Real-world last-mile delivery datasets are crucial for research in logistics, supply chain management, and spatio-temporal data mining. Despite a plethora of algorithms developed to date, no widely accepted, publicly available last-mile delivery dataset exists to support research in this field. In this paper, we introduce LaDe, the first publicly available last-mile delivery dataset with millions of packages from the industry. LaDe has three unique characteristics: (1) Large-scale. It involves 10,677k packages of 21k couriers over 6 months of real-world operation. (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. (3) Diversity. The dataset includes data from various scenarios, including package pick-up and delivery, and from multiple cities, each with its unique spatio-temporal patterns due to their distinct characteristics such as populations. We verify LaDe on three tasks by running several classical baseline models per task. We believe that the large-scale, comprehensive, diverse feature of LaDe can offer unparalleled opportunities to researchers in the supply chain community, data mining community, and beyond.
LaDe is composed of two subdatasets: i) LaDe-D, which comes from the package delivery scenario. ii) LaDe-P, which comes from the package pickup scenario. To facilitate the utilization of the dataset, each sub-dataset is presented in CSV format.
LaDe can be used for research purposes. Before you download the dataset, please read these terms. Then put the data into "/data/raw/".
The structure of "/data/raw/" should be like:
* /data/raw/
* delivery
* delivery_sh.csv
* ...
* pickup
* pickup_sh.csv
* ...
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.
| City | Description |
|---|---|
| Shanghai | One of the most prosperous cities in China, with a large number of orders per day. |
| Hangzhou | A big city with well-developed online e-commerce and a large number of orders per day. |
| Chongqing | A big city with complicated road conditions in China, with a large number of orders. |
| Jilin | A middle-size city in China, with a small number of orders each day. |
| Yantai | A small city in China, with a small number of orders every day. |
Below is the detailed field of each sub-dataset.
Code for tasks on Cainiao-LaDe (Last-mile Delivery dataset).
route_prediction: code for route prediction task
stg_prediction: code for spatio-temporal graph forecasting.
time_prediction: code for estimated time of arrival (ETA) prediction.
For the detailed running instructions of each task, plese see the corresponding readme in its directory.
