Factors Influencing Pavement Friction during Snowstorms
Bibliographic record
Abstract
Operating an effective winter road maintenance program is a necessity for cities that face severe winter seasons. Snowstorms leave roads in a slippery surface condition that disrupts traffic flows and compromises drivers’ safety. Decision-makers use a variety of tools to control snow and ice on the roads, which include applying anti-icing chemicals before snowstorms, applying deicing substances on fresh snow, and clearing snow off the roads using snowplows. However, the influence of these tools on improving the overall road surface conditions has not been investigated. In this study, a location-specific and event-based framework was utilized to understand the impact of the different weather variables as well as maintenance operations on the variability of the pavement friction coefficients during snowstorms in urban environments. Using multilinear regression and ordinary least squares, friction coefficient models were calibrated. The final model was found to be a good fit for the data (R2 = 0.723). The model showed that the total precipitation during snowstorms, extremely low temperatures, and the potential for black ice formation worsen pavement friction significantly. On the other hand, plowing operations, the application of anti-icing chemicals before snowstorms, and frequent deicing operations all have a statistically significant impact on improving pavement friction. The model presented in this paper can be used to predict pavement friction on urban arterial and collector roads during snowstorms of different magnitudes, which could help the authorities in predicting the road surface conditions during forecasted snowstorms and deciding on the best course of action under these conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".