Analysis of Speed Related Behavior of Kuwaiti Drivers Using the Driver Behavior Questionnaire
Bibliographic record
Abstract
The Manchester Driver Behaviour Questionnaire (DBQ) is widely used to measure driving styles and investigate the relationship between driving behaviour and accidents involvement. Recent evaluations of different population groups have taken place throughout the world, including countries in the Arabian Gulf. This study seeks to extend the application of the DBQ to Kuwait with its mix of native and expatriate drivers, by examining the relationships between speed-related behavior and accident involvement using a speed-related score (SRS). For this purpose, 536 respondents (425 Kuwaitis and 111 Non-Kuwaitis) were asked to complete a questionnaire based on the DBQ parameters as well as background information. The results showed that young Kuwaiti male drivers scored highest in most of the areas. Factor analysis resulted in four significant dimensions; speed-related violations, anger related violations, errors, and lapses. The study focused on the speed related violation score (SRS) as the dependent variable. The statistical analysis using ANOVA and t- test showed that there is a significant effect of such factors as accident involvement, age, gender, nationality, education level, driving experience and marital status. Some countermeasures to reduce accidents were identified focusing on those groups with higher SRS values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".