Optimal-Tolling Using Reinforcement Learning

This article explores traffic management's intricacies through the lens of optimal tolling, employing advanced reinforcement learning techniques. The research delves into the dual perspectives of drivers and traffic operators, addressing the critical decision-making processes involved in lane choice and toll pricing. By modeling traffic flows based on historical data from the Georgia Department of Transportation, the study employs neural networks to simulate traffic dynamics and density-speed relationships accurately. Deep Q-Network (DQN) agents are central to the investigation, representing drivers and traffic controllers rigorously trained within a simulated environment to optimize lane choices and toll prices. This innovative approach not only maximizes revenue for traffic controllers but also ensures efficient traffic flow, striking a balance between speed and cost for drivers. The study's findings demonstrate a significant advancement in traffic management strategies, offering a scalable and data-driven solution to urban congestion and toll lane utilization challenges. This work presents a novel framework that has the potential to revolutionize traffic control systems and provide insights into the practical applications of reinforcement learning in complex real-world scenarios.