Factors Influencing Pavement Friction during Snowstorms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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