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To meet the demand for efficient vehicle passage at unsignalized intersections in intelligent connected environments, 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 objective of minimizing the maximum vehicle passing time. The proposed method first encodes the vehicle passing sequences 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 intersection 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 convergence speed, stability, and optimization effectiveness. The experimental results show that the improved BKA exhibits strong optimization search capability in solving the vehicle scheduling optimization problem at unsignalized intersections.
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Basic Information:
China Classification Code:U491;TP18
Citation Information:
[1]CAO Meng,ZHANG Funa,JIANG Xinyu ,et al.Vehicle scheduling optimization at unsignalized intersection based on Petri net and improved black-winged kite algorithm[J].Journal of Nantong University (Natural Science Edition),2026,25(01):14-23.
Fund Information:
南通市基础科学研究项目（JC2021203）
2025-04-23
2025-04-23
2025-04-23