Identifying driving safety profiles from smartphone data using unsupervised learning

Abstract A large number of drivers with different driving characteristics co-exist on the road network. Assessing a person’s driving profile and detecting aggressive and unsafe driving behavior is essential to enhance road safety, reduce fuel consumption and – at a macroscopic level - tackle congestion. Nowadays, driving data can be massively collected via sensors embedded in mobile phones, avoiding the expensive and inefficient solutions of in-vehicle devices. In this paper, these data are used to detect unsafe driving styles based on two-stage clustering approach and using information on harsh events occurrence, acceleration profile, mobile usage and speeding. First, an initial clustering was performed in order to separate aggressive from non–aggressive trips. Subsequently, to distinguish “normal” trips from unsafe trips, a second level clustering was performed. In this way, trips have been categorized into six distinct groups with increasing importance with respect to safety. Findings reveal that about 50% of the trips were characterized as “safe trips”, while in 23.5% of the trips drivers were driving above the speed limit and only 7.5% of the trips are characterized by distracted driving. The further analysis of drivers in relation to the grouping of their trips showed that drivers cannot maintain a stable driving profile through time, but exhibit a strong volatile behavior per trip. Finally, a discussion is provided on the implications of the main findings in research and practice.