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LaDe

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Logistics
Last-mile Delivery
Spatial-Temporal
Graph
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
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  ---
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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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  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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- 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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  * ...
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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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  # 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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  # 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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  ## 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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  ## 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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1
  ---
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  license: apache-2.0
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+ task_categories:
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+ - time-series-forecasting
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+ tags:
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+ - Spatial-Temporal
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+ - Graph
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+ - Logistic
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+ size_categories:
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+ - 10M<n<100M
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  ---
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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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  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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+ 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/".
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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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  * ...
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  ```
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+ 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.
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+
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+
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+ | City | Description |
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+ |------------|----------------------------------------------------------------------------------------------|
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+ | Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
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+ | Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
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+ | Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
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+ | Jilin | A middle-size city in China, with a small number of orders each day. |
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+ | Yantai | A small city in China, with a small number of orders every day. |
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+
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  # 3. Description
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+ Below is the detailed field of each sub-dataset.
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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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  # 4. Leaderboard
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+ Blow shows the performance of different methods in Shanghai.
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  ## 4.1 Route Prediction
 
 
 
 
 
 
 
 
 
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+ Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.
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+
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+ | Method | HR@3 | KRC | LSD | ED |
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+ |--------------|--------------|--------------|-------------|-------------|
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+ | TimeGreedy | 57.65 | 31.81 | 5.54 | 2.15 |
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+ | DistanceGreedy | 60.77 | 39.81 | 5.54 | 2.15 |
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+ | OR-Tools | 66.21 | 47.60 | 4.40 | 1.81 |
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+ | LightGBM | 73.76 | 55.71 | 3.01 | 1.84 |
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+ | FDNET | 73.27 ± 0.47 | 53.80 ± 0.58 | 3.30 ± 0.04 | 1.84 ± 0.01 |
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+ | DeepRoute | 74.68 ± 0.07 | 56.60 ± 0.16 | 2.98 ± 0.01 | 1.79 ± 0.01 |
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+ | Graph2Route | 74.84 ± 0.15 | 56.99 ± 0.52 | 2.86 ± 0.02 | 1.77 ± 0.01 |
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  ## 4.2 Estimated Time of Arrival Prediction
 
 
 
 
 
 
 
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+ | Method | 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 ± 2.20 | 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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  ## 4.3 Spatio-temporal Graph Forecasting
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+
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+
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+ | Method | MAE | RMSE |
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+ |-------|-------------|-------------|
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+ | HA | 4.63 | 9.91 |
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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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