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Enregistrement W7067660956

Modeling Freight Network Robustness and Criticality in Ontario, Canada

2022· dissertation· en· W7067660956 sur OpenAlexaboutno aff

Notice bibliographique

RevueScholarship at UWindsor (University of Windsor) · 2022
Typedissertation
Langueen
DomaineEngineering
ThématiqueInfrastructure Resilience and Vulnerability Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWork (physics)LimitingContext (archaeology)Fuzzy logicNettingIntellectualization
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This dissertation explores criticalities that arise in a freight transportation network for the multi-regional economically active province of Ontario, Canada. A significant economic contributor and generator of freight trips, Ontario relies on its transportation system for the movement of goods. A combination of network performance and economically driven measures are used to evaluate the impacts of link disruptions, identify criticalities in the network, and produce a more holistic view of freight transportation activities. The Network Robustness Index (NRI) is applied to capture the impacts of travel conditions on the network due to link disruptions. A methodology is introduced for estimating industry-level freight demand and shipment value flows to ascertain the economic importance at the link-level. Major trade routes, including the Montreal-Windsor corridor along Highway 401 and highways leading to major border crossings with the United States, as well as highways in the Toronto region and links to northern Ontario consistently appear critical in the analysis. In combination, these measures are useful for developing a framework for assessing the effectiveness of proposed infrastructure improvements in mitigating the impacts of critical link failures. The first chapter of the research presented in this thesis is dedicated to evaluating the effectiveness of the NRI to capture the impacts of link disruptions with respect to freight activity. Chapter 2 employs a sensitivity analysis to explore the network-wide impacts of increasing degrees of disruption on six segments deemed critical due to their frequency of use as part of shortest-path routes between origins and destinations. While the most severe impacts are noted closer to the disrupted network segments, complete link failures or closures along heavily traveled routes appear to have significant impacts through the network as freight and passenger flows must reroute. To better capture the nature of freight activity for the entire province of Ontario, Chapter 3 applies the NRI to each of the network’s 35,254 links, simulating traffic assignments for the province’s freight demand to note the impact that each link’s failure has on network conditions. Chapter 4 adds the economic perspective by introducing a methodology for disaggregating freight flows into six mutually exclusive industry categories, following the assumption that spatial interactions will vary among different industries due to the nature of the goods carried and their respective markets. Additionally, the average shipment value is estimated for each industry group to illustrate the eco nomic importance of network links, given by the value of goods they carry. This analysis allows for a better understanding of the economic activities of freight being undertaken in the province. A set of highly critical portions of the network are highlighted consistently. Finally, these measures are brought together in a framework, where network criticalities are compared to the locations of proposed infrastructure improvements. A comparison is made among four highway expansion segments planned along highly critical portions of the network, evaluating the resulting impacts of these improvements with respect to operating conditions on the network, economic throughput, and greenhouse gas emissions. Each chapter of this research proposes policy guidelines meant to identify network criticalities, mitigate the negative impacts of critical link failures, and compare the effects of proposed infrastructure improvements and investments. The goal of these policy guidelines is to ensure that the maximum benefit is achieved, both in terms of network conditions, as well as with respect to promoting economic productivity.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,673
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,190
Écart entre enseignants0,181 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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