Improved Guidelines for Recalibration of Predictive Models over Time Based on Model Uncertainty
Notice bibliographique
Résumé
The Highway Safety Manual (HSM) summarizes the safety performance functions (SPFs) of various facility types. The primary use of SPFs is to estimate the safety performance (i.e., the number of crashes by severity level) of different facilities based on geometric and traffic variables. The SPFs were developed using the negative binomial (NB) regression model based on crash data obtained from a selected number of states and cities in the United States and Canada. Applied directly to the local jurisdictions, SPFs may yield biased or incorrect results. Therefore, calibration of the SPFs or predictive models is an important step before applying them to local jurisdictions. Moreover, it is also necessary to recalibrate SPFs over time to account for variations in factors that cannot be accounted for directly in SPFs, such as changes in driver behavior, crash-reporting thresholds, etc. The calibration factor (for a specific facility type) is defined as the ratio of the observed number of crashes to the predicted number of crashes. The HSM recommends that SPFs be recalibrated every 2 to 3 years. However, these guidelines are not based on sound research or reliable criteria. The lack of appropriate guidelines can lead to two types of errors: recalibrating of the models when it is not needed, and not recalibrating them when such a need arises.\n \nThe aim of this thesis is to develop guidelines regarding when or how often SPFs should be recalibrated. To this end, two methodologies were created related to the variance or uncertainty associated with the SPFs, and the guidelines were developed using statistical principles. These guidelines are that SPFs should be recalibrated when (i) the total number of crashes that occur in a network of similar types of facilities falls beyond the prediction intervals of the predicted or estimated total number of crashes in that same network; or (ii) the calibration factor developed in a specific year is statistically significantly different than 1 (based on coefficient of variation (CV) of the SPF and the Calibration Factor C). \n\nBoth approaches were tested on several intersections and segment datasets from Michigan and Toronto. The results show that both approaches are feasible and could provide safety analysts with better and more reliable guidelines regarding when SPFs should be recalibrated. However, the methodologies developed in this thesis cannot be applied to the SPFs developed in the HSM since the information needed to evaluate the variance of SPFs is not available in this manual. The results of both the methodologies were compared to the results of a methodology recently proposed in the literature that can be applied to HSM SPFs and uses a fixed threshold value of C-factor error estimate (say 10%). This study indicated that the 10% error is a reasonable value to use for re-calibrating models.\n\nThe shortcomings of these methodologies include the need to develop a new SPF (which is time-consuming and work-intensive process) and to collect extensive data every year. When data is available every year, the practitioner might as well estimate a new calibration factor every year instead of needing to know the frequency of recalibration or use an approximate method (Cproxy). Future research in this area should focus on identifying the minimum data requirements for both methodologies proposed in this thesis.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,061 | 0,299 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,007 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,008 |
| Science ouverte | 0,009 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».