Variable Speed Limit

Variable speed limits (VSLs) dynamically adjust speed limits on roadways to improve traffic flow and safety. Current research heavily utilizes multi-agent reinforcement learning (MARL) and other advanced control algorithms to optimize VSL systems, often deploying these models in real-world settings and evaluating their performance against rule-based systems. This work focuses on improving the adaptability and robustness of VSL control to real-world traffic complexities, including driver behavior variations and imperfect adherence to posted limits. The resulting improvements in traffic efficiency and safety have significant implications for transportation management and autonomous driving systems.