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Vehicle-dynamics data now more readily available thanks to moderate-cost embedded data logging solutions have been used to study drivers behavior acceleration braking and yaw rate through naturalistic driving research aimed at detecting critical safety events In addition self-reported measures have been developed to describe these events and to assess various individual risk factors such as sensation seeking lack of experience anger expression while driving and sensitivity to distraction In the present study we apply both of these methods of gathering driving data in order to assess risk proneness as accurately as possible Data were obtained from 131 drivers who filled in an introductory questionnaire pertaining to their driving habits Their vehicles were equipped with an external automatic data-capture device for approximately two months During that period the participants reported critical safety events that occurred behind the wheel by a pressing a button connected to the device and b describing the events in logbooks They also filled in weekly questionnaires and at the end of the participation period a final questionnaire with various self-reported measures pertaining to their driving activity We processed the data by a performing a multiple correspondence analysis of the characteristics assessed via the automatic data capture and self-reports and b categorizing the participants via hierarchical clustering of their coordinates on the dimensions obtained from the correspondence analysis This allowed us to identify a group of drivers n 43 at risk based on several self-reported measures in particular their recent crash involvement and the frequency of critical acceleration deceleration events as an objectivemeasure However the at-risk drivers did not themselves report more critical safety events than the other two groups

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Evaluating individual risk proneness with vehicle dynamics and self-report data × toward the efficient detection of At-r...

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