Trajectory feature extraction and multi‐criteria k nearest neighbour based job‐to‐crowd matching for the crowdshipping last mile delivery

• Published in: IET control theory & applications Vol. 17; no. 17; pp. 2304 - 2312
• Main Authors: Tsai, Pei‐Wei, Xue, Xingsi, Zhang, Jing
• Format: Journal Article
• Language: English
• Published: Stevenage John Wiley & Sons, Inc 01-11-2023; Wiley
• Subjects: Algorithms; data analysis; Feature extraction; Freight transportation; Global positioning systems; GPS; intelligent control; Matching; Professionals; Solution space; Travel
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/cth2.12489

Abstract
Sustainable freight transportation is one of the essential concepts in the smart city. Under this concept, many people connected with mobile devices produce location data and potential opportunities for transporting small objects in a more environmentally friendly and sustainable way. Crowdshipping, which utilises public people as transportation, is one of the terminal solutions in the last mile delivery scenario. Nevertheless, precisely assigning the delivery to the right crowd willing to accept the job is challenging because the solution space is too large to perform a full search. This article proposes a trajectory feature extraction algorithm and a task‐to‐crowd matching (T2CM) algorithm for coping with the job‐to‐crowd assignment problem. A simulation based on the real‐world dataset is conducted on three different scenarios to justify the outcome from our proposed method to the job assignment results.