IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v627y2023ics0378437123006854.html
   My bibliography  Save this article

Fundamental diagram of mixed traffic flow considering time lags, platooning intensity, and the degradation of connected automated vehicles

Author

Listed:
  • Li, Ruijie
  • Sun, Siyuan
  • Wu, Yunxia
  • Hao, Huijun
  • Wen, Xuguang
  • Yao, Zhihong

Abstract

To investigate the impact of connected automated vehicles (CAVs) on the characteristics of mixed traffic flow this paper proposed a fundamental diagram to consider time lags, platooning intensity, and the degradation of CAVs. First, the cooperative adaptive cruise control (CACC), adaptive cruise control (ACC), and intelligent driver model (IDM) are used to describe the car-following behavior characteristics of CAVs, and degraded CAVs (DCAVs), and HDVs, respectively. Second, a fundamental diagram model of mixed traffic flow under different penetration rates of CAVs is derived. The model is related to CAVs degradation, time lags, and platooning intensity. Then, a sensitivity analysis is investigated, including platooning intensity, free-flow speed, and minimum safety distance. Finally, the proposed model is verified by a simulation experiment designed by SUMO. The results show that the free-flow speed, penetration rate, and platooning intensity of CAVs positively affect the traffic capacity and optimal traffic density of mixed traffic flow. On the contrary, the time lags and minimum safety distance harm the traffic capacity and optimal traffic density of mixed traffic flow.

Suggested Citation

  • Li, Ruijie & Sun, Siyuan & Wu, Yunxia & Hao, Huijun & Wen, Xuguang & Yao, Zhihong, 2023. "Fundamental diagram of mixed traffic flow considering time lags, platooning intensity, and the degradation of connected automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    • Handle: RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006854
    DOI: 10.1016/j.physa.2023.129130
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123006854
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.129130?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006854. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.