Shared mobility-on-demand systems can improve the efficiency of urban mobility through reduced vehicle ownership and parking demand. However, some issues in their implementations remain open, most notably the issue of rebalancing non-occupied vehicles to meet geographically uneven demand, as is, for example, the case during the rush hour. This is somewhat alleviated by the prospect of autonomous mobility-on-demand systems, where autonomous vehicles can relocate themselves; however, the proposed relocation strategies are still centralized and assume all vehicles are a part of the same fleet. Furthermore, ride-sharing is not considered, which also has an impact on rebalancing, as already occupied vehicles can also potentially be available to serve new requests simultaneously. In this paper we propose a reinforcement learning-based decentralized approach to vehicle relocation as well as ride request assignment in shared mobility-on-demand systems. Each vehicle autonomously learns its behaviour, which includes both rebalancing and selecting which requests to serve, based on its local current and observed historical demand. We evaluate the approach using data on taxi use in New York City, first serving a single request by a vehicle at a time, and then introduce ride-sharing to evaluate its impact on the learnt rebalancing and assignment behaviour.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning


    Beteiligte:
    Gueriau, Maxime (Autor:in) / Dusparic, Ivana (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    356439 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Rebalancing shared mobility-on-demand systems: A reinforcement learning approach

    Wen, Jian / Zhao, Jinhua / Jaillet, Patrick | IEEE | 2017


    Deep Reinforcement Learning based Charging Pricing for Autonomous Mobility-on-Demand System

    Lu, Ying / Liang, Yanchang / Ding, Zhaohao et al. | BASE | 2022

    Freier Zugriff

    REGIONAL BATCHING TECHNIQUE WITH REINFORCEMENT LEARNING BASED DECISION CONTROLLER FOR SHARED AUTONOMOUS MOBILITY FLEET

    SYED ARSLAN ALI / BACHET ABD ELRAHMAN | Europäisches Patentamt | 2022

    Freier Zugriff


    Agent-based simulation of a shared, autonomous and electric on-demand mobility solution

    Jager, Benedikt / Agua, Fares Maximilian Mrad / Lienkamp, Markus | IEEE | 2017