Evaluating Rainy Weather Effects on Driving Behaviour Dimensions of Driving Behaviour Questionnaire
Why this work is in the frame
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Bibliographic record
Abstract
This study aims to develop a modified version of the existing driving behaviour questionnaire (DBQ) by including items related to driving behaviour under rainy conditions to evaluate driving behaviour changes and their implications. A survey of 680 drivers in Iran was conducted with the modified DBQ considering rainy conditions. Exploratory and confirmatory factor analysis concluded a four-factor solution (high velocity with a law violation, slips, positive and cautious behaviours, and aggressive driving behaviours) with a 52% explanation of variance. One of the most affected driving behaviours during rainfall is the tendency of high velocity with law violation behaviours. Compared to male drivers, female drivers showed lower high-velocity behaviours with law violation when driving in dry weather and in rainy weather. Married drivers have not only less tendency to drive fast or violate the law compared to single drivers but are also less susceptible to these actions during rain. It was observed that young drivers under 25 did not change their aggressive driving behaviours in rainy conditions. The results from this study are valuable resources to help transportation agencies to understand drivers’ likely behaviour in rainy conditions and develop appropriate countermeasures to minimize the risky behaviours. Also, since aggressive driving, high acceleration, and speed variance have been reported to result in high fuel consumption and emissions, the findings from this study are valuable resources to understand the relationship between weather, driver behaviour, and emissions in future studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it