Hugging Face's logo

Datasets:
Cainiao-AI
/
LaDe

Size:
10M<n<100M
ArXiv:
Tags:
Logistics
Last-mile Delivery
Spatial-Temporal
Graph
License:
Dataset card Viewer Files Community
4
jinyan218 commited on
Commit
46d1433
·
1 Parent(s): 3a3ccab

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +52 -29
README.md CHANGED
@@ -1,5 +1,13 @@
1
  ---
2
  license: apache-2.0
 
 
 
 
 
 
 
 
3
  ---
4
  # 1. About Dataset
5
  **LaDe** is a publicly available last-mile delivery dataset with millions of packages from industry.
@@ -13,7 +21,7 @@ If you use this dataset for your research, please cite this paper: {xxx}
13
  LaDe is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario.
14
  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.
15
 
16
- 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/".
17
  The structure of "/data/raw/" should be like:
18
  ```
19
  * /data/raw/
@@ -25,10 +33,20 @@ The structure of "/data/raw/" should be like:
25
  * ...
26
  ```
27
 
28
- Each sub-dataset contains 5 csv files, with each representing the data from a specific city.
29
- Below is the detailed field of each sub-dataset.
 
 
 
 
 
 
 
 
 
30
 
31
  # 3. Description
 
32
  ## 3.1 LaDe-P
33
  | Data field | Description | Unit/format |
34
  |----------------------------|----------------------------------------------|--------------|
@@ -80,41 +98,46 @@ Below is the detailed field of each sub-dataset.
80
 
81
 
82
  # 4. Leaderboard
 
83
  ## 4.1 Route Prediction
84
- | Method | HR@3 | KRC | LSD | ED |
85
- |--------------|----------------|----------------|----------------|----------------|
86
- | TimeGreedy | 59.38 | 39.65 | 5.30 | 2.26 |
87
- | DistanceGreedy | 60.81 | 42.78 | 5.46 | 1.95 |
88
- | OR-Tools | 62.23 | 44.87 | 4.77 | 1.90 |
89
- | LightGBM | 70.33 | 54.44 | 3.36 | 1.94 |
90
- | FDNET | 68.55 ± 0.10 | 51.99 ± 0.12 | 4.28 ± 0.02 | 1.89 ± 0.01 |
91
- | DeepRoute | 71.57 ± 0.07 | 56.33 ± 0.13 | 3.31 ± 0.06 | 1.86 ± 0.01 |
92
- | Graph2Route | 71.41 ± 0.04 | 56.46 ± 0.02 | 3.18 ± 0.01 | 1.88 ± 0.01 |
93
 
 
 
 
 
 
 
 
 
 
 
 
94
 
95
 
96
  ## 4.2 Estimated Time of Arrival Prediction
97
- | Model | MAE | RMSE | ACC@30 |
98
- |----------|-------|-------|--------|
99
- | LightGBM | 30.99 | 35.04 | 0.59 |
100
- | SPEED | 23.75 | 27.86 | 0.73 |
101
- | KNN | 36.00 | 31.89 | 0.58 |
102
- | MLP | 21.54 ± 2.2 | 25.05 ± 2.46 | 0.79 ± 0.04 |
103
- | FDNET | **18.47 ± 0.25** | **21.44 ± 0.28** | **0.84 ± 0.01** |
104
 
 
 
 
 
 
 
 
105
 
106
 
107
  ## 4.3 Spatio-temporal Graph Forecasting
108
- | Method | MAE | RMSE |
109
- |-------|-----------------|-----------------|
110
- | HA | 5.26 | 11.39 |
111
- | DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
112
- | STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
113
- | GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
114
- | ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
115
- | MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
116
- | AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
117
- | STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
 
 
118
 
119
 
120
 
 
1
  ---
2
  license: apache-2.0
3
+ task_categories:
4
+ - time-series-forecasting
5
+ tags:
6
+ - Spatial-Temporal
7
+ - Graph
8
+ - Logistic
9
+ size_categories:
10
+ - 10M<n<100M
11
  ---
12
  # 1. About Dataset
13
  **LaDe** is a publicly available last-mile delivery dataset with millions of packages from industry.
 
21
  LaDe is composed of two subdatasets: i) [LaDe-D](https://huggingface.co/datasets/Cainiao-AI/LaDe-D), which comes from the package delivery scenario.
22
  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.
23
 
24
+ LaDe can be used for research purposes. Before you download the dataset, please read these terms. And [Code link](https://github.com/wenhaomin/LaDe). Then put the data into "/data/raw/".
25
  The structure of "/data/raw/" should be like:
26
  ```
27
  * /data/raw/
 
33
  * ...
34
  ```
35
 
36
+ Each sub-dataset contains 5 csv files, with each representing the data from a specific city, the detail of each city can be find in the following table.
37
+
38
+
39
+ | City | Description |
40
+ |------------|----------------------------------------------------------------------------------------------|
41
+ | Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
42
+ | Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
43
+ | Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
44
+ | Jilin | A middle-size city in China, with a small number of orders each day. |
45
+ | Yantai | A small city in China, with a small number of orders every day. |
46
+
47
 
48
  # 3. Description
49
+ Below is the detailed field of each sub-dataset.
50
  ## 3.1 LaDe-P
51
  | Data field | Description | Unit/format |
52
  |----------------------------|----------------------------------------------|--------------|
 
98
 
99
 
100
  # 4. Leaderboard
101
+ Blow shows the performance of different methods in Shanghai.
102
  ## 4.1 Route Prediction
 
 
 
 
 
 
 
 
 
103
 
104
+ Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
105
+
106
+ | Method | HR@3 | KRC | LSD | ED |
107
+ |--------------|--------------|--------------|-------------|-------------|
108
+ | TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 |
109
+ | DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 |
110
+ | OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 |
111
+ | LightGBM | 73.76 | 55.71 | 3.01 | 1.84 |
112
+ | FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
113
+ | DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
114
+ | Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
115
 
116
 
117
  ## 4.2 Estimated Time of Arrival Prediction
 
 
 
 
 
 
 
118
 
119
+ | Method | MAE | RMSE | ACC@30 |
120
+ | ------ |--------------|--------------|-------------|
121
+ | LightGBM | 30.99 | 35.04 | 0.59 |
122
+ | SPEED | 23.75 | 27.86 | 0.73 |
123
+ | KNN | 36.00 | 31.89 | 0.58 |
124
+ | MLP | 21.54 ± 2.20 | 25.05 ± 2.46 | 0.79 �� 0.04 |
125
+ | FDNET | 18.47 ± 0.25 | 21.44 ± 0.28 | 0.84 ± 0.01 |
126
 
127
 
128
  ## 4.3 Spatio-temporal Graph Forecasting
129
+
130
+
131
+ | Method | MAE | RMSE |
132
+ |-------|-------------|-------------|
133
+ | HA | 4.63 | 9.91 |
134
+ | DCRNN | 3.69 ± 0.09 | 7.08 ± 0.12 |
135
+ | STGCN | 3.04 ± 0.02 | 6.42 ± 0.05 |
136
+ | GWNET | 3.16 ± 0.06 | 6.56 ± 0.11 |
137
+ | ASTGCN | 3.12 ± 0.06 | 6.48 ± 0.14 |
138
+ | MTGNN | 3.13 ± 0.04 | 6.51 ± 0.13 |
139
+ | AGCRN | 3.93 ± 0.03 | 7.99 ± 0.08 |
140
+ | STGNCDE | 3.74 ± 0.15 | 7.27 ± 0.16 |
141
 
142
 
143