An energy loss-based vehicular injury severity model

The prevention of traffic crashes requires an in-depth analysis of the severity of crashes. The term ‘severity’ in the traffic safety literature may refer to the probability of crashes, the damage magnitude, or both (Shelby, 2011). The degree of crash harm depends on the relationship between physical injuries and crash mechanisms, but understanding is often limited by complicated crash mechanics (Carlson, 1979). Therefore, statistical models, which exploit previous crash data, are used to predict injury severity with several explanatory variables. For example, factors in most vehicular injury analyses include crash mechanical properties, driver characteristics (such as age, gender, and alcohol or drug use), safety equipment, vehicle use, and surrounding conditions (Massie et al., 1995, Abdel-Aty et al., 1998, Abdel-Aty et al., 2011, Yau, 2004, Chang and Wang, 2006, Keall et al., 2004, Smink et al., 2005).

There is no consensus on how to evaluate severity. To assess multiple discrete severity outcomes, the Abbreviated Injury Score (AIS) and Maximum Abbreviated Injury Score (MAIS) are frequently used to classify the extent of injuries (Carlson and Kaplan, 1975, Carlson, 1977, Augenstein et al., 2003, Digges and Dalmotas, 2001, Conroy et al., 2008). They have seven classifications in general: 0 – Property Damage Only (PDO), 1 – Minor Injury, 2 – Moderate Injury, 3 – Serious Injury, 4 – Severe Injury, 5 – Major Injury, and 6⁺ – Fatality (Untreatable cases). Similarly, there are also some indices like Injury Severity Score (ISS) evaluating one injury severity score derived from three different body regions (Greenspan et al., 1985, Ehrlich et al., 2006). This paper uses MAIS for the severity evaluation, as it was recorded per single vehicle and was commonly provided by the datasets employed here.

To specify the crash severity, Mackay (1968), applying momentum conservation theory, first defined Delta-V as the change of speed before and after the collision. The probability and magnitude of injuries and fatalities are often based on the Delta-V of involved vehicles. Delta-V has been applied for the measurement of vehicular crash severity for several decades (Joksch, 1993, Roberts and Compton, 1993, Evans, 1994). Evans (1994) evaluated this model as one of the best analytical methods when real crash testing is unavailable. The fundamental mechanical meaning of Delta-V is related to the forces that occupants receive inside the crashing vehicles (Carlson, 1979). In reaction to the imposed force after the collision, occupants strike the surfaces in the front of the vehicle, which is the main reason for injuries in most cases. However, the recent application of Delta-V depends on four crash modes (Front, Near side, Far side, and Rear detected by the event data recorder), which may not be comparable so that different directions yield different conclusions (Andricevic et al., 2018). Delta-V fails to consider the influence of long crash pulse on injury (Ydenius, 2010, Tsoi and Gabler, 2015). The limitations of Delta-V have prompted the discussion of alternative crash indicators.

Injury severity studies typically establish the statistical relationship between the dependent variable, injury severity, and several independent variables. Ordered logistic regression (OLR) models and their variants, with different levels of severity assessments, perform well (Yasmin et al., 2015, Ferreira et al., 2017, Quddus et al., 2002, Duncan et al., 1998, Kockelman and Kweon, 2002, Ye and Lord, 2014, Bogue et al., 2017, Abay et al., 2013). Table 1 summarizes severity indices, crash indicators, and regression models in previous automobile crash severity studies.

However, few logistic models have been estimated to explain injury severity due to impact energy. This study uncovers the correlation between the injury severity of occupant and a multi-directional energy-related predictor.

This paper considers dynamic crash mechanisms and then proposes an energy loss-based vehicular injury severity (ELVIS) evaluation indicator. The model is first calibrated by a dataset with separate two types of collision, considering the longitudinal and lateral impacts simultaneously. It aims to include both magnitude and direction elements in its expression. By comparing with the Delta-V, we explain the occupant injury severity due to energy absorption in a specific collision. Additionally, because of the discrete and ordinal nature of the dependent variable, the ordered logistic regression model is used to estimate model coefficients. The rest of the paper is structured as followed: Section 2 introduces how data get processed in this study, and Section 3 provides a theoretical framework for the ELVIS function. In Section 4, we evaluate the regression model by the calibrated parameter and different probability distributions; meanwhile, comparing the Delta-V model with the new model with their statistical fitness and predicted accuracy. The discussion (Section 5) interprets the findings from the ELVIS model. A brief conclusion (Section 6) summarizes the outcomes.