Mixed Traffic Control

Mixed traffic control focuses on optimizing the flow of vehicles, including both human-driven and autonomous vehicles, at intersections and across road networks. Current research heavily utilizes reinforcement learning (RL), often coupled with techniques like imitation learning and graph neural networks, to develop control strategies that improve safety, efficiency, and reduce emissions. These advancements aim to address challenges posed by heterogeneous vehicle behaviors and complex traffic dynamics, ultimately leading to more efficient and sustainable transportation systems. The impact extends to both theoretical advancements in AI and RL, and practical improvements in urban traffic management.