The Impact of Cold and Snow on Weekday and Weekend Highway Total and Passenger Cars Traffic Volumes
Bibliographic record
Abstract
Presented in this paper is an investigation of the impact of cold and snow on daily traffic volumes of total traffic and passenger cars. It is based on a detailed case study of five years of Weigh-In-Motion data recorded continuously at a highway site in Alberta, Canada. Dummy-variable regression models are used to relate daily traffic volumes with snowfall and categorized cold variables. The importance of all the independent variables used in the model are established by conducting tests of statistical significance. The total traffic and passenger car volumes are influenced by both the snowfall and the cold categories. Plots of the partial effect of each independent variable on the dependent variable are generated. It is found that a daily snowfall of 10 cm may cause a 25% reduction in the daily volume of passenger cars, and temperatures below -25°C may reduce the passenger car volumes by 10% or more. It is believed that the developed traffic-weather models of this study can benefit highway agencies in developing more advanced imputation method or identifying weather adjustment factors for accurate estimation of AADT from short duration traffic counts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".