Abstract Combining ride-sourcing and public transit services (with ride-sourcing service to address the first/last-mile issues) can bring many benefits, such as saving passengers’ trip fares, improving drivers’ earnings, reducing gas emissions, and alleviating traffic congestion. However, it still remains a challenging issue to coordinate ride-sourcing and public transit services through real-time order dispatching. In this paper, we model the order dispatching in a multi-modal transportation system as a large-scale sequential decision-making problem. A centralized algorithm is then proposed to dispatch idle drivers to arriving passenger orders and determine whether to advise passengers to use a combined mode of ride-sourcing and public transit services (if yes, the algorithm also needs to recommend an appropriate transportation hub). In particular, our proposed algorithm contains a reinforcement learning approach that estimates the long-term expected rewards, and an Integer Linear Programming (ILP) that matches idle drivers and waiting passengers in real-time based on both immediate revenue and the estimated long-term rewards. By evaluation on the real-world on-demand data and metro system in Manhattan, the proposed method shows remarkable improvement on the system’s efficiency under different density of supply and demands.

    Highlights An order dispatch model is proposed to coordinate ride-sourcing and public transit. We describe the multi-modal system as a sequential decision-making problem. The model combines reinforcement learning and Integer Linear Programming. The model is evaluated on real-world mobility data and metro networks. The model shows superiority and robustness in system efficiency improvement.


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    Titel :

    Coordinating ride-sourcing and public transport services with a reinforcement learning approach


    Beteiligte:
    Feng, Siyuan (Autor:in) / Duan, Peibo (Autor:in) / Ke, Jintao (Autor:in) / Yang, Hai (Autor:in)


    Erscheinungsdatum :

    2022-02-12




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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