南通大学学报（自然科学版）2026，Vol.25Issue(1)：14-23,10.DOI:10.12194/j.ntu.20241118002
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
Vehicle scheduling optimization at unsignalized intersection based on Petri net and improved black-winged kite algorithm
摘要
Abstract
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.关键词
智慧交通系统/Petri网/黑翅鸢算法/死锁避免策略/Circle混沌映射/Levy飞行策略Key words
intelligent transportation system/Petri net/black-winged kite algorithm/deadlock avoidance policy/Circle chaotic mapping/Levy flight strategy分类
信息技术与安全科学引用本文复制引用
曹猛,张富娜,姜心雨,黄程,刘慧霞,张思晗,孙文强..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[J].南通大学学报（自然科学版）,2026,25(1):14-23,10.基金项目
南通市基础科学研究项目(JC2021203) （JC2021203）
