Ride Sourcing Market

Ride-sourcing markets are complex two-sided platforms aiming to efficiently match drivers and riders, optimizing for profit and user satisfaction. Current research heavily emphasizes the development and deployment of sophisticated algorithms, including reinforcement learning and stochastic generalized Nash equilibrium models, to improve matching efficiency, dynamic pricing, and platform competition strategies. These advancements leverage machine learning to address challenges like real-time decision-making under uncertainty and the trade-off between profit maximization and minimizing wait times for both drivers and riders. The resulting improvements in operational efficiency and revenue generation have significant implications for the transportation industry and the broader field of algorithmic market design.