1. School of Information Sci and Eng, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018,China
2. Jiaxing Key Lab of Smart Transportations, School of Information Sci and Eng, Jiaxing University, Jiaxing, Zhejiang 314001, China
3. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, Zhejiang 310027,China
Abstract：Due to the limited global search capability,traffic signal control (TSC) methods based on traditional particle swarm optimization (PSO) algorithms are prone to falling into local optima.In addition,the TSC model with a fixed signal cycle lacks flexibility in dealing with complex traffic flows that change over time.To address these issues,a TSC method based on an improved chaotic particle swarm optimization (ICPSO) algorithm is proposed,which utilizes chaotic motion to enhance global search capabilities to overcome local optima.The proposed ICPSO algorithm introduces a neighborhood radius parameter for elite particles with higher fitness in the population,implements neighborhood chaos search,and retains the advantageous characteristics of particles while improving their ability to escape local optima.In addition,a variable-period TSC model (VTSC) is designed to dynamically adjust the signal cycle length based on time-varying traffic flow,in order to flexibly respond to complex traffic conditions.In order to evaluate the performance of the proposed method,simulation experiments are conducted in the VISSIM simulation environment.The experimental results show that compared with the baseline method,the proposed method reduces the average queue length by 9.34% and the maximum queue length by 15.28%,and reduces the average delay time by 9.45% and the average number of stops by 5%.
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2024, Vol. 45 
