Cainiao-AI/LaDe

Dataset Download: https://huggingface.co/datasets/Cainiao-AI/LaDe/tree/main Dataset Website: https://cainiaotechai.github.io/LaDe-website/ Code Link:https://github.com/wenhaomin/LaDe Paper Link: https://arxiv.org/abs/2306.10675

1. About Dataset

LaDe is a publicly available last-mile delivery dataset with millions of packages from industry. It 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, 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. [image]

2. Download

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. And Code link. 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  
        * ...
    * road-network  
        * roads.csv
    * data_with_trajectory_20s
        * courier_detailed_trajectory_20s.pkl.xz

road-network/roads.csv records the road network of the five cities.

datawithtrajectory_20s/* records the trajectory of courier.

import pandas as pd
>>> import pandas as pd
>>> df = pd.read_pickle("courier_detailed_trajectory_20s.pkl.xz")
>>> df.head(3)
    ds                        postman_id        gps_time           lat           lng
0  321  106f5ac22cfd1574b196d16fed62f90d  03-21 07:31:58  3.953700e+06  3.053400e+06
1  321  106f5ac22cfd1574b196d16fed62f90d  03-21 07:32:18  3.953700e+06  3.053398e+06
2  321  106f5ac22cfd1574b196d16fed62f90d  03-21 07:32:41  3.953700e+06  3.053398e+06

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.

3. Description

Below is the detailed field of each sub-dataset.

3.1 LaDe-P

3.2 LaDe-D

4. Leaderboard

Blow shows the performance of different methods in Shanghai.

4.1 Route Prediction

Experimental results of route prediction. We use bold and underlined fonts to denote the best and runner-up model, respectively.

4.2 Estimated Time of Arrival Prediction

4.3 Spatio-temporal Graph Forecasting

5. Citation

If you find this helpful, please cite our paper:

@misc{wu2023lade,
      title={LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry}, 
      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},
      year={2023},
      eprint={2306.10675},
      archivePrefix={arXiv},
      primaryClass={cs.DB}
}