An analysis of COVID-19 effects on the trends of traffic violations
Notice bibliographique
Résumé
Traffic violations can pose significant challenges to public safety and road infrastructure. The consequences of such violations may be managed based on the insights from their observed trends. Following the COVID-19 outbreak and changes in driving behavior, the violation patterns were affected. This study examines traffic violations in the Isfahan province of Iran between 2016 and 2022, focusing on seat belt and speeding violations. Two analytical approaches, time series analysis and count data modeling, were employed to explore various aspects of these violations. Time series analysis involved analyzing aggregated monthly violation records to forecast trends before and during the pandemic. A comparison of projected and observed patterns revealed remarkable shifts in traffic violations, especially after the start of the vaccination campaign in February 2021. This study also found that recording speeding violations was influenced by the maintenance of the speed-control cameras. The second approach focused on police-issued violation records across three periods: two pre-pandemic phases (Pre1 and Pre2) and a pandemic phase (Pand). A set of zero-truncated Poisson models assessed individual and environmental factors in Pre1 and Pre2, such as car type, license plate, driver characteristics, time of day, day type, road hierarchy, and season. The results showed that these factors significantly impacted violation probabilities. To analyze the effects of COVID-19 on these influential factors, another zero-truncated Poisson model was applied to the Pand phase, along with t -tests comparing the coefficients across the three phases. The findings revealed statistically significant changes in how these factors influenced seat belt and speeding violations. Notably, driver characteristics, day type, and season became more determinant for seat belt violations in the Pand phase, while the importance of license plate type decreased. • Notable shifts in violation patterns were observed due to the COVID-19 outbreak, especially after the start of vaccination. • Speeding violation tickets were found to be affected by the functionality and maintenance of the speed-control cameras. • Individual and environmental factors, such as car type and season, were found to influence violation probabilities. • Significant alterations in how the significant factors influenced violations have occurred due to the COVID-19 outbreak.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| 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 ».