Before-and-After Empirical Bayes Evaluation of Achieving Bare Pavement using Anti-Icing on Urban Roads
Bibliographic record
Abstract
Canadian municipalities are increasingly choosing to achieve bare pavement (BP) for snow and ice control during fall/winter seasons. When a snowstorm event is forecasted, one strategy is to apply anti-icing chemicals to the pavement surface to prevent the snow and ice from forming a bond with the road surface. Such an approach facilitates a more efficient plowing operation and reduces the amount of deicing chemicals needed to achieve BP. This study assesses the safety performance of achieving BP using anti-icing compared with the traditional reactive winter road maintenance (WRM) approach on urban roads using the before-and-after Empirical Bayes technique. Results suggest that achieving BP significantly reduces all collision types and severities on midblocks with a reduction value in the range of 13.7% to 19.7%. Attaining BP at intersections was found to be very effective in reducing injury collisions with an estimated reduction of 12.5%. When sites were grouped based on a WRM priority-basis, it was found that anti-icing was effective for reducing the majority of collision types and severities at the different priority levels with reductions ranging from 8.7% to 49.83% on midblocks and between 5.37% and 13% at intersections. All reductions were statistically significant. The monetary benefits of the reductions in property-damage only and nonfatal injury collisions were estimated at 60 million Canadian dollars using a 1.92% interest rate and a 2-year service life. These findings provide unequivocal evidence that achieving BP using anti-icing can lead to significant societal safety benefits that economically translate to huge collision cost savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".