Validating self-reported driving behaviours as determinants of real-world driving speeds

Self-reported driver behaviour has long been a tool used by road safety researchers to classify drivers and to evaluate the impact of interventions yet the relationship with real-world driving is challenging to validate due to the need for extensive, detailed observations of normal driving. This study examines this association by applying the large UDRIVE naturalistic driving study data involving 96 car drivers, comprising 131,462 trips and 1,459,110 km travelled over a duration of 32,096 hours, to compare individual questions and composite indicators based on the Driver Behaviour Questionnaire with real world driving. Self-reported speed behaviour was compared to the measured values under urban and highway conditions. Generalised Linear Mixed Models were developed to examine the relationships between the observed speed behaviours with DBQ errors and violations scores in conjunction with traffic and environmental factors. Drivers' self-reported data on speed selection seldom aligned with their real-world behaviour and there were no meaningful differences between many of the response categories. The DBQ violations and errors scales showed a highly significant correlation with driving speed indicators however they had a low explanatory power compared to other traffic situational and driving factors. Overall, the study highlights the need to validate self-reported driving data against the accuracy and relevance to real-world driving.