Investigating temporal trends in risk factors related to injury severity of crashes with pedestrians in urban areas
Notice bibliographique
Résumé
OBJECTIVE: The implementation of road safety policy in urban areas can potentially change the severity profile of crashes, as well as how risk factors influence crash severity. In this sense, this study aims to empirically evaluate possible changes in the severity profile of crashes with pedestrians and in the influence of risk factors for pedestrian injuries after the efforts of the Decade of Action for Road Safety in the city of Fortaleza, Brazil. METHODS: This was done using data from crashes with pedestrians between 2009 and 2019; divided into three periods. Two categorical modeling analyses were performed using the mixed logit modeling approach, including sociodemographic, environmental, vehicle, road type, and traffic control device factors. In the first analysis, a single model was estimated, and time (period) was included as an explanatory variable; in the second one, models were estimated for each period. RESULTS: According to temporal analysis, a reduction was evident in the severity profile of crashes with pedestrians over the decade of action. In general, the safety interventions seemed to have little or no impact on pedestrian gender, young pedestrians (up to 15 years old), crashes at night, crashes during weekends and crossings near traffic lights. Regarding crashes on arterial roads, the results suggest an increase in the marginal effects for fatal crashes after the decade of action, while other variables, such as heavy vehicles and expressways, showed positive marginal effects in all periods, indicating that the direction of their effect did not change. This is a potential indication that the overall safety impact of policies during the decade were not effective for these types of crashes. It was possible to identify considerable reduction in the marginal effects for older pedestrians (60+) for both severe and fatal crashes. CONCLUSION: Although it is not possible to claim that this change comes from specific actions or controlled factors, the results presented here indicate an improvement in road safety for these users, in line with the goals of the Safe Systems Approach and the Decade of Action for Road Safety to reduce severe and fatal traffic injuries.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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 ».