THE IMPACT OF HEATWAVES ON TRAFFIC SAFETY ACROSS FIVE CITIES IN QUÉBEC’S PROVINCE
Bibliographic record
Abstract
Climate change profoundly affects various aspects of life, prompting increased research into its impact on traffic safety. Understanding the association between climate change, particularly heatwaves, and traffic collisions requires robust methodologies encompassing different urbanization features. This study focuses on five major cities in Québec to assess the relationship between heatwaves and traffic crashes. A case time series design was employed to analyze data from May to September between 2015 and 2022, incorporating two types of heatwaves based on daily mean temperatures. Data on traffic crashes, injuries, and fatalities were sourced from the Société de l’assurance automobile du Québec (SAAQ). Control for non-pharmaceutical COVID-19 interventions was also considered using the QC-nP-COVID-19 index. The results reveal a positive association between heatwaves and traffic collisions in Montréal and Longueuil, with respective increases of 10% (95% CI: 3, 17%) and 9% (95% CI: 1, 16%) on heatwave days compared to non- heatwave days. Significant increases in traffic injuries were observed in Montréal and Longueuil during heatwave days, with Incidence Rate Ratios of 5 (95% CI: 1, 9) and 11 (95% CI: 5, 26) respectively. However, heatwaves were not associated with changes in traffic fatality outcomes across all cities. The findings in Montréal and Longueuil may suggest a potential „urban heat island effect,“ emphasizing the need for consistent consideration of this phenomenon in road safety studies and interventions. This study underscores the importance of addressing the impact of heatwaves on traffic safety, particularly in urban areas, to mitigate negative outcomes and improve overall road safety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".