Modeling Intra- and Inter-community Information for Route and Time Prediction in Last-mile Delivery

Last-mile delivery, which refers to delivering packages from the depot to customers, is a crucial step for logistics service. The Route and Time Prediction (RTP) in last-mile package delivery is beneficial to improve customers’ experience and supervise couriers’ behavior. However, the limited raw information brings great challenges to accurately predict the route and delivery time. In this paper, we propose a deep model named I
²
RTP, which explores the heterogeneous representation of the package’s community to help predict the delivery route and estimate the arrival time of each package. Specifically, for the entire delivery route prediction, we model the inter- and intra-community information to learn the route features from global and local perspectives. Besides, by integrating the community representation with package features, our model could make more accurate predictions of the next-delivery package and its time duration. Experiments on the offline dataset and the online deployment on Cainiao’s Delivery System demonstrate the effectiveness of our proposed method, as well as validate the rationality of the global and local prediction pipeline.