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.
Enhancing Adaptive Traffic Control Systems with Deep Reinforcement Learning and Graphical Models
20.11.2024
5041519 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch