An energy loss-based vehicular injury severity model

Ang Ji; David Levinson

doi:10.1016/j.aap.2020.105730

An energy loss-based vehicular injury severity model

Accid Anal Prev. 2020 Oct:146:105730. doi: 10.1016/j.aap.2020.105730. Epub 2020 Aug 21.

Authors

Ang Ji¹, David Levinson²

Affiliations

¹ The University of Sydney, School of Civil Engineering, Sydney, Australia. Electronic address: ang.ji@sydney.edu.au.
² The University of Sydney, School of Civil Engineering, Sydney, Australia.

PMID: 32835953
DOI: 10.1016/j.aap.2020.105730

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

MeSH terms

Accidents, Traffic / statistics & numerical data*
Humans
Injury Severity Score*
Logistic Models
Machine Learning
Motor Vehicles / classification*
Wounds and Injuries / epidemiology
Wounds and Injuries / etiology*