An efficient matching method for dispatching autonomous vehicles

Ming Li, Nan Zheng, Xinkai Wu, Xiang Huo

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther


Autonomous vehicles (AVs), as an emerging transportation tool, are widely discussed for improving traffic mobility. Particularly, when combining with prevalent shared transportation modes, i.e., car-sharing and ride-sharing, AVs show promising future. However, the efficiency of searching for an optimal match between shared autonomous vehicles and passengers has long become a critical problem. This research tackles this issue by developing a model which has better efficiency. The essential task is to dispatch vehicles to serve passengers, which is similar to vehicle routing problem. But unlike existing vehicle routing models, our method makes use of the similarity of passengers' demand instead of directly matching vehicles and passengers. In this way, a cluster-based algorithm is proposed to find near-optimal solutions. Numerical experiments on large-size are performed to present the validity of the optimization model and efficiency of the proposed algorithm. Results indicate that the cluster-based algorithm could bring benefit to minimizing the number of vehicles and total travel distance.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538670248
Publication statusPublished - Oct 2019
EventIEEE Conference on Intelligent Transportation Systems 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019
Conference number: 22nd


ConferenceIEEE Conference on Intelligent Transportation Systems 2019
Abbreviated titleITSC 2019
CountryNew Zealand

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