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：现实世界的最后一公里配送数据集对于物流、供应链管理及时空数据挖掘研究至关重要。尽管目前已开发出大量算法，但尚不存在被广泛接受的公开最后一公里配送数据集以支撑该领域研究。本文首次提出工业界公开的拥有数百万包裹的最后一公里配送数据集\texttt{LaDe}。LaDe具有三个独特特征：(1) 大规模性：涵盖21k名快递员6个月真实运营中的10,677k个包裹；(2) 信息全面性：提供原始包裹信息（如位置与时间要求）及任务事件信息（记录取件与完成等事件发生时快递员的位置与时间）；(3) 多样性：包含多个城市中包裹取件与配送等多场景数据，各城市因人口等特征差异呈现出独特时空模式。我们通过为每项任务运行若干经典基线模型，在三个任务上验证了LaDe的有效性。我们相信，LaDe的大规模性、全面性与多样性特征将为供应链社群、数据挖掘社群及其他领域的研究者提供前所未有的机遇。数据集主页公开访问地址为：https://huggingface.co/datasets/Cainiao-AI/LaDe。