IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v674y2025ics0378437125004133.html
   My bibliography  Save this article

Reduction strategy of rear-end collision risks for connected and automated vehicles on freeways with different weather conditions

Author

Listed:
  • Jiang, Yufeng
  • Qin, Yanyan
  • Zhu, Li
  • Li, Gen
  • Wang, Hao

Abstract

Car-following behavior is significantly influenced by weather conditions. Adverse weather conditions, in particular, negatively affect this behavior and increase rear-end collision risks. Equipped with advanced technologies, connected and automated vehicles (CAVs) have potential in reducing collision risks. To mitigate the collision risks of mixed fleet with CAVs and human-driven vehicles (HVs) on freeways with various weather conditions, this paper proposes a distance-based control strategy for CAVs. Specifically, the Gipps model was employed to represent car-following behavior of HVs under different weather conditions. Based on this, a car-following strategy for CAVs is developed to enhance their adaptability to distance situations with the vehicle ahead. To validate the effectiveness of the proposed CAV strategy, simulation experiments under both speed homogeneity and heterogeneity conditions were conducted, which analyzed the effects of weather condition, vehicle speed, and CAV penetration rate on rear-end collision risks. Furthermore, we examined how distribution patterns of CAVs in mixed fleet influenced the rear-end collision risk. The results demonstrate that the proposed CAV strategy can effectively enhance fleet stability and reduce rear-end collision risks caused by emergency braking under clear, rainy, and foggy freeway conditions. When compared to a fleet of pure HVs, reduction in surrogate measures of ITC and DRAC exceeds 75 % and 60 %, respectively, at different speed levels for a fleet of pure CAVs. Additionally, during the early stages of CAVs adoption on freeways, it is recommended to place CAVs in the middle of the mixed fleet across different weather conditions. As CAVs become more widely adopted, they are suggested to be positioned at the front of the mixed fleet to minimize the overall rear-end collision risks. While the speed heterogeneity weakens this trend, the minimum collision risk occurs when CAVs position in the front of the mixed fleet.

Suggested Citation

  • Jiang, Yufeng & Qin, Yanyan & Zhu, Li & Li, Gen & Wang, Hao, 2025. "Reduction strategy of rear-end collision risks for connected and automated vehicles on freeways with different weather conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 674(C).
  • Handle: RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125004133
    DOI: 10.1016/j.physa.2025.130761
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437125004133
    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.2025.130761?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:674:y:2025:i:c:s0378437125004133. 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.