PRICING AND INVESTMENT IN A TRANSPORTATION NETWORK: THE CASE OF TORONTO AIRPORT. IN: AIR TRANSPORT
Bibliographic record
Abstract
In this paper, a transportation network model is applied to simulate Toronto International (Malton) Airport in order to determine marginal social cost prices of using Malton's facilities throughout the day. The results are then evaluated in terms of an economic surplus criterion to investigate policy issues such as whether congestion tolls should be assessed and the timing and location of additional airport capacity. The Canadian Ministry of Transport is proposing to build an international airport at Pickering, 30 miles northeast of Toronto, in order to alleviate future congestion and reduce noise at Malton. Findings suggest that congestion pricing should be introduced at Malton. It is further suggested that the expansion of airport facilities is not necessary until the mid-1980s, and that when expansion occurs it should initially occur at Malton rather than Pickering. These findings contradict the recommendations of the Ministry of Transport, which were made using engineering criteria for capacity expansion and comparisons of physical magnitudes of the two airport sites. However, transportation network models that incorporate prices and determine equilibrium flows represent an advance over these planning models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".