Evaluating the effect of subarctic weather conditions on road traffic intensity
Bibliographic record
Abstract
Regions with a subarctic climate are characterized by long winters, heavy snowfall and extremely cold temperatures. Weather conditions can change rapidly and are subject to a high level of seasonal variation. These factors influence drivers’ decisions regarding the trips they have to take. Existing literature has confirmed that adverse weather decreases traffic volumes, but no study has yet employed comprehensive and historical data from a subarctic climate. This article examines the effect of weather conditions on traffic intensity in the Northwest Territories using traffic counter data from four locations along the territory's highway network between 1993 and 2019. We use a linear regression model with specifications that control for time-fixed effects and location-fixed effects to isolate the influence of temperature, rain, snow and wind on traffic volumes. To date, few studies have examined the effect of temperature, rain, snow and wind in an extreme cold weather climate. Our study also validates the conclusions of international literature in the Northern Canadian context. It is both a contribution to transportation and circumpolar scholarship.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".