Driving Performances Assessment Based on Speed Variation Using Dedicated Route Truck GPS Data

Abstract

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. INDEX TERMS Driving pattern, dedicated route, global positioning system, trajectory, speed variation, risky driving performance.

Identifying Origin-Destination Trips from GPS Data – Application in Travel Time Reliability of Dedicated Trucks

“…Other potential applications for splitting truck GPS trips using the method proposed in this paper are, for example, truck travel demand forecasting [25,26], freight fleeting performances [27], fuel consumption and emission estimations [28], roadway bottlenecks identification [22], and so forth. For the logistics and trucking industry, it would be interesting to know the punctuality of a truck on a route to ensure timely delivery and performance stability [29], which will be showcased later in this paper.…”

“…The specific geospatial information of the target routes is estimated from the dataset using a trajectory similarity measure. Interested readers can refer to the previous study [29]. As an example, Figures 2a and 2b plot a truck route on a geo-coordinate plane.…”

Predicting Damage Incidents, Fines, and Fuel Consumption from Truck Driver Data: A Study from the Netherlands

“…Figueredo et al (25) and Zhou and Zhang (26) investigated risky truck driving behavior and used this to classify drivers into different risk profiles; nevertheless, they did not establish a direct association with accident involvement. A prevalent issue in truck driving behavior analysis is that drivers often switch trucks, as Li et al acknowledged in their truck driver profiling study (27).…”