An energy loss-based vehicular injury severity model. (October 2020)

• Record Type: Journal Article
• Authors: Ji, Ang; Levinson, David
• Is Part Of: Accident analysis and prevention. Volume 146(2020)
• Publication Date: 2020-10
• Subjects: Injury severity -- Regression model -- Vehicle crashes -- Energy absorption; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105730
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: An in-depth analysis of the severity of crashes is needed to mitigate traffic crashes. Regression models are to analyze the relationship between injury and crash mechanisms. Occupants receive less impact from collisions by protective structures and high stiffness. The mass ratio of vehicles affects energy absorption, especially in lateral structures. 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.