Abstract (EN):
Driver inattention has been a major contributor to road crashes through the last years. Additionally, professional drivers are more expose to this risk due to the long work journey. The present study aims to explore the data collected by a commercial driver-monitoring system (DMS) in order to identify profiles of professional drivers. The DMS emitted an alert when distraction or drowsiness were detected, generating additional information such as timestamp, GPS and instant speed. The Hierarchical Clustering Approach and the K-means method were used to identify distinct profiles. The results showed a clear distinction among the clusters with respect to the exposition variables (time and distance related) as well as the number of inattention events. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.
Language:
English
Type (Professor's evaluation):
Scientific