Ride-hailing platforms allow people to request rides, but sometimes they do not respond quickly enough. Due to the complex urban transportation system and the large geographical area of the city, the imbalance between supply and demand becomes a common problem. To solve this problem, many studies have proposed taxi repositioning methods. However, each of two mainstream methods (region-based and taxi-based) has its own advantages and disadvantages. In this paper, we propose the RSTR model, which is a two-layer model that takes advantages of the two mainstream methods. We model the problem as a partially observable Markov decision process and design the optimization objective. To generate more accurate repositioning strategies and improve model training outcomes, we propose a many-to-many scheduling mode and demonstrate its effectiveness. Extensive experiments on real-world datasets show that RSTR can effectively balance supply and demand and outperform other baseline methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    RSTR: A Two-Layer Taxi Repositioning Strategy Using Multi-Agent Reinforcement Learning


    Beteiligte:
    Yu, Hao (Autor:in) / Guo, Xi (Autor:in) / Luo, Xiao (Autor:in) / Wu, Ziyan (Autor:in) / Zhao, Jing (Autor:in)


    Erscheinungsdatum :

    01.03.2025


    Format / Umfang :

    3373118 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning

    Shou, Zhenyu / Di, Xuan | ArXiv | 2020

    Freier Zugriff


    Drone taxi system using multi-agent reinforcement learning and drone taxi operation method using the same

    JOONGHEON KIM / YUN WON JOON / JAE HYUN KIM et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    DRONE TAXI SYSTEM BASED ON MULTI-AGENT REINFORCEMENT LEARNING AND DRONE TAXI OPERATION USING THE SAME

    KIM JOONGHEON / YUN WON JOON / KIM JAE-HYUN et al. | Europäisches Patentamt | 2022

    Freier Zugriff