Exploration Of Theoretical And Application Issues In Using Fully Bayesian Methods For Road Safety Analysis
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Résumé
The Fully Bayesian (FB) approach to road safety analysis has been available for some time, but it is largely unevaluated and untested. This study is trying to bridge the gap by conducting a thorough evaluation of FB method for black spots identification and treatment effect analysis. First, an evaluation is conducted on the univariate FB versus the empirical Bayesian (EB) method for single level severity data through the development of various models, and multivariate FB versus univariate FB for multilevel severity data, as well as the performance of various ranking and evaluation criteria for black spots identification. It is confirmed that the FB method is superior to the EB with respect to key ranking criteria (expected rank, mode rank and median rank of posterior PM, etc.). The multivariate FB method is better than univariate FB for the multilevel severity crashes. Then a teat of the FB before-after method for treatment effect analysis is performed. Two FB testing frameworks were employed. First the univariate before-after fully Bayesian (FB) method was examined using three simulated datasets. Then multivariate Poisson log normal (MVPLN), univariate Poisson log normal (PLN) and PB (Poisson gamma) models were evaluated using two groups of California unsignalized intersections. Hypothetical treatment sites were selected from these datasets such that a significant effect would be estimated by the naive before-after method that does not account for regression to the mean. This study confirmed that FB methods can indeed provide valid results, in that they correctly estimate a treatment effect of zero at these hypothetical treatment sites after accounting for regression to the mean. Finally the EB and the validated FB before after methods were applied to evaluation of two treatments: the conversion of rural intersections from unsignalized to signalized control; and the conversion of road segments from a four-lane to a three-lane cross-section with two-way left turn lanes (also known as road diets). The result indicates that both FB and EB method can provide comparable treatment effect estimates. This would suggest it is still appropriate to conduct treatment effect analysis using the EB method for univariate crash data, but that it is essential in so doing to account for temporal trends in crash frequency.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| 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 | 0,000 | 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 découle