Abstract: Predicting a couriers pick-up arrival time, i.e., estimating the arrival time of the courier after a user places a package pick-up order, is a fundamental task in logistics platforms. Accurate pick-up arrival time prediction can optimize logistics efficiency and improve user experience. The problem faces the following challenges: 1) The couriers pick-up arrival time is affected by a variety of complex spatiotemporal factors, including the spatiotemporal characteristics of the target order, as well as the correlations between the target order and other unpicked-up orders. 2) During the execution of the pick-up task, new orders will be continuously assigned to the courier by the system, resulting in changes in the package pick-up route, which brings great difficulties to the arrival time prediction. In response to the above challenges, a multi-task deep spatial-temporal network (named MSTN4PAT) is proposed to accurately predict package pick-up arrival time, which learns the complex spatiotemporal patterns of couriers pick-up arrival time from massive historical data. MSTN4PAT fully exploits the intrinsic relationship between the origin and destination of the target order, uses multi-task learning to model the interaction between orders, and efficiently integrates various features from the perspectives of feature width and feature depth to achieve accurate arrival time predictions. The experimental results on two real-world datasets show that MSTN4PAT significantly outperforms other comparative medels. 

Key words: package pick-up, estimated arrival time, multi-task leaning, spatial-temporal data mining, spatial-temporal correlation