Achieving Optimized Allocation of Road Resources: A Path Planning Strategy for a Multivehicle Evolutionary Game Based on a Correlation Model

Number of vehicles in metropolitan areas is rapidly increasing, leading to worsening traffic congestion. There is an urgent need for the implementation of effective vehicle routing planning (VRP) to increase road traffic efficiency. However, the existing path planning algorithms focus primarily on the optimization of single vehicles, neglecting the correlations in routing demands and the collective behavior of vehicles. To address these issues, this paper proposes evolutionary game-based multivehicle route planning (EG-MVRP). By constructing a vehicle grouping model and applying evolutionary game theory, we analyze the interest conflicts and coordination requirements among different vehicle groups, which leads to the formulation of an optimized cooperative path planning strategy for multiple vehicles. The experimental results demonstrate that EG-MVRP significantly enhances the efficiency of intragroup path planning, alleviates local congestion, and improves the overall operational efficiency of the traffic network by minimizing excessive competition among multiple vehicle groups on the same road sections. In addition, the proposed method offers clear advantages in reducing travel time and fuel consumption. The research presented in this paper offers novel ideas and methods for future traffic management and planning, offering significant practical application value.