MUNI METRO GOES POP: IMPLEMENTING PROOF OF PAYMENT FARE COLLECTION ON MUNI METRO
Bibliographic record
Abstract
The San Francisco Municipal Railway (Muni) is the first light rail system in the United States to convert from traditional fare collection to proof of payment (POP) fare collection. The issues and problems Muni has encountered in making this conversion will be discussed, along with how the problems have been solved, and why Muni made the decisions it did. The lessons learned have applications mainly for traditional systems with extensive street running such as Boston, Philadelphia, Pittsburgh, and Toronto, but could also have applications for new systems. At Muni, POP fare collection is being implemented on a system with a mixture of right-of-way (including subway), boarding locations, station and platform configurations, and operating environments, many of which resemble traditional streetcar operation more than they resemble modern light rail. In addition, POP at Muni is initially being implemented largely without the installation of wayside ticket vending machines (TVMs). At Muni, POP is being implemented with onboard fare payment still allowed at many locations, in contrast to other POP systems in the United States. POP fare collection is being phased in at Muni incrementally on a line-by-line basis. This has created special problems for enforcement and passenger information because POP lines run beside lines still using traditional fare collection. The process of conversion involves a large amount of passenger education, which has presented challenges for creating effective and understandable signage and information programs. POP fare collection began at Muni in October 1993, when Muni opened two new surface stations as limited platform-only POP stations, with wayside TVMs. This was Muni's first introduction to POP, although POP was not in effect on board the vehicles operating the line that serves the stations. Muni's transitional E-line was inaugurated in January 1998 as an all-POP line, with fare inspections on board the vehicles for the first time. In August 1998, Muni expanded POP to the N-Judah Line (Muni's heaviest LRV line). POP is being incrementally expanded to all of Muni's lines over the next few years. Enforcement has recently made a transition from the San Francisco Police Department to Muni's new civilian fare inspection force.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".