Development and Application of a Winter Weather Traffic Imputation Model: A Comparative Study Against Machine Learning Techniques During the Winter Season
Bibliographic record
Abstract
This study examines how winter weather conditions influence traffic patterns for both passenger vehicles and trucks, using data collected from weigh-in-motion (WIM) stations and nearby weather monitoring sites along Alberta’s Highways 2 and 2A. To explore how snowfall and temperature affect traffic volumes, we developed Ordinary Least Squares Regression (OLSR) models. The findings indicate that passenger car volumes drop more sharply than truck volumes under increased snowfall, with the decline being particularly notable on Highway 2, a rural stretch. In contrast, Highway 2A showed an uptick in truck traffic, likely due to detours from adjacent routes with less winter maintenance. For estimating missing traffic data during severe weather, we employed both OLSR and a machine learning technique, k-Nearest Neighbor (k-NN). In comparing the two approaches, OLSR demonstrated superior accuracy and consistency, making it more effective for filling in missing traffic data throughout the winter season. The performance of the OLSR model underscores its reliability in addressing data gaps during adverse winter conditions. Additionally, this study contributes to sustainable transportation by improving data accuracy, which aids in better resource allocation and enhances road safety during adverse weather. The findings support more efficient traffic management and maintenance strategies, including optimizing winter road maintenance and improving sustainable infrastructure planning, thereby aligning with the goals of sustainable infrastructure development.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".