Title: CARTS: Cooperative Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction
Primary area: reinforcement learning
Keywords: Traffic Signal Control (TSC), Reinforcement Learning, Multiagent systems

Abstract: Existing traffic signal control systems often rely on overly simplistic, rule-based approaches. Even reinforcement learning (RL)-based methods tend to be suboptimal and unstable due to the inherently local nature of control agents. To address the potential conflicts among these agents, we propose a cooperative architecture named CARTS (CooperAtive Reinforcement learning for Traffic Signal control). CARTS introduces multiple reward terms, weighted with an age-decaying mechanism, to optimize traffic signal control at a global scale. Our framework features two types of agents: local agents that focus on optimizing traffic flow at individual intersections, and a global agent that coordinates across intersections to enhance overall throughput. Importantly, the system is designed to reduce both vehicle waiting time and carbon emissions. We evaluated CARTS using real-world traffic data obtained from traffic cameras in an Asian country. Despite incorporating a global agent during training, CARTS remains decentralized at inference time, requiring no centralized coordination during deployment. Experimental results show that CARTS consistently outperforms state-of-the-art methods across all evaluated performance metrics. Moreover, CARTS effectively links carbon emission reduction with global agent coordination, providing an interpretable and practical approach to sustainable traffic signal control.