Evaluating the Safety Impacts of Countermeasures for Winter Weather Collisions
Bibliographic record
Abstract
In Canada, winter weather is a fact of life, and one unfortunate consequence of our sometimes-snowy climes is weather related collisions. Based on geography and associate microclimate, certain highway corridors are particularly susceptible to weather conditions that complicate winter driving, and the result is a higher-than-expected concentration of winter-weather-related collisions. One such location is the section of Highway 401 that passes through Northumberland County, in southeastern Ontario. In recent years, the Ministry of Transportation has implemented a number of countermeasures aimed at reducing the frequency of winter-weather-related collisions along this corridor. Included in those measures are warning messages on static and dynamic signs, snow fencing, and measures to prohibit highway access and communicate detours during weather-related highway closures. As a Traffic Engineering Services Retainer assignment for the Ministry of Transportation of Ontario (MTO), IBI Group was asked to assess the safety effectiveness of the applied countermeasures. The assessment was conducted by means of an Empirical Bayes before-after study. In addition to providing an account of the safety effectiveness of the countermeasures in their current application, the assessment also attempted to develop collision modification factors (CMF) for the various treatments for future use. Based on the available data and the types of countermeasures that were applied along the corridor, CMFs were initially developed to account for the aggregate impacts of all treatments at the project level. The analysis produced CMFs that suggest a 37% reduction in total collisions (40% reduction in fatal + injury collisions and 36% reduction in property damage only collisions) across the study corridor. Subsequent analysis produced CMFs that isolated the impacts of installing snow fencing. The results of that analysis suggest a 33% reduction in total collisions that is attributable to snow fencing alone. However, the specific micro-climate within the study area is such that it may amplify the benefits of the applied treatments, and were they applied elsewhere the countermeasures might not achieve the same effect. As a result, caution should be exercised in adopting the CMFs described herein for application in any other context.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".