Ridesharing Platform

Ridesharing platforms, aiming to efficiently match riders and drivers, are a focus of intense research. Current efforts concentrate on improving matching algorithms (e.g., using reinforcement learning and stable matching techniques), predicting rider demand and driver behavior (leveraging graph neural networks and recurrent networks), and optimizing resource allocation to minimize costs and maximize efficiency (including exploring dynamic pricing and incentivizing ride-sharing). These advancements have significant implications for transportation planning, urban mobility, and environmental sustainability, as evidenced by studies demonstrating real-world emission reductions from ride-sharing and the development of systems to improve safety and security.