<ul><li>The rapid expansion of ride-sourcing services presents operational challenges, such as vehicle rebalancing.</li><li>A scalable mean-field control and reinforcement learning model is proposed for precise vehicle repositioning.</li><li>An accessibility constraint is integrated to ensure equitable service distribution.</li><li>Empirical evaluation using real-world data-driven simulation demonstrates the efficiency and robustness of the approach.</li></ul>

Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantees via Constrained Mean-Field Reinforcement Learning

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