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--- |
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license: apache-2.0 |
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tags: |
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- Logistics |
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- Last-mile Delivery |
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- Spatial-Temporal |
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- Graph |
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size_categories: |
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- 10M<n<100M |
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--- |
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Dataset Download: https://huggingface.co/datasets/Cainiao-AI/LaDe/tree/main |
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Dataset Website: https://cainiaotechai.github.io/LaDe-website/ |
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Code Link:https://github.com/wenhaomin/LaDe |
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Paper Link: https://arxiv.org/abs/2306.10675 |
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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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# 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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* road-network |
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* roads.csv |
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``` |
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road-network/roads.csv records the road network of the five cities. |
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Each sub-dataset (delivery, pickup) 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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If you find this helpful, please cite our paper: |
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```shell |
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@misc{wu2023lade, |
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title={LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry}, |
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author={Lixia Wu and Haomin Wen and Haoyuan Hu and Xiaowei Mao and Yutong Xia and Ergang Shan and Jianbin Zhen and Junhong Lou and Yuxuan Liang and Liuqing Yang and Roger Zimmermann and Youfang Lin and Huaiyu Wan}, |
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year={2023}, |
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eprint={2306.10675}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.DB} |
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} |
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``` |
