Highlights The aim of this study is achieving higher on-time arrival reliability for customers. The reliability is improved with reliable path method and historical traffic information. A system-beneficial path guidance is proposed for mitigating road network congestion. Total travel time of the road network is reduced by the system-beneficial path guidance.

    Abstract Dynamic shared autonomous taxi (SAT) systems are regarded as a promising means of improving travel flexibility. With no human drivers, SATs urgently require precise traffic information in order to plan accurate paths independently; in addition, on-time arrival is an essential service quality in SAT systems. In this study, taxis are assumed to be replaced with ride-sharing autonomous vehicles. To improve the probability of on-time arrival, the reliable path concept and collected travel time information are used to facilitate path finding for SATs, and the potential benefits are examined. Two simulation scenarios—one based on historical traffic information and the other based on real-time traffic information—are executed to evaluate the information’s usefulness in reliable path finding. In simulation results, reliable path scenarios showed a higher on-time arrival ratio than shortest path scenarios, in which the shortest path algorithm is used in path finding for SATs, and the historical information-based scenarios showed a higher on-time arrival ratio than the real-time information-based scenarios. A system-beneficial path finding method is proposed and is verified to be effective for mitigating road network congestion.


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    Titel :

    Dynamic shared autonomous taxi system considering on-time arrival reliability


    Beteiligte:
    Liu, Zhiguang (Autor:in) / Miwa, Tomio (Autor:in) / Zeng, Weiliang (Autor:in) / Bell, Michael G.H. (Autor:in) / Morikawa, Takayuki (Autor:in)


    Erscheinungsdatum :

    2019-04-17


    Format / Umfang :

    17 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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