Tackling Fare Evasion on Calgary Transit’s CTrain System
Bibliographic record
Abstract
Calgary Transit is a medium-sized integrated transit system with a staff of over 2,800 providing bus, community shuttle, and light rail transit (LRT) service. In 1981 Calgary Transit became one of the first transit agencies in North America to operate an LRT system; it was called CTrain. Today CTrain is considered to be one of the most successful LRT startup operations based on ridership. CTrain carries over 275,000 riders on an average weekday. Since its inception CTrain has grown from a single line to three lines comprised of 37 stations and platforms and 48 km (30 mi) of dual track with a fourth line tentatively scheduled to open in December 2012. CTrain operates on the surface and is characterized as an open system, in which there are no turnstiles or barriers controlling access. Consequently, Calgary Transit relies on voluntary compliance and proof of payment from customers for fare payment. Calgary Transit peace officers, hired in 1981, conduct regular fare-checking activities to ensure compliance. Since 1993 Calgary Transit has conducted annual fare evasion studies to estimate current fare evasion levels to monitor the effectiveness of fare checking and enforcement efforts. This paper describes the fare evasion study methodology employed by Calgary Transit and historical fare evasion study results, and details the findings of the 2011 study. In addition, the paper outlines Calgary Transit’s efforts to increase compliance through public awareness activities and describes the findings from a survey of over 1,400 fare evaders conducted as part of the 2011 fare evasion study. The paper concludes with a discussion of the role of fare enforcement as a means of controlling crime and disorder on transit systems. The authors contend that during the day the primary role of proof of payment checks is to serve the revenue and business interests of transit agencies while during the evening the primary focus is on passenger behavior, which has a secondary impact on reducing fare evasion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".