Chaotic characteristics of mixed traffic flow integrated with intelligent connected vehicle
(1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong 264005, China; 3. Taizhou Yihualu Data Lake Information Technology Co., Ltd., Taizhou, Jiangsu 225500, China)
Abstract：To investigate the chaotic characteristics of mixed traffic flow and discern the factors influencing the degree of chaos in mixed traffic platoons, the Cao method and the improved Cao method were employed based on the traditional traffic flow theory to determine the delay time and embedding dimension of the mixed traffic flow. The phase space of mixed traffic flow sequences was reconstructed to determine the chaotic characteristics by calculating the maximum Lyapunov exponent. The influential parameters of the proportion of intelligent connected vehicle (ICV) using cooperative adaptive cruise control (CACC) and the delay time in mixed traffic flow were analyzed. The results show that when the maximum Lyapunov exponent of the headway sequence during the car-following process is less than zero, chaos exists in the mixed traffic flow. Increasing the proportion of CACC vehicles can mitigate chaos in certain time intervals. The car-following system tends to be stable when the proportion of CACC vehicles reaches 0.6. The delay time of CACC vehicles significantly affects chaos, and maintaining low communication delays is essential for CACC vehicles to effectively suppress chaos.
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