Nonadditive Public Transit Fare Pricing Under Congestion with Policy Lessons from a Case Study in Toronto, Ontario, Canada
Bibliographic record
Abstract
With increasing urbanization and the development of technologies that support automated fare collection, policy makers need decision-support tools to evaluate differentiated public transit fare pricing policies. However, the state-of-the-art tools that consider congestion effects account only for additive fares. A stochastic user equilibrium model with elastic demand was extended to handle nonadditive station-to-station–based fares and was solved by using a method of successive averages. In this paper, an illustrative example is used to show how simple price elasticities alone are not enough to predict the effects of a fare on demand within even a simple eight-node congested network. The first case study of a fare pricing policy was conducted in Toronto, Ontario, Canada; in this case, a distance-based policy was used for the Toronto Transit Commission subway system with respect to downtown and nondowntown subpopulations. The analysis found that compared with the base scenario of a Can$3 fixed fare, there are Pareto-improving fare policies (e.g., fixed rate of Can$2 and variable rate of Can$0.06/km), but the same policy might not be Pareto-improving for all subpopulations. These findings call for more sophisticated fare pricing policies for Toronto (e.g., zone-based) that can cater to specific needs of subpopulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".