Traffic Signal Control

Traffic signal control aims to optimize traffic flow, reduce congestion, and improve safety and sustainability in urban areas. Current research heavily utilizes reinforcement learning (RL), often incorporating graph neural networks, transformers, and large language models to improve coordination across multiple intersections and adapt to real-time conditions, including malfunctions and varying traffic demands. These advancements are leading to more efficient and adaptable traffic management systems, with a focus on improving both simulation-based models and real-world deployment through techniques like domain randomization and offline learning. The resulting improvements in traffic flow, reduced emissions, and enhanced safety have significant implications for urban planning and transportation management.