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An energy loss-based vehicular injury severity model

Author

Listed:
  • Ji Ang
  • David Levinson

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

Abstract

How crashes translate into physical injuries remains controversial. Previous studies recommended a predictor, Delta-V, to describe the crash consequences in terms of mass and impact speed of vehicles in crashes. This study adopts a new factor, energy loss-based vehicular injury severity (ELVIS), to explain the effects of the energy absorption of two vehicles in a collision. This calibrated variable, which is fitted with regression-based and machine learning models, is compared with the widely-used Delta-V predictor. A multivariate ordered logistic regression with multiple classes is then estimated. The results align with the observation that heavy vehicles are more likely to have inherent protection and rigid structures, especially in the side direction, and so suffer less impact.

Suggested Citation

  • Ji Ang & David Levinson, 2020. "An energy loss-based vehicular injury severity model," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:elvis
    DOI: 10.1016/j.aap.2020.105730
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    File URL: https://doi.org/10.1016/j.aap.2020.105730
    File Function: First version, 2020
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    More about this item

    Keywords

    Injury severity; Regression model; Vehicle crashes; Energy absorption;
    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

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