Professional drivers constitute a high-risk road user group mainly due to the increased driving time and distance travelled, the heavy weight of vehicles and the special traffic and operating rules to be followed while driving. In that context, the objective of the present study is to: (i) explore the speeding and aggressive behavior of professional drivers based on detailed driving analytics collected by smartphone sensors, and (ii) investigate whether incentives in a social gamification scheme can improve driving behavior. For that purpose, high-resolution smartphone data collected from a naturalistic driving experiment with a sample of 19 professional drivers were utilized and analysed by means of Generalized Linear Mixed-Effects Models. The findings suggest that speeding and aggressive driving behavior are correlated both to exposure and behavioral driving indicators. Additionally, results capture and quantify the positive effects of awarding safe driving, thus providing needed impetus for larger-scale applications as well as relevant policy interventions.
ID | pc408 |
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Tags | big data, driver behaviour, naturalistic driving, speed, statistical modelling, telematics |