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.

@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}
}