In a mobility-on-demand system, travel requests are handled by a fleet of shared vehicles in an on-demand fashion. An important factor that determines the operational efficiency and service level of such a mobility-on-demand system is its operational policy that assigns available vehicles to open passenger requests and relocates idle vehicles. Previously described operational policies are based on control theoretical approaches, most notably on receding horizon control. In this work, we employ reinforcement learning techniques to design an operational policy for a mobility-on-demand system. In particular, we propose a cascaded learning framework to reduce the number of state-action pairs which allows for more efficient learning. We train our model using the AMoDeus simulation environment and real taxi trip travel data from the city of San Francisco. Finally, we demonstrate that our reinforcement learning based operational policy for mobility-on-demand systems outperforms state-of the art fleet operational policies that are based on conventional control theoretical approaches.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning to Operate a Fleet of Cars


    Beteiligte:
    Fluri, Christian (Autor:in) / Ruch, Claudio (Autor:in) / Zilly, Julian (Autor:in) / Hakenberg, Jan (Autor:in) / Frazzoli, Emilio (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    808399 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Boston builds subway while cars operate above

    Hollingsworth, F.N. | Engineering Index Backfile | 1932


    Boston builds subway while cars operate above

    Hollingsworth, F.N. | Engineering Index Backfile | 1932


    Riderless cars operate on A.C. over 6000-ft. Haul

    Emerson, R.F. | Engineering Index Backfile | 1931