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--- |
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license: apache-2.0 |
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task_categories: |
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- Last-mile Related Research |
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tags: |
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- Spatial-Temporal |
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- Graph |
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- Logistics |
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- Last-mile Delivery |
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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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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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If you use this dataset for your research, please cite this paper: {xxx} |
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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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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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* 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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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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| 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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# 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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| **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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## 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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# 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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| 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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| 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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# 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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``` |
