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In urban areas, the problem of recurring daily congestion is constantly increasing. A possible solution is seen in the application of Adaptive Traffic Signal Control (ATSC) systems for the control of signalized intersections. While available ATSC systems can achieve an increase in the Level of Service, the focus of ATSC research has shifted towards the application of Reinforcement Learning (RL) techniques, which allow the controller to learn the optimal control policy by direct interaction with the environment. This paper describes the fundamentals of traffic signal control, RL algorithms and approaches, and their application to ATSC, with a discussion on the impact of Connected and Autonomous Vehicles on future traffic signal control. In conclusion, a summary of research questions and possible directions for future research in the domain of RL-based traffic signal control is given.
Traffic Signal Control ; Urban Intersections ; Machine Learning ; Connected and Autonomous Vehicles
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