Applying multilevel analysis and the Driver Behavior Questionnaire (DBQ) on unsafe actions under a road safety policy

PLoS One. 2022 Nov 16;17(11):e0277750. doi: 10.1371/journal.pone.0277750. eCollection 2022.

Abstract

The aims of this research are: to investigate and develop a multilevel analysis of unsafe actions or risky behaviors; to study the influence of road safety policy factors on risky behaviors; and to analyze personal characteristics that influence risky behaviors. Data were collected using 1,474 samples from locations countrywide at the district level, including 76 clusters, via the Driver Behavior Questionnaire (DBQ) and road safety policy. The results indicate that, for the district-level model, the participation factor directly and negatively influenced risky behaviors, and government support indirectly had a negative impact through participation. Thus, people's participation in the area caused a decrease in unsafe behaviors. Meanwhile, safety policy support in the area partially caused people to participate at a significant level. At the personal level, income, having a driver's license, past violations, and past accidents significantly affected risky behaviors, especially having a driver's license, which had a negative influence. This meant that people who had a driver's license facilitated a positive effect in terms of decreasing risky behaviors, while people with past violations and past accidents influenced this situation positively. The more traffic law violations and accidents the participants had, the more they engaged in unsafe actions. Based on the findings, acknowledging and solving the problem of unsafe driving at a spatial level can address the issue by supporting different measures to help people in the area improve the situation. In addition, we should assist people who have a driver's license by offering them useful training to decrease traffic law violations and inform them about accidents.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic* / prevention & control
  • Automobile Driving*
  • Humans
  • Multilevel Analysis
  • Policy
  • Surveys and Questionnaires

Grants and funding

This research was funded by the Suranaree University of Technology Research and Development Fund, Grant number IRD7-704-63-12-17, and the APC was funded by Suranaree University of Technology. Sajjakaj Jomnonkwao received funding for this work.