Doing in one go : delivery time inference based on couriers' trajectories

• Main Authors: Ruan, Sijie, Xiong, Zi, Long, Cheng, Chen, Yiheng, Bao, Jie, He, Tianfu, Li, Ruiyuan, Wu, Shengnan, Jiang, Zhongyuan, Zheng, Yu
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering::Information systems::Database management; Delivery Time Inference; Trajectory Data Mining
• Online Access: https://hdl.handle.net/10356/148159
• Institution: Nanyang Technological University

The rapid development of e-commerce requires efficient and reliable logistics services. Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the accurate delivery time of each parcel manually, which provides vital information for applications like delivery insurances, delivery performance evaluations, and customer available time discovery. Couriers' trajectories generated by their PDAs provide a chance to infer the delivery time automatically to ease the burdens on the couriers. However, directly using the nearest stay point to infer the delivery time is under satisfactory due to two challenges: 1) inaccurate delivery locations, and 2) various stay scenarios. To this end, we propose Delivery Time Inference (DTInf), to automatically infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which detects stay points from trajectories, and separates stay points and waybills by delivery trips, 2) Delivery Location Correction, which infers true delivery locations of waybills by mining historical deliveries, and 3) Delivery Event-based Matching, which selects the best-matched stay point for waybills in the same delivery location to infer the delivery time. Extensive experiments and case studies based on large scale real-world waybill and trajectory data from JD Logistics confirm the effectiveness of our approach. Finally, we introduce a system based on DTInf, which is deployed and used internally in JD Logistics.