基于Petri网与改进黑翅鸢算法的无信号交叉口车辆优化调度方法OA

To meet the demand for efficient vehicle passage at unsignalized intersections in intelligent connected envi-ronments,this study proposes a new vehicle scheduling optimization method for unsignalized intersections based on a Petri net model,a deadlock avoidance strategy,and an improved black-winged kite algorithm(BKA),with the objec-tive of minimizing the maximum vehicle passing time.The proposed method first encodes the vehicle passing se-quences and establishes a mapping between vehicle numbers and BKA individuals.Then,a real-time online deadlock detection and repair strategy is applied to each individual to ensure control feasibility.Furthermore,two improvement strategies are developed for the BKA,the improved Circle chaotic mapping and the Levy flight strategy,to improve the algorithm's solving speed and solution accuracy.Finally,experiments are conducted on a two-way four-lane inter-section under multiple typical scenarios,and comparisons are made between the improved BKA,the original BKA,and the genetic algorithm to verify the significant advantage of the proposed method in minimizing the maximum passing time.Statistical analysis further demonstrates that the proposed method achieves the best performance in terms of con-vergence speed,stability,and optimization effectiveness.The experimental results show that the improved BKA ex-hibits strong optimization search capability in solving the vehicle scheduling optimization problem at unsignalized in-tersections.