Enhancing Adaptive Traffic Control Systems with Deep Reinforcement Learning and Graphical Models

Intelligent traffic signal control plays a crucial role in improving urban transportation networks by reducing congestion and increasing traffic flow efficiency. This paper presents a deep reinforcement learning (DRL) approach for managing traffic signals at multiple intersections. This method enhances both the adaptability and efficiency of traffic control systems. Our model integrates a probabilistic graphical framework with DRL, facilitating the extraction of interpretable and actionable insights from complex traffic data. We have developed and validated our model using a simulated environment that reflects real-world traffic conditions, demonstrating substantial improvements in traffic throughput and reduction in average travel times. The proposed model outperforms traditional traffic signal control algorithms. It also offers a robust framework for real-time traffic management, contributing to the development of future smart city infrastructure. The model achieves a 95% success rate, reduces average queue lengths by over 50%, and cuts intersection delays by up to 80%, compared to traditional methods. These improvements highlight its efficiency in optimizing traffic flow and minimizing congestion.