Modeling snow and cold effects for classified highway traffic volumes
Bibliographic record
Abstract
This paper discusses about the effect of snowfall, temperature, and their interaction on two vehicle classes: passenger cars, and trucks on a primary highway in Alberta, Canada. The investigation is based on large data collected from the Weigh-In-Motion (WIM) site located at Leduc, on Highway 2A. The variations on traffic volume for vehicle classes are analyzed by means of a dummyvariable regression model with seven cold categories. The models are calibrated to estimate the temperature impact on daily traffic variations and, more specifically, to quantify the interaction effect of snowfall and temperature on classified traffic volume. The study results suggested distinctive patterns in traffic variations for passenger cars and trucks. The daily passenger car volume reduction is 12% when the temperature goes below −25°C and, by interaction between snow and cold, it was reduced by 36% at the temperature range −25°C ~ −20°C with 16cm snowfall. Conversely, the daily truck traffic is generally increased for all cold categories. In particular, truck traffic is not really affected by snow and cold interaction even at extreme winter weather conditions. The paper contributes to the literature by analyzing the winter weather effects on truck traffic, in particular. This study may be useful for developing efficient highway monitoring programs, and winter road maintenance programs, etc.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".