IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-031-55048-5_23.html
   My bibliography  Save this book chapter

A Statistical Approach to Estimate Severe Accident Vehicle Collision Probability Inside a Multi-lane Road Tunnel with Unidirectional Traffic Flow

Author

Listed:
  • Jajati K. Jena

    (Cyient Limited
    Lulea University of Technology)

  • Ajit K. Verma

    (Western Norway University of Applied Sciences)

  • Uday Kumar

    (Lulea University of Technology)

  • Srividya Ajit

    (Indian Institute of Technology Bombay)

Abstract

Dynamic risk estimation of the tunnel is an important aspect of tunnel safety. Severe accident collision probability is an important parameter in the dynamic tunnel risk assessment process as it is needed to build a probabilistic dynamic risk model of a tunnel. This helps in continuous monitoring of the risk of the tunnel from severe accidents and can enable tunnel management to take appropriate control measures to restrict the risk when it crosses a certain threshold. This paper tries to estimate the severe accident probability of collision when a vehicle enters a lane of a tunnel at a certain speed. The three-lane Bhatan tunnel on Mumbai-Pune Expressway in India was considered for the analysis and modeling of the traffic flow. A traffic simulation of one year is performed with 13 million vehicles to come out with the number of overtaking that can happen with a given speed of a vehicle. An exponential regression model was used to predict the number of overtaking. A suitable Weibull distribution was used to predict the severe accident collision probability using the number of overtaking.

Suggested Citation

  • Jajati K. Jena & Ajit K. Verma & Uday Kumar & Srividya Ajit, 2024. "A Statistical Approach to Estimate Severe Accident Vehicle Collision Probability Inside a Multi-lane Road Tunnel with Unidirectional Traffic Flow," Springer Series in Reliability Engineering,, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-55048-5_23
    DOI: 10.1007/978-3-031-55048-5_23
    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:ssrchp:978-3-031-55048-5_23. 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.