Exploratory Method for Practitioners Analyzing the Impact of Integrated Fare Structures in Decentralized Metropolitan Regions
Bibliographic record
Abstract
Metrolinx, the regional transportation agency tasked with improving the coordination and integration of all transportation modes in the Greater Toronto and Hamilton Area, has developed an exploratory method for analyzing the effects of new fare structures that integrate the fare systems of multiple transit service providers in the region. The method uses a data set of all weekday trips made in the region segmented by modes used and origin–destination information. A formula derived from the mode choice modeling theory is used to obtain fare elasticity based on unit cost, mode share, and time of day. The distribution of elasticities produced is then calibrated according to a literature review of fare elasticities, and in the future, it will be done according to local market research. The result is a spreadsheet-based tool that provides analysts with an ability to test more complex changes to fare systems, including testing fare integration between agencies and introducing fares by distance, mode, time of day, or a combination of those features. Exploratory in nature, the method is not a replacement for comprehensive market research or fare pilots. However, it addresses the shortcomings of traditional fare analyses that use only aggregate elasticities for diverse market segments by better reflecting the spectrum of transit user sensitivities associated with specific travel characteristics. Furthermore, it provides analysts with a straightforward tool to test the effects of complex fare structures more commonly used in Europe and Asia enabled by smart card and open payment technology on ridership, revenue, emissions, and social equity.
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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 | 0.046 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".