Trajectory feature extraction and multi‐criteria k nearest neighbour based job‐to‐crowd matching for the crowdshipping last mile delivery
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Published:2023-05-12
Issue:17
Volume:17
Page:2304-2312
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ISSN:1751-8644
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Container-title:IET Control Theory & Applications
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language:en
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Short-container-title:IET Control Theory & Appl
Author:
Tsai Pei‐Wei1ORCID,
Xue Xingsi2ORCID,
Zhang Jing3
Affiliation:
1. Department of Computing Technologies Swinburne University of Technology Hawthorn Victoria Australia
2. Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China
3. School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian China
Abstract
AbstractSustainable 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.
Funder
Swinburne University of Technology
National Natural Science Foundation of China
Natural Science Foundation of Fujian Province
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering