Future paths for regional fare collection in Atlanta: a case study analysis of the planning and implementation of next generation fare collection systems for regional transit in North America
Bibliographic record
Abstract
The Atlanta region will soon be faced with a choice as to how it will go about planning for and implementing its next regional fare collection system that will replace the current BREEZE system. In 2006, MARTA became the first transit agency in the United States to implement an all contactless smartcard for use on its services. However, there have been many advances in new technologies and the consumer payment preferences have evolved since the initial implementation. These developments, coupled with the rapid consumer adoption of smartphones and changing attitudes within the financial payments industry towards transit properties, have recently led four major transit agencies within North America to implement new fare collection systems based on open payments, the development of mobile ticketing applications, or a combination. This research uses a case study methodology to answer several questions related to the planning and implementation of regional fare collection systems in Chicago (CTA), Dallas (DART), Philadelphia (SEPTA) and Toronto (TTC). Based on the experience of the case study agencies, the implementation of Atlanta's next fare collection system is sure to be a long and arduous process. However, by utilizing the lessons learned from DART, CTA, SEPTA and TTC, MARTA and the other regional operators (Cobb Community Transit, Gwinnett County Transit and the Georgia Regional Transportation Authority) will be better poised to provide their patrons with additional means of paying fares while, at the same, minimizing the disruption to the existing fare collection system during the transition period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".