The impact of COVID-19 on road safety in Canada and the United States
Bibliographic record
Abstract
The COVID-19 pandemic has led to the implementation of unprecedented public health measures. The effect of these lockdown measures on road safety remain to be fully understood, however preliminary data shows reductions in traffic volume and increases in risky driving behaviors. The objective of the present study is to compare self-reported risky driving behaviors (speeding, distracted driving, drinking and driving, and drugged driving) during the pandemic in Canada and the U.S. to determine what differences exist between these two countries. Data was collected using the Road Safety Monitor (RSM), an annual online public opinion survey that investigates key road safety issues, administered to a representative sample of N = 1,500 Canadian drivers and N = 1,501 U.S. drivers. Respondents were asked about the likelihood of engaging in risky driving during the pandemic as compared to before COVID-19. Results show the majority of respondents indicated their behavior did not change, and most positively, a small proportion reported they were less likely to engage in these risky driving behaviors. However, notable proportions indicated they were more likely to engage in risky driving behaviors during the pandemic, as compared to before COVID-19. Of those who indicated this, U.S. drivers had significantly higher percentages compared to their Canadian counterparts. Behaviors most often reported by this sub-section of drivers who admit to being more likely to engage in risky driving during the pandemic were speeding (7.6%) and drinking and driving (7.6%) in the U.S., and speeding (5.5%) and distracted driving (4.2%) in Canada. Logistic regression results confirm that country was a significant factor, as U.S. drivers had greater odds of reporting they were more likely to engage in these risky driving behaviors, with the exception of speeding. Age also had a significant effect, as increasing age was associated with lower odds of reporting that these risky driving behaviors were more likely during the pandemic. Conversely, sex did not have a significant effect. Overall, the current findings suggest that a small proportion of drivers reported being more likely to engage in risky driving behaviors and the pandemic may have led to changes in the profiles of those drivers engaging in risky driving behaviors during lockdown measures. These results have important implications for policies and can inform how to manage road safety during future lockdowns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".