IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-97-1949-5_69.html
   My bibliography  Save this book chapter

Causal Analysis of Subway Construction Safety Accidents Based on Random Forest

Author

Listed:
  • Shuang Du

    (Chongqing Jiaotong University
    Chongqing Jiaotong University)

  • Xiaosen Huo

    (Chongqing Jiaotong University)

  • Liudan Jiao

    (Chongqing Jiaotong University)

  • Tong Hao

    (Chongqing Jiaotong University)

Abstract

In recent years, safety accidents during subway construction have been occurred frequently in China, resulting in significant casualties and property losses. To minimize the losses caused by such accidents effectively and identify key factors influencing subway construction safety, this study employs the Human Factors Analysis and Classification model (HFACS). Using 107 typical subway construction safety accident investigation reports as data samples, the study constructs an HFACS model that is applicable to subway construction safety accidents, and screens out 21 accident factors. Additionally, a prediction model for subway construction safety accident levels is developed using the Random Forest method to rank the accident factors in terms of importance. The results show that the accuracy of the prediction model using Random Forest reaches 0.88, and five key factors that have a greater impact on accident severity are identified. Based on these findings, the study puts forward targeted pre-control measures and recommendations to reduce the probability of subway construction accidents effectively.

Suggested Citation

  • Shuang Du & Xiaosen Huo & Liudan Jiao & Tong Hao, 2024. "Causal Analysis of Subway Construction Safety Accidents Based on Random Forest," Lecture Notes in Operations Research,, Springer.
    • Handle: RePEc:spr:lnopch:978-981-97-1949-5_69
    DOI: 10.1007/978-981-97-1949-5_69
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:lnopch:978-981-97-1949-5_69. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.