Probabilistic Graph Models: A Key to Boosting Deep Reinforcement Learning in Urban Traffic Networks

Intelligent traffic signal control is a critical solution for optimizing urban transportation networks by reducing congestion and enhancing traffic flow efficiency. This study introduces a deep reinforcement learning (DRL) framework integrated with probabilistic graph models (PGMs) to address the challenges of adaptability and interpretability in traffic management. The PGMs enable a structured representation of complex dependencies within traffic systems, allowing for interpretable insights into dynamic traffic conditions. The proposed model is trained and validated in the SUMO simulation environment, accurately reflecting real-world traffic scenarios. Results demonstrate substantial improvements in throughput, reduced travel times, and minimized intersection delays compared to traditional control methods. The integration of PGMs with DRL not only enhances decision-making processes but also improves scalability and robustness in real-time applications. This innovative approach lays a solid foundation for implementing adaptive and intelligent traffic management systems, paving the way for smarter urban infrastructures capable of handling evolving transportation demands.