Off-Board Fare Payment Using Proof-of-Payment Verification
Bibliographic record
Abstract
The objective of this synthesis was to document the state of the practice in terms of experiences related to the application of proof-of-payment (PoP) on transit systems in North America and internationally, updating the information provided in the 2002 TCRP Report 80, Toolkit for Self-Service, Barrier-Free Fare Collection. The subject is more complex than evasion rates. It involves related subjects such as inspection rates, enforcement techniques, duties of fare inspection personnel, adjudication processes, and the kinds of penalties involved for evasion. In addition, there is the need for acquiring capital equipment and, perhaps, handheld verification devices if smartcards are used. PoP fare collection has evolved to where it can be found on bus rapid transit, regular bus service, heavy rail transit, streetcars, passenger ferries, and commuter rail. A literature review, organized into five issue groupings related to PoP fare collection, is provided, as well as the results of a selected, on-line survey of transit agencies in the United States and Canada that yielded a 100% response rate (33 of 33 responses). Seven case studies offer detailed reviews of transit agency PoP fare collection experiences in Buffalo and New York City, New York; Dallas, Texas; Los Angeles and San Francisco, California; Minneapolis, Minnesota; and Phoenix, Arizona. Six areas deserving future study are identified as well.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".