A Hierarchical Traffic-Balanced Route Planning Method for Connected Vehicles

Urban traffic congestion has a great impact on commute time, energy consumption, and carbon emissions. To deal with traffic congestion, the vehicle cooperative routing method coordinates the routing behavior of vehicles according to dynamic traffic demands. However, city-wide cooperative routing is faced with extremely high communication and computing complexity, which makes it difficult to guarantee real-time performance. To this end, we propose a hierarchical traffic-balanced route planning method for connected vehicles based on edge computing. Specifically, the road network is divided into grids, and an improved back-pressure algorithm is used to guide the inter-grid traffic flow. Furthermore, to balance the intra-grid traffic flow, the vehicle routing is scheduled by evolutionary game method at each intersection. The simulation results show that the algorithm can effectively balance the utilization of road network resources, increase the throughput of the road network and reduce the total traffic time.