Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies
This addresses traffic management efficiency for urban areas, but it is incremental as it builds on existing MARL methods in a simulated environment.
This study tackled urban traffic congestion at intersections by applying multi-agent reinforcement learning (MARL) to optimize traffic signal coordination in a simulation, resulting in statistically significant improvements such as reduced average waiting times and improved throughput compared to a fixed-time baseline.
Urban traffic congestion, particularly at intersections, significantly impacts travel time, fuel consumption, and emissions. Traditional fixed-time signal control systems often lack the adaptability to manage dynamic traffic patterns effectively. This study explores the application of multi-agent reinforcement learning (MARL) to optimize traffic signal coordination across multiple intersections within a simulated environment. Utilizing Pygame, a simulation was developed to model a network of interconnected intersections with randomly generated vehicle flows to reflect realistic traffic variability. A decentralized MARL controller was implemented, in which each traffic signal operates as an autonomous agent, making decisions based on local observations and information from neighboring agents. Performance was evaluated against a baseline fixed-time controller using metrics such as average vehicle wait time and overall throughput. The MARL approach demonstrated statistically significant improvements, including reduced average waiting times and improved throughput. These findings suggest that MARL-based dynamic control strategies hold substantial promise for improving urban traffic management efficiency. More research is recommended to address scalability and real-world implementation challenges.