IDEAS home Printed from https://ideas.repec.org/p/nex/wpaper/injuryseverity.html
   My bibliography  Save this paper

Injury severity prediction from two-vehicle crash mechanisms with machine learning and ensemble models

Author

Listed:
  • Ji Ang
  • David Levinson

    (TransportLab, School of Civil Engineering, University of Sydney)

Abstract

Machine learning algorithms aim to improve the power of predictors over conventional regression models. This study aims to tap the predictive potential of crash mechanism-related variables using ensemble machine learning models. The results demonstrate selected models can predict severity at a high level of accuracy. The stacking model with a linear blender is preferred for the designed ensemble combination. Most bagging, boosting, and stacking algorithms perform well, indicating ensemble models are capable of improving upon individual models.

Suggested Citation

  • Ji Ang & David Levinson, 2020. "Injury severity prediction from two-vehicle crash mechanisms with machine learning and ensemble models," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:injuryseverity
    DOI: 10.1109/OJITS.2020.3033523
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1109/OJITS.2020.3033523
    File Function: First version, 2020
    Download Restriction: no
    File URL: https://libkey.io/10.1109/OJITS.2020.3033523?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. Li, Kun & Xu, Haocheng & Liu, Xiao, 2022. "Analysis and visualization of accidents severity based on LightGBM-TPE," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    More about this item

    Keywords

    Injury severity; machine learning algorithms; vehicle crashes; ensemble technique; crash mechanisms;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    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:nex:wpaper:injuryseverity. 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: David Levinson (email available below). General contact details of provider: https://edirc.repec.org/data/nexmnus.html .

    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.