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A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics

Author

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  • Yunze Wang

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    Key Laboratory of Traffic Safety and Control of Hebei Province, Shijiazhuang 050043, China
    School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Ranran Xu

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    Key Laboratory of Traffic Safety and Control of Hebei Province, Shijiazhuang 050043, China
    School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Ke Zhang

    (College of Information Engineering, Fuyang Normal University, Fuyang 236041, China)

Abstract

Autonomous driving technology and vehicle-to-vehicle communication technology make the hybrid driving of connected and automated vehicles (CAVs) and regular vehicles (RVs) a long-existing phenomenon in the coming future. Among the existing studies, IDM models are mostly used to study the performance of homogeneous traffic flow. To explore the stability of mixed traffic flow, an extended intelligent driver model (IDM) based car-following model was proposed for mixed traffic flow (MTF) with both CAVs and RVs, considering the headway, the speed and acceleration of multiple front vehicles, as well as the response characteristics of RV drivers. Through the linear stability analysis, the criterion for the stability of MTFs was derived, and the relationship among the penetration rate of CAVs, equilibrium velocity and traffic stability in MTF are discussed. Based on the above theoretical model, a numerical simulation was conducted in two typical scenarios of starting and braking. The results showed that, at the microscopic scale, the vehicle in the Cooperative Adaptive Cruise Control (CACC) mode could significantly decelerate in response to the interference from other vehicles in the same traffic environment. At the macroscopic scale, as the penetration rate of CAVs increased, the overall acceleration fluctuation of the traffic flow decreased. At the same penetration rate of CAVs, the higher density of CAVs coincided with the higher stability of the MTF. When the penetration rate of CAVs was 50%, the degree of distribution had the greatest impact on the MTF. When the penetration rate of CAVs exceeded 70%, the degree of distribution had little impact on the MTF. This research can provide basic theoretical support for the management and control of MTF in the future.

Suggested Citation

  • Yunze Wang & Ranran Xu & Ke Zhang, 2022. "A Car-Following Model for Mixed Traffic Flows in Intelligent Connected Vehicle Environment Considering Driver Response Characteristics," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11010-:d:905805
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    References listed on IDEAS

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    2. Li, Zhipeng & Li, Wenzhong & Xu, Shangzhi & Qian, Yeqing, 2015. "Stability analysis of an extended intelligent driver model and its simulations under open boundary condition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 526-536.
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    4. Yang, Da & Jin, Peter (Jing) & Pu, Yun & Ran, Bin, 2014. "Stability analysis of the mixed traffic flow of cars and trucks using heterogeneous optimal velocity car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 371-383.
    5. G. F. Newell, 1961. "Nonlinear Effects in the Dynamics of Car Following," Operations Research, INFORMS, vol. 9(2), pages 209-229, April.
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    Cited by:

    1. Shan Guan & Chicheng Ma & Jianjun Wang, 2023. "Traffic Flow State Analysis Considering Driver Response Time and V2V Communication Delay in Heterogeneous Traffic Environment," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
    2. Wu, Xinyu & Xiao, Xinping, 2024. "An improved stochastic car-following model considering the complete state information of multiple preceding vehicles under connected vehicles environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 644(C).
    3. Chengju Song & Hongfei Jia, 2022. "Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs," Sustainability, MDPI, vol. 14(20), pages 1-12, October.

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