Abstract In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, routing, and dynamic pricing are covered. Most of the literature has appeared in the last few years, and several core challenges are to continue to be tackled: model complexity, agent coordination, and joint optimization of multiple levers. Hence, we also introduce popular data sets and open simulation environments to facilitate further research and development. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.

    Highlights A comprehensive survey of literature on reinforcement learning approaches to core problems in ridesharing. Topics covered include order matching, vehicle repositioning, pricing, routing, and ride-pooling. Open datasets and simulation environments are introduced. Challenges and future opportunities in reinforcement learning for ridesharing are discussed.


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    Titel :

    Reinforcement learning for ridesharing: An extended survey


    Beteiligte:
    Qin, Zhiwei (Tony) (Autor:in) / Zhu, Hongtu (Autor:in) / Ye, Jieping (Autor:in)


    Erscheinungsdatum :

    2022-08-07




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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