Author(s)

Pengfei Li¹, Jianjun Shi¹, Xiaoming Liu²

¹Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China.
²Ministry of Transport of the People’s Republic of China, Beijing, China.

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. The present study aims to research driver pattern and classification feature. Driver behavior self-reported investigation was conducted with standardized driver behavior questionnaire (DBQ) by 225 nonprofessional drivers on the internet in Beijing. Questionnaire’s reliability was verified by statistics analysis. Confirmatory factor analysis (CFA) was used to analyze the underlying factor structure. Speed advantage, space occupation, the contend right of way and the contend space advantage were extracted from the ques-tionnaire results to quantify driver characteristics. Based on fuzzy C-means (FCM) algorithm and taking the four factors as pattern features, the number of driver classification distribution was discussed. Then the number of driver classification was determined by statistical indices. The comparison of classification results with the survey finding on whether the driver occurred in traffic accidents within five years shows that the classification result is the same as the actual driving conditions. Finally, correlation between the demographic and types of driving behavior has been analyzed. Female were more likely than male to careful driving, and the older the driver and the less driving experience, the more careful and moderate driving behavior is.

Driver Behavior Questionnaire, Behavior Pattern , FCM, Driver Classification

Li, P. , Shi, J. and Liu, X. (2017) Driving Style Recognition Based on Driver Behavior Questionnaire. Open Journal of Applied Sciences, 7, 115-128. doi: 10.4236/ojapps.2017.74010.