ADAPTIVE TRAFFIC SIGNAL CONTROL USING AI AND DISTRIBUTED SYSTEMS FOR SMART URBAN MOBILITY

Urban traffic jams remain a pressing issue due to increasing vehicle concentrations, inadaptive traffic signal management, and unreliable road conditions. Fixed or semi-adaptive traditional traffic control systems cannot adaptively change in real-time traffic environments, leading to wasted time, excessive fuel usage, and greater pollutant emissions. This study proposes a comprehensive framework for the optimization of urban traffic, leveraging predictive modeling, adaptive signal control, and distributed messaging infrastructure. The platform collects real-time sensor information, employs machine learning techniques for short-term traffic prediction, and optimizes signal timing through heuristic approaches such as Simulated Annealing (SA) and Reinforcement Learning (RL). The asynchronous messaging architecture supports flexible communication among prediction modules, controllers, and external services to facilitate flexibility and scalability. Experimental validation was conducted using the SUMO traffic simulator, where a standard fixed-cycle signal scheme was compared to the proposed adaptive scheme. Results indicate that there is a 21.6% reduction in average waiting time, a 12.9% reduction in CO₂ emissions, and a 15.6% improvement in fuel efficiency. These findings validate the effectiveness of the system in mitigating congestion and enabling green urban mobility. Future work will involve incorporating vehicle-to-everything communication data (V2X communication), multi-intersection coordination, and deep learning traffic flow predictions in order to better enhance adaptability and robustness at the large-scale deployment stage.