Adaptive Traffic Signal Timing Optimization for Urban Intersections Using Reinforcement Learning

Abstract

Urban traffic congestion remains a pressing challenge, particularly in developing regions where infrastructure development struggles to keep pace with rapid urbanization. Fixed-time traffic signal systems, commonly deployed in countries like Yemen, lack the adaptability needed to manage dynamic and unpredictable traffic flows efficiently. This thesis addresses these limitations by proposing and evaluating reinforcement learning (RL)–based algorithms for adaptive traffic signal control, with a dual focus on improving traffic flow and reducing environmental impact. The research introduces two core innovations: a decentralized Q-learning algorithm and a deep reinforcement learning framework based on Proximal Policy Optimization with Masking (PPO-Mask). While Q-learning demonstrated substantial improvements over traditional control strategies in reducing vehicle delays and emissions, its scalability was limited in complex traffic environments. To overcome these challenges, the PPOMask model was developed, incorporating action masking to ensure safer decision-making and faster convergence in highdimensional settings. Simulations conducted using the SUMO platform across both synthetic and real-world scenarios demonstrated that PPO-Mask consistently outperformed Q-learning and fixed-time baselines across all key performance metrics. This work contributes a robust, scalable, and cost-effective approach to adaptive traffic signal control that is particularly suitable for low-resource urban environments. It also provides a comparative framework and practical insights that can inform future integration of AI-driven traffic control strategies into broader urban mobility planning.