Modeling Spatial-Temporal Constraints and Spatial-Transfer Patterns for Couriers' Package Pick-up Route Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 12; pp. 13787 - 13800
• Main Authors: Wen, Haomin, Lin, Youfang, Hu, Yuxuan, Wu, Fan, Xia, Mingxuan, Zhang, Xinyi, Wu, Lixia, Hu, Haoyuan, Wan, Huaiyu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Behavioral sciences; Constraint modelling; Customization; Decision making; decision making process; Logistics; Package pick-up route prediction; Planning; Predictive models; Route selection; Routing; routing patterns; spatial-temporal constraints; Task analysis
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3301661

Couriers' package pick-up route prediction is a fundamental task in the emerging intelligent logistics systems. It is beneficial for order dispatching and arrival-time estimation by leveraging the predicted routes to improve those downstream tasks. However, the package pick-up route prediction problem is challenging since couriers' behaviors are affected by both strict Spatial-Temporal Constraints (STC) and personalized spatial-transfer patterns (STP). Specifically, couriers have to consider explicitly spatial-temporal requirements such as the locations of packages and the promised pick-up time when selecting future routes. In addition, couriers have personalized mobility patterns between different locations, which are implicit patterns hidden behind couriers' historical trajectories and cannot be ignored to precisely depict their behaviors. This paper proposes a novel framework, named CP-Route, to predict a specific courier's future package pick-up route under strict spatial-temporal constraints, and enhance the prediction performance by learning the spatial-transfer patterns (i.e., the routing patterns) of couriers. A sophisticated encoder is designed to capture the STC and STP, and a mixed-distribution-based decoder is designed to simultaneously consider the influence of spatial-temporal constraints and routing patterns on couriers' final decisions. Extensive experiments conducted on an industry-scale logistics dataset demonstrate the superiority of our proposed framework against the existing baseline methods. The online A/B test shows our contribution to the improvement of arrival time prediction.