Ride Pooling

Ride pooling optimizes transportation efficiency by matching multiple passengers with a single vehicle, aiming to reduce costs, congestion, and environmental impact. Current research emphasizes developing sophisticated algorithms, including reinforcement learning and agent-based modeling, to improve vehicle dispatching, pricing strategies, and customer matching, often incorporating elements of predictive modeling and optimal transport theory. These advancements aim to create more profitable and sustainable ride-pooling systems by balancing the needs of passengers, drivers, and operators, with a focus on real-time optimization and improved service quality. The resulting improvements in efficiency and resource allocation have significant implications for urban planning, transportation management, and the broader field of logistics.