Optimizing Ride-Hailing Services with IoT-Integrated Reinforcement Learning for Passenger Matching

Optimization of passenger matching to improve service efficiency and customer happiness is both possible and difficult in the ride-hailing sector, which has grown rapidly. This paper proposes optimizing ride-hailing passenger matching using IoT and reinforcement learning (RL). To gather real-time traffic, passengers, and vehicle data, integrate Internet of Things (IoT) sensors into ride-hailing vehicles. Using IoT data, an RL agent dynamically assigns passengers to drivers. Journey duration, driver proximity, and passenger preferences are considered by the RL model to match passengers. This technique improves passenger matching efficiency and reduces wait times and journey durations via thorough simulations and real-world trials. Our findings show that the IoT -integrated RL system outperforms heuristics-based systems, making ride-hailing more smooth and rewarding for passengers and drivers. It explores the scalability and practical application of our approach on current ride-hailing platforms, emphasizing the potential for considerable service quality and operational cost savings. It addresses IoT data privacy and security problems and recommends solutions to protect user data.