Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.