Optimizing Traffic Signal Control Using Reinforcement Learning Methods: Hybrid Approach
Urban traffic congestion remains a major challenge for modern cities, requiring intelligent traffic signal control (TSC) strategies capable of adapting to dynamic traffic conditions. This paper proposes a hybrid reinforcement learning approach for traffic signal control that combines the complementary learning mechanisms of Q-learning, SARSA, and Monte Carlo algorithms to improve both learning efficiency and control performance. The proposed approach is implemented and evaluated using the Simulation of Urban MObility (SUMO) simulator on a realistic road network corresponding to the “Route de Sefrou” in Fez, Morocco. The traffic signal controller is trained through continuous interaction with the simulated environment and compared with the three individual reinforcement learning algorithms under identical experimental conditions. The experimental results demonstrate that the proposed hybrid approach provides more efficient traffic management, faster convergence, and greater learning stability than the individual algorithms. These findings demonstrate the potential of hybrid reinforcement learning as an effective solution for adaptive traffic signal control in realistic urban environments.