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Last-mile Delivery
Spatial-Temporal
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  license: apache-2.0
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  license: apache-2.0
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+ # 1 About Dataset
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+ **LaDe** is a publicly available last-mile delivery dataset with millions of packages from industry.
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+ It has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation.
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+ (2) Comprehensive information, it offers original package information, such as its location and time requirements, as well as task-event information, which records when and where the courier is while events such as task-accept and task-finish events happen.
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+ (3) Diversity: the dataset includes data from various scenarios, such as package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations.
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+
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+ If you use this dataset for your research, please cite this paper: {xxx}
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+
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+ # 2 Download
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+ LaDe is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario.
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+ ii) [LaDe-P](https://huggingface.co/datasets/Cainiao-AI/LaDe-P), which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format.
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+
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+ LaDe can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](xxx). Then put the data into "/data/raw/".
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+ The structure of "/data/raw/" should be like:
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+ ```
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+ * /data/raw/
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+ * delivery
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+ * delivery_sh.csv
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+ * ...
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+ * pickup
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+ * pickup_sh.csv
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+ * ...
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+ ```
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+
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+ Each sub-dataset contains 5 csv files, with each representing the data from a specific city.
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+ Below is the detailed field of each sub-dataset.
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+
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+ # 3 Description
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+ ## 3.1 LaDe-P
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+ | Data field | Description | Unit/format |
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+ |----------------------------|----------------------------------------------|--------------|
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+ | **Package information** | | |
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+ | package_id | Unique identifier of each package | Id |
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+ | time_window_start | Start of the required time window | Time |
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+ | time_window_end | End of the required time window | Time |
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+ | **Stop information** | | |
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+ | lng/lat | Coordinates of each stop | Float |
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+ | city | City | String |
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+ | region_id | Id of the Region | String |
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+ | aoi_id | Id of the AOI (Area of Interest) | Id |
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+ | aoi_type | Type of the AOI | Categorical |
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+ | **Courier Information** | | |
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+ | courier_id | Id of the courier | Id |
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+ | **Task-event Information** | | |
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+ | accept_time | The time when the courier accepts the task | Time |
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+ | accept_gps_time | The time of the GPS point closest to accept time | Time |
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+ | accept_gps_lng/lat | Coordinates when the courier accepts the task | Float |
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+ | pickup_time | The time when the courier picks up the task | Time |
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+ | pickup_gps_time | The time of the GPS point closest to pickup_time | Time |
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+ | pickup_gps_lng/lat | Coordinates when the courier picks up the task | Float |
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+ | **Context information** | | |
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+ | ds | The date of the package pickup | Date |
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+
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+
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+ ## 3.2 LaDe-D
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+ | Data field | Description | Unit/format |
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+ |-----------------------|--------------------------------------|---------------|
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+ | **Package information** | | |
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+ | package_id | Unique identifier of each package | Id |
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+ | **Stop information** | | |
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+ | lng/lat | Coordinates of each stop | Float |
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+ | city | City | String |
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+ | region_id | Id of the region | Id |
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+ | aoi_id | Id of the AOI | Id |
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+ | aoi_type | Type of the AOI | Categorical |
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+ | **Courier Information** | | |
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+ | courier_id | Id of the courier | Id |
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+ | **Task-event Information**| | |
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+ | accept_time | The time when the courier accepts the task | Time |
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+ | accept_gps_time | The time of the GPS point whose time is the closest to accept time | Time |
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+ | accept_gps_lng/accept_gps_lat | Coordinates when the courier accepts the task | Float |
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+ | delivery_time | The time when the courier finishes delivering the task | Time |
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+ | delivery_gps_time | The time of the GPS point whose time is the closest to the delivery time | Time |
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+ | delivery_gps_lng/delivery_gps_lat | Coordinates when the courier finishes the task | Float |
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+ | **Context information** | | |
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+ | ds | The date of the package delivery | Date |
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+
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+
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+ # 4 Leaderboard
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+ ## 4.1 Route Prediction
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+ | Method | HR@3 | KRC | LSD | ED |
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+ |--------------|----------------|----------------|----------------|----------------|
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+ | TimeGreedy | 59.38 | 39.65 | 5.30 | 2.26 |
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+ | DistanceGreedy | 60.81 | 42.78 | 5.46 | 1.95 |
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+ | OR-Tools | 62.23 | 44.87 | 4.77 | 1.90 |
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+ | LightGBM | 70.33 | 54.44 | 3.36 | 1.94 |
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+ | FDNET | 68.55 ± 0.10 | 51.99 ± 0.12 | 4.28 ± 0.02 | 1.89 ± 0.01 |
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+ | DeepRoute | 71.57 ± 0.07 | 56.33 ± 0.13 | 3.31 ± 0.06 | 1.86 ± 0.01 |
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+ | Graph2Route | 71.41 ± 0.04 | 56.46 ± 0.02 | 3.18 ± 0.01 | 1.88 ± 0.01 |
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+
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+
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+
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+ ## 4.2 Estimated Time of Arrival Prediction
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+ | Model | MAE | RMSE | ACC@30 |
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+ |----------|-------|-------|--------|
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+ | LightGBM | 30.99 | 35.04 | 0.59 |
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+ | SPEED | 23.75 | 27.86 | 0.73 |
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+ | KNN | 36.00 | 31.89 | 0.58 |
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+ | MLP | 21.54 ± 2.2 | 25.05 ± 2.46 | 0.79 ± 0.04 |
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+ | FDNET | **18.47 ± 0.25** | **21.44 ± 0.28** | **0.84 ± 0.01** |
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+
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+
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+
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+ ## 4.3 Spatio-temporal Graph Forecasting
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+ | Method | MAE | RMSE |
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+ |-------|-----------------|-----------------|
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+ | HA | 5.26 | 11.39 |
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+ | DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
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+ | STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
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+ | GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
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+ | ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
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+ | MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
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+ | AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
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+ | STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
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+
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+
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+
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+ # 5 Citation
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+ To cite this repository:
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+
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+ ```shell
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+ @software{pytorchgithub,
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+ author = {xx},
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+ title = {xx},
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+ url = {xx},
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+ version = {0.6.x},
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+ year = {2021},
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+ }
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+ ```