Evaluating the effects of fare characteristics on fare equity: A scoping review
Bibliographic record
Abstract
While public-transit fares can represent barriers to some people to use public-transit systems, they remain a major source of funding for operating it. Given the ubiquitous nature of fares in public-transit systems worldwide, understanding how characteristics of fare structures affect the distribution of fare burden (i.e., fare equity) is crucial. To do so we conducted a scoping review of the current literature on public-transit fare equity. We defined fare equity in the form of vertical equity (based on the ability-to-pay principle) and market equity (based on the beneficiary-pay principle). We then screened through 511 unique studies, retaining 24 for analysis. Findings were grouped based on fare attributes (e.g., distance-, time-, service- and user-based fare modulations), fare type and fare integration before combining results in a conceptual model. Distance-, time- and service-based fares were shown to have a positive effect on market equity while only income-based fares always positively impacted vertical equity. User-based fares have shown clear negative effects on market fare equity. The effects of most fare characteristics on fare equity were either not well researched or dependent on local contexts. Lastly, a lack of assessment of the synergies between fare characteristics in their effect on fare equity was also observed. Potential opposite effects of fare characteristics on vertical and market fare equity points to the necessity for public-transit agencies to choose which form of fare equity to promote. Recommendations for practitioners and researchers based on our findings are provided to guide the field of fare equity forward.
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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 | 0.021 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".