Consideration of Weather Conditions to Estimate Missing Traffic Data
Bibliographic record
Abstract
Estimating missing traffic data is an essential task for transportation agencies. Several imputation methods have been developed in the literature to estimate the missing traffic volumes. However, none have considered the variation in traffic patterns caused by severe winter weather conditions. Highway traffic volumes are highly influenced by weather conditions; therefore, a detailed investigation was carried out to develop relationships between weather and highway traffic volumes and to use them for reliable estimation of missing traffic volumes. The study was based on hourly traffic data from permanent traffic counter sites located on provincial highways of Alberta, Canada, using 11 years of data from 1995 to 2005. Weather data were obtained from Environment Canada weather stations located within 10 mi of the chosen permanent traffic counter sites. Cold and snowfall represented the winter conditions. Multiple regression analysis was used to develop relationships between hourly traffic volumes, categorized cold, and total snowfall. The study models showed a strong association between traffic volumes and weather conditions. Weekend traffic was more susceptible to weather than weekday traffic. In cases of extreme cold (≤25°C), the peak hours experienced fewer reductions in traffic (6% to 13%) than off-peak hours (10% to 17%). The amount of reduction in traffic volume caused by each centimeter of snowfall varied from 0.5% to 2.0%. The traffic–weather relationships developed were used to estimate missing hourly volumes. The errors were 30% to 75% less than the traditional methods used by highway agencies.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".