Abstract: To further alleviate traffic congestion and improve road network efficiency, this study proposed an urban vehicle route guidance method integrating personalized routing strategies and traffic signal control based on hierarchical multi-agent reinforcement learning(MARL). Route guidance agents and traffic signal control agents were deployed at intersections to provide personalized routing policies and optimize traffic light control, thereby balancing urban traffic flow. To overcome the limitations of predefined graph structures in representing dynamic traffic state features, the traffic signal control agents employed an adaptive graph convolutional network to autonomously capture spatial correlations among peer agents. Concurrently, the route guidance agents integrated meanfield game to analyze aggregated vehicle actions, effectively capturing inter-vehicle interactions for coordinated decision-making while delivering destination-specific routing strategies. To prevent local congestion and severe traffic imbalance, a multi-agent proximal policy optimization(MAPPO)algorithm was adopted, enabling centralized training and decentralized execution for cooperative signal control agents to implement directional flow restriction. A hierarchical reinforcement learning framework facilitated information sharing and collaboration among heterogeneous agents. Extensive experiments were conducted on the SUMO simulation platform using multiple real-world open-source traffic datasets, with comparisons against baseline methods. Results demonstrated that the proposed method reduced average travel time by at least 11.05% and decreased average delay time by at least 19.90%, significantly enhancing urban traffic efficiency.

Key words: reinforcement learning, route guidance, signal control, mean field game, adaptive graph convolution