Filling Delivery Time Automatically Based on Couriers' Trajectories
Sijie Ruan, Xi Fu, Cheng Zhi Long, Zi Xiong, Jie Bao, Ruiyuan Li, Yiheng Chen, Shengnan Wu, Yu Zheng · IEEE Transactions on Knowledge and Data Engineering · 2021
Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the delivery time of each parcel manually, which is essential for delivery insurances, delivery performance evaluations, and customer available time discovery. Stay points extracted from couriers' trajectories provide a chance to fill the delivery time automatically to ease their burdens. However, it is challenging due to inaccurate delivery locations and various stay scenarios. To this end, we propose the improved Delivery Time Inference (DTInf+), to infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which organizes waybills and stay points by delivery trips, 2) Delivery Location Mining, which obtains the delivery location for each address and each Geocoded waybill location by mining historical delivery caused stay points, and 3) Delivery Event-based Matching, which jointly selects the best-matched stay point for waybills at the same delivery location based on Pointer Network-like model SPSelector to infer the delivery time. Extensive experiments and case studies based on real-world datasets from JD Logistics confirm the effectiveness of our approach. Finally, a system powered by DTInf+ is deployed in JD Logistics.