Modeling and analysis of heterogeneous traffic flow with mixed regular and connected human-driven vehicles considering driver stochasticity

Ying Luo, Yanyan Chen, Kaiming Lu, Jian Zhang, Tao Wang and Zhiyan Yi
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Ying Luo: Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, P. R. China
Yanyan Chen: Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, P. R. China
Kaiming Lu: Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, P. R. China
Jian Zhang: Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, P. R. China
Tao Wang: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, P. R. China
Zhiyan Yi: Department of Civil & Environmental Engineering, University of Utah, Salt Lake City, UT 84112, USA

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 06, 1-25

Abstract: This paper presents a novel microscopic modeling framework to explore the impact of driver stochasticity and other related factors on heterogeneous traffic flows, consisting of both regular human-driven vehicles (RHDVs) and connected human-driven vehicles (CHDVs). We apply the stochastic optimal velocity car-following model for RHDVs, while an extended version of the stochastic continuous car-following model, taking into account optimal velocity guidance and driver compliance, is devised for CHDVs. The stability of CHDV car-following model is examined theoretically and numerically. Simulation experiments are conducted to analyze the stochastic heterogeneous traffic flow properties (i.e. stability and safety) with the proposed microscopic modeling framework under diverse parameter settings including CHDV penetration rate, driver compliance, and driver stochasticity. Results show that traffic flow stability and safety improve with the increase in CHDV penetration rate and driver compliance but deteriorate as the driver’s stochasticity strength increases. CHDVs significantly enhance traffic stability when the CHDV penetration rate or driver compliance exceeds a certain threshold, typically ranging between 0.5 and 0.7. Additionally, there is an interaction between driver stochasticity and CHDV penetration rate in their effect on traffic flow stability, where the contribution of stochasticity to traffic oscillations first increases and then decreases as the penetration rate rises. When the penetration rate is between 0 and 0.4, the traffic risk in emergency situations can be significantly reduced as the penetration rate increases. These findings may guide the management and control strategies of heterogeneous traffic flow.

Keywords: Heterogeneous traffic flow; connected human-driven vehicles; driver stochasticity; stochastic linear stability; traffic safety (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502449

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