Modeling the Impact of Transit Fare Change on Passengers’ Accessibility
Bibliographic record
Abstract
Accessibility “to” and “through” public transit has been one key transit planning indicator that reflects service quality. Occasionally, transit agencies may consider a fare change to maintain operations or to attract more passengers. However, transit agencies do not usually consider the effect of such fare change on passengers’ accessibility. This paper investigates that effect. A multinomial logit mode choice model is developed to measure the monetary value of transit users’ travel time. Then, the cumulative opportunity measure of accessibility is used to examine the change in job accessibility after a recent transit fare increase in the city of Kelowna, British Columbia, Canada. The results show that the loss in job accessibility resulting from transit fare increase is inversely proportional to the length of the trip, given a flat fare structure. The findings of this paper should be kept in mind before a transit agency rethinks transit fare structures. For example, a transit agency could consider applying a zone-based fare structure as opposed to a flat fare structure to ensure better equity for all transit users.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".