Understanding Traffic Safety Culture: Implications for Increasing Traffic Safety
Bibliographic record
Abstract
Despite the success of various engineering, education, and enforcement measures, fatalities and injuries from traffic collisions remain one of the major global problems. It has been advocated that addressing this massive problem requires a fundamental transformation in the traffic safety culture of road users. Measuring and understanding traffic safety culture has gained growing attention in the field of traffic safety. This study, believed to be the first of its kind in Canada, aimed to ( a) measure traffic safety culture related to distracted driving, impaired driving, and speeding; ( b) investigate how perceptions of these major issues are associated with self-reported behavior and support for related enforcement and policy; and ( c) explore the effect of respondents’ sociodemographic characteristics on traffic safety culture. A telephone survey based on a stratified random sample of approximately 1,000 residents in the Edmonton region of Alberta, Canada, was conducted in 2014. Descriptive analysis, multivariate confirmatory factor analysis, and structural equation modeling were performed. The results demonstrate statistically significant correlations among perceived threat to personal safety, acceptability of behaviors, self-reported behaviors, support for enforcement, and support for law and policy. Perceived threat to personal safety has a statistically significant influence on self-reported behavior, support for enforcement, and support for law and policy. Various sociodemographic characteristics have a significant effect on the perceived threat of traffic behaviors to personal safety. The results can be used to guide educational campaigns to transform traffic safety culture from one that is risk receptive to one that is protective.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".