A Smartphone-based Connected Vehicle Solution for Winter Road Surface Condition Monitoring
Notice bibliographique
Résumé
The monitoring of winter road surface conditions (RSCs) is essential to transportation agencies and the traveling public, since the former needs to be aware of the location and severity of existing RSCs in order to effectively maintain safe roadways with minimal environmental impact, while the latter uses RSC information to make informed travel decisions. However, current RSC monitoring practice still relies on methods that are time-consuming, labour-intensive and lacking in objectivity, therefore limiting their ability to provide sufficient spatial and temporal coverage across a road network. This research was motivated by the need for accurate, timely and reliable RSC monitoring for winter maintenance personnel and the travelling public. To achieve this objective, the field performance of a smartphone-based RSC monitoring system was evaluated on a section of Highway 6 in Ontario, Canada during the winter of 2014. A comparison between this system and current monitoring methods indicated that the former was capable of providing reliable results particularly at the maintenance route level; however, classification accuracy was found to vary according to RSC type. \n \nTo improve the results produced by the smartphone-based system, this thesis proposes a connected- vehicle (CV) based RSC monitoring system that utilizes Road Weather Information System (RWIS) data in addition to the smartphone-based system’s data. Three techniques in artificial neural networks (ANNs), random trees (RTs), and random forests (RFs) were tested as the underlying models of the CV system, and the results indicated that all three models successfully increased the classification accuracy of the smartphone-based system. RFs were found to provide the most accurate RSC classifications for the standard (three-class) classification scheme while RTs were found to be most accurate when using a more detailed (five-class) classification scheme. Model transferability was also tested using data captured from a different test site during the winter of 2015; and it was found that although the proposed CV system significantly increased the reliability of RSC classifications, the underlying models were non-transferable and would therefore require local calibration before being used at different sites across a road network.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».