What question did this study set out to answer?

The study aims to determine which assessment methods best predict traffic offences among professional truck drivers.

February 14, 2026Open Access

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

Key Points

The study aims to determine which assessment methods best predict traffic offences among professional truck drivers.
Compared self-report questionnaires, performance-based tests, and driving simulator measures.
Participants completed different questionnaires assessing personality traits and driving behaviours.
Performance was measured through driving scenarios in a truck simulator with speed variations.
Drivers were classified by their self-reported traffic offences into higher and lower offence groups.
Statistical analyses identified significant differences between groups and assessed predictive validity.
The Road Traffic Behaviours Questionnaire (KZD) showed significant differentiation between driver groups (p = 0.034, d = 0.76).
Simulator performance indicated significant differences; offence-reporting drivers had smaller speed reductions (p = 0.033, d = −0.68).
Higher empathy was noted in offence-free drivers, indicating its role in traffic safety.
General impulsiveness and venturesomeness showed no significant differences between groups.
Moderate predictive validity was found across several measures (AUC: 0.64–0.70).

Abstract

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.

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Cite This Study

Baran et al. (Thu,) studied this question.

synapsesocial.com/papers/699011522ccff479cfe57ede https://doi.org/https://doi.org/10.3390/bs16020271

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