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

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.

[1]  Takashi Imamura,et al.  Modeling driver operation behavior by linear prediction analysis and auto associative neural network , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Qiang Liu,et al.  Research and Design of Intelligent Vehicle Monitoring System Based on GPS/GSM , 2006, 2006 6th International Conference on ITS Telecommunications.

[3]  Charles C. MacAdam,et al.  Understanding and Modeling the Human Driver , 2003 .

[4]  Kangwon Shin,et al.  Evaluation of the Scottsdale Loop 101 automated speed enforcement demonstration program. , 2009, Accident; analysis and prevention.

[5]  M. Treiber,et al.  Estimating Acceleration and Lane-Changing Dynamics from Next Generation Simulation Trajectory Data , 2008, 0804.0108.

[6]  Jianqiang Wang,et al.  Longitudinal driving behaviour on different roadway categories: an instrumented-vehicle experiment, data collection and case study in China , 2015 .

[7]  Masayoshi Tomizuka,et al.  An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control , 2014 .

[8]  R. Bertini,et al.  Transit Buses as Traffic Probes: Use of Geolocation Data for Empirical Evaluation , 2004 .

[9]  Adrian B Ellison,et al.  Personality, risk aversion and speeding: an empirical investigation. , 2010, Accident; analysis and prevention.

[10]  Adam Duran,et al.  GPS Data Filtration Method for Drive Cycle Analysis Applications , 2012 .

[11]  Linda Reichwein Zientek,et al.  Book Review: Exploratory and Confirmatory Factor Analysis: Understanding Concepts and Applications , 2007 .

[12]  Tomer Toledo,et al.  In-Vehicle Data Recorder for Evaluation of Driving Behavior and Safety , 2006 .

[13]  Alexandre M. Bayen,et al.  Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment , 2009 .

[14]  Masamichi Shimosaka,et al.  Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems , 2010 .

[15]  Ingrid van Schagen,et al.  Driving speed and the risk of road crashes: a review. , 2006, Accident; analysis and prevention.

[16]  Peter Bonsall,et al.  Modelling safety-related driving behaviour: impact of parameter values , 2005 .

[17]  Walid Abdelwahab,et al.  Determining Need for and Location of Truck Escape RAMPS , 1997 .

[18]  Mohan M. Trivedi,et al.  Driving style recognition using a smartphone as a sensor platform , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[19]  Z Bareket,et al.  USING NEURAL NETWORKS TO IDENTIFY DRIVING STYLE AND HEADWAY CONTROL BEHAVIOR OF DRIVERS , 1998 .

[20]  Yang Li,et al.  The Driving Safety Field Based on Driver–Vehicle–Road Interactions , 2015, IEEE Transactions on Intelligent Transportation Systems.

[21]  Long Dong,et al.  A New Model for Predicting Dynamic Surge Pressure in Gas and Drilling Mud Two-Phase Flow during Tripping Operations , 2014 .

[22]  H. Koh,et al.  Chemical fingerprinting of Isatis indigotica root by RP-HPLC and hierarchical clustering analysis. , 2005, Journal of pharmaceutical and biomedical analysis.

[23]  R Zito,et al.  Global positioning systems in the time domain: How useful a tool for intelligent vehicle-highway systems? , 1995 .