Data-Driven Sustainability Validation of Winter Traffic Model through Spatial Transferability of the Model’s Parameters between Functionally Homogeneous and Heterogeneous Highway Segments
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Notice bibliographique
Résumé
Transportation agencies in the cold region are responsible for developing winter traffic models and verifying their sustainability to save financial and human resources while enhancing the suitability of the developed models. To do this, they operate traffic monitoring sites to collect traffic volume and loading data in their network using technologies such as permanent traffic counters (PTCs) and weigh-in-motion (WIM). None of the previous studies have conducted spatial transferability of the winter traffic models’ parameters between homogeneous and heterogeneous road segments during the winter season. This research pursues this using traffic data collected from six WIM sites in Alberta, Canada. Winter traffic models were developed for two WIM sites that serve commuter traffics. The other four WIM sites serving different travel populations besides commuter traffic were exhaustively utilized to test the developed models. The raw WIM data were aggregated into three vehicle types to develop winter traffic models by associating traffic data with climatic information. Two spatial transferability tests for the developed models were designed and carried out. The first test was conducted between the two modeling sites for which the winter traffic models were developed. The first experiment pursued a cross-spatial transferability test between homogeneous road segments. The second experiment tested the transferability of model parameters between heterogeneous road segments that represent a different road function other than commuter type. The models’ parameters developed for the two commuter segments were transferred to the other four sites to test their spatial transferability. This research has demonstrated that the winter traffic models developed for the roads serving one specific travel population can be transferred with high accuracy to homogeneous and heterogeneous road segments. It revealed that a more suitable model structure could be selected for each site and vehicle class, considering the accuracy of the test results. This research contributes to planning and designing traffic monitoring or weighing site deployment to save financial and human resources.
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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 | 0,000 | 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 | 0,000 | 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