Considering demographics of other involved drivers in predicting the highest driver injury severity in multi-vehicle crashes on rural two-lane roads in California. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Hossain, Md Julfiker; Ivan, John N.; Zhao, Shanshan; Wang, Kai; Sharmin, Sadia; Ravishanker, Nalini; Jackson, Eric
• Is Part Of: Journal of transportation safety & security. Volume 15:Issue 1(2023)
• Page Start: 43
• Page End: 58
• Publication Date: 2023-01-02
• Subjects: Driver's demographics -- driver injury severity -- most severely injured driver -- partial proportional odds -- prediction accuracy; Transportation -- United States -- Safety measures -- Periodicals; Transportation -- Security measures -- United States -- Periodicals; Transportation -- Safety measures; Transportation -- Security measures; United States; Periodicals; 353.9805
• DOI: 10.1080/19439962.2022.2033899
• Languages: English
• ISSNs: 1943-9970

Abstract

Abstract: The injury severity of a driver in a crash is significantly related to the driver's age and gender and vehicle characteristics. Previous studies have used only information about the most severely injured driver to represent the crash severity, ignoring other drivers involved in the crash, which can also be important to explain the crash severity. This study uses demographic information of all drivers involved in a multi-vehicle crash to predict the injury severity of the most severely injured driver using a partial proportional odds model. Models incorporating demographic information and vehicle characteristics of all drivers and vehicles involved in a crash were compared with models considering only information about the most severely injured driver in terms of significance of factors and prediction accuracy. The results indicate that although young drivers are likely to have lower levels of injury severity compared to working-age drivers, injury severity increases if the proportion of young drivers increases in a multi-vehicle crash. Drivers indicated to be not at fault frequently were more severely injured than drivers at fault. Finally, the inclusion of all drivers' demographic information shows an improvement in the prediction accuracy of crash severity of the most severely injured driver.