Information-Based Traffic Assignment for the Analysis of Geopolitical Equity
Bibliographic record
Abstract
The uneven spatial distribution of transport costs and benefits within an urban agglomeration generates important inequities among the various cities of which it is composed. As a result, the problem of equitable resource allocation for the provision of road transport infrastructure becomes highly geopolitical. This paper demonstrates method for analyzing geopolitical equity based on the bridge-usage patterns revealed by auto-drivers in a large-scale travel survey conducted in the Greater Montreal Area. A traffic assignment algorithm is applied to a validated sample of trips to construct complete itineraries from this partial path information. The itineraries are used to calculate the amount of road transport supplied and consumed by each of the numerous geopolitical entities that make up the urban region. Road transport benefits are assumed to be derived from consumption while costs are associated with supply. The comparison of benefits and costs assigned to each territory confirms that the central city of Montreal is burdened with most of the costs while the distant suburbs incur the greatest benefits from major bridge and freeway infrastructure. At present, the local government of the central city is not compensated for this inequity which can be attributed to the spillover effects of the major infrastructure provided by the provincial and federal levels of government. The paper proposes a solution to address the imbalance.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".