Intelligent Traffic Signal Management through Vision and Reinforcement Learning
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13792Keywords:
Traffic control systems, Reinforcement learning control, Intelligent transportation systems, Multi-agent systems, Modeling and simulation of transportation systems, Urban MobilityAbstract
Urban traffic congestion remains a major challenge in modern cities. Traditional fixed-time traffic signal controllers cannot adapt to dynamic traffic conditions. This work presents an autonomous traffic signal control agent based on Deep Reinforcement Learning (DRL) that uses only photorealistic images of the intersection as input, removing the dependency on costly physical sensor infrastructure. The validation is performed in a high-fidelity co-simulation platform combining CARLA, for visual realism, and SUMO, for microscopic traffic dynamics. Results show reductions of up to 22.68% in average waiting time at the training intersection, an improvement of 23.76% when retraining the agent in an intersection with a different topology, and positive synergy in multi-agent settings, where cooperation between adjacent intersections improves the reward by up to 59.78%.
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Copyright (c) 2026 Diego Caballero, Jaime Villa, Rubén Fernández, Mohamed Abderrahim, Araceli Sanchis, José María Armingol
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