Sustainable road safety : development, transference and application of community-based macro-level collision prediction models
Notice bibliographique
Résumé
The enormous social and economic burden imposed on society by road collision injuries is a major global problem. As a result, it is of ongoing interest of governments to discover ways of reducing this burden. The traditional engineering approach has been to address road safety in reaction to existing collision histories. While this approach has proven to be very successful, road safety authorities are also pursuing more proactive engineering approaches. Rather than working reactively to improve the safety of existing facilities, the proactive engineering approach focuses on improving the safety of planned facilities. Proactive programs rely heavily on reliable empirical techniques, including macro-level collision prediction models (CPMs). The three objectives of this research were to: 1. Develop community-based macro-level CPMs for the Capital Regional District (CRD) in BC, Canada and the City of Ottawa in Ontario, Canada. 2. Perform a road safety evaluation of the Canada Mortgage and Housing Corporation’s (CMHC) recently promoted Fused Grid model for sustainable subdivision development. 3. Use these models to conduct a Black Spot analysis of each region. Results were in line with intuitive expectations of each objective. First, following the recommended development and transferability guidelines, 64 community-based macro-level CPMs were successfully developed for the CRD and City of Ottawa. These models can be used by community planners and engineers as a decision-support tool in proactive road safety improvement programs. Second, the safety level of five road network patterns was evaluated using these macro-level CPMs. It was concluded that the 3-way offset and Fused Grid road networks were the safest over all, followed by the cul-de-sac and Dutch SRS road networks. The grid network was the least safe road pattern. Finally, black spot studies were also conducted, and four black spots were selected for in-depth analysis on diagnosing safety problems, and evaluating possible remedies. The results of this research demonstrate the potential of community-based, macro-level CPMs as new empirical tools for road safety planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce the road collision burden on communities worldwide.
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 | 0,000 |
| 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 ».