IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0227609.html
   My bibliography  Save this article

A highway crash risk assessment method based on traffic safety state division

Author

Listed:
  • Dongye Sun
  • Yunfei Ai
  • Yunhua Sun
  • Liping Zhao

Abstract

In order to quantitatively analyze the influence of different traffic conditions on highway crash risk, a method of crash risk assessment based on traffic safety state division is proposed in this paper. Firstly, the highway crash data and corresponding traffic data of upstream and downstream are extracted and processed by using the matched case-control method to exclude the influence of other factors on the model. Secondly, considering the weight of traffic volume, speed and occupancy, a multi-parameter fusion cluster method is applied to divide traffic safety state. In addition, the quantitative relationship between different traffic states and highway crash risk is analyzed by using Bayesian conditional logistic regression model. Finally, the results of case study show that different traffic safety conditions are in different crash risk levels. The highway traffic management department can improve the safety risk management level by focusing on the prevention and control of high-risk traffic safety conditions.

Suggested Citation

  • Dongye Sun & Yunfei Ai & Yunhua Sun & Liping Zhao, 2020. "A highway crash risk assessment method based on traffic safety state division," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-14, January.
    • Handle: RePEc:plo:pone00:0227609
    DOI: 10.1371/journal.pone.0227609
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227609
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0227609&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0227609?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhao, Jingya & Liu, Qingchao, 2024. "Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    2. Eui-Jin Kim & Oh Hoon Kwon & Shin Hyoung Park & Dong-Kyu Kim & Koohong Chung, 2021. "Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-21, May.

    More about this item

    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:plo:pone00:0227609. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.