Determining the required operating subsidy of Canadian Transit Agencies: an examination of public transit operating deficits in Canada
Bibliographic record
Abstract
Public transit systems can reduce road congestion, decrease air pollution, and provide affordable transportation within the areas they service. Despite continued growth in Canadian urban transit ridership levels over the past years, public transit systems across Canada consistently earn less revenue than they spend to remain operational. The difference between transit agencies' operating revenue and operating expenditures (known as the operating deficit) is covered by government subsidies which allow public transit to continue working. As more funding becomes available for new transit projects and current transit services, it is essential to understand how much each transit agency requires to remain in operation. This thesis conducts a regression analysis on the determinants of transit demand to estimate the expected per-trip operating deficit a transit agency will experience. Data collected by Statistics Canada and the Canadian Urban Transit Association (CUTA) is used to generate two models which can predict the per-trip subsidy a public transit agency will require based on transit demand levels in the region it services. Data collected from the years 1981, 1986, 1991, 1996, 2001, 2006, and 2016 from 104 Canadian Transit agencies is used in this study. Transit demand is accounted for with Canadian census data, and findings show that demographic and socioeconomic factors that impact ridership levels among transit users have varying effects on the per-trip subsidy a transit service requires. These effects reveal diseconomies of scale within large, urban transit agencies and explain the relationship between transit demand and transit costs. The results from this analysis allow policymakers to predict expected costs for current transit infrastructure, and determine expected future costs for new transit projects.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".