Motor vehicle driver injury severity analysis utilizing a random parameter binary probit model considering different types of driving licenses in 4-legs roundabouts in South Australia

A roundabout may not provide an acceptable level of control and can be confusing to inexperienced drivers. Therefore, the purpose of this study is to identify the contributing factors that lead to specific driver injury severity by utilizing a random parameter binary probit model sustained by different experiences of motor drivers at 4-legs roundabouts in South Australia. Four models were estimated based on seven years of crash data (2012-2018), considering different types of motorist-driving license: learner, provisional, full, and for all datasets, including unknown licensures. The model estimates variables have been categorized into a driver, crash, temporal, spatial, vehicle, roadway characteristics, and vehicle movements. The results showed there are differences between resulting crash-injury severities when driver experience has been observed. Besides, several parameters were found to be random and normally distributed: safety equipment, crash type (rear-end crash), number of involved vehicles, weekdays indicator, stats area (crash occurred within metropolitan), vehicle type (passenger car), and posted speed limit (more than 50 km/hr.). In addition, the log-likelihood and the trans-ferability test indicated that the data should be separated and analyzed according to the driver's license. Findings can help authorities to improve driver safety considering the influence of the driver experience.

Automated summary

By analyzing crash data from 4-leg roundabouts in South Australia, the study found that driver experience significantly influences crash severity, providing insights for authorities to enhance driver safety measures.