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KDD 2020 | Doing in One Go: Delivered Time Inference Based on Coueriers' Trajectories

Accepted Papers

Doing in One Go: Delivered Time Inference Based on Coueriers' Trajectories

Sijie Ruan: Xidian University; Zi Xiong: Wuhan University; Cheng Long: Nanyang Technological University; Yiheng Chen: JD Logistics; Jie Bao: JD Intelligent Cities Research; Tianfu He: Harbin Institute of Technology; Ruiyuan Li: Xidian University; Shengnan Wu: JD Logistics; Zhongyuan Jiang: Xidian University; Yu Zheng: Xidian 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.

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