AI-Based Optimization of Urban Traffic Signal Systems Using Reinforcement Learning

Abstract

Rapid urbanization and the continuous growth of vehicular population have intensified traffic congestion challenges in modern cities, leading to increased travel delays, fuel consumption, and environmental pollution. This study presents an Artificial Intelligence (AI)-based optimization framework for urban traffic signal systems using Reinforcement Learning (RL) techniques to improve traffic flow efficiency and intelligent transportation management. The proposed system integrates real-time traffic data collection, adaptive signal control, and machine learning algorithms to dynamically optimize traffic signal timing based on changing road conditions. A simulation-based methodology was adopted in which traffic parameters such as vehicle density, queue length, waiting time, and traffic flow patterns were analyzed using RL agents trained through continuous interaction with the traffic environment. The developed model employed state-action-reward mechanisms to achieve adaptive decision-making and efficient signal coordination at urban intersections. Experimental and simulation results demonstrated significant reductions in vehicle waiting time, traffic congestion, and average travel delay when compared with conventional fixed-time traffic control systems. The AI-driven approach also improved traffic throughput, minimized fuel consumption, and enhanced intersection efficiency under varying traffic conditions. Furthermore, the intelligent optimization framework exhibited strong scalability and adaptability for deployment in smart city transportation infrastructures. The study concludes that reinforcement learning-based traffic signal optimization provides an effective and sustainable solution for intelligent urban mobility management by enabling real-time adaptive control, reducing transportation inefficiencies, and supporting the development of safer, smarter, and environmentally sustainable urban traffic systems.