As inter-vehicle communication and automatic driving technology continue to develop, but are not yet popular, regular vehicles, connected vehicles and connected autonomous vehicles (CAVs) will coexist on the road for a long time. This mixed traffic environment highlights the need to theoretically analyze the impacts of some connected and autonomous technologies (i.e., accurate detection technology, inter-vehicle communication technology, data storage technology and inter-vehicle cooperation technology) on the stable operation of heterogeneous traffic. According to the characteristics of the vehicles equipped with different technologies, this paper extends the corresponding car-following models based on the optimal velocity model. Through these analytical models, these connected and autonomous technologies are quantified and the linear stability analyses are conducted. Numerical simulation shows that the inter-vehicle communication between three vehicles, and two previous time-step data storage or two future time-step inter-vehicle cooperation are sufficient to stabilize the mixed traffic. As CAV penetration rates increase, the stability of heterogeneous traffic is improved. Furthermore, the stability of heterogeneous traffic is weakened when the size of the largest single fleet increases. These theoretical results can serve as a quantitative tool for scholars and vehicle designers before drawing any qualitative conclusions of related technologies on heterogeneous fleet stability to avoid wasting resources such as data storage capacity and inter-vehicle communication ranges.


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    Titel :

    Modeling Heterogeneous Traffic Mixing Regular, Connected, and Connected-Autonomous Vehicles Under Connected Environment


    Beteiligte:
    Cui, Shaohua (Autor:in) / Cao, Feng (Autor:in) / Yu, Bin (Autor:in) / Yao, Baozhen (Autor:in)


    Erscheinungsdatum :

    2022-07-01


    Format / Umfang :

    3022626 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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