Evaluating Pay-on-Entry Versus Proof-of-Payment Ticketing in Light Rail Transit
Bibliographic record
Abstract
Pay-on-entry (POE) fare control for on-street transit allows effective revenue protection. However, the POE systems requires single door boarding and therefore increased stop dwell times compared with proof-of-payment (POP) systems. Most light rail transit (LRT) uses POP but is often criticized for poor revenue protection. This paper explores the trade-offs between the POE and POP systems through the comprehensive modeling of revenue protection, dwell time, ridership, revenue, and operational resource impacts in a case study of the conversion of the Melbourne, Victoria, Australia, POP system to a POE system; the paper uses data from LRT in Toronto, Ontario, Canada, which uses a POE system. The results show that the POE system increases journey times (+15%) and decreases ridership (−10%) and that 49 (+14%) additional light rail vehicles are required. POE conversion costs a net 29.4 million Australian dollars (A$) per annum and A$276 million for new vehicles, compared with a fare evasion reduction of A$8.1 million per annum. A 30-year discount cash flow analysis of the POE system results in a benefit–cost ratio of 0.44. The results are most sensitive to POE stop dwell time but not to fare evasion rates. Stop dwell times have a significant impact on LRT financial performance; alternative methods of revenue protection, such as increased inspection rates, are more effective than the POE system. The results justify the widespread adoption of POP systems in LRT and should provide a strong basis for defending against criticisms of the higher fare evasion rates of POP systems. The results should also act as a wake-up call to any LRT (or bus) system that still uses a POE rather than POP system and provide a basis for assessing the impacts of POP conversion for all modes.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".