Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach
arXiv:2503.24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile applications. Despite convenience, these platforms confront significant operational challenges, particularly vehicle rebalancing-strategic repositioning of a fleet of vehicles to address spatiotemporal mismatches in supply and demand. Inadequate rebalancing results in prolonged rider waiting times and inefficient vehicle utilization, but also leads to fairness issues, such as the inequitable distr
The increasing complexity and scale of ride-sourcing operations, coupled with the computational advancements in AI, make sophisticated rebalancing solutions highly relevant and feasible at this moment.
This development addresses a core operational challenge for ride-sourcing companies, directly impacting efficiency, rider experience, and market fairness, with implications for urban logistics and transportation AI.
The ability to guarantee service accessibility while scalably rebalancing ride-sourcing vehicles, using advanced AI techniques, could lead to more efficient and equitable urban mobility platforms.
- · Ride-sourcing companies (Uber, Lyft)
- · Urban commuters and customers
- · AI/ML research and development
- · Smart city initiatives
- · Inefficient ride-sourcing algorithms
- · Traditional taxi services (indirectly)
Improved profitability and market share for ride-sourcing platforms due to enhanced operational efficiency and customer satisfaction.
Accelerated adoption of AI-driven optimization in broader logistics and last-mile delivery services, setting new industry standards.
Potential for urban planners to integrate AI-optimized ride-sourcing data and models for dynamic traffic management and infrastructure development, leading to smarter, more responsive cities.
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