A Transport-Economic Analysis Framework for Border Crossing Infrastructure Investment Policies
Bibliographic record
Abstract
Border crossings serve two critical purposes: ensuring the safety and security of a nation; and facilitating trade and movement of people between countries. Inefficient border crossings resulting from insufficient infrastructure investments create bottlenecks to economies. Despite the importance of border crossings, studies aimed at optimizing border crossing investments are limited. \nThis thesis introduces an innovative transport-economic modelling framework to optimize border crossing infrastructure investments. The framework migrates from a stylized CGE modelling approach by explicitly linking transportation models of border crossing activities to a Computable General Equilibrium (CGE) model of the global economy. The framework combines the capabilities of a CGE model with several transportation models and datasets to determine border crossing investment priorities. The framework addresses some of the limitations of prior studies in the literature by incorporating queuing theory and mode choice theory to comprehensively measure the economic impacts of border crossing investments. \nThe developed framework is applied to Canada-US border crossings to determine short- and long-term border crossing investment priorities. Simulation results suggest that reducing delay times at border crossings can have sizeable impacts on the Canadian economy. The impacts on Canada’s GDP and welfare are always positive and can range up to $ 92.44M USD and $ 79.83M USD per year, respectively. The impacts of infrastructure investment on the export of Canadian industries varies from a reduction of $ 0.86M USD to an increase of $ 8.47M USD per year. Analysis results suggest that Ambassador Bridge, Sarnia, and Fort Erie are the three most important borders for Canadian economy. The analysis results suggest that the magnitude of the effects of border crossing investment and the border crossing investment priorities are highly sensitive to border crossing delay modelling and less sensitive to mode shifts resulting from investment in one mode of transportation. This research concludes with border crossing priorities and the policy implications.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".