Safe system approach to preventing cyclist fatalities: safety by design for urban and rural environments
Notice bibliographique
Résumé
Abstract Background Cyclists are vulnerable road users, with preventable deaths increasing by 48% over the past decade. This study aimed to review the epidemiology of cyclist fatalities to identify risk factors for targeted interventions through a safe system approach, with a focus on urban and rural environments. Methods Data on fatal cyclist and motor vehicle collisions (CMVC) and injuries were collected from the Office of the Chief Coroner (2013-19), including selected crash investigations and expert reviews by a multidisciplinary team. Descriptive analyses were conducted, and urban vs. rural CMVC were compared using Pearson chi-square and Mann-Whitney U tests. Results There were 83 fatal cyclist collisions (81% male), with 6% children, 13% youth, 69% adults, and 12% seniors (median age = 48, ISS = 75). The head was the most severely injured body region across all age groups (median AIS = 5), except for children, whose thoracic injuries were more severe. Overall, 62% of cyclists were not wearing helmets, and 24% were impaired. Expert review found that 60% of child cyclist fatalities were run over, all of whom were ≤ 6 years. Distractions from cell phones (1%) or headphones (8%) may have contributed to CMVC. Urban collisions (49 cyclists; 59%) accounted for all child deaths and had significantly more collisions involving intersections (57% vs. 6%; p < 0.001), low-speed crashes (33% vs. 0%; p < 0.001), bike lanes (29% vs. 0%; p < 0.001), and heavy vehicles (31% vs. 6%; p = 0.006). Rural collisions were associated with higher speeds (> 50 km/h, 94% vs. 49%; p < 0.001), dark lighting (44% vs. 4%; p < 0.001), and riding on the roadway with traffic (56% vs. 16%; p < 0.001). No rural CMVCs had sidewalks or bike lanes (0% vs. 84%; 0% vs. 33%; p < 0.001). Conclusion Cyclists face severe injury and death risks in both urban and rural settings. A safe system approach recognizes human vulnerability and the inevitability of mistakes. Engineering countermeasures, such as road separation, better lighting in rural areas, traffic calming, and vehicle safety features (i.e., guard rails, advanced headlights, and cyclist detection), support CMVC prevention. Public health campaigns and legislative action, along with equitable implementation across urban and rural areas, facilitate improving cyclists’ safety.
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) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,047 | 0,001 |
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 ».