Feasibility of Using Traffic Data for Winter Road Maintenance Performance Measurement
Notice bibliographique
Résumé
Winter road maintenance (WRM) operations, such as plowing, salting and sanding, are significant to maintain both safety and mobility of highways, especially in countries like Canada. Traditionally, WRM performance is measured using bare pavement regain time and snow depth/coverage, which are reported by maintenance or quality assurance personnel based on periodic visual inspection during and after snow events. However, the increasing costs associated with WRM and the lack of objectivity and repeatability of traditional performance monitoring methods have stimulated significant interest in developing alternative performance measures. \nThis research is motivated by the need to develop an outcome based WRM performance measurement system with a specific focus on investigating the feasibility of inferring WRM performance from traffic state. The research studies the impact of winter weather and road surface conditions (RSC) on the average traffic speed of rural highways with the intention of examining the feasibility of using traffic speed from traffic sensors as an indicator of WRM performance. Detailed data on weather, RSC, and traffic over three winter seasons from 2008 to 2011 on rural highway sites in Iowa, US is used for this investigation. Three modeling techniques are applied and compared for modeling the relationship between traffic speed and various road weather and surface condition factors, including multivariate linear regression, artificial neural network (ANN), and time series analysis. Multivariate linear regression models are compared by temporal aggregation (15 minutes vs. 60 minutes), types of highways (two-lane vs. four-lane), and model types (separated vs. combined). The research also examines the feasibility of estimating/classifying RSC based on traffic speed and winter weather factors using multi-layer logistic regression classification trees. \nThe modeling results have shown the expected effects of weather variables including precipitation, temperature and wind speed, and verified the statistically strong relationship between traffic speed and RSC. The findings suggest that speed could potentially be used as an indicator of bare pavement conditions and thus the performance of WRM operations. It is also confirmed that the time series model could be a valuable tool for predicting real-time traffic conditions based on weather forecast and planned maintenance operations, and the multi-layer logistic regression classification tree model could be applied for estimating RSC on highways based on average traffic speed and weather conditions.
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 | 0,000 |
| Science ouverte | 0,001 | 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 ».