Publications

• Sorted by Date • Classified by Publication Type • Classified by Research Category •

A timely match for ride-hailing and ride-pooling services using a deep reinforcement learning approach

Yiman Bao, Jie Gao, Jinke He, Frans A. Oliehoek, and Oded Cats. A timely match for ride-hailing and ride-pooling services using a deep reinforcement learning approach. Transportation Research Part C: Emerging Technologies, 187:105644, 2026.

Download

pdf [6.4MB]  ps.gz ps HTML 

Abstract

Efficient matching in ride-hailing and ride-pooling services depends not only on how matches are constructed, but also on when the platform triggers a matching operation. Many systems use batched matching with a fixed time interval to accumulate requests before matching, which increases the candidate set but cannot adapt to real time supply-demand fluctuations and may induce unnecessary waiting. This paper proposes a reinforcement learning approach that learns when to trigger matching based on current system conditions. We formulate the timing problem as a finite-horizon Markov decision process and train the policy using the Proximal Policy Optimization algorithm. To address sparse and delayed feedback, we introduce a finite-horizon, potential-based reward shaping scheme that preserves the optimal policy while densifying the learning signal; the same framework applies to both ride-hailing and ride-pooling, where detour delay is incorporated into the reward for pooling. Using a data-driven simulator calibrated on NYC trip records, the learned policy adapts matching timing decisions to the current state of waiting requests and available drivers and outperforms fixed-interval, rule-based dynamic, and first-dispatch baselines. It reduces total waiting time by 3.1% in ride-hailing and 20.1% in ride-pooling, and detour delay by 36.1% in pooling, while maintaining short matching times.

BibTeX Entry

@article{Bao26TRC,
    author =    {Yiman Bao and Jie Gao and Jinke He and Frans A. Oliehoek and Oded Cats},
    title =     {A timely match for ride-hailing and ride-pooling services using a deep reinforcement learning approach},
    journal =   {Transportation Research Part C: Emerging Technologies},
    volume =    {187},
    pages =     {105644},
    year =      {2026},
    issn =      {0968-090X},
    doi =       {https://doi.org/10.1016/j.trc.2026.105644},
    OPTurl = {https://www.sciencedirect.com/science/article/pii/S0968090X26001324},
    keywords =   {refereed},
    abstract = {
        Efficient matching in ride-hailing and ride-pooling services depends
        not only on how matches are constructed, but also on when the
        platform triggers a matching operation. Many systems use batched
        matching with a fixed time interval to accumulate requests before
        matching, which increases the candidate set but cannot adapt to
        real time supply-demand fluctuations and may induce unnecessary
        waiting. This paper proposes a reinforcement learning approach that
        learns when to trigger matching based on current system conditions.
        We formulate the timing problem as a finite-horizon Markov decision
        process and train the policy using the Proximal Policy Optimization
        algorithm. To address sparse and delayed feedback, we introduce a
        finite-horizon, potential-based reward shaping scheme that
        preserves the optimal policy while densifying the learning signal;
        the same framework applies to both ride-hailing and ride-pooling, where
        detour delay is incorporated into the reward for pooling. Using a
        data-driven simulator calibrated on NYC trip records, the learned
        policy adapts matching timing decisions to the current state of
        waiting requests and available drivers and outperforms
        fixed-interval, rule-based dynamic, and first-dispatch baselines.
        It reduces total waiting time by 3.1% in ride-hailing and 20.1% in
        ride-pooling, and detour delay by 36.1% in pooling, while
        maintaining short matching times.
    }
}

Generated by bib2html.pl (written by Patrick Riley) on Thu Apr 02, 2026 14:56:28 UTC