Assessing the relationship between the Driver Behavior Questionnaire and the Driver Skill Inventory: Revealing sub-groups of drivers
DOI: 10.1016/j.trf.2014.06.008
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Summary
This study investigates the relationship between two widely used self-report instruments for assessing driving behavior: the Driver Behavior Questionnaire (DBQ) and the Driver Skill Inventory (DSI). The primary motivation was to determine whether drivers demonstrate consistency in their self-assessment of driving ability by comparing reported aberrant behaviors (DBQ) with perceived driving skills (DSI). Additionally, the researchers aimed to identify distinct sub-groups of drivers based on these measures to understand heterogeneity in traffic safety risks and to inform targeted interventions. The study utilized a survey of 3,908 Danish drivers aged 18–84, selected randomly from the national driving license register. Participants completed the DBQ, which measures the frequency of violations, errors, and lapses, and the DSI, which assesses self-rated perceptual-motor and safety skills. Statistical analysis involved calculating standardized sum scores for the five factors (three from DBQ, two from DSI) and applying k-means cluster analysis to identify driver sub-groups. The optimal solution, determined by F-values and interpretability, consisted of four clusters. These clusters were then compared using ANOVA regarding demographic characteristics, annual mileage, accident involvement, fines, and speed preferences. The results revealed four distinct driver profiles: "skilled safe drivers," "violating unsafe drivers," "unskilled unsafe drivers," and "unskilled safe drivers." The analysis indicated that drivers generally exhibit consistency in their self-reporting; low self-rated skills corresponded with high frequencies of aberrant behaviors, and vice versa, in three of the four clusters. The "violating unsafe" cluster, predominantly young men, reported high perceptual-motor skills but low safety skills, alongside the highest rates of violations, accidents, fines, and speed. The "unskilled unsafe" cluster reported low skills across both dimensions and the highest frequency of errors and lapses. Conversely, the "unskilled safe" cluster, largely older women, reported low skills but also low aberrant behaviors, suggesting under-confidence rather than actual incompetence. The findings imply that self-reported measures can effectively identify high-risk driver sub-groups, supporting the use of differentiated preventive strategies. The study suggests that interventions should address three distinct areas: actual driving skills (requiring training), attitudes toward safety (requiring attitude change), and self-assessment awareness. Specifically, "violating unsafe" drivers may need attitudinal interventions, while "unskilled unsafe" drivers require both skill training and attitude adjustment. The consistency observed in most drivers challenges the notion that overconfidence is a universal issue, highlighting instead that specific groups may suffer from under-confidence or a lack of motivation to change despite awareness of their risks.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-19 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | failed | — | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
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- dbq psychometrics
- human error taxonomy
- decision making risk perception
- sex gender
- cultural cross national
- induced exposure
Information type
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- Empirical Findings: observational prevalence
- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model