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Record W7067660956

Modeling Freight Network Robustness and Criticality in Ontario, Canada

2022· dissertation· en· W7067660956 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2022
Typedissertation
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)LimitingContext (archaeology)Fuzzy logicNettingIntellectualization
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.190
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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