Driving errors, driving violations and accident involvement
ABSTRACT A survey of over 1600 drivers is reported, the results of which are consistent with those reported in an earlier study (Reason et al. 1990), which identified a three-fold typology of aberrant driving behaviours. The first type, lapses, are absent-minded behaviours with consequences mainly for the perpetrator, posing no threat to other road users. The second type, errors, are typically misjudgements and failures of observation that may be hazardous to others. The third type, violations, involve deliberate contraventions of safe driving practice. In the present study the survey instrument used, the Driver Behaviour Questionnaire, was also shown to be reliable over time. Each type of behaviour was found to have different demographic correlates. Most importantly, accident liability was predicted by self-reported tendency to commit violations, but not by tendency to make errors or to have lapses. The implications for road safety are discussed.
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ABSTRACT: The Driver Behavior Questionnaire is a well-used self-report measure of aberrant driving.•Debate continues regarding the underlying structure of the measure.•We applied a bifactor model to data from a large national cohort study of novice drivers .•A general factor and specific violations factor were linked to crashes.•A 12 item version of the Driver Behavior Questionnaire is presented.Accident Analysis & Prevention 01/2015; 74. · 1.87 Impact Factor
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ABSTRACT: This study aims to conduct researches on the correlation between driver safety consciousness and several indices, and propose an objective evaluation criterion, which can covert immeasurable safety consciousness to measurable objective indices. A number of taxi drivers were selected as study subjects, and their safety consciousness was evaluated by using fuzzy comprehensive evaluation method. Combined with the number of accidents these drivers involve in a year, the correlation between driver safety consciousness and accident numbers was investigated. And combined with GPS surveillance data, the correlation between driver safety consciousness and the mean speed, speed dispersion, and max vehicle speed was analyzed. Then the identification model between max vehicle speed and driver safety consciousness level was established. The results show that driver safety consciousness level is correlated with accident numbers, and not correlated with the mean speed, correlated with vehicle speed dispersion, and highly correlated with max vehicle speed. According to fuzzy identification model, max vehicle speed obtained from statistics circle can be used to evaluate and categorize driver safety consciousness. Verification shows that the driver safety consciousness evaluation model, which based on the max vehicle speed, is effective. It can overcome the demerits of scale evaluation, effectively identify driver safety consciousness level, and provide guidance for driver safety education, management and training.Procedia - Social and Behavioral Sciences 07/2014; 138:11–21.
- Proceedings of the Australasian Road Safety Research, Policing and Education Conference, Adelaide, South Australia; 11/2008