Adaptive Traffic Management System Using Reinforcement Learning

Abstract

Traffic congestion is a significant issue in urban areas, leading to increased travel delays, fuel consumption, and environmental pollution. Traditional traffic management systems, which use fixed-timer signals, rule-based controls, manual intervention by traffic police, and electronic sensor-based methods, often struggle to adapt to dynamic traffic conditions. To address these challenges an Adaptive Traffic Management System (ATMS) using Reinforcement Learning (RL) is proposed to optimize signal timings and improve traffic flow. The system dynamically adjusts traffic signal timings based on real-time traffic flow, ensuring efficient traffic movement. Key features of this approach include prioritization of emergency vehicles, reduced waiting time for emergency vehicles and zero waiting time for emergency vehicles. Additionally, our system minimizes pollution and prevents potential collisions by optimizing traffic flow. The system leverages the SUMO (Simulation of Urban MObility) platform to simulate real-world traffic scenarios and evaluate performance. Reinforcement learning enables the system to learn from traffic patterns and make real-time decisions to minimize congestion. The model is trained using deep Qlearning to optimize signal control at intersections. The adaptive system reduces average waiting time, travel delays, and fuel consumption compared to conventional traffic light control methods. Through iterative learning, the model adjusts signal phases dynamically, ensuring smooth and efficient vehicle movement. The simulation results demonstrate significant improvements in road network efficiency and reduced congestion. The proposed system can also handle unpredictable traffic disruptions such as accidents or roadblocks. Performance is evaluated based on key metrics, including average vehicle delay, queue length, and throughput. The flexibility of reinforcement learning allows the system to adapt to varying traffic conditions without manual intervention. Implementing AI-driven adaptive traffic control can enhance urban mobility and commuter experiences. By optimizing traffic signal control, this approach contributes to sustainable and efficient transportation networks. This research highlights the potential of reinforcement learning in addressing traffic congestion through intelligent decision-making.