Performance of traffic network path planning optimized by genetic algorithm in dynamic Traffic simulation

Abstract

In complex dynamic traffic environments, existing path planning methods are often limited by insufficient convergence efficiency, poor solution stability, and uneven load distribution. This paper proposes an optimization strategy based on a genetic algorithm (GA). This strategy incorporates an adaptive mutation operator to adjust mutation probabilities during evolution to maintain population diversity dynamically. A partial matching crossover operator is used to improve the recombination efficiency of high-quality gene segments. Furthermore, three metrics—travel time, path length, and path balance—are simultaneously incorporated into the fitness function to achieve optimization under multi-dimensional constraints. The experimental results demonstrate that the average travel time of the optimized path is 32.5 s, which significantly improves the traffic efficiency compared with the Dijkstra algorithm (42 s). The path length is 850 meters, which is better than the 860 meters of the A* (A-star) algorithm, and the path complexity is significantly lower than that of other algorithms. The results demonstrate the effectiveness and applicability of the proposed optimization strategy in improving convergence speed, path stability, and traffic flow balance.