Clustering of Several Typical Behavioral Characteristics of Commercial Vehicle Drivers Based on GPS Data Mining: Case Study of Highways in China

Abstract

Some studies of driving behavior have been based on data mining to create a mechanism that relates data derived from vehicle monitoring, driver behavioral characteristics, and road safety to each other. To make the best of GPS data collected by transportation businesses and explore the potential rules of commercial vehicle driver behavioral characteristics, the parameters related to driving behavioral characteristics are extracted according to GPS data attributes based on factor analysis, and eight parameters of driving behavioral characteristics are transformed into a few aggregated variables containing clear information about driving behavior. With these variables as indicators, a cluster analysis of commercial vehicle driver behavioral characteristics in the selected case is carried out through hierarchical clustering. The results show that commercial vehicle driver behavioral characteristics can be effectively aggregated into four kinds: acceleration–deceleration, speeding-prone, acceleration, and deceleration. Of the four kinds, drivers with relatively serious acceleration–deceleration behavior are also characterized by three other relatively serious behaviors; such drivers have relatively high driving risks, so transportation businesses need to focus their supervision on those drivers. The research results have some relevance to the supervision and training of commercial vehicle drivers in China.