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 \texttt{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. The dataset homepage is publicly available at https://huggingface.co/datasets/Cainiao-AI/LaDe.

翻译：真实世界的最后一公里配送数据集对于物流、供应链管理和时空数据挖掘研究至关重要。尽管迄今为止已开发出大量算法，但尚未存在广泛接受、公开可用的最后一公里配送数据集以支持该领域研究。本文提出\texttt{LaDe}，这是首个来自工业界、包含数百万包裹的公开最后一公里配送数据集。LaDe具有三大独特特征：（1）大规模。涵盖21,000名快递员在6个月真实运营中的10,677,000个包裹数据。（2）信息全面。提供原始包裹信息（如位置和时间要求）以及任务事件信息（记录任务接收、任务完成等事件发生时快递员的位置与时间）。（3）多样性。数据集包含来自多种场景的数据，如包裹取件和配送，并覆盖多个城市，这些城市因其人口等不同特征而具有独特的时空模式。我们通过为每项任务运行多个经典基线模型，在三个任务上验证了LaDe。我们相信，LaDe的大规模、全面性和多样性特征将为供应链领域、数据挖掘领域及其他领域的研究人员提供无与伦比的机遇。数据集主页公开访问地址为https://huggingface.co/datasets/Cainiao-AI/LaDe。