The impact of COVID-19 on road safety in Canada and the United States
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».