Eco-Intelligent Traffic Signal Control via Deep Reinforcement Learning

Abstract

Traffic congestion remains a major challenge in modern urban areas, leading to delays, increased fuel consumption, and environmental pollution. Traditional traffic signal systems rely on fixed schedules and often fail to adapt to real-time traffic dynamics. This paper proposes an adaptive traffic signal control framework using Deep Reinforcement Learning (DRL), specifically the Proximal Policy Optimization (PPO) algorithm, to optimize signal phases based on real-time traffic conditions. The control problem is formulated as a Markov Decision Process, where the agent learns optimal actions from vehicle-level metrics such as waiting time, queue length, speed, and distance to the intersection. The system is trained and evaluated in a SUMO-based simulation environment and benchmarked against a conventional fixed-time controller. Experimental results show that the RL-based controller reduces average waiting time and queue lengths by up to 37.5% and 33.3%, respectively. It also achieves moderate improvements in speed, fuel consumption, and CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>emissions. These results highlight the potential of DRL for intelligent traffic management in smart city infrastructures.