What Do I Have and Where is it Located? Quantifying Ontario’s Municipal Lane Kilometres
Notice bibliographique
Résumé
An essential requirement of a good asset management plan is data. There are many benefits to having good data: trust, reduced liability, improved asset knowledge, improved budgeting, and improved customer service. Without this, it is impossible to make strategic asset management decisions. In 1995, the Ministry of Transportation of Ontario ended the Conditional Grant Program that provided partial funding for municipalities to support maintenance, rehabilitation, and reconstruction of their roadways. This decision also impacted the collection of road inventory, condition and performance data. At this time, it was estimated that Ontario municipalities owned and maintained approximately 275,000 lane kilometres of road. Since this time, there have been numerous changes within the province that would affect the lane kilometre value: downloading of provincial highways to local municipalities; municipal amalgamations; and system growth/development. In an attempt to recapture some of this missing road infrastructure data, the Municipal Performance Measure Program (MPMP) was created in 2000. Under this program, Ontario municipalities are required to report efficiency and effectiveness performance measures for the services they are responsible for delivering as part of their Financial Information Return (FIR). Although mandated, there has never been 100% compliance by Ontario’s 444 municipalities. Fast forward to 2012, 343 Ontario municipalities (77%) submitted lane kilometre data through MPMP. Recognizing the importance of this value in an asset management context, the Ministry of Municipal Affairs and Housing (MMAH) initiated the Roads and Bridges Data Improvement Project. The goal of the project was to fill in the missing gaps and to create a complete data set for the number of Ontario lane kilometres that are under municipal jurisdiction and confirm the accuracy of the information being provided. Through rigorous follow-up with individual municipalities, MMAH was able to obtain 100% participation and determine that Ontario municipalities are responsible for 301,886 lane kilometres of road. The paper focuses on five key areas of the Roads and Bridge Improvement Project: context and goals; the data improvement process; projects results; data verification; and observations/lessons learned. The results of this effort is an accurate starting point to begin collecting important road infrastructure data that can be used to make strategic asset management decisions at both the provincial and municipal levels of government and allow for accurate benchmarking comparisons to take place.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».