Assessing the Relationship Between Self-Reported Driving Behaviors and Driver Risk Based on Naturalistic Driving Study

Drivers are prone to overlook their risky behaviors and bad habits during daily driving. These behaviors and habits may be associated with their risk of crash involvement. Safety countermeasures can be more effectively developed if the relationships between risky behaviors and crash risk are better understood, but the low incidence of crashes has limited the accuracy of most research. The objectives of this study are therefore: 1) to determine the extent to which driver involvement in both crashes and near crashes (CNCs) is related to driving behavior, and 2) to assess the relationship between each type of risky behavior and individual driver CNC risk. Driver and crash data were required from the Shanghai Naturalistic Driving Study (SH-NDS), and a k-mean cluster method was adopted to classify the drivers into three CNC groups (high-, moderate- and low-risk drivers). Drivers self-reported their driving behaviors and bad driving habits by completing the Manchester Driver Behavior Questionnaire (DBQ). Principal component analysis of the 24 DBQ items led to a five-component structure including aggressive violations, ordinary violations, lapses, inattention errors, and inexperience errors. Two logistic regression models were developed to investigate the correlation between the five DBQ components and drivers’ CNC levels. Conclusions are as follows: 1) High-risk drivers were more likely to have inattention errors (e.g., miss “yield” rule) and ordinary violations (e.g., run a red light) than the other drivers, 2) aggressive violations (e.g., race against others) and ordinary violations were positively related to the probability of being a high- or moderate-risk driver.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
  • Corporate Authors:

    Transportation Research Board

    ,    
  • Authors:
    • Wang, Xuesong
    • Xu, Xiaoyan
    • Chai, Chen
  • Conference:
  • Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: References;
  • Pagination: 5p

Subject/Index Terms

Filing Info

  • Accession Number: 01698138
  • Record Type: Publication
  • Report/Paper Numbers: 19-03855
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:47AM