Correlates of unsafe driving in non-professional drivers: insights from a data-driven modeling approach

Aberrant driving behavior is a major determinant of traffic crashes and injuries. It encompasses deliberate violations, errors, and lapses. Understanding the specific driver characteristics associated with each behavioral domain is critical for improving road safety. This study aimed to identify demographic, exposure-related, and behavioral covariates associated with violations, errors, and lapses as measured by the Driver Behavior Questionnaire.

A secondary analysis was conducted using a population-based dataset of 1,764 non-professional drivers from Egypt. Drivers completed questionnaires assessing socio-demographic factors, driving exposure, and habitual behaviors. Three separate penalized linear regression models using elastic net regularization were developed for violations, errors, and lapses total scores. Model tuning was optimized by repeated 10-fold cross-validation, followed by refitting selected predictors.

Low education, mobile phone use, eating while driving, and driving while sleepy were independently associated with higher violation scores (p < 0.001). Errors were significantly associated with increasing age, female sex, low education, non-use of seatbelts, eating while driving, and sleepiness (p ≤ 0.005). Lapses were predicted by increasing age, female sex, low education, non-use of seatbelts, and driving while sleepy (p ≤ 0.001). Driving experience was inversely associated with all aberrant behavior domains (p < 0.001). Model diagnostics confirmed adequate fit, low multicollinearity, and robust predictive performance.

Distinct behavioral, demographic, and exposure-related factors predict violations, errors, and lapses among non-professional drivers. These findings underscore the multifactorial nature of aberrant driving and support the development of behavior-specific, evidence-based interventions to enhance road safety and inform precision-focused management of driver behavior strategies.