide
+		
+			CQ Press
+			2012
+			
+		
+	
+	Tech. rep
+	"Order JO 7210.3X Facility Operation and Administration," Tech. rep., Federal Aviation Administration, 2012.
+
+
+
+	
+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
+		
+			BanavarSridhar
+		
+		
+			ShonRGrabbe
+		
+		
+			GanoBChatterji
+		
+		
+			KapilSSheth
+		
+		10.2514/atcq.9.1.1
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			9
+			1
+			
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Bilimoria, K., Sridhar, B., Chatterji, G., Sheth, K. S., and Grabbe, S., "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, Vol. 9, No. 1, 2001, pp. 1-20.
+
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+
+	
+		Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis
+		
+			PKopardekar
+		
+		
+			ASchwartz
+		
+		
+			SMagyarits
+		
+		
+			JRhodes
+		
+	
+	
+		th USA-Europe ATM R&D Seminar
+		Barcelona, Spain
+		
+			July 2007
+		
+	
+	Kopardekar, P., Schwartz, A., Magyarits, S., and Rhodes, J., "Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis," 7 th USA-Europe ATM R&D Seminar , Barcelona, Spain, July 2007.
+
+
+				
+			
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+I. IntroductionI n 2011, Cleveland Air Route Control Center (ZOB) redesigned significant portions of its airspace to address traffic imbalances among sectors.The two primary factors responsible for these traffic pattern changes were the de-hubbing of Pittsburgh International Airport by US Airways in 2004, and the transition from turbo-prop to regional jet aircraft, which fly at higher altitudes.A team of Cleveland Center staff modified their airspace design to conform with the new traffic patterns, and an analysis of these changes was previously performed and documented in Ref. 1.In the meantime, starting in December 2007 significant operational changes within the neighboring New York Air Route Control Center (ZNY) were under way.Included in these changes are the re-routing of arrivals and departures to and from airports in the New York and Philadelphia metropolitan areas.This airspace and procedural redesign is motivated in part by the fact that one third of all US flights are directly affected by delays in this region. 2The changes to routes and procedures are expected to reduce the number of delays locally and nationally by expediting arrivals and departures at airports in those areas.As of May 2012, two of the four stages of redesign have been implemented in ZNY. 2 Because a significant portion of traffic within Cleveland Center consists of en route flights to and from New York Center, a new set of jet routes has been proposed for this traffic that connect with the new routes in New York Center.These routes (known as Q-routes) are being phased in to replace the existing routes (known as J-routes).With so much of the traffic in Cleveland Center (particularly in the eastern portion of the center) being rerouted, it is natural to expect that controller workload will be affected in those sectors where the reroutes have occurred.The existing sectors have been designed with the existing route structure in mind, and the new routes may alter traffic volume distribution that increases workload in some sectors while decreasing it in others, resulting in an undesirable imbalance in sector loads within an area of specialization (AoS).Furthermore, if new routes are located closer to sector boundaries, it is natural to assume that controller workload of the sectors that share those boundaries will increase due to an increase in point-outs and handoffs of aircraft on routes that encroach on those boundaries.With this in mind, a team of Cleveland Center staff and controllers proposed a new candidate design for the high and super-high sectors with most of the changes concentrated in areas where the majority of route changes exist.Thus, this paper compares three simulation scenarios: a baseline scenario consisting of the existing routes and existing sectors, a scenario consisting of the new routes with the existing sectors, and a scenario with the new routes and the new sectors.The first two scenarios are compared to determine the extent and location of the impact the new route structure has on the sector performance.Then, the third scenario is compared to the first in order to evaluate the overall effect of the route and proposed sector redesigns.This paper is organized as follows: Section II highlights the major route changes proposed for Cleveland Center.The new sector design, which is proposed as a response to the new routes, is also presented.Section III details the method used for the airspace analysis.This includes a description of the traffic data used, as well as the metrics used to evaluate the performance of the three scenarios.Section IV begins with a comparison of the second scenario (new routes with existing sectors) to the baseline scenario, then proceeds to an analysis of how the new routes perform with the new sectors.Section V presents concluding remarks on the analysis and results.
+II. Cleveland Center Route and Sector Changes
+A. Route ChangesAs stated, several of the routes through Cleveland Center were redesigned to connect with the new routes in New York Center, which have been changed to improve efficiency, and reduce delay of the nation's most congested airspace.The new routes primarily affect arrivals and departures in and out of the major airports of the New York Metroplex (JFK, EWR, LGA, PHL, TEB), with some departures from Toronto (YYZ) affected as well.Examples of simulated flight data under the existing and new flight routes are shown in Fig. 1 where only flights into and out of these airports are shown from several 16-hour segments of selected days.The volume of the traffic has not changed, but note how the flights in the new route design are more distinctly and precisely segregated into lanes according to their origin and destination airports rather than being concentrated in the center of the region.The existing super-high sectors are shown in Fig. 1, and from this snapshot of traffic it is clear how the distribution of traffic is drastically changed within the sectors.Not only has the volume of flights within individual sectors been altered, the location of major flows has also changed with several east-west routes in close proximity to horizontal sector boundaries.It is clear from a subjective perspective that these route changes pose potentially negative effects on existing sector workload.
+B. Sector ChangesIn order to address the problems of controlling the new route structure within the old sector design, a team at Cleveland Center proposed a new design for the high and super-high sectors that might better accommodate the new routes.Fig. 2 shows the new sector design in red overlaid on the existing sector design in black.Sectors are classified by their existing status as high or super-high sectors (ultra-high sectors are included with super-highs), but this distinction is not always consistent according to altitude, and is used herein primarily to better organize and visualize the analysis.Low sectors are not included in this analysis since the rerouting involves en route aircraft at flight level (FL) 240 and above.However, this currently-proposed sector design change results in some gaps and mismatches with the existing low sectors.At the time of this analysis a new low sector design had not been completed.Note that most of the changes are in the eastern portion of the center where the majority of route changes occur.Note, also, the extensive redesign of sectors 58, 59, and 64.Sector 64, which currently sits above sectors 68 and 69, is proposed to be relocated above sector 59, while sector 58's ceiling has been lowered to accommodate the change.It is emphasized that this sector design has only been proposed, and at the time of this analysis, has not been finalized.The next section discusses the approach used to evaluate the effect the new routes has on the existing sector design and the performance of this proposed design with the new routes in place.
+III. ApproachThe method used to evaluate Cleveland Center's design changes involves using the performance of the existing airspace under the existing route structure as a baseline (referred herein as Scenario A).First, the performance of the new route structure under the existing sector design (Scenario B) is compared to Scenario A to identify those sectors with the greatest impact due to the new routes.Then, the performance of the new routes under the new versions of those sectors (Scenario C) is evaluated.Finally, a comparison between Scenarios A and C is made to identify those sectors showing the greatest overall impact of the new route structure and the new sector design.For all three scenarios, the Future ATM Concepts Evaluation Tool (FACET) 3 is used to record the number of aircraft in each sector (also referred to as sector count ), as well as several other workload factors for each sector.Since the new routes have yet to be flown, simulated flight tracks are used in the three scenarios.Simulations use historical filed flight plans (as recorded prior to departure), but en route trajectory specifics such as top of descent/ascent are based on prescribed database parameters.En route flight plan updates and controller traffic management are absent, and any existing letters of agreement for local traffic procedures are not enforced.While such simulations lack the fidelity that historical data tracks would provide, this evaluation involves a comparison between the three scenarios, and the data presented herein is not meant to be an accurate absolute measure of real world operations.For Scenarios B and C, which simulate the new routes, an algorithm was introduced in the FACET code that reroutes flights meeting specific origin/destination criteria along the new Q-route waypoints within Cleveland Center.The flight traffic data is selected from 30 days between June and October of 2012.For each day, only traffic between 6 and 10 pm EDT is used, which is when most of the traffic occurs.The selected days are all among the highest traffic volume days (as recorded by Cleveland Center) and are judged to be relatively free of major weather impacts throughout Cleveland Center and nearby airspace, such as that near Chicago, and the major airports along the northern east coast of the US.Weather-related factors are not included in this analysis.
+A. Sector LoadingThe raw traffic data produced by a FACET simulation consists of several workload factors recorded every minute over a 16-hour period for the selected 30 days, resulting in nearly 29,000 data points per workload factor for every sector.The first factor used in this analysis is called sector loading.This factor is derived from the sector count factor and the assumed capacity of each sector for which the published (if available) or estimated Monitor Alert Parameter (MAP) is used. 4The MAP values of the proposed new sectors are assumed herein to remain unchanged, since, at the time of this analysis, the new routes and sectors had yet to be implemented.Sector loading is found by dividing its sector count by its MAP value.Thus, a value of 1 (or 100%) indicates that the sector is at full capacity.The maximum value of the sector count values during every fixed 15-minute interval is determined, reducing the data set to roughly 2000 points per sector.Dividing by the sector's MAP value results in a corresponding set of 2000 sector loading values from the 30 selected days.A typical sector loading distribution plot is shown in Fig. 3.Note that the mean value of this sector is shown as is the MAP threshold line at 100%.To illustrate how this plot can be read, it can be seen, for example, that in roughly 20% of the 2000 data points, this sector has a 15-minute max sector loading of between 40 -50% as indicated by the orange bar .Also, for some small but not insignificant part of the time, the sector is over-loaded because it experiences sector counts exceeding its MAP value.By comparing the sector loading distributions between the three scenarios, quantitative insight can be gained into how the new route structure and proposed sector design affects Cleveland's airspace.For example, sector load distributions that have values beyond 100% loading and/or mean lines to the right of about 45% indicate the sector may be over-loaded.Likewise, load distributions with mean lines to the left of about 25% suggest the sector might be under-loaded.Also, when sector loading distributions are plotted together from multiple sectors within a region or an AoS, similar sector loading distributions are preferred because they indicate that workload is well balanced among those sectors.
+B. Additional Workload FactorsAs mentioned, FACET also records additional flight factors at every 60-second interval for every sector.Some of the factors can be used directly as sector workload factors, while others can be combined to calculate new ones.These factors are:1. Predicted conflicts
+Transitioning FlightsAt every minute in the simulation, FACET records the number of flights climbing faster than 300 ft/min, t c , and the number of flights descending faster than 300 ft/min t d .Rather than evaluate these data separately, two workload factors were calculated from them: the total of climbing and descending aircraft at every minute t cd = t c + t d , and a measure of the ratio of simultaneously climbing and descending sets of flights t simultrns , which is calculated as follows:t simultrns =    0, t c = t d = 0 min(tc,t d )max(tc,t d ) • t cd , otherwise.This workload factor is upper-bounded by t cd .The motivation of this workload factor can be illustrated by example.Given a scenario where a sector has six aircraft climbing and/or descending faster than 300 ft/min (t cd = 6), it is reasonable to assume that if all six were descending aircraft (t simultrns = 0), this would require less workload than if three were climbing and three were descending (t simultrns = 6).
+Flights Near Horizontal Sector BoundariesFigure 4 shows eight flights within and one flight outside of a notional sector.Flights within 4 nmi on either side of the sector's boundaries are counted as boundary flights.In this case, there are four boundary flights at this instant in time.This factor was used as a simple proxy for handoffs and point-outs.Note that this metric does not count flights near vertical boundaries, and that some flights counted as near horizontal boundaries may not actually be point-outs or boundary crossings.
+±4 nmiFigure 4: Flights near horizontal sector boundaries.
+Vertical Sector Boundary CrossingsThis metric counts flights that have entered or exited each sector during the last minute via the floor or ceiling of the sector as a vertical boundary crossing for that sector.The above factors are evaluated in the same way as the sector loading metric discussed above.Distributions of max 15-minute values are found and compared between the three scenarios.While over fifty factors have been suggested in the literature (see Ref. 5 for example), sector counts/loading, short term conflicts, transitioning flights, and handoffs/point-outs were identified by subject matter experts (SMEs) as being among the most critical.The last two factors, flights near horizontal sector boundaries, and vertical sector boundary crossings, are used as a proxy approximation for handoffs/point-outs.As will be discussed below, only two of these factors, sector loading and flights near horizontal sector boundaries, were shown to be significantly affected by the Cleveland Center route changes.
+C. Scenario Workload Factor DifferenceFrom the simulations, each of the six workload factors discussed above can be compared and contrasted between the three scenarios in several ways.The difference between the means and medians of the factors, for example, is a reasonable method.However, the shape of the factor distributions may change greatly with little effect on median and mean, so differences between those values will miss such changes.Also, the range of the recorded factors can vary greatly between sectors, scenarios and the factors themselves.Therefore, it it desired to derive a common method of measuring the difference between the workload factor distributions from one scenario to another.Noting that the sum of the individual distribution values (area under the curve) of the distribution plots (see Fig. 3) must equal one, it is proposed that a good method for measuring the change in a workload factor from one scenario to another is to compute the sum of the absolute value of the difference of the distribution entries along the range of the factor's values.The difference in sector loading between Scenarios A and B, for instance, is computed as:SL A-B = n i |sl A i -sl B i |.(1)Here, i is the index of distribution values along the range of the workload factor (up to n values), and sl A i is the fraction of occurrences of sector loading values of the i th entry of the distribution plot for Scenario A. For example, referring to Fig. 3, n = 14 because it has a total of 14 distribution data points, and the orange bar (the sixth data point) shows that sl A 6 = 0.22.SinceIn the following section, this metric is used to identify the factors, and then the sectors, for which the new routes would have the greatest effect on the existing sectors.It is used again later in the analysis to identify those sectors with the greatest performance change with the new route and sector design.
+IV. ResultsThis analysis is separated into two parts.The first part identifies the workload factors and the sectors most affected by the new routes by comparing Scenario B to Scenario A. Once identified, the performance of the new design with the new routes (Scenario C) is investigated.The second part of the analysis involves identifying those sectors most affected overall by both the new design and new routes by comparing Scenario C to Scenario A.
+A. Existing Sectors Affected by New RoutesHere, the performance of the new routes flying through the existing sectors (Scenario B) is compared to the baseline of existing routes flying in existing sectors (Scenario A).The appropriate workload factors will indicate the sectors in greatest need of redesign to better accommodate the new routes.The difference metric developed above is used to identify (1) those workload factors most affected by the route changes, and (2) those sectors most affected in terms of those workload factors.Once identified, a deeper analysis is presented of how those sectors were affected (positively or negatively) by the new routes, and then how their new design accommodates the new routes (Scenario C).Of all the workload factors discussed above, and subjectively considering the route changes depicted in Fig. 1, it is reasonable to assume that the sector loading (SL for short) workload factor might exhibit the greatest difference between Scenarios A and B. Note that the total traffic volume through the entire center will remain unchanged since the same set of historical flights are simulated in all three scenarios.However, the spatial and temporal distribution of those flights will differ between Scenarios A and B. It is clear from Fig. 1 that fewer flights are concentrated in the middle of Cleveland Center, with a greater number of flights being spread northward and southward, and segregated into distinct lanes.Furthermore, the appearance of new traffic lanes in the new route design suggests that there could be an increase in the number of flights near horizontal sector boundaries (HB for short).Thus, from a qualitative perspective, one would expect that SL and HB would show the greatest contrast between Scenarios A and B.  The distribution difference formula of ( 1) is applied to all six workload factors and all high and super-high sectors.Fig. 5 shows the magnitudes of the results (indicated by circle size and color), and confirms the hypothesis that sector loading and flights near horizontal sector boundaries are the two workload factors affected the most by the route changes.A comprehensive analysis on all the sectors and all workload factors was performed, however Fig. 5 shows that several super-high sectors stand out as being significantly affected by the new route design in terms of the SL and HB.The analysis for a selection of these sectors is presented in the following sections, and though all workload factors were analyzed in this work, since the SL and HB factors showed the greatest distinction between scenarios, the discussion will focus on them.
+Sector 59Fig. 5 shows that Sector 59 is greatly affected by the new route design in terms of SL and HB.A look at the 15-minute peak SL and HB workload distributions in Fig. 6 shows how drastically traffic changed in this sector.Comparing the baseline Scenario A curves (shown in black) to the Scenario B curves (shown in dashed blue) it is clear that the new routes substantially reduce traffic loads in this sector, while substantially increasing the number of flights near horizontal sector boundaries.This is obvious when the flight tracks of New York Metroplex arrivals and departure are compared in Fig. 7.Note that while there is less traffic in Fig. 7b, there is a route on the northern boundary that remains within ±4-nmi of the boundary for the majority of the sector's length.This is not an ideal arrangement, so it is reasonable to expect that this boundary should be moved away from this route and made to be parallel to the routes in that region.Fig. 7c shows the proposed design for Sector 59.It has been moved to the north, and its floor and ceiling altitudes have been changed to FL 350 and above.Note that the northern and southern boundaries are also parallel to the surrounding flows, to keep a consistent distance between routes and boundaries.Fig. 6 shows that the new design with the new routes (depicted in solid red) results in a mean SL and HB that is lower than even in Scenario A. Finally, it is worth noting that Sector 59 also benefited from the greatest reduction in predicted conflicts.It is interesting to note that Sector 59 is one of most altered sectors compared to its original design.
+Sector 49Sector 49 displayed the second greatest difference in sector loading with the new routes.Fig. 8 shows that this sector benefited from the new routes in terms of reducing its SL as well as HB, though the latter factor was probably reduced primarily due to a reduction in overall sector volume.As with Sector 59, the new routes pose an issue on the northern boundary with an east-west route skirting that edge.This is addressed in the new design by moving the boundary to the south and aligning it with the major flows.See Figs.9b and9c.The result is a drastic drop in HB as well as in SL, as shown by the red distribution lines in Fig. 8.
+B. Sectors Affected by New Routes and New DesignIn this part of the analysis, the sectors affected the most overall by the new routes and the new design are identified.Again, the difference metrics for the SL and HB factors are used, but this time they are calculated between the existing routes and sectors (Scenario A) and the new routes with proposed sectors (Scenario C).Several sectors once again stand out as having significant changes in performance, and some of the sectors that were not identified in the previous subsection of having been greatly affected by the new routes are shown here among the most affected sectors overall.The top two sectors as shown in Fig. 12 with the greatest difference in sector loading are sectors 57 and 59.Sector 59 was analyzed in the previous section, and sectors 57, 58, and 59 share the same footprint (differing only by altitude) in both the existing and the proposed designs.Therefore, it is not surprising that both the SL and HB factors trend similarly downward in Sector 57 from Scenario to Scenario C as Sector 59 does.
+Sector 69Sector 69 shows up third from the top in Fig. 12, and it is the only sector in the top tier of this plot that exhibits an increase in sector loading mean, as shown in Fig. 14a.With barely any change from Scenario A to Scenario B in both these factors, the increase observed in Scenario C comes from the increase in altitude range of the sector.Formerly spanning the FL 330-379 strata, the proposed new design spans FL 340 and above, filling half the volume of what used to be Sector 64 (moved to the north in the proposed design).An increase in sector loading is not always a bad thing if the sector was formerly underutilized.Previous work with SMEs indicated that an underutilized sector can lead to inattention. 6However, the SL distribution of Scenario C exhibits a significantly thick tail in the 55-85% range, suggesting that this proposed sector design may be lead to excessive volume and number of over-MAP conditions.
+Sector 38From the HB difference plot shown in Fig. 13, Sector 38 is shown to have the greatest change between Scenarios A and C. The change in sector loading is minor.Looking at the HB distribution comparison in Fig. 16 reveals a major increase in this metric, and Fig. 17c reveals why.Sector 38 shares its southern boundary with Sectors 77 and 79, and this boundary (as discussed in the Sector 79 section above) is crossed twice by a major flow.In fact, the mean of this metric is the highest of all the sectors in this study, suggesting that this boundary be redesigned.
+V. ConclusionsAn analysis of proposed route and sector changes in Cleveland Air Route Traffic Control Center was detailed in this paper.The analysis was carried out by studying the simulated sector performance of six workload factors using three scenarios: the existing sector design with existing routes (baseline), the existing sectors with the new routes, and the proposed sector design with the new routes.A difference metric was derived to measure how much each workload factor distribution changed between the scenarios for each sector.First, this metric was used to determine that when the new routes were flown through the existing sectors, among the six workload factors, the distributions for sector loading (SL) and the number of flights near horizontal sector boundaries (HB) showed the most significant difference.Next, the metric was used to identify those sectors most significantly affected in terms of SL and HB, and a detailed analysis of several of those sectors was presented.Among those existing sectors that showed the greatest impact by the new routes, several were impacted in arguably negative way, but most of those issues were resolved in the newly proposed sector designs (e.g.Sector 59).However, when a comparison between the baseline scenario and the new routes in the new sectors was made, some candidate sector designs stood out as potentially problematic with increased SL (e.g.Sector 69), or an increase in HB due to routes that cross boundaries multiple times (e.g.Sector 38).Admittedly, there are several operation details that this analysis may have neglected that might explain these design choices.Nevertheless, this finding may motivate a modification of the proposed design, which could then be evaluated using the methodology presented in this paper.Figure 1 :1Figure 1: Route changes for en route New York Metroplex flights.Existing super-high sectors shown.
+Figure 2 :2Figure 2: Existing (black) and proposed (red) sector designs.Note floor and ceiling changes for some sectors identified by black and red colored font.
+Figure 3 :3Figure 3: Example sector loading distribution.
+2. Transitioning flights (a) Total number of flights climbing faster than 300 ft/min.(b) Total number of flights descending faster than 300 ft/min.3. Flights near horizontal sector boundaries 4. Vertical sector boundary crossings 1. Predicted Conflicts Predicted conflicts are estimated by simulating every flight forward in time along its filed flight plan 3D trajectory in 15-second intervals for 15 minutes into the future.A predicted conflict is counted for the sector if any pair of these flight projections originating from the sector results in the aircraft flying within 8 nmi horizontally and 1000 feet vertically of each other.
+Figure 5 :5Figure 5: Distribution difference calculated for all sectors and workload factors between Scenarios A and B.
+= 0.395 B: mean = 0.316 C: mean = 0.313 (a) 15-minute peak sector loading distributions for Sector 59.
+15-minute peak flights near horizontal sector boundaries distributions for Sector 59.
+Figure 6 :6Figure 6: Workload distributions for Scenarios A, B, and C for Sector 59.
+Figure 7 :7Figure 7: Existing (black) and proposed (red) Sector 59 with existing (blue) and new (green) routes.
+Figure 12 :12Figure 12: Sector loading distribution difference SL A-C for Scenarios A and C.
+Figure 13 :13Figure 13: Number of flights near horizontal sector boundaries distribution difference HB A-C for Scenarios A and C.
+15-minute peak sector loading distributions for Sector 69.
+= 2.974 B: mean = 3.044 C: mean = 3.483 (b) 15-minute peak flights near horizontal sector boundaries distributions for Sector 69.
+Figure 14 :14Figure 14: Workload distributions for Scenarios A, B, and C for Sector 69.
+Figure 15 :15Figure 15: Existing (black) and proposed (red) Sector 69 with existing (blue) and new (green) routes.
+Scenario A: Existing Sector 64 with existing routes.FL = 330-379.ZOB 64 (b) Scenario B: Existing Sector 64 with new routes.FL = 330-379.ZOB 64 (c) Scenario C: Proposed Sector 64 with new routes.FL = 340-999.
+Figure 19 :19Figure 19: Existing (black) and proposed (red) Sector 64 with existing (blue) and new (green) routes.
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			n i f i = 1 for any workload factor f , this difference metric is in the range of [0, 2].6 of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
+			of
+			American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337Thismaterial is declared a work of the U.S. and is not subject to copyright protection in the United States.
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+American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on August 14, 2013 | http://arc.aiaa.org| DOI: 10.2514/6.2013-4337This material is declared a work of the U.S. and is not subject to copyright protection in the United States.
+AcknowledgmentsThe authors would like to thank the Cleveland Center airspace design team consisting of Connie Atlagovich, Eric Gaines, Brian Hanlon, Doug Odell, Craig Pass, Mike Ruples, and Ron Wood for providing us with the data required for this analysis and for answering our questions about airspace design.
+			
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+			
+This material is declared a work of the U.S. and is not subject to copyright protection in the United States.
+Sector 79In terms of the HB workload factor, Sector 79 experiences the greatest change with the new routes.This is clearly shown in Fig. 10b, where the distribution (and mean) are shifted significantly to the right.The reason for the higher occurrence of flights near horizontal sector boundaries can be seen in Fig. 11b where the southern boundary is crossed at a very shallow angle by the new PHL arrival route.This issue is addressed in the new sector design shown in Fig. 11c, however; this design introduces a new problem with the northern boundary, which is crossed twice by a new route of Chicago-bound flights.Thus, HB remains as high as prior to the sector redesign as shown in Fig. 10b.One benefit of the new design is a reduction in sector loadingespecially in over-MAP loads which can be observed on the tail of the SL distribution of Fig. 10a.It should be mentioned that Sector 77 (third highest in HB A-B in Fig. 5), which is the high sector sitting directly below 79 with the same footprint, has nearly identical performance results between all three scenarios.
+Sector 64This section is concluded with a look at Sector 64, which, like Sector 38, also shows a significant change in HB distribution from Scenario A to C that results in an increase in mean HB.See Fig. 18.The proposed design significantly changes the sector, moving it north so that it sits vertically above Sector 58.However, even though the change is significant, the mean and distribution is still low when compared to most of the other sectors.Looking at the major flows in Fig. 19c doesn not reveal any obvious potential issues for this design.			
+			
+
+				
+
+
+	
+		Cleveland Center Airspace Redesign Analysis
+		
+			MDrew
+		
+		
+			MBloem
+		
+		
+			KDBilimoria
+		
+	
+	
+		12 th AIAA Aviation Technology Integration and Operations Conferrence
+		Indianapolis, IN
+		
+			September 2012
+			
+		
+	
+	Drew, M., Bloem, M., and Bilimoria, K. D., "Cleveland Center Airspace Redesign Analysis," 12 th AIAA Aviation Tech- nology Integration and Operations Conferrence, Indianapolis, IN, 17 -19 September 2012.
+
+
+
+	
+		/PHL) Metropolitan Area Airspace Redesign
+		
+			RNovia
+		
+		
+	
+	
+		Congressional Update Presentation
+		New York/New Jersey/Philadelphia (NY/NJ
+		
+			16 March 2012
+		
+	
+	Novia, R., "New York/New Jersey/Philadelphia (NY/NJ/PHL) Metropolitan Area Airspace Redesign," Congressional Update Presentation: http://www.faa.gov/air_traffic/nas_redesign/regional_guidance/eastern_reg/nynjphl_redesign/ briefings/media/20120316.pdf, FAA, 16 March 2012.
+
+
+
+	
+		FACET: Future ATM Concepts Evaluation Tool
+		
+			KarlDBilimoria
+		
+		
+			BanavarSridhar
+		
+		
+			ShonRGrabbe
+		
+		
+			GanoBChatterji
+		
+		
+			KapilSSheth
+		
+		10.2514/atcq.9.1.1
+	
+	
+		Air Traffic Control Quarterly
+		Air Traffic Control Quarterly
+		1064-3818
+		2472-5757
+		
+			9
+			1
+			
+			2001
+			American Institute of Aeronautics and Astronautics (AIAA)
+		
+	
+	Bilimoria, K., Sridhar, B., Chatterji, G., Sheth, K. S., and Grabbe, S., "FACET: Future ATM Concepts Evaluation Tool," Air Traffic Control Quarterly, Vol. 9, No. 1, 2001, pp. 1-20.
+
+
+
+	
+		Federal Aviation Administration
+		10.4135/9781544377230.n127
+	
+	
+		Federal Regulatory Guide
+		
+			CQ Press
+			2012
+			
+		
+	
+	Tech. rep
+	"Order JO 7210.3X Facility Operation and Administration," Tech. rep., Federal Aviation Administration, 2012.
+
+
+
+	
+		Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis
+		
+			PKopardekar
+		
+		
+			ASchwartz
+		
+		
+			SMagyarits
+		
+		
+			JRhodes
+		
+	
+	
+		th USA-Europe ATM R&D Seminar
+		Barcelona, Spain
+		
+			July 2007
+		
+	
+	Kopardekar, P., Schwartz, A., Magyarits, S., and Rhodes, J., "Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis," 7 th USA-Europe ATM R&D Seminar , Barcelona, Spain, July 2007.
+
+
+
+	
+		Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations
+		
+			MichaelBloem
+		
+		
+			MichaelDrew
+		
+		
+			ChokFungLai
+		
+		
+			KarlBilimoria
+		
+		10.2514/6.2012-5592
+	
+	
+		12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
+		Indianapolis, IN
+		
+			American Institute of Aeronautics and Astronautics
+			September 2012
+			
+		
+	
+	12 th AIAA Aviation Technology Integration and Operations Conferrence
+	Bloem, M., Drew, M., Lai, C. F., and Bilimoria, K. D., "Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations," 12 th AIAA Aviation Technology Integration and Operations Conferrence, Indianapolis, IN, 17 - 19 September 2012.
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+I. IntroductionT raditional methods of characterizing air traffic in the National Air Space (NAS) have relied on mea- surements such as number of operations, flight counts, delay (total, average, and peak), airspace demand, etc.These data are useful for understanding and analyzing the performance of the national air transportation system, and are the basis for the majority of the work in the air traffic management field.Several methods of modeling, predicting, and optimizing the performance of the NAS using these metrics have been proposed.For example, Sridhar et al. 1 construct a linear time-varying model of aggregate traffic flow using flight counts as the state variable.An autoregressive model for predicting sector demand is presented by Chen and Sridhar in Ref. 2 that uses historical data and takes convective weather conditions into account.There has been less work in applying frequency analysis techniques to air traffic data, though related spectral analysis techniques that operate on the stochastic properties of the traffic have been successfully used to model and predict both air and automotive traffic.In Ref. 3, for example, a spectral analysis on airport performance is conducted based on histograms of arrival rates.The authors found that the approach was useful for graphically distinguishing the operational differences between airports.In Ref. 4, a spectral analysis technique of using historical traffic flow covariance matrix modal functions is used to forecast automotive traffic.Results were found to be comparable to other prediction techniques based on time series models.In this paper, the authors have begun to investigate the use of the Discrete Fourier Transform (DFT) on airspace traffic count data.The DFT is used to characterize various NAS airspace regions, and to detect disturbances in the those regions that are not as apparent in the time domain.Nominal traffic in several Air Route Traffic Control Centers (ARTCCs) is characterized by applying the DFT to simulated traffic data.Simulations of various delay-causing Traffic Management Initiatives (TMIs) such as playbook routes, metering, and Ground Delay Programs are also studied in the frequency domain using the DFT.Finally, the DFT is used to search for those TMI frequency domain signatures in the historical data.This paper is organized as follows: In Section II the mathematical background to the DFT is presented, and its method of application is described.The results of the analysis are shown in Section III, which is divided into three parts.The first part presents a characterization of some ARTCCs using the DFT on simulated data.The second part demonstrates the use of the DFT for characterizing three TMIs, and the third part shows the results of applying the DFT to historical flight data.Conclusions and future work are presented in Section IV.
+II. Background
+A. The Discrete Fourier TransformThis section begins with a brief overview of the Fourier transform, which transforms a signal from the time domain into the frequency domain.It is based on the idea that a signal in time can be decomposed into a sum of sines and cosines over an infinite range of frequencies.The resulting plot of the Fourier transform is the amplitude of the sinusoids at those frequencies.As an example, consider the signal shown in Fig. 1a, which is made up of 3 sinusoids of different frequency and amplitude.If a sample of this signal is long enough to be assumed infinite, the plot of the Fourier transform yields 3 distinct spikes shown in Fig. 1b at 10, 100, and 250 Hz with amplitudes 4, 2, and 3 respectively.Thus, the signal has been transformed into the frequency domain and no information has been lost.In fact, given the signal of Fig. 1b, the time series signal can be reconstructed using what is known as the Fourier transform synthesis equation.x[n] = 1 N N -1 k=0 X[k] • e i2πkn/N ,(1)where the complex X[k] terms are the Fourier series coefficients.Equation ( 1) is known as the DFT synthesis equation, or the inverse DFT equation.One way to interpret this result is that any N -length discrete sampled time series can be thought of as the sum of N/2 + 1 sines and cosines at N/2 + 1 discrete frequencies ranging from 0 to F s /2, where F s is the sampling frequency.The amplitudes of the sines and cosines are given by the X[k] terms, which come from the DFT analysis equation:X[k] = N -1 n=0 x[n] • e -i2πkn/N .(2)As with the continuous time Fourier transform, once the signal has been transformed into the frequency domain, (using Eq. ( 2)), it can be completely reconstructed using Eq.(1) despite the DFT having only a finite N/2 + 1 number of frequency points.In practice, the Fast Fourier Transform is used to efficiently calculate equation (2), and in this paper MATLAB's 'fft' function 5 is used to generate the results.Fourier transforms (specifically the DFT) show up in countless applications in science and engineering.Much has been written about their theory and application.See Refs.6 and 7 for more details.
+B. Window FunctionsImplicit in the mathematics of the DFT is the assumption that the time series signal x[n] is infinite and periodic-that is, the signal repeats itself from negative infinity to positive infinity.This has to be the case because sines and cosines are defined as extending from negative to positive infinity, thus x[n] in (1) is not finite.Furthermore, x[n] must be periodic since an infinite number of sinusoids are required to synthesize an aperiodic signal, and the DFT uses only a finite number of frequencies.Since real-world signals are not infinite, and most are not periodic, the DFT is calculated with the assumption that values of x[n] for n < 0 and n > N -1 are all zero.This process is known as windowing because it is analogous to filtering out all samples of an imaginary infinite and periodic signal except for those in 0 ≤ n ≤ N -1.When calculating the DFT of a finite aperiodic signal using the Fast Fourier Transform (FFT) algorithm, this simple type of window function, known as a rectangular window, is implicitly applied.Unfortunately, for reasons beyond the scope of this paper, windowing a signal produces artificial anomalies in the DFT plot.There are alternative methods of windowing, however, that trade off some negative characteristics of those anomalies.The proper choice of a windowing function is critical and varies depending on application.In order to determine the appropriate windowing function for this type of data, a single air traffic simulation was generated that contained a known frequency signature in an Air Route Traffic Control Center (ARTCC, or Center).(This specific simulation will be referenced later in the paper with more details.)Using one day's recorded center counts from this simulation, and applying four popular windowing functions to the data prior to computing the DFT, produces the results shown in Fig. 2. Here, and in the remainder of this paper, DFT amplitude is plotted against period instead of frequency because it is more intuitive in this application.Recall that period T is simply the inverse of frequency f , and since the count data is sampled at 1-minute intervals, the resulting period is in minutes.The simulation run in Fig. 2 is known to have periodic content at approximately 13.4 minutes, and all four plots indicate this.The smaller spikes to the left of 13.4 minutes are the harmonics of this fundamental periodic content.Periodic signals often produce a spike at their fundamental frequency f f along with smaller spikes at multiples of that frequency 2f f , 3f f , etc. (In terms of fundamental period, these spikes are shown at T f /2, T f /3, etc.)While the rectangular window results shown in Fig. 2a preserves the DFT amplitude the best, it also contains a lot of artificial noise-especially near the fundamental frequency spike at 13.4 minutes.Using this window, it might be difficult to notice important periodic events in the DFT plots.The Blackman-Nuttall window shown in Fig. 2d smooths out the most noise of all four windows, but it also severely reduces the amplitude of the fundamental frequency.The Hanning and Hamming windows shown in Figs.2b and2c are very similar in their formulations and results.Throughout the remainder of this paper, the Hamming windowing function will be used to condition the time series count data prior to calculating the DFT.This window function is one of the most commonly used windows in signal processing, and strikes an acceptable balance between noise reduction and amplitude attenuation for this research.For more information on window functions, see Refs.6, 7, and 8.
+III. ResultsThe results of this analysis are divided into three parts.In the first subsection, the DFT is applied to selected centers in an effort to characterize their nominal traffic.In the second subsection, various traffic management initiatives (TMIs) are applied in simulation in order to identify their DFT signature.Finally, in the third subsection historical data are analyzed from the DFT perspective in an attempt to identify events in the NAS that would otherwise be overlooked from the raw time series data.
+A. DFT for Characterizing Center TrafficThe DFT can be calculated for any element in the NAS that can produce a time series of air traffic counts.Examples of such time series include center or sector counts versus time, and airport departures versus time.This work begins by selecting 30 weekdays of traffic from 2012 with the lowest amount of NASwide delay in minutes.Using this data set, simulations of nominal traffic are run using the Future ATM Concepts Evaluation Tool (FACET). 9Herein, nominal traffic indicates that only the filed flight plan is used to simulate the behavior of each flight.This is as opposed to simulations where additional controller actions have been simulated, or historical flight data, which is updated according to actual radar-track-recorded aircraft positions.In Fig. 3a, the air traffic counts in Cleveland Center (ZOB) for each of the 30 days are plotted for 24 hours, sampled in 1-minute intervals.The blue line shows the mean of these counts.Treating each of the 30 days of count data as a time series signal, the DFT is computed (after the Hamming window of Fig. 2c is applied) and the results are shown in Fig. 3b.Again, the mean of these DFT results are shown in blue.Fig. 3b shows that, as expected, the dominant periodic content of the counts data occurs at 1440 minutes.This is the 24-hour cyclical behavior of traffic, and the DFT of any daily traffic data signal would exhibit this characteristic.More interesting information may be found by looking at the higher frequency (periodicity of less than 240 minutes) events that exist closer to the origin of Fig. 3b.For the remainder of this paper, the DFT plots will focus on this higher frequency range because it is expected that disruptions in traffic flow will be exhibited here.Also, while the periodic 24-hour cyclical nature of traffic is known, traffic events of lower periodicity are not as well understood or cataloged.Of the 30 days represented in Fig. 3, the center count data of December 6, 2012 is closest to the mean counts (that is, the sum of the absolute error from the mean counts is the least), and thus the most similar to a "typical" low-delay delay.In Fig. 4 the center counts and DFT plots are shown for this date in black.Note that there is a spike at approximately 110 minutes in the DFT plot (Fig. 4b), indicating that a significant event exists in the data with that periodicity.Suspecting that this might be caused by flights originating from and heading towards Detroit Metropolitan Wayne County Airport (DTW), one of the major airports supplying traffic to Cleveland Center, these flights were subtracted from the center counts, and the DFT was recomputed.The remaining center counts and DFT results are plotted in blue in Figs.4a and4b.The DTW flight counts and DFT results are shown in maroon.It is clear from Fig. 4b that the spike at 110 minutes is primarily due to the DTW flights.4a from the solid black line, all that can be easily surmised is that the flight volume has been reduced.However, if it is known that flights to and from DTW airport in Cleveland Center have a signature periodicity of approximately 110 minutes, this volume reduction could be identified as being caused by a disruption in DTW airport operations by viewing the data in the frequency domain.A reduction in the DFT plot at approximately 110 minutes would suggest the reduction in center-wide traffic volume as a reduction in DTW flights.Generally, air traffic count data in a center (or sector) is the result of several other contributing entities of the NAS (e.g.specific airports, neighboring air space, specific jet routes, etc.).If those entities exhibit a periodic signature, an operational disruption in them would be hidden in the time series data, but made more obvious in the DFT plot (frequency domain).This is clearly demonstrated in the Cleveland center DFT plot, and several other centers exhibit similar characteristics.For example, most of the spikes in the DFT of Denver Center (ZDV) counts can be attributed to flights to and from Denver International Airport (DEN).This is shown in Fig. 5 where the DFT of only DEN flights in Denver Center for May 14, 2012 (shown in maroon) shadows the DFT plot of total Denver Center counts.May 14 exhibited traffic most similar to the mean traffic in this center.Unfortunately, it is not always clear how to explain the DFT plot in its entirety for all centers.This is partially due to the limited time series sample size in addition to the noise introduced by windowing as discussed above.It is suspected, however, that there are reliable explanations for the source of large spikes that appear in this range in center count DFT plots.Further analysis and interaction with subject matter experts may be needed to identify them.For example, in Fig. 6 when a similar analysis is done with Atalanta Center (ZTL) and Atlanta International Airport (ATL) for January 4, 2012, the major DFT spikes of the total center data (in black) cannot be attributed to ATL traffic (in maroon) despite ATL being a major source of traffic in that center.Again, the traffic of January 4 was most similar to the mean traffic in Atlanta Center.In times of convective weather, or traffic congestion of any sort, traffic management initiatives (TMIs) are implemented to reroute, meter, or delay aircraft.In this subsection, examples of some TMIs are implemented in simulation to observe and catalog their effects in the frequency domain via the DFT.
+B. Catalog of Initiatives
+Playbook ReroutingOne of the common TMIs used by controllers to reroute traffic around convective weather is known as the severe weather avoidance playbook CAN 1 East.This involves rerouting east-bound flights heading toward the northeast airports (e.g., New York Metropolitan, Washington, Philadelphia, and Boston area) north into Minneapolis Center (ZMP) and over the Great Lakes region.The first high altitude sector in ZMP to be affected by the increase in traffic is ZMP20.Thus, choosing the high volume date of July 6, 2012, and implementing the CAN 1 East playbook in simulation in FACET from 16:00 -24:00 UTC, the traffic counts and DFT results for that sector are compared against the baseline nominal traffic data in Fig. 7.As expected, Fig. 7a shows that traffic volume increases substantially with the additional playbook rerouted aircraft.In the frequency domain, this additional stream of aircraft is identified by an increases in the amplitude of the DFT plot between 30 and 50 minutes of period.Though it is not yet understood why this playbook route increases the periodicity of the sector counts within this range, other sectors through which this playbook stream traverses exhibit similar behavior.
+Miles in Trail MeteringIf the CAN 1 East playbook-affected stream of aircraft is metered at the the Rapid City waypoint (RAP), which is upstream of ZMP center, the metered playbook stream of traffic can be detected in the DFT plot.In Fig. 8b it is shown that without any metering, the traffic in ZMP11 shows an increase in amplitude between 35 and 55 minutes of period (similar to ZMP20).As the miles-in-trail (MIT) of separation is increased on this stream, the DFT plot approaches that of the baseline DFT curve.This is because as the playbook traffic stream is spread out in distance, the traffic density of the stream is and thus the increase in single sector counts due to this stream is lessened.In other words, the traffic looks more like the original baseline traffic-both in the time and frequency domains.The curve for 50 MIT does, however show a telltale spike at approximately 6.7 minutes.This corresponds to the distance in time between flights traveling at approximately 450 knots.(The spike at 3.35 minutes for the 25 MIT simulation is harder to detect within the noise.)In fact, the plots shown in Fig. 2 were generated from the DFT of total ZMP20 sector counts with 100 MIT applied to the CAN 1 East flow, producing a spike at approximately 13.4 minutes.
+Ground Delay ProgramsSpikes in the center count DFT plots can often be attributed to traffic associated with a major airport in the center.With this in mind, simulations were carried out to determine if Ground Delay Programs (GDPs), which involve delaying flights on the ground at their origins to limit the arrival rate at an airport, could be detected using the DFT.Four simulations were carried out involving Denver Center (ZDV) and Denver International Airport (DEN) using traffic data from May 14, 2012.The nominal arrival rate for DEN is 120 aircraft per hour.Denver Center counts are recorded from 12:00 -20:00 UTC when DEN is at the nominal arrival rate, and then for 3 additional simulations where this rate has been significantly reduced.Figure 9 shows both the time series and DFT results for these simulations.Note that because the time series data is shorter (8 hours instead of 24) the nominal DFT plot takes a different shape than it does in Fig. 5.This is because the amount of data is reduced, so there is less resolution in the DFT plot.However, there is still a significant spike shown between 90 and 180 minutes of period.The simulated GDPs are not in full effect until 17:00 UTC, so the time series center count data of Fig. 9a is nearly identical from 12:00 -17:00 UTC.Nevertheless, the GDP effects are contrasted nicely in Fig. 9b, with the DEN signature spike being progressively reduced as the arrival rate at DEN is reduced.To clarify, arrival rates (measured in flights per hour) and traffic periodicity plotted in DFT plots (measured in minutes) are not the same thing.In other words, the nominal arrival rate of 120 flights per hour is not related to the spike in Fig. 5 being near 120 minutes.Rather, the reduction in arrivals at DEN has reduced the signature of DEN traffic within Denver Center, which happens to exhibit a periodicity of between 90 and 180 minutes.Also, it should be mentioned that a reduction in volume as exhibited in the time series data of Fig. 9a does not necessarily correlate to a reduction in DFT magnitude.Recall that the DFT is displaying the amplitude of periodic events that make up a time series signal.All things being equal, a mere reduction in mean traffic counts would produce an identical DFT plot at all periodicities except at the infinite-length period (0 th frequency).In this work, all traffic count data has been mean-shifted prior to calculating the DFT in order to eliminate this effect.12:00 13:00 14:00 15:00 16:00 17:00 18:00 20:00 0
+C. Identifying Historical NAS EventsSo far, the DFT has been effective at distinguishing characteristic traffic flows within the NAS as well as the effects of playbook, metering, and GDP TMIs implemented in simulation.Unlike simulation results, however, historical traffic data includes the effects of controller and airline actions as they respond to traffic demands, weather constraints, and other tactical changes that are not represented by the original filed flight plans used in the simulated results.Thus, it becomes more difficult to identify specific traffic flows and TMI actions in the presence of all the other day-to-day operational variances.Nevertheless, with some investigation, specific events in the NAS data record can be highlighted by the DFT analysis.From operational record data, the CAN 1 East playbook was implemented on July 26, 2012 at 20:15 UTC and extending into the following day.In Fig. 10, the DFT of the historical counts in sector ZMP11 from 16:00 -24:00 UTC are compared to that of three other days chosen from the list of low-delay dates surrounding July 26.This is done to reduce the effects of seasonal traffic variance.The CAN 1 East playbook caused an increase in amplitude for the July 26 results (shown in green) at nearly the same period as the simulated CAN 1 East playbook results shown in Fig. 8b.It is not known exactly what TMI events caused the nearby spikes shown for the July 6 (blue) and August 2 (maroon) results, but historic radar data shows that there was significant convective weather in the region, and traffic in Sector 11 was disrupted.June 28 (black), by contrast, was clear of any convective weather within the region for this time span.On August 16, 2012, the Vulcan (VUZ) playbook, which routes flights from the west to northeastern destinations southward through Atalanta Center (ZTL), was put into effect from 15:00 UTC into the following day.These flights pass through sector ZTL23, and all flights destined to Newark, LaGuardia, JFK, and Teterboro airports were metered to 35 miles-in-trail.Again, in Fig. 11 the DFT results of this date are compared to those of three nearby days from the low-delay list of dates.As clearly highlighted by the green line, which is lower than the other dates through most of the plot's domain, the effect of this metered flow through this sector is to reduce the amount of high frequency (low period) content of traffic.In the time domain plot of Fig. 12 the volume of counts for this day is increased as expected.Time series data for ZMP11 on July 26 is not shown but is similar to Fig. 12, in that any trend that would identify the presence of a playbook stream is difficult to observe.Although the three other dates were chosen to be low-delay from a NAS-wide perspective, it is obvious from Fig. 11 that something is substantially different about August 7, 2012.Looking into historic NEXRAD radar data, it was discovered that there were severe thunderstorms throughout the region.In response to the weather, historic TMI records for this time period show that there were significant metering actions throughout Atlanta Center, and traffic in Sector 23 was disrupted, resulting in more high frequency (low period) events in the counts data.Note that this effect cannot be easily determined by the time domain counts data shown in Fig. 12.The DFT is shown to highlight these differences in traffic more effectively than the raw time series data.
+IV. Conclusions and Future WorkIn this paper, a method of analyzing traffic data within the NAS airspace was developed and applied to simulated and historical data.It was shown that a frequency domain analysis provides an alternative method of analyzing and understanding the data.Traffic within control centers and sectors can be characterized by their DFT plots to a level of detail that is not possible using time domain plots.It was found that applying the DFT to simulated nominal traffic counts within a control center allowed specific sources of the traffic to be identified by their periodic signatures.Often, when those sources are disrupted (as with a GDP, for instance, or any other TMI) the disruption can be detected at the center-wide level in the frequency domain, but not in the time domain.As presented here, the effects of playbook rerouting, metering, and GDPs could all be detected using the DFT method on simulated data.Despite those TMI signatures being less obvious and coherent in the historical DFT analysis, they could still be detected in many cases.In fact, while looking for the signature of the Vulcan playbook in historic data, the detrimental effects of thunderstorms and the resulting TMI actions were noticed in the DFT results of a different day.These results would have been difficult to detect by time series data alone.Thus, the DFT may be an effective tool for looking through historical data and checking if certain initiatives were in place, or for detecting significant weather events and their impacts on traffic.It has been shown that delay-inducing events like TMIs produce frequency domain signatures within airspace regions of the NAS, and it is hypothesized that such signatures, to the extent they can be detected in historical data, may be useful for analyzing behavior.Eventually, such methods may be used to predict NAS behavior for daily real-time planning and control purposes by detecting traffic events more precisely.To succeed in doing so, more work needs to be done to identify the frequency domain signatures of specific traffic sources in centers and sectors, along with other initiatives like Airspace Flow Programs (AFPs).Each airspace is different, and varies in its frequency domain response to specific TMI actions.Also, because windowing and sample size play a major role in the DFT resolution and accuracy, more work needs to be done to determine how to best apply the technique-especially with historical data where controller actions tend to have smaller duration and flight plans are not followed as closely as they are in simulation.Figure 1 :1Figure 1: Signal x(t) = 4 sin(2π(10t)) + 2 sin(2π(100t)) + 3 sin(2π(250t)) in time and frequency domains.
+Figure 2 :2Figure 2: A comparison of four window functions applied to the same simulated traffic count data.DFT amplitude versus period.
+DFT of center counts
+Figure 3 :3Figure 3: Simulated nominal traffic Cleveland Center counts and DFT for 30 selected days.Mean counts and DFT shown in blue.
+ZOB Counts w/o DTW Flights DTW Flight Counts in ZOB (b) DFT of center counts
+Figure 4 :4Figure 4: Simulated nominal traffic Cleveland Center counts and DFT for December 6, 2012.
+Figure 6 :6Figure 6: DFT of Atlanta Center counts for January 4, 2012.
+Figure 7 :7Figure 7: Sector ZMP20 counts and DFT for July 6, 2012, 16:00 -24:00 UTC.
+16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 24:(b) DFT of sector counts
+Figure 8 :8Figure 8: Sector ZMP11 Counts and DFT for July 6, 2012, 16:00 -24:00 UTC.
+Rate = 120 GDP: DEN Arrival Rate = 60 GDP: DEN Arrival Rate = 30 GDP: DEN Arrival Rate = 15 (b) DFT of center counts
+Figure 9 :9Figure 9: Denver Center Counts with varying ground delay programs for Denver International Airport, May 14, 2012, 12:00 -20:00 UTC.
+Figure 10 :10Figure 10: DFT of sector counts for ZMP11 for selected dates 16:00 -24:00 UTC.
+Figure 11 :11Figure11: DFT of sector counts for ZTL23 for selected dates 16:00 -24:00 UTC.
+Figure 12 :12Figure12: Sector counts for ZTL23 for selected dates 16:00 -24:00 UTC.
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2420
+			Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2420
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2420
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2420
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2420
+			of 11 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on June 20, 2014 | http://arc.aiaa.org| DOI: 10.2514/6.2014-2420
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+I. IntroductionW hen characterizing the state of air traffic in the National Airspace System (NAS) for historical or real- time analyses, most data are evaluated in the time domain.These data are useful for understanding and analyzing the performance of the NAS, and are the basis for the majority of the work in this field.Several methods of modeling, predicting, and optimizing the performance of the NAS using these metrics have been proposed.For example, Sridhar et al. in Ref. 1 construct a linear time-varying model of aggregate traffic flow using flight counts as the state variable.In Ref. 2, an autoregressive model for predicting sector demand is presented that uses historical data and takes convective weather conditions into account.Recent work by Drew and Sheth in Ref. 3 demonstrates that insight into NAS operations can be found by transforming time domain traffic count data into the frequency domain.Specific traffic flows (e.g flights to and from a major regional airport) are shown to exhibit a periodic signature that can be readily identified in the frequency domain by using the Discrete Fourier Transform (DFT).Furthermore, some disruptions in regional traffic counts (like those caused by weather, or by controller operations) are shown to be more clearly identified in the frequency domain than in the time domain.One of the drawbacks to the DFT is that once transformed into the frequency domain, all notion of time is lost.Thus, if a disruption is noticed in the frequency domain data, it is impossible to determine when the disruption occurred.The desire to resolve data into both its frequency and time domain content is what gives rise to wavelet analysis.Though it is impossible to have perfect resolution in both domains simultaneously, the wavelet analysis provides a notion of both the frequency and time domain content of a signal.Primarily a tool used for signal filtering and compression, wavelet analysis has also found uses in other domains.In Ref. 4 for example, wavelets are used to analyze the audio signal of a mechanical watch.By matching known modal frequencies provided by a finite element analysis to the 2D time and frequency results of the wavelet analysis, malfunctions of the watch's movement can be detected.In Ref. 5 the authors present a guide for analyzing time series data by performing a wavelet analysis on ocean temperature data.In Ref. 6, the authors apply wavelets to the problem of detecting disease outbreaks from time series disease counts data sets.In Refs.7, 8, 9 the authors apply wavelets to graph models of data and road traffic analysis, and wavelets are found to be capable of characterizing the network performance and of identifying failures in traffic links.In order to better analyze NAS traffic and identify disruptions (potentially prior to becoming difficult to manage) the authors have begun investigating the use of wavelet analysis on air traffic data.In this paper two techniques of wavelet analysis are performed.The first approach is to apply the wavelet transform in a traditional fashion to airspace traffic count time series data.The second approach is to explore the use of wavelets on a network graph model of the national airspace.Simulations of several Traffic Management Initiatives (TMIs) are analyzed using these methods and compared to their nominal traffic flow behavior.This paper is organized as follows: Section II begins with a brief description of both the Continuous Wavelet Transform (CWT), and the Spectral Graph Wavelet Transform (SGWT).Section III contains the results of the CWT analysis, and Section IV contains those of the SGWT analysis along with a description of the center based graph network traffic model.Concluding remarks are in Section V.
+II. BackgroundMuch has been written about wavelet transforms in all their varied forms and applications.In this section, only a broad conceptual overview is presented to familiarize the reader with the general idea of wavelet transforms and their application in this context.
+A. Continuous Wavelet TransformFigure 1: The Morlet mother wavelet function Unlike the Fourier transform, which correlates a signal expressed in time (time series data) with infinitely long sines and cosines, the wavelet transform correlates the time domain signal with scaled and shifted versions of a 'mother wavelet' function ψ(t).There are many choices for a mother function, but certain conditions must be met.For instance, it must have a zero mean and be localized in time and frequency.An example is the Morlet wavelet shown in figure 1.The Continuous Wavelet Transform (CWT), which is used in this work, computes the inner product of a signal x(t) with the scaled and shifted version of the mother wavelet.It is defined as follows:4 C(s, τ ) = ∞ -∞ x(t) 1 √ s ψ * t -τ s dt,(1)where ψ * (t) is the complex conjugate of the mother wavelet, s is the scaling factor, and τ is the shift in time.The shift in time τ provides temporal resolution, while the shift in scale s provides an approximation of frequency resolution.High frequency content within the signal will have high correlation with small scaling factors, whereas large scaling factors, which stretch the mother wavelet, will capture low frequency events in x(t).In practice, the scale term can be converted into 'psuedo-frequency'-or, as presented herein for ease of interpretation, 'psuedo-period'.An example of the CWT applied to air traffic is shown in figure 2. Here, one day of air traffic counts in Cleveland Air Route Traffic Control Center (ARTCC) is shown in black and the CWT coefficients are shown as colored contours.In this plot, the Morlet wavelet is used.The warmer and cooler colors represent peaks and valleys respectively at their indicated periodicity.For instance, as found in Ref. 3, flights within Cleveland Center going in and out of Detroit Metropolitan Wayne County Airport are responsible for a periodicity in the data of about 110 minutes.This is validated by the CWT with red and blue contours shown centered at 110 minutes.Unlike the Fourier transform results in Ref. 3, the CWT plot has the added benefit of clearly displaying when that periodicity is present-in this case it begins at about 12:00 UTC and persists until about 21:00 UTC where a trend of higher periodicity emerges.
+B. Spectral Graph Wavelet TransformRecently, work has been done in applying traditional wavelet techniques, which operate on a temporal data signal x(t), to the signals on a network line graph model.The motivation is that the so-called Spectral Line Graph Wavelet Transform could reveal and detect the impact of disruptive traffic events in the context of its impact on the overall traffic network.Formal derivations of the SGWT can be found in Ref.
+0, otherwiseThe edge weights in this work are set to one, however, less trivial weighting models could be easily employed.At each vertex of the graph resides a graph signal f : V → R N , consisting of sampled data associated with that network node.For example, the m th component of f could represent the traffic volume (or speed, etc.) at node m.For any simple graph G, the Laplacian matrix L can be defined as L = D -A, where D is the degree matrix having only diagonal elements d m equal to the sum of weights of all edges connected to vertex m.A is the adjacency matrix defined above.The Laplacian matrix has many useful properties in graph theory. 10he eigenvalues and eigenvectors of L found by L χ l = λ l χ l for l = 0, 1, . . ., N -1 produce eigenvectors χ l .Noting the analogy with the exponential eigenfunctions of the classical Fourier transform, it can be shown that the graph Fourier transform f of the function f on the vertices of G can be defined as:9 f (l) = N n=1 χ * l (n)f (n). (2)The graph wavelet coefficients defined at each vertex n and scale s are then found with Here g is a wavelet generating kernel analogous to the mother wavelet function found in the CWT.Recall that for the CWT, the mother wavelet provides localization in time and frequency depending on how it is stretched and scaled.The SGWT with its kernel function g provides localization in frequency (of signals whose domain is the graph vertices-not time), and graph space.Therefore, as the mother wavelet of the CWT is selected to provide the best resolution tailored for the application, the wavelet generating kernel g must also be selected to provide an appropriate trade-off between localization in the frequency and graph space domains.For this work the wavelet generating kernel function is a cubic spline with parameters matching those in the examples of Ref. 9 so that sufficient resolution on the vertices of the graph can be achieved.It must be pointed out that despite the discrete nature of the graph vertex domain, g is a continuous function, and s can be any positive real number.The domain of g must span the spectral range of the graph (given by the largest eigenvalue of L).Furthermore, similar to how the mother wavelet of the CWT is scaled to find correlation with a signal at different psuedo-frequencies, the SGWT kernel function g is scaled by s to find find correlation with the signal residing on the graph vertices.An example of this function is shown in figure 3 for various scale values.To be clear, the SGWT is very different from the CWT, which produces a view of time series data revealing the temporal location of its frequency components.The independent variables s (for periodicity) and τ (for time shift) of Eq. ( 1) allow the creation of plots like those of figure 2. In contrast, note that the SGWT equation of Eq. ( 3) has independent variables s for network scale, and n for vertex location.There is no knowledge of time (though successive calculations can be done for each time sample of the data), and here scale is related to how events are correlated across the network.Rather than identifying the periodicity of a signal as it moves through the time domain, the SGWT identifies the periodicity of a signal as it exists on one graph vertex relative to another.Lower scale values capture how events in the network vary over large vertex ranges (low frequency, or high periodicity within the network).Higher scale values (high frequency) highlight events that have a greater correlation with their more immediate neighbors in the network.An example plot of SGWT coefficients is shown in figure 4 based on one day of NAS Center graph network traffic.Results for individual vertices are shown as horizontal rows.Note that though the SGWT does not operate on time-domain signals, it can be computed sequentially on the network for each sample in time.Thus, for this plot it was calculated 1440 times using a fixed scale of s = 0.78.Again, warmer colors indicate higher values and cooler colors lower.A more thorough discussion of how to interpret SGWT plots will be presented in Section IV.W f (s, n) = N -1 l=0 g(sλ l ) f (l) χ l (n).(3)
+III. Continuous Wavelet Transform Analysis ResultsThe results of this work are divided into two parts.In this section a traditional wavelet analysis is performed on air traffic data using the CWT.This begins by recording historical sector counts sampled every minute using the Future Air Traffic Management (ATM) Concepts Evaluation Tool (FACET). 11Specific air traffic streams, like flights of a particular altitude range, can be filtered out if desired and recorded separately.Simulations of various Traffic Management Initiatives (TMIs) can also be conducted in FACET and compared to simulated baseline traffic in order to observe their effects in the wavelet results.The CWT contour plots are created using the 'cwtft' function of MATLAB's wavelet toolbox. 12This function uses an FFT algorithm to calculate the CWT coefficients using the desired mother wavelet.
+A. Wavelet ChoiceAs mentioned, computing the CWT involves a mother wavelet with which the time series data is compared.Depending on the wavelet chosen, the resulting wavelet plot will exhibit a trade off between time and frequency resolution.Thus, the first step in the analysis is to choose the appropriate mother wavelet for the type of analysis involved.Wavelet analysis seeks a compromise between time and frequency resolution, and some wavelets can be biased towards one of those domains.In figure 5, two different wavelets are applied to the same time domain signal.In figure 5a, the Morlet-12 wavelet is used.Here 12 is the base frequency of the mother wavelet.With a high base frequency, it is broad in the frequency domain, but narrow in the time domain.In figure 5b, the DOG-2 (Derivative of Gaussian) wavelet is used.It has broad time domain signal, but narrow in frequency.Thus, the periodic signature at 110-minutes is sharply resolved in figure 5a in terms of its periodicity, but it is not as clear when in time it starts to show up in the signal.(Some time between 8:00 and 12:00 UTC.)In figure 5b, the DOG-2 wavelet shows poor resolution in frequency, since it is difficult to pinpoint the periodicity of the same signature.On the other hand, it is clear that it does not start before 12:00 UTC.A wavelet that strikes a good compromise between time and frequency domain resolution for air traffic is the Morlet wavelet with the standard base frequency of 6 (Morlet-6).The results of this wavelet applied to the same traffic data are shown in figure 2, which depicts much sharper warm and cold colors.
+B. Historical Traffic AnalysisNoting that known traffic flow signatures (like the Detroit Metropolitan Wayne County Airport flights in figure 2) can be observed with wavelet analysis, the next step is to apply it to several days of air traffic to see if off-nominal traffic patterns can be detected.Cleveland Center (ZOB) is one of the most congested ARTCCs in the country.Focusing on flights above flight level 250 in ZOB during July 2014, the wavelet coefficients for a 24-hour period of air traffic counts are calculated for each day.Then, seven mean days of coefficients are calculated for each day of the week from the month.Figure 12 in the Appendix shows the difference between every July day's wavelet coefficients and the mean coefficients computed for that day of the week.Select dates are shown in figure 6 below.For example, note the dark red and blue contours shown toward the end of July 3 and the beginning of July 4 shown in figures 6a and 6b.This indicates that the wavelet coefficients for these two days are significantly different from those of the average Thursday and Friday of this month.Thus, plots with more reds and blues (representing high peaks and low valleys) indicate possible off-nominal traffic conditions.Figure 6 demonstrates that the wavelet analysis highlights differences in traffic flow characteristics that are difficult to detect in the original time domain data.Several days stand out as being significantly different in the wavelet domain.During the July 3-4 time period there was a large system of convective weather that moved through the Cleveland and New York regions.Similarly, July 14 (figure 6c) also shows a large difference from mean toward the end of the day.Weather records show that there were severe thunderstorms west of Cleveland Center near Chicago, as well as within the center itself.Operational data indicate that due to heavy volume, the playbook route CAN-BRAVO-EAST 13 was put into effect from 17:00 UTC to 2:00 UTC the following day.July 23, which also exhibits major wavelet domain contrast in figure 6d, also witnessed severe convective weather in the Midwest and the Northeast, resulting in a CHICA-ROUTE-OUT playbook to be put in place from 17:00 to 23:59 UTC.Again, the wavelet analysis detects this off-nominal traffic by a signature in the frequency domain.Unlike the Fourier analysis, however, it also indicates the time at which this traffic anomaly took place.
+C. Simulated TMI DetectionIn figure 12, July 17 is shown as having high volume but near-nominal behavior in terms of the CWT analyis (indicated by the lack of dark red and blue contours).Hoping that the CWT could detect the presence of a single TMI within a center, two simulations of July 17, 2014 historical traffic were conducted in FACET, and the CWT was applied to the resulting ZOB traffic counts.In the first simulation, traffic was flown without any TMIs applied.In the second, the Green Bay (GRB) playbook route was implemented from 12:00 -20:00 UTC.This playbook routes east-bound flights into Cleveland Center.Also, 30 Miles in Trail (MIT) metering was applied through the DJB fix (near Cleveland).The resulting counts and contours for both simulations are shown in figure 7. Note that these plots are displaying the CWT contours as directly applied to the counts.They are not the difference from a mean day's contours as in figure 12.In the previous subsection, the CWT was able to detect off-nominal historical traffic due to a combination of factors involving weather and various TMIs.However, it appears difficult to observe a difference in the CWT results between figure 7a and figure 7b.Thus, it seems that this application of the CWT is not capable of detecting a single TMI event, and it is not yet clear how to distinguish or identify one TMI or traffic pattern from another.Individual traffic streams, like those traveling to and from a specific airport, can occasionally be identified due to their operational patterns (as was seen in figure 2).However, historical and  simulated analyses like these have yet to reveal a reliable signature for many traffic flows-either nominal or off-nominal.This may not be possible due to the relatively small data volume and finite nature of traffic time series data.Hence, the focus was moved to analyzing traffic behavior with the SGWT method described above to look at reroute disturbances.
+IV. Spectral Graph Wavelet Transform AnalysisUsing a network model as a traffic network realization is not a new technique.For the purposes of this work, a network graph of continental Air Route Traffic Control Centers is created such that the vertices of the graph are the centers, with edges connecting each center vertex to all contiguous neighboring center vertices.That is, each pair of control centers that share a boundary are connected with a graph edge.This graph network is depicted in figure 8, where vertices are shown with black circles and edges are shown in blue.Note that neither the locations of the vertices, nor the edge lengths as shown is indicative of weights or any other mathematical attribute of the model.The vertices are shown located at the centroid of each control center, but this is only for convenient graphical depiction.All edge weights are set to one in this work.This graph has 20 vertices and 45 edges.Both historical and simulated traffic can be processed and converted into this network flow model by recording the number of flights that are traveling from one center to another at each time sample (1-minute).This is recorded for all 45 edges.For instance, if there are 15 flights traveling from Chicago Center to Cleveland Center, and 12 flights traveling in the reverse direction, the value of the signal recorded at the edge connecting both centers will be 27 at that time step.Flights that are departing and arriving within the same center, as well as flights transitioning to international airspace are not counted.The traffic flow data that exist in this network graph model resides on the edge of the graph, but the SGWT analysis deals with signals on the vertices f .This leads to defining a 'line graph' based on the original graph model.In graph theory, given a graph G, each vertex of the line graph L(G) represents an edge of G, and the line graph edges exist between those vertices of L(G) if the edges they represent in G share a common vertex in G. 14 The resulting line graph edges of the NAS center network graph are shown in figure 8 in dark red, with vertices shown as green triangles at the midpoints of each of the original graph's edges.It consists of 45 vertices and 176 edges.The line graph is only used for computational purposes in the SGWT.For the remainder of this paper, the original center network graph with traffic flow along its edges is an acceptable way of visualizing and describing the model.The first step in exploring this analysis is to run a historical simulation in FACET.Historically-scheduled flights are flown according to their filed flight plans without any controller intervention or changes due to weather, etc. Choosing August 22, 2014, which was a high volume low delay day, the recorded traffic flow is converted into the network model structure.Then the SGWT coefficients are calculated for a range of scale values for all 45 center-to-center edges.This is performed using MATLAB 12 and the SGWT Toolbox from Ref. 15.The results are shown in figure 13 in the Appendix.It can be difficult to interpret these results, but from figure 13, it is clear that the network has the highest coefficients for a scale range at roughly 0.08 to 1.0 due to the warmer colors distributed in this range of the plots.Recall that in this context the range of scale values is based on the Laplacian matrix of the graph, with larger values related to the high frequency (on the vertex domain) content of the signals across the network, and vice versa for smaller scale values.For example, if a warm color is depicted at a low scale value (low frequency = long period) at a specific link location, it indicates that the traffic at that link has a strong cause and effect relation to traffic at a relatively long distance across the network.Disruptions in traffic on such links would likely have a large effect on traffic a long distance away.On the other hand, if a link shows warm colors for higher scale values, its traffic is more strongly correlated to closer neighboring links in the network.Disruptions on those links would likely affect closer neighboring links.A more illustrative view of these results can be found by calculating the coefficients at a specific scale and plotting the results at their corresponding edges in the center network.Such a depiction is shown in figure 9, which shows the SGWT results for a scale value of 0.08.Each color band shown on every graph edge represents the the 24-hour period of August 22, and can be thought of as representing a slice at 0.08 through each of the plots in figure 13.Accordingly, the colors tend to be warmer in the first quarter and last half of each band where volume is highest.At the low scale value of 0.08, the results depict the correlation between edges that are farther away in the network (low frequency across the network).In other words, warmer colors on links in this plot indicate flows that have a stronger cause/effect relation with traffic at a farther distance across the network.Figure 9 reveals that flows with the greatest long-distance impact include the northerly flows of Salt Lake (ZOB)↔ Denver (ZDV), Denver (ZDV) ↔ Minneapolis (ZMP), and Minneapolis (ZMP) ↔ Chicago (ZAU).Of note on the East Coast, is the New York (ZNY) ↔ Washington DC (ZDC) flow, which clearly has a greater correlation to faraway flows than other edges in the region.Figure 10 shows the same analysis at a scale of 1.0, which depicts a more localized correlation of traffic flows.Warmer colored bands indicate times and links where traffic is better correlated to closely neigh-  13 suggest that traffic in the NAS, from a center network perspective, has a strong long distance cause/effect relationship.Thus, traffic events and disturbances in one center can have a large effect on traffic many centers away.This is shown in figure 13 where most of the links show warmer colors at the low scale side of the scale spectrum.In order to determine how sensitive the SGWT technique is to TMIs, both the CAN-1-East and CAN-1-West playbook routes were implemented in simulation for the entire day of August 22.This is an unlikely scenario in real operations, since traffic blocking convective weather does not persist for 24-hours, but it is desired to see to what extent and for what time of the day the SGWT may show a distinction in results.Using a scale of 0.08, which will highlight low frequency (long period) signatures across the network, the results are shown in figure 11.Both CAN-1 playbooks route cross-country flights to the north and into Canadian airspace north of the Great Lakes.Comparing figure 11 to figure 9, many of the links show a significant difference when the playbook route is implemented.The most striking of which is the Minneapolis (ZMP) ↔ Chicago (ZAU) flow, which shows markedly less red and orange at peak time, indicating that when the playbook route is implemented, this link has less of a cause/effect correlation with traffic at distant edges.The same is true for the New York (ZNY) ↔ Washington DC (ZDC) link.Some of this difference is caused by a reduction in volume at these links, but when the same analysis is done comparing plots using a scale value of 1.0, less of a distinction is observed, indicating that the SGWT is capturing the effects of the playbook at a long periodicity of the signal across the center network graph.The previous figures reveal that the SGWT can highlight interesting characteristics of traffic flow in the NAS.It can also detect the presence of major playbook routes as well as provide some notion of the extent to which the playbook route affects the NAS as a whole.However, more work is required to determine if it can identify, and even predict the severity and extent of a disruption in the NAS due to weather or TMI actions.Unfortunately, it is not yet clear how, or even if this is possible, since it remains difficult to quantify and interpret the SGWT results.In most cases, differences between the baseline and TMI simulations are minimal.Prior applications of the SGWT include internet and highway traffic where the volume per graph edge is much greater, and disruptions have a more noticeable impact.Disruptions and delays in air traffic caused by TMIs, on the other hand, can be much more subtle and/or localized in time and space.
+V. Concluding RemarksIn prior work, a frequency domain analysis was shown to provide insight into air traffic streams that was not possible in the time domain alone.In this paper, two additional frequency domain techniques were applied to air traffic.The Continuous Wavelet Transform was shown to provide similar insight that the Fourier transform did, but with the added benefit of preserving some time domain resolution.Though some off-nominal TMI-and weather-affected traffic was detected in the historical data using the CWT, no conspicuous signature could be found to reliably detect and identify specific patterns in the traffic.The CWT analysis of TMI simulations failed to reveal major differences when compared to baseline nominal traffic results.Then, the Spectral Graph Wavelet Transform was applied to a center-based network graph of the NAS.This alternate method was also applied to nominal and off-nominal traffic simulations.The SGWT highlighted some characteristics of traffic flow in the NAS, revealing the relative extent for which traffic events in one area affect the traffic in distant regions of the NAS.It also identified the location of traffic differences caused by a major TMI playbook route, and hinted at the extent (spatially) to which the rerouting affected the NAS as a whole.However, it is still difficult to reliable identify and quantify off-nominal traffic conditions and their effects on the NAS using either transform.It is suspected that this is due to several factors.First, since traffic within a center consists of many traffic streams, the detection of a single affected stream can be very difficult in either the time or frequency domains.If the scope is narrowed to the sector level, the volume of the traffic data is reduced, and noise (caused by spatial and temporal flight variation) tends to dominate the results.Second, even within a large airspace, the number of fights is small compared to packets traversing a router, or cars on a stretch of highway.When disruptions in the NAS occur, planes do not simply stop moving, unlike internet or road traffic where flow rates routinely drop to zero at affected nodes.To the extent that the most extreme traffic events can be detected using either the CWT or SGWT, it is difficult to argue that similar patterns are not as clear in time domain data.Third, related to the problem of volume, is the relatively short time domain and finite nature of the data.With a sampling period of 1-minute, there are only 1440 points per day.This particularly affects the CWT analysis, which performs better with longer signal length.On this point, however, this work is not without merit.It may be discovered that by widening the time scope of the data to weeks, or months, such a frequency analysis may provide insight to long-term trends in the NAS, like airline strategy, seasonal and/or weather effects, etc.Figure 2 :2Figure 2: Aircraft count in Cleveland Air Route Traffic Control Center (ZOB), and contours of the corresponding continuous wavelet transform.
+8,9.Here, only a conceptual overview is presented.Given a weighted connected graph G = {V, E, w} with a finite set of vertices V , |V | = N , a set of edges E, and weight function w that resides on the edges, an N × N adjacency matrix A can be defined with entries a m,n such that a m,n =    w(e), if e ∈ E connects vertices m and n
+Figure 3 :3Figure 3: Wavelet generating kernel function g(s • x) with various scale values.
+Figure 4 :4Figure 4: SGWT results of NAS network for August 22, 2014.Scale s = 0.78.
+Figure 5 :5Figure 5: A comparison of wavelet analysis between two wavelets on the same air traffic data.
+Figure 6 :6Figure 6: Traffic counts and wavelet analysis for flights above FL250 in Cleveland Center on select July 2014 dates.Wavelet contours shown are the difference between that day and the mean of that day of the week's wavelet contours.See figure 12 for the entire month.
+(a) Baseline, no TMIs implemented.(b) Green Bay playbook implemented from 12:00 -20:00 UTC with 30 MIT.
+Figure 7 :7Figure 7: Aircraft count and CWT contours for flights above FL 250 in ZOB.Simulated traffic based on historical flights from July 17, 2014.
+Figure 8 :8Figure 8: NAS Center network graph G shown in blue, and line graph L(G) shown in green.
+Figure 9 :9Figure 9: SGWT results for simulated traffic based on flights of August 22, 2014.Scale = 0.08
+Figure 10 :Figure 11 :1011Figure 10: SGWT results for simulated traffic based on flights of August 22, 2014.Scale = 1.0
+
+
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI: 10.2514/6.2015-2731
+			of 14 American Institute of Aeronautics and Astronautics Downloaded by NASA AMES RESEARCH CENTER on July 2, 2015 | http://arc.aiaa.org| DOI:
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diff --git a/file200.txt b/file200.txt
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+	
+	
+		
+IntroductionNew technologies developed for use by Air Traffic Controllers (ATC) and airline ramp operators are studied in a Human in the Loop (HITL) simulation study.The Ramp Traffic Console (RTC), shown in figure 1 below, was designed along with the Spot and Runway Departure Advisor (SARDA) Decision Support Tool (DST) proposed to aid ramp controllers in reducing taxi delay.SARDA was first evaluated as a decision support tool for air traffic controllers to meter flights from the spot to the runway (Hayashi et al, 2013).Air Traffic Control Towers (ATCT) are equipped with multiple electronic systems that have been developed over time to facilitate controllers in the management of air traffic.Advanced Electronic Flight Strips (AEFS) is one such technology that is likely to be subsumed into Terminal Flight Data Management (TFDM) as a part of a larger effort to integrate multiple existing electronic systems.In a 2012 study of a prototype ATCT TFDM system, Controller-Pilot communications were used to measure cognitive workload (Lockande, 2016).This study found that controllers utilizing the prototype TFDM system reported lower workload than the control group.While RTC is designed for use by airline operators, like AEFS and TFDM, RTC is intended to replace paper strips with a digitally integrated information source to present integrated flight data.In the current study, SARDA advisories are presented to the ramp controller as a tactical surface scheduler (DST) designed to meter flights from the gate.The RTC has a novel user interface displayed on a 27" multi-touch screen monitor, used by ramp controllers in place of paper strips and paper maps, and includes the SARDA pushback advisories.During simulated operations, ramp controllers gave instructions to pilots via radio communications to manage traffic and ensure airplanes were safely separated while efficiently taxiing to their destination.This task required the controllers to engage in a variety of high-level cognitive functions, including planning, managing, monitoring, problem solving, and coordinating with other ramp controllers, pilots, and air traffic controllers.The CLT ramp is divided into four sectors, North, East, South and West, with most airplanes needing to taxi through multiple sectors.Ramp controllers hand off airplanes to each other at the sector boundaries.Handoffs are also made to air traffic controllers at various points, called spots, intersecting with the Federal Aviation Administration controlled active movement area on their way to and from the arrival or departure runway.Outbound departure flights are handed off to the Air Traffic Controller (ATC) at the spots and inbound arrival flights are received from the ATC at the spots and directed to their gate.Consequently, the ramp controllers were required to communicate with other sector controllers as well as air traffic controllers and multiple pilots to efficiently manage all the departure and arrival flights to and from their gates on the ramp.The RTC and SARDA concept were developed initially for use at the Charlotte Douglas International Airport (CLT).The simulation study reported in this paper is one in a series of studies to evaluate SARDA and RTC from the ramp controller's point of view.Human-in-the-Loop (HITL) simulations are used as a safe and controlled environment to evaluate new concepts and decision support tools.The goal of the present study was to evaluate virtual flight strips on RTC as compared to the use of paper strips in ramp traffic management.The research questions explored here are regarding the effect of using virtual flight strips on RTC as compared to using paper strips shown in Figure 2 below, on the workload and usability ratings of the ramp controller participants.
+MethodsThe virtual flight strips as presented on RTC were tested in a HITL simulation study in Future Flight Central (FFC), a high-fidelity tower simulator at NASA Ames Research Center.This study included eight 90-minute data collection runs over three days.There were two RTC training sessions for a total of 3 hours and 20 minutes of controller training using RTC.There were four ramp controller participants.In four of the data collection runs, the ramp controllers used paper strips and paper maps while controlling ramp traffic, and in the other four runs, the ramp controllers used the virtual flight strips on RTC.There were two traffic scenarios used in the simulation and each was repeated twice in the paper condition and in the RTC condition.Two participants were active ramp controllers from CLT, a third was a retired FAA controller, and the fourth participant was an active ramp controller from another airport.The four ramp controller participants used the RTC in the simulated ramp operations environment while usability and workload data was collected from the users under the two different conditions.In one condition the participants used paper strips and paper map, while in the second condition participants used the virtual flight strips and movable map on RTC.The two ramp controllers who were current CLT controllers were rotated through sector assignments such that each worked both scenarios in the paper and RTC conditions.The other two ramp controllers who were not active CLT controllers remained in one of the "less busy" sectors that were deemed to have less impact on the operation.Postrun and post-study workload and usability questionnaires were administered to all four of the sector controllers.User workload is commonly assessed with subjective measures, which require the participants to report on their subjective psychological experience.These measures include self-reported subjective ratings on certain scales, such as the NASA Task Load Index (TLX) (Hart & Staveland, 1988).Workload for the purposes of the present study is defined by four components of the NASA-TLX (Task Load Index).The four components include Mental Demand (Thinking, deciding, calculating, searching, etc.), Physical Demand (Hands and arm movement, force), Temporal Demand (Time pressure), and Frustration (Stress, annoyance, irritation).Controllers were asked to rate each of the four components of their workload after every run on a scale of 1-10.For example, see Figure 3 for the "mental demand" question response format.A performance sub-scale was not included.Along with workload, usability of the RTC was also assessed.There are several definitions of usability (J.Jeng, 2005, provides a good review of various definitions).In this paper, the definition used by the International Organization for Standardization (ISO, 1998) will be followed.It defines usability as the extent to which the users of a product are able to work effectively, efficiently, and with satisfaction.Following the definition used by the International Organization for Standardization (ISO, 1998), usability for the purposes of this paper is defined by three aspects of usability, effectiveness, efficiency, and satisfaction.Traffic management performance questions were included in the post run questionnaire with the aim of determining the "effectiveness" aspect of usability.Resources and efficiency questions were included in the post-run questionnaire with the aim of determining the "efficiency" aspect of usability.The post-study survey questions were designed to assess the "satisfaction" aspect of usability.After each run, the controllers were asked questions regarding their traffic management performance and resources and efficiency using a response format with a scale of 1 "Referred to Always" to 7 "Referred to Never."For example, one Traffic Management and Performance aspect of Usability is assessed by the controller's response to the question shown in Figure 4 below: Post Run and Post Study questionnaire responses were gathered and the results were analyzed to assess controller workload and usability ratings under both conditions, virtual flight strips on RTC and paper strips.To determine the effect of condition (Paper or RTC) on controller workload and usability ratings, mean post run responses on the workload and usability related questions were collected from all four sector controllers and a two-way repeated measures Analysis of Variance (ANOVA) was performed to determine effect of flight strip type on participant workload and usability.
+ResultsThe mean post run workload ratings and ANOVA results shown in Table 1 and are graphed with standard error bars at a 95% confidence level in Figure 5 below.These results show that the mean workload ratings for the RTC condition are lower than the mean ratings for the Paper condition across all four components of workload.With respect to the Mental Demand aspect of workload, the participants reported a higher mean workload rating of 5.7 for the Paper condition as compared to a mean workload rating of 3.9 in the RTC condition however, as can be seen in Table 1, this was not a statistically significant main effect.There was a statistically significant main effect across the other three aspects of workload.With respect to the Time Pressure aspect of workload, the participants reported a higher mean workload rating of 4.9 in the Paper condition as compared to a mean rating of 2.4 in the RTC condition.With respect to the Physical Demand aspect of workload, the participants reported a higher mean workload rating of 4.6 in the Paper condition, and 2.8 in the RTC condition.Finally, looking at the Frustration aspect of workload, the participants reported a higher mean workload rating of 3.6 in the Paper condition, and 1.3 in the RTC condition.Because the response scale for the post run usability questions was presented in reverse order such that "Always" is the lower anchor (1) on the scale, and "Never" is the upper anchor (7) on the scale, for ease of discussion, an inverse scale of the means is reported in this paper to account for the opposite phrasing of the questions.The mean usability ratings of the post run traffic management and performance questions, meant to assess the "effectiveness" aspect of usability, were higher in the RTC condition as compared to the Paper condition for all of the seven questions.The means and standard errors are shown in Table 2 and graphed in Figure 6 below.The results of the analysis showed a statistically significant main effect of condition for questions 2, 3, and 5 that asked about "maintaining organized traffic flow," "minimizing taxi delay," and "maintaining pressure on the runways" respectively (see Table 2).Looking at question 2 which asked if the participant "maintained well organized traffic flows," the participants reported a higher rating of 6.6 for RTC as compared to a mean rating of 6.1 in the Paper condition.Looking at question number 3 which asked if the participant "minimized taxi delay of each aircraft," the participants reported a higher mean rating of 6.5 in the RTC condition than the mean rating of 5.9 in the paper condition.For question number 5 which asked if the participant "maintained pressure on the departure runways," the participants reported a higher mean rating of 6.7 in the RTC condition than the mean rating 5.9 in the paper condition.All of the other traffic management questions had higher mean usability ratings in the RTC condition as compared to the paper condition, although this difference was not statistically significant (see Table 2).For question number 1 which asked if the participants "maintained sufficient separation among planes," the participants reported a higher mean rating of 6.9 for the RTC condition than the mean rating of 6.7 for the Paper condition.For question number 4 which asked if the participant "avoided sending airplanes into head on course or gridlock", the participants reported a higher mean response of 6.9 in the RTC condition than the mean rating of 6.6 in the Paper condition.For question number 6 which asked if the participant "metered their departures", the participants reported a higher mean response of 6.6 in the RTC condition than the mean response of 6.2 in the paper condition.Finally, for question number 7 which asked if the participant "responded to the pilots call promptly", the participants reported a higher mean response of 6.9 in the RTC condition than the mean response of 6.8 in the Paper condition.Looking at the results overall for the Traffic Management questions, there is a trend toward increased mean usability ratings in the RTC condition as compared to the paper condition for the traffic management and performance questions which were meant to assess the "effectiveness" aspect of usability, with the mean participants rating being higher in the RTC than the paper condition for all of these questions.The mean participant response values for the post run usability resources and efficiency questions meant to assess the "efficiency" aspect of usability are shown Table 3 and graphed in Figure 7 below.The mean rating was higher in the Paper condition for questions 3 and 4, and the mean was the same for RTC and Paper conditions for question 6.However, none of these results demonstrated a statistically significant main effect of condition on participant usability ratings (See Table 3).Questions 1, 2, and 5 of the resources and efficiency questions resulted in a higher mean rating in the RTC virtual strip condition as compared to the paper strip condition.Looking at the Resources and Efficiency question 1 which asked if "the information needed was easily accessible," the participants reported a higher rating of 6.0 for RTC virtual strips as compared to a mean rating of 5.4 in the paper strip condition.Similarly, looking at question 2 which asked if "the was available but required some work to get to it," the participants reported a mean rating of 3.4 for RTC and 3.3 for Paper.Question number 5 asked the participants if "they collaborated with other controllers and took action to help them," the participants reported a higher rating of 6.9 in the RTC virtual strip condition as compared to a rating of 6.8 in the Paper condition.Questions 3 and 4 of the Resources and Efficiency questions the results show a higher mean rating in the Paper condition as compared to the RTC condition.Looking at question 3 which asked "if information need to keep track of held aircraft was available," the participants reported a higher mean rating of 5.5 in the Paper condition as compared to the mean rating of 5.4 in the RTC condition.The Resources and Efficiency question 4 asked "if the actions required the minimum number of steps," with a higher mean participant rating of 5.4 in the Paper condition as compared to the mean RTC rating of 4.9.Finally, for question 6 which asked "if other controllers handled traffic in the way it was requested," the mean participant rating was the same in both Paper and RTC conditions with a mean rating of 6.88 for both RTC and Paper.6.9 0.125 6.9 0.125 0, p=1.0To assess the satisfaction aspect of usability, a set of 18 specific preference questions were included in the post study questionnaire.The responses were collected from all four controller participants with responses on a scale of 1 (Prefer Paper) to 7 (Prefer RTC).The results shown in Figure 8 above indicate that very high level of satisfaction ratings were achieved all the questions ranging from tracking aircraft status, and being aware of the direction of the flight, to managing sector handoff to ease of reading of information.In sum, results from the Post Run questionnaire indicate lower workload ratings for RTC condition, with only one of the workload elements not statistically significantly lower.Usability ratings for Traffic management performance questions are lower in the RTC condition than in the paper condition showing a preference for RTC over Paper, with not all of the questions showing a statistically significant difference.Usability ratings for Resources and efficiency questions showed mixed results.Post Study Usability responses and satisfaction ratings indicated a clear preference for RTC.
+DiscussionThe mean participant ratings for workload were lower in the RTC virtual strips condition as compared to the Paper condition for all four aspects of workload.There was a statistically significant main effect of condition for all aspects of workload measured except for the mental demand aspect of workload, which was similar for paper and virtual strips.It is possible that this mental workload result would decrease with increased training and increased familiarity.The participants had a minimal amount of training with the RTC virtual strips prior to the data collection.The total amount of time spent training with RTC was 3 hours and 20 minutes; it is possible that with more time training the participants might have reported lower mean mental demand workload rating for RTC condition as compared to the Paper condition resulting in a statistically significant main effect.The participants in this study had been using only traditional paper strips to manage traffic in their experience as professional ramp and air traffic controllers, and RTC was a new tool.The participant ratings for mental demand aspect of workload were lower in the RTC condition than in the paper condition, however this was not a statistically significant difference, perhaps more time training in preparation for the data collection runs, or a greater number of data collection runs might have allowed the participants to gain more experience with the tool resulting in a decrease in the mental demand aspect of workload of using the RTC virtual strips to perform their role as ramp controllers in the HITL.Also, due to the nature of the simulation study with a limited number of controller positions and a limited number of data collection runs, there were only four participants and only eight 90-minute data collection runs.Perhaps, future studies might include a greater number of participants and or data collection runs, thereby increasing the statistical power of the study.The participant ratings for the "effectiveness" aspect of usability were higher in the RTC virtual strip condition than the Paper condition for all of the Traffic Management Performance questions, with statistically significant results for some of these questions.The trend shows that RTC was more efficient than paper on all questions except for two.The lower RTC rating regarding managing the strips was possibly due to lack of familiarity and usage; potentially the participants did not perceive a difference in the efficiency between the two conditions (RTC virtual strips and Paper strips) or the lack of sufficient data in this study.Looking at the results of the Resources and Efficiency questions in relation to the results of the Traffic Management questions, the Traffic Management questions received a more consistently favorable and statistically significant positive rating for RTC than the Resources and Efficiency questions, perhaps the participants found using the RTC virtual strips to be more effective than using the paper strips.At the same time, these results might be interpreted to indicate that for some aspects of efficiency, the results were not a clear indication of a preference for RTC.Again, perhaps this is a function of the participants being new to the RTC virtual strips and given more time and experience using the RTC virtual strips, the participants rating of the efficiency aspect of usability might improve.Participants' ratings from the post study questionnaire for the "satisfaction" aspect of usability indicate a definite preference for the RTC over the Paper condition.Overall these results indicate a trend towards increased mean participant Usability ratings when using the RTC virtual strips as compared to using the paper strips across the three aspects of Usability assessed: effectiveness, efficiency and satisfaction.As in the TFDM prototype system study by Lockande (2012), the workload results from the current study indicate reduced workload in the RTC virtual strip condition as compared to corresponding baseline or paper strip condition.Similar to the Lockande (2012) study, one possibility is that a reduction in workload is a function of the RTC displaying data on the virtual flight strips that is digitally updated.Like the TBFM prototype used by Lockande, the RTC also integrates other operational data and presents it to the ramp controller in real time such that the ramp controller is not seeking out and verifying information regarding, for instance, Traffic Management Initiatives, or airport configuration, thereby reducing overall workload.The workload results indicating reduced Workload when using RTC along with the Usability results indicating a trend toward increased Usability when using RTC seem to indicate that the participants favored the RTC virtual strips as compared to the Paper condition.Future studies of the RTC may benefit from more training runs, as well as having either a greater number of participants or a greater number of data collection runs to increase the statistical power of the analyses.Recently, RTC has undergone a design refactoring, removing the touch capability, and going to a mouse only design.This refactoring was prompted by a couple of reasons.During the HITL testing of RTC, feedback from some of the controllers indicated that they prefer using the mouse over touch screen functionality.Also, it was decided to a larger 32' screen size for screen sharing with another technology in the field.Going to a larger screen meant possible degradation of touch screen precision along with possible increased fatigue while using the larger display.The controller feedback information along with deciding to go to a larger screen size resulted in the decision to go to a mouse only design.The SARDA tactical surface scheduler has also undergone some development and maturation as it has been integrated along with the RTC with a set of other Air Traffic Management Technologies as a part of NASA's ATD-2 effort (Malik et al, 2016).The ATD-2 Phase One field testing began in September of 2017 where RTC is currently in use by ramp controllers at CLT.Given that additional development and maturation has been completed on the RTC and the tactical scheduler tool, it will be important to follow up on this study to determine the impact of this refactoring on ramp controller user workload and usability ratings.Fig. 1 .Fig. 2 .12Fig. 1.RTC with virtual strips
+Fig. 3 .3Fig. 3. Post Run Questionnaire Workload question format
+Fig. 4 .4Fig. 4. Post-Run Questionnaire Usability question format
+Fig. 5 .5Fig. 5. Mean Participant Workload Rating
+Fig. 6 .6Fig. 6.Mean participant ratings of Traffic Management Performance
+Fig. 7 .Fig. 8 .78Fig. 7. Resources and Efficiency Participant Mean Response
+
+Table 1 .1Mean Workload response all sectors
+Mean Participant Workload Ratings Across Four Aspects of WorkloadMeanMeanAspect ofResponseSEResponseSEWorkloadPaperPaperRTCRTCF(1,3)=Mental Demand5.70.823.91.673.59, p=.155Time Pressure4.90.572.40.5048.46, *p=.006Physical Demand4.61.322.81.4384.26, *p=.003Frustration3.60.311.30.3429.73, *p=.012
+Table 2 .2Participant ratings of Traffic Management and Performance
+Traffic Management Performance Questions Mean Response with Standard Error and F values6. Metered Departures6.20.4936.60.12.73, p=.4567. Responded Promptly6.80.1886.90.063.33, p=.604QuestionMean Paper S.E. Mean RTC S.E.F (1,3)=1. Maintained Separation6.70.1576.90.1259, p=.0582. Maintained Flow6.10.3736.60.29512, *p=.043. Minimized Delay5.90.3296.50.25 22.09, *p=.0184. Avoided Grid-lock6.60.1616.90.0636.82, p=.0885. Maintained Pressure on Runway5.90.2586.70.23754, *p=.005
+Table 3 .3Resources and Efficiency Mean Participant Resonse Resources and Efficiency Questions Mean Response with Standard Error and F valuesQuestionMean PaperMean RTCS.E.F (1,3)=1. Information Accessible5.40.9046.00.654 1.86, p=.2662. Information Available,3.30.7533.40.74.03, p=.878but required work3. Held Aircraft5.50.7295.40.582.16, p=.718Information Available4. Actions Required5.40.4394.90.161 1.85, p=.267Minimum Steps5. Collaborated6.80.256.90.125.27, p=.6386. Others Handled Trafficas Expected
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+AcknowledgementsThe author acknowledges the work of the team of people who made this research possible.I express my special thanks to Miwa Hayashi, Yoon Jung, Savita Verma, Katherine Lee and Victoriana Delosantos.
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