A Survey on Traffic Signal Control Methods
This is an incremental survey that synthesizes existing knowledge to foster interdisciplinary research in intelligent transportation.
This survey addresses the problem of traffic signal control by reviewing existing methods and recent reinforcement learning literature, aiming to minimize vehicle travel time through improved coordination at intersections.
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.