Towards adaptive traffic signal control through foundation models and reinforcement learning

Traffic Signal Control (TSC) is pivotal for managing urban traffic flow and enhancing intersection safety. Traditional TSC systems are rule-based and tailored to specific intersections, requiring substantial training and resources, which restricts their flexibility. This paper proposes a novel adaptive, scalable solution utilizing Foundation Models (FM) and Reinforcement Learning (RL), designed to handle diverse urban intersections efficiently without extensive retraining. The approach leverages advanced neural network architectures, including attention mechanisms, to improve generalization capabilities across different intersection topologies. A safety control mechanism aligned with traffic regulations ensures the safe operation of traffic signals, significantly enhancing the system’s reliability. By systematically classifying intersection types, the method tailors the control strategies to specific traffic scenarios, further reducing implementation times and expertise requirements. This FM- and RL-based approach not only reduces resource demands but also promises more efficient traffic flow and improved safety in various urban settings.