Designing computable general equilibrium models for transportation applications
Bibliographic record
Abstract
This paper presents a review of Computable General Equilibrium (CGE) model applications for spatial economic and transport interaction modelling. This paper has three objectives (1) To deliver an up to date and comprehensive literature review on applications of CGE models in transportation, (2) To analyze the different methodological approaches and their theoretical and practical advantages and disadvantages, and (3) To ultimately provide guidance on designing CGE models for various transportation analyses. The content of the paper is as follows: first, a brief introduction to CGE models is provided. The history of CGE models is traced, ranging from their origins and seminal applications in economics, to their eventual adoption in transportation research. This is followed by a comprehensive review of the application of CGE models to transport projects and policies. Various applications in transportation are reviewed in terms of their intended application, as well as their treatment of space and time. Finally, these applications are contrasted with respect to their methodological approaches, with a close examination of various influential model choices. Here, the essential design choices made within these model applications are explained and debated, to clearly elaborate on the workings of the models and the design choices facing CGE model developers.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".