Game Theory-Based Fastest Route Plan Method for Transportation Network Applications

This paper presents a vehicle route planning method based on game theory principles and innovative utility functions. By addressing the complexities of real-time traffic congestion, the proposed framework offers a dynamic allocation strategy for rational decision-making. The utility function, which inte-grates traffic flow volume, road capacity, and congestion effects, provides accurate travel time estimations. Mathematical analysis and validation, including genetic algorithms, underscore the framework's robustness. Equilibrium solutions reveal allocation strategies responsive to varying road conditions. Comparative scenarios demonstrate the utility function's effectiveness in guiding enterprises' decisions. This research extends beyond static models, envisioning a future of data integration, multi-objective optimization, adaptive learning, and eco-friendly navigation. By converging these routes, the paper sets the stage for a smarter, more sustainable transportation landscape.