It was hypothesized that a driver is not safe when travel speed is too high and also not necessarily safe when travel speed is too low. Based on this hypothesis, this paper studied the risky driving performances by measuring speed variations of a driver’s recurrent trips in two perspectives: 1) driver profiles, which scored the risk on-road driving of each driver and 2) driving patterns, which reflected the risk speed patterns of a type of drivers. The proposed method was tested on a 30-day global positioning system (GPS) dataset, collected from 100 trucks. The study first split the raw dataset into trips and finds the most repeatedly traveled route. Next, the frequency and amplitude of the speed variations from trips of each truck are calculated to establish driver profiles. A risk score is used to rank the truck drivers, i.e., a higher score indicates that the truck driver is more likely to conduct risky driving performances. All trucks are featured in four pre-defined driving patterns according to the different types of speed variations. The geospatial speed distribution of several trucks is manually examined from the raw dataset to verify the results. The contribution lies in providing a method to evaluate a driver’s risk performance through mass truck GPS data. The proposed method would help for monitoring on-road risky driving performances in large fleet management and also providing knowledge about driving styles among drivers which would be beneficial in study driver assistant system
