Notice bibliographique
Résumé
Several past studies have attempted to quantify the impact of winter weather conditions on highway mobility in terms of traffic volume, speed, and capacity. While consistent in their general findings, these studies have shown considerably different results in terms of effect size and contributing factors. More importantly, most of these studies have not attempted to model the effects of winter maintenance operations on mobility or isolate these effects from those due to snowstorm characteristics, rendering their results and the proposed methods of limited use for estimating the benefits of maintenance activities. This research attempts to address this gap through a statistical analysis of a data set that is unique in terms of spatial and temporal coverage and data completeness. The data set includes both event based and hourly observations of road weather and surface conditions, maintenance operations, traffic volume and speed, as well as several other measures, from 21 highway sections across the province of Ontario. \nEvent based information is available for six winter seasons (2000 to 2006) at 19 of the sites. For this event based data a matched pair technique was employed to determine the changes in traffic volumes and speeds under matched conditions with and without snow events. A regression analysis was subsequently performed to relate the changes in traffic volume and speed over an event to changes in various contributing factors such as highway type, snow event characteristics and road surface conditions. A case study was conducted to illustrate the application of the developed models for quantifying the mobility impact of road surface condition and the mobility benefit of winter maintenance operations. \nComplete hourly records were available for all 21 sites for three winter seasons. This was used to perform the evaluation on an hourly basis. A matching technique is employed to assign hour-by-hour median speeds observed under typical weather and road surface conditions to each hour of a snowstorm event. A regression analysis is subsequently performed to relate changes from average hourly speed to various contributing factors such as highway type, weather conditions and maintenance operations. Effects of maintenance operations are represented by an intermediate variable called road surface condition index (RSI). A case study is conducted to illustrate the application of the developed models for quantifying the mobility impact of winter snowstorms and the mobility benefit of maintenance operations. \nThe models developed in these analyses confirmed the relationships between weather variables and traffic volume and speed described in the literature. In addition a strong association between road surface condition and traffic volumes and speed was identified.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,001 | 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 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 ».