Behind the Wheel of a Truck Simulator: Comparison of Self-Reported, Performance-Based, and Simulation Methods for Predicting Driver Traffic Offences

Traffic violations represent a significant public health concern, with professional drivers substantially impacting road safety. This pilot study compared self-report questionnaires (general personality versus domain-specific), performance-based tests, and driving simulator measures to determine which assessment method best predicts traffic offences among professional truck drivers. Participants (N = 27) completed the Impulsiveness–Venturesomeness–Empathy Questionnaire (IVE), the Road Traffic Behaviours Questionnaire (KZD), and the Vienna Risk-Taking Test Traffic (WRBTV) and performed standardised driving scenarios in a truck simulator. Performance was assessed using speed variations in five validated decision-making situations. Drivers were classified into two groups based on relatively higher and relatively lower numbers of self-reported traffic offences. The KZD demonstrated the strongest group differentiation (p = 0.034, d = 0.76). Simulator performance was significantly different between the groups (p = 0.033, d = −0.68), with offence-reporting drivers showing smaller speed reductions. The WRBTV and the IVE empathy subscale approached significance (p = 0.056 and p = 0.059, respectively). Higher empathy characterised offence-free drivers, suggesting social–emotional factors may contribute to traffic safety. General impulsiveness and venturesomeness showed no group differences. The results indicate that domain-specific questionnaires and behavioural assessments offer superior predictive validity compared to general personality measures for identifying potentially unsafe drivers. ROC analysis revealed moderate predictive validity across significant measures (AUC: 0.64–0.70), with differential patterns of sensitivity and specificity among predictors. The findings suggest implementing tiered screening approaches using domain-specific questionnaires as initial cost-effective tools, followed by simulator assessment for at-risk drivers, enabling transport companies and regulatory bodies to identify high-risk drivers proactively.