Demonstrating a Correlation Between the Maturity of Road Safety Practices and Road Safety Incidents
Notice bibliographique
Résumé
OBJECTIVE: The objective of this study is to demonstrate a correlation between the maturity of a country's road safety practices and road safety incidents. METHODS: Firstly, data on a number of road injuries and fatalities for 129 countries were extracted from the United Nations Global Status on Road Safety database. These data were subdivided according to road safety incident and accident causation factors and normalized based on vehicular fleet (per 1000 vehicles) and road network (per meter of paved road). Secondly, a road safety maturity model was developed based on an adaptation of the concept of process maturity modeling. The maturity of countries with respect to 10 road safety practices was determined through the identification of indicators recorded in the United Nations Global Status of Road Safety Database. Plots of normalized road safety performance of the 129 countries against their maturity scores for each road safety practice as well as an aggregation of the road safety practices were developed. An analysis of variance was done to determine the extent of the correlation between the road safety maturity of the countries and their performance. In addition, a full Bayesian analysis was done to confirm the correlation of each of the road safety practices with injuries and fatalities. RESULTS: Regression analysis for fatalities, injuries, and combined accidents identified maturity with respect to road safety practices associated with speed limits and use of alternative modes as being the most significant predictors of traffic fatalities. A full Bayesian regression confirms that there is a correlation between the maturity of road safety practices and road safety incidents. CONCLUSION: Road safety practices associated with enforcement of speed limits and promotion of alternative modes are the most significant road safety practices toward which mature countries have concentrated their efforts, resulting in a lower frequency of fatalities, injury rates, and property damage accidents. The authors argue that the use of gross domestic product (GDP) as a predictor of road safety incidents suffers from the presumptive assumption that the only criterion that matters is national income, therefore erroneously predicting that richer countries all perform approximately the same in fatalities and that developing nations are at various stages, with high variability and uncertainty in prediction. It is proposed that an aggregation of individual maturity scores from road safety practices will lead to a better indicator for policy because it connects externalities (fatalities/injuries) with intrinsic factors for which policies can be oriented.
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 | 0,001 | 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 | 0,001 |
| 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 ».