A predictive analysis of crash proneness among freight drivers: insight into latent risk dimensions

This research investigates the complex determinants of crash occurrences among freight vehicle drivers, a neglected issue in developing countries like Iran, where high crash rates highlight the urgent need for further study. The primary aim is to establish a predictive model for drivers’ proneness to crashes based on survey data from a diverse driver cohort. The dependent variable of this study is the frequency of the reported accident, and a set of computed variables from questionnaires as independent predictors. In-depth analysis of factors identified a new structure classifying these predictors into three latent dimensions: demographic attributes, personality traits, and levels of perceived risk. The meaningful analyses of factors contributing to crash proneness emphasized demographic elements: driver mid-aged, recent traffic violations, high annual driving mileage, and limited driving experiences are significant predictors of crashing that excessively heighten crash risk. Additionally, certain personality traits, in particular psychoticism, and a negative attitude toward perceiving risks increase proneness to crashing. These findings point out the pressing need for targeted, evidence-based interventions, education, and policy reforms regarding the different driver profiles that manifest certain vulnerabilities. In this regard, there is a need to take an approach that comprehensively embraces the aspects of social norms, individual beliefs, emotional well-being, and personality factors in an effort toward enhanced road safety and reduction in crashes involving freight vehicles. By so doing, policy framers and interested stakeholders will be better placed to develop effective strategies to counter the increasing trend in freight vehicle crashes.