Impact of Weather Conditions on Traffic: Case Study of Montreal’s Winter
Bibliographic record
Abstract
Congestion is the number one issue in many metropolitan areas. It decreases quality of life and generates negative impacts on the environment and the economy. Apart from recurring congestion causes, many events and circumstances can affect the traffic conditions: weather, roadworks, road incidents and other special events. The former is very important especially in a northern city like Montreal, where winter conditions last four to six months each year.This study relies on GPS data from vehicle fleets combined with weather data from weather stations to assess traffic conditions on the whole highway network of the Greater Montreal Area. The proposed methodology uses logistic regression models to model the probability of congestion to describe the effects of weather conditions (snowfall), road conditions (icy) and visibility on traffic conditions, defined using the Speed Limit Ratio. This research is part of the development of a congestion monitoring and analysis tool for the region of Montreal, which is also presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".