Momentum Based Reward Design for Low Emission Traffic Signal Control
For urban traffic management, this work offers an improved reward design that enhances DRL-based signal control, though it is an incremental improvement over existing reward functions.
The paper proposes a Momentum-Based Reward Function (MBRF) for deep reinforcement learning in adaptive traffic signal control, which encourages vehicle movement rather than penalizing congestion. Evaluated in SUMO, MBRF achieves better throughput-emission trade-offs and more stable learning than delay/queue-based rewards and classical controllers.
Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution. Traditional traffic signal control systems often fail to adapt to dynamic traffic conditions. Adaptive traffic signal control can improve urban traffic without changing road infrastructure. Deep Reinforcement Learning (DRL) has shown strong performance for this task, but existing delay and queue-based rewards often produce short-sighted or unstable policies. This paper proposes a Momentum-Based Reward Function (MBRF) that encourages vehicles to keep moving rather than penalizing congestion alone. The method is evaluated in SUMO (Simulation of Urban MObility) using standard traffic metrics such as waiting time, queue length, throughput, and CO2 emissions. Results show that the proposed reward produces better throughput-emission trade-offs and more stable learning behavior than delay or queue-based rewards, as well as classical controllers such as Max Pressure and LQF.