Effects of Winter Weather and Maintenance Treatments on Highway Safety
Bibliographic record
Abstract
This research has conducted an analysis of the effects of winter weather and maintenance treatments on the safety of highways as related to factors such as weather, road, and treatment characteristics. The ability to assess and quantify these effects is essential for a comprehensive cost-benefit analysis of alternative maintenance strategies and methods and effective communication of the impacts of these strategies and methods to the decision-makers and the public. Two highway routes from Ontario, Canada were selected and data on daily accident occurrences, weather conditions and winter maintenances operations were obtained for this analysis. A statistical analysis was performed on the integrated dataset with the goal of identifying those weather and maintenance factors that had a significant impact on crash frequency. The modeling results indicate that weather conditions such as temperature and precipitation (mainly snow fall) had a significant effect on the crash risk. Anti-icing and pre-wetting operations were found to have improved road safety at one of the study sites. Sanding operations were found to have a positive effect on the safety at both maintenance routes. The research however could not statistically confirm the safety effect of conventional maintenance operations- plowing and salting with dry salt.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".