Proactive Urban Expressway Guidance: A Hybrid Approach Using Reinforcement Learning and Traffic Prediction Models

Addressing traffic congestion is significant in enhancing urban mobility. Traditional navigation systems use real-time traffic status to push the temporally shortest path to drivers, forming selfish-routing, which guide traffic flow to the same roadway, thereby causing imbalance traffic flow distribution and navigation-induced congestion. Besides, navigation based on real-time detected traffic metrics may cause the congestion oscillation during periods with high travel demand variations. This paper proposes the Traffic Prediction and Reinforcement Learning-based Navigation Guidance (TP-RLNG) approach for active control of traffic flow on urban expressways. The TP-RLNG introduces a differentiated guidance approach with multiple origin-destination (OD) pairs, replacing the conventional all-or-nothing route guidance strategies. To enhance the stability, the TP-RLNG integrates traffic prediction and reinforcement learning (RL)-based dynamic optimization to prospectively harmonize traffic supply and demand overall the expressway system. In contrast to the reinforcement learning navigation guidance (RLNG) (no prediction) approach, the integration of traffic prediction model in TP-RLNG enhances the anticipation of critical nodes and enables proactive traffic management. We use SUMO-based traffic simulation to examine the efficacy of TP-RLNG and alternative approaches in controlling traffic flow within the Hangzhou Liushi Expressway network under varying demand patterns. The results underscore the ability of TP-RLNG to enhance road network efficiency and mitigate urban expressway congestion. The findings indicate that, relative to RLNG approaches, our methodology achieves a reduction in average travel time of 9.3%.