Real-Time Monitoring and Forecasting Ice Layer Thickness Growth Rate and Grip Loss on a Road Network During Winter Storm Events
Bibliographic record
Abstract
Winter storm severity, road surface conditions, traffic volume, and vehicle speed can influence the risk of winter-related vehicular crashes. The severity of a winter storm depends on many climatic factors, including precipitation type, intensity and duration of the storm, wind speed, air temperature, and pavement surface temperature. The rapid growth of snow and ice layer thicknesses on the road surface significantly reduce the grip between tires and the road surface, leading to hazardous travel conditions. Our investigation shows that vehicular crash rates in winter months have an inverse linear relationship with the 10th percentile grip statistics, which is a function of how quickly the road was plowed and salted and the bare pavement conditions regained. We found a logarithmic relationship between the ice and snow layer thicknesses and the grip loss. We developed a new model for forecasting ice layer thickness growth rate as a function of the relative humidity, dewpoint temperature, and pavement temperature. Real-time monitoring and forecasting the spatial and temporal variability of the grip on a road network can help road authorities to better optimize the salt application strategy for a given winter storm event and prioritize the timing and frequency for the deployment of their fleet of winter maintenance vehicles on different salt routes to minimize vehicular crash rates.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".