  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.
