Developing a Risk Assessment Model for a Highway Site during the Winter Season and Quantifying the Functional Loss in Terms of Traffic Reduction Caused by Winter Hazards Conditions
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a methodology to quantify traffic change triggered by the combined effect of weather hazards based on winter weather hazards models. The winter weather hazards models for three vehicle types were developed with weigh-in-motion (WIM) data collected in the commuter highway in the cold Canadian region for 5 years. The developed model was utilized to simulate the variations of the percentage reduction for each vehicle type based on the 239 pairs of weather combinations composed of six cold categories and various amounts of snowfall. The first phase involved measuring the marginal effect of weather factors such as cold category (or temperature) on the percentage reduction in traffic volume. The second phase involved utilizing the same winter weather traffic model to quantify the effect of combined weather factors on the percentage reduction. The percentage reduction of the total traffic and passenger cars increased as temperature deteriorated and snowfall increased. Truck traffic decreased as snowfall increased, but interestingly, as temperature deteriorated, it was estimated that the truck traffic volume increased. This phenomenon assumed that truck traffic moves from low-maintenance to high-maintenance highways as the weather deteriorates. The methodology to quantify traffic volume changes can be adopted by highway agencies to determine the timing of the snowplow operation based on the risk assessed in terms of traffic volume reduction. It can also be used to predict the percentage reduction of traffic and then determine whether to open or close a highway.
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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 | 0.001 | 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