An energy loss-based vehicular injury severity model

Ji Ang and David Levinson

No 2022-01, Working Papers from University of Minnesota: Nexus Research Group

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.

Keywords: Injury severity; Regression model; Vehicle crashes; Energy absorption (search for similar items in EconPapers)
JEL-codes: R41 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Accident Analysis and Prevention. 146 October 2020, 105730.

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Persistent link: https://EconPapers.repec.org/RePEc:nex:wpaper:elvis

DOI: 10.1016/j.aap.2020.105730

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