Individual driver risk assessment using naturalistic driving data.
Driving risk varies substantially among drivers. Identifying and predicting high-risk drivers will greatly benefit the development of proactive driver education programs and safety countermeasures. The objective of this study is twofold: (1) to identify factors associated with individual driver risk and (2) predict high-risk drivers using demographic, personality, and driving characteristic data. The 100-Car Naturalistic Driving Study was used for methodology development and application. A negative binomial regression model was adopted to identify significant risk factors. The results indicated that the driver's age, personality, and critical incident rate had significant impacts on crash and near-crash risk. For the second objective, drivers were classified into three risk groups based on crash and near-crash rate using a K-mean cluster method. The cluster analysis identified approximately 6% of drivers as high-risk drivers, with average crash and near-crash (CNC) rate of 3.95 per 1000miles traveled, 12% of drivers as moderate-risk drivers (average CNC rate=1.75), and 84% of drivers as low-risk drivers (average CNC rate=0.39). Two logistic models were developed to predict the high- and moderate-risk drivers. Both models showed high predictive powers with area under the curve values of 0.938 and 0.930 for the receiver operating characteristic curves. This study concluded that crash and near-crash risk for individual drivers is associated with critical incident rate, demographic, and personality characteristics. Furthermore, the critical incident rate is an effective predictor for high-risk drivers.
Department of Statistics, Virginia Tech Transportation Institute, Virginia Tech, 406A Hutcheson Hall, Blacksburg, VA 24061-0439, USA. Electronic address: feng.guo@vt.edu.
Feng
F
Guo
Youjia
Y
Fang
D000063:Accidents, Traffic / Q000517:prevention & control* / Q000523:psychology; D000328:Adult; D000367:Age Factors; D001334:Automobile Driving / Q000523:psychology / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007206:Individuality; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010551:Personality; D010555:Personality Inventory; D018570:Risk Assessment / Q000379:methods*; D012309:Risk-Taking; D055815:Young Adult