A Stochastic Traffic Flow Model-Based Reinforcement Learning Framework For Advanced Traffic Signal Control

In addressing the complex challenge of Traffic Signal Control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are sensitive to minor input changes, which complicates accurate predictions. This ambiguity hampers algorithm convergence, speed, and overall performance. Additionally, existing DRL methods for TSC employ high-dimensional state spaces, escalating computational complexity. This study addresses these challenges by introducing an innovative approach, SLFMLight, that integrates a stochastic traffic flow model with DRL algorithm for TSC. Our method employs an innovative network update algorithm that integrates traffic flow prediction in Q-value learning process to enhance interpretability and accelerate algorithm convergence. Utilizing mode-based multi-actor networks to handle diverse traffic conditions, SLFMLight excels in decision-making towards complex traffic scenarios, especially in congested ones. Concise state definition improves computational efficiency. SLFMLight contributes to the advancement of intelligent traffic management by providing an effective DRL solution that improves interpretability, efficiency, and adaptability in TSC.