Injury severities from heavy vehicle accidents: An exploratory empirical analysis

Abstract

Despite the importance of heavy vehicles in Australia's transportation system, little is known on the factors influencing injury severity from accidents involving a single heavy vehicle. Heavy vehicular crashes have been one of the main causes of fatal injuries in Australia, and this raises safety concerns for transport authorities, insurance companies, and emergency services. Although there have been several potential attempts to identify the factors contributing to heavy vehicle crashes and injury severity, it is still necessary to reduce the number of traffic crashes and lower the fatality rate involving heavy vehicles. The aims of this study were investigating the effects of heavy trucks' presence in accidents on the injury severity level sustained by the vehicle driver and detecting the contributing factors that lead to specific injury severity levels. Fixed- and random-parameter ordered probit and logit models were applied for predicting the likelihood of three injury severity categories severe, moderate, and no injury based on data from crashes caused by heavy trucks in Victoria, Australia in 2012–2017. The results showed that the random-parameter ordered probit model performed better than the other models did. Twenty variables (i.e., factors) were found to be significant, and 12 of them were found to have random parameters that were normally distributed. Since some of the investigated factors had different effects on the type of injury severity in Australia, this paper does not recommend generalizing the findings from other case studies. Based on the findings, Victoria state authorities can have insight and enhanced understanding of the specific factors that lead to various types of injury severity involving heavy trucks. Consequently, the safety of all road users, including heavy vehicle drivers, can be enhanced.

Keywords

• Heavy vehicle
• Injury severity
• Random parameter
• Ordered probit model