The influence of heatwaves on traffic safety in five cities across Québec with different thermal landscapes
Bibliographic record
Abstract
BACKGROUND: This study assesses the impact of heatwaves on road safety in five Québec cities (Montréal, Québec City, Laval, Longueuil, and Sherbrooke) from June to September 2015-2022. Using Urban Heat Island (UHI), 90th and 95th percentile thresholds for heatwaves, we analyze their effects on collisions, traffic injuries, and killed and seriously injured (KSI) cases. METHODS: Traffic data were analyzed across two heatwave definitions, utilizing a time-stratified case-crossover design. UHI was approximated using the annual maximum of mean warm-season land surface temperatures (LST) derived from Landsat 8 (30 m resolution) over three consecutive years, identifying areas that stay hotter during the day and radiate excess heat at night. Heatwaves were defined as periods of at least two consecutive days with mean temperatures exceeding the historical 90th or 95th percentile of mean temperatures. Negative Binomial regression models were used to examine associations between UHI, heatwave events and traffic incidents. Models controlled for time varying variables such as rainfall, seasonality, and COVID-19 impacts. RESULTS: Heatwaves, particularly at the 95th percentile threshold, significantly increased traffic incidents in Montréal and Longueuil. In Sherbrooke, the 90th percentile threshold showed significant effects on collisions and injuries, while Québec City and Laval exhibit no significant associations. UHIs show a modest overall increase in collisions (IRR: 1.07) but limited effects on traffic injuries and KSI. Differences across cities highlight Montréal's higher IRR for collisions under heatwaves and lower IRR for KSI compared to Québec and Longueuil respectively. DISCUSSION AND CONCLUSION: The results indicate that cities like Montréal and Longueuil, with slightly stronger UHI and higher susceptibility to heatwaves, face increased road safety risks. However, UHI levels in Montréal were not significantly different from those in other cities, and heatwaves at the 95th percentile showed variability across regions. These findings highlight the need for targeted climate-adaptive strategies, such as green spaces and heat-reflective materials, to mitigate risks. Integrating climate resilience into urban planning remains critical as extreme weather events grow more frequent.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".