Association of Highway Traffic Volumes with Cold and Snow and Their Interactions
Bibliographic record
Abstract
Presented in this paper is the association of highway traffic flow with severity of cold, amount of snow and various combinations of cold and snow intensities by giving detailed consideration to factors such as highway type and location. The study is based on hourly traffic flow data from 350 permanent traffic counter sites located on the provincial highway system of Alberta, Canada, and weather data obtained from Environment Canada weather stations located within 10 miles of the selected permanent traffic counter sites, during the period of 1995-2005. Multiple regression analysis is used in the modeling process. The model parameters include three sets of variables: amount of snowfall as a quantitative variable, categorized cold as a dummy variable and an interaction variable formed by the product of the above variables. The developed models closely fit the real data with R-square values greater 0.99. The study results indicate that the association of highway traffic flow with cold and snow varies with day of week, hour of day and severity of weather conditions. Traffic volume on a day decreases with the increase in severity of cold and snow. A reduction of 1% to 2% in traffic volume for each centimeter snowfall is observed when the mean temperatures are above 0°. For the days with zero precipitation, reductions in traffic volume due to mild and severe cold are 1% and 31%, respectively. An additional reduction of 0.5% to 3% per each centimeter of snowfall results when snowfall occurs during severe cold 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".