Risky Driver Identification Using Beta Regression Based on Naturalistic Driving Data

Abstract

Naturalistic driving data are widely used to investigate factors related to road safety. Crashes and near-crashes can be regarded as the critical events on the road. The existing studies typically modeled crash and near-crash events at the trip level. However, individual drivers may have different risk levels, and other factors such as distraction can also play a role. This study uses variables automatically derived from naturalistic driving data. Driver distraction is detected from videos using facial landmarks. Based on the collected variables, a beta regression model is developed to identify the significant variables affecting drivers’ risk levels. It is found that the average acceleration rate, number of hard accelerations, driver distraction, and age are significant variables. The findings from this study can be used to identify risky drivers and improve the design of automated vehicles by eliminating human errors and risky driving patterns. Moreover, advanced driver assistance systems (ADAS) can be promoted to alert drivers to risky driving behaviors. The proposed model is also easy to implement in real driving conditions as most of the variables can be extracted automatically. Relevant agencies can also use the model to identify risky drivers and provide proactive customized education programs.