Analyzing the effects of changing global trade patterns on domestic freight systems
Bibliographic record
Abstract
Global trade patterns are continuously changing as economies and trade policies develop and interact. Recent developments and forecasts suggest changes in such patterns are likely to continue. Global trade patterns ultimately manifest themselves in freight flows on global and domestic transportation systems, but the translation of economic flows into transportation patterns is not straightforward. Moreover, while countries may benefit from global trade, the transportation impacts are felt locally, as passenger and freight movements compete for domestic infrastructure capacity.This thesis introduces a joint transportation and trade modelling framework to analyze the effects of changing global trade patterns on domestic freight operations. A unique multi-scale modelling framework confronts the notion that it is convenient but unrealistic to draw a geographic boundary around the economy and freight transportation system. Innovative harmonization, transformation, estimation, and optimization-based methods are developed to jointly model trade at the global, national, and regional levels. Multinomial logit (MNL) models are used to include the influence of transportation disutility in global trade choice behaviours, extending the multi-scale model into the domain of random-utility-based multi-regional input-output (RUBMRIO) models, which are first comprehensively introduced and reviewed.To demonstrate the feasibility of the proposed approach, a Canada-centric model was developed that includes forty-eight countries, Canada's ten provinces and three territories, and Ontario's eleven economic regions. Validation results show that it is possible to link spatial scales with a reasonable degree of accuracy. And while Canada's industrial outputs are the sum of its provincial and regional outputs, individual provinces and regions are not just microcosms of the larger country's behaviour. Example applications of food and paper product demand shocks from the United States, as well as doubling and halving of global transportation costs, demonstrate that the economic impacts from scenarios related to global trade and their implications for freight demand and traffic patterns differ for each province of Canada and region of Ontario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".