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Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective

Author

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  • Jin, Shuang
  • Sun, Di-Hua
  • Zhao, Min
  • Li, Yang
  • Chen, Jin

Abstract

With the development of automated driving and communication technologies, the connected automated vehicles (CAVs) gradually enter market and become more popular. At the same time, the mixed traffic composed of conventional vehicles and CAVs will gradually become a very common form of traffic. In fact, the driving process of CAV is a typical cyber physical process which couples tightly the cyber factor of traffic information with the physical components of the vehicles. In this paper, we present a mixed traffic model from the perspective of cyber physical fusion, the model focuses on the fact that the CAVs can obtain the information from multiple vehicles ahead and the drivers have a reaction delay in driving process. The stability condition of the proposed model is derived via linear stability analysis. Furthermore, we investigate the fuel consumption and CO2 emission using the model we propose under different penetration rates of CAVs. The results show that the stability of mixed traffic is related to driver’s reaction delay, the penetration rate of CAVs, and the information from multiple vehicles ahead that CAVs can obtain. Numerical simulations are conducted to verify the analytical results. The simulation results demonstrate that the model proposed in this paper can better reflect the real advantages of CAV in mixed traffic, and further show that the model in this paper is more realistic. More specifically, the information obtained from multiple vehicles ahead including conventional vehicle and CAV can improve the stability of mixed traffic and traffic efficiency to a greater extent, and the driver’s reaction delay will destabilize mixed traffic. Besides, when the penetration rate of CAVs is high, the fuel consumption and CO2 emission in mixed traffic can be greatly reduced.

Suggested Citation

  • Jin, Shuang & Sun, Di-Hua & Zhao, Min & Li, Yang & Chen, Jin, 2020. "Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437120300480
    DOI: 10.1016/j.physa.2020.124217
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    References listed on IDEAS

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    1. Zhu, Wen-Xing & Zhang, H.M., 2018. "Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 274-285.
    2. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Modeling connected and autonomous vehicles in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 269-277.
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    Citations

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    Cited by:

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    3. Montanino, Marcello & Monteil, Julien & Punzo, Vincenzo, 2021. "From homogeneous to heterogeneous traffic flows: Lp String stability under uncertain model parameters," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 136-154.
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    6. Liu, Zhongcheng & Sun, Dihua & Zhao, Min & Jin, Shuang & Zhang, Yicai, 2022. "Pinning control strategy and stability analysis of mixed platoon: A cyber–physical perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    7. Cheng, Rongjun & Lyu, Hao & Zheng, Yaxing & Ge, Hongxia, 2022. "Modeling and stability analysis of cyberattack effects on heterogeneous intelligent traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
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    9. Muhammad Azam & Sitti Asmah Hassan & Othman Che Puan, 2022. "Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis," Sustainability, MDPI, vol. 14(17), pages 1-34, August.
    10. Montanino, Marcello & Punzo, Vincenzo, 2021. "On string stability of a mixed and heterogeneous traffic flow: A unifying modelling framework," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 133-154.
    11. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    12. Wu, Yuanyuan & Wang, David Z.W. & Zhu, Feng, 2022. "Influence of CAVs platooning on intersection capacity under mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    13. Mohammed Al-Turki & Nedal T. Ratrout & Syed Masiur Rahman & Imran Reza, 2021. "Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives," Sustainability, MDPI, vol. 13(19), pages 1-22, October.
    14. Yao, Zhihong & Gu, Qiufan & Jiang, Yangsheng & Ran, Bin, 2022. "Fundamental diagram and stability of mixed traffic flow considering platoon size and intensity of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    15. Hua, Xuedong & Yu, Weijie & Wang, Wei & Zhao, De, 2023. "Impact of multi-class stochastic cyberattacks on vehicle dynamics and rear-end collision risks for heterogeneous traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).

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