Highlights A real-world deployed reinforcement learning-based algorithm for ride-hailing vehicle repositioning. A practical framework incorporating offline learning and online decision-time planning. Effective algorithmic designs for small-fleet and large-fleet scenarios.

    Abstract We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms. Our approach learns the spatiotemporal state-value function using a batch training algorithm with deep value networks. The optimal repositioning action is generated on-demand through value-based policy search, which combines planning and bootstrapping with the value networks. For the large-fleet problems, we develop several algorithmic features that we incorporate into our framework and that we demonstrate to induce coordination among the algorithmically-guided vehicles. We benchmark our algorithm with baselines in a ride-hailing simulation environment to demonstrate its superiority in improving income efficiency measured by income-per-hour. We have also designed and run a real-world experiment program with regular drivers on a major ride-hailing platform. We have observed significantly positive results on key metrics comparing our method with experienced drivers who performed idle-time repositioning based on their own expertise.


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    Title :

    Real-world ride-hailing vehicle repositioning using deep reinforcement learning


    Contributors:
    Jiao, Yan (author) / Tang, Xiaocheng (author) / Qin, Zhiwei (Tony) (author) / Li, Shuaiji (author) / Zhang, Fan (author) / Zhu, Hongtu (author) / Ye, Jieping (author)


    Publication date :

    2021-07-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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