“Is There a Bus?”: Ridership Changes from All-day, Every-day Transit Service in Toronto
Bibliographic record
Abstract
This thesis explores the relationship between public transit ridership and service provision on bus routes in Toronto, Canada. The study examines how increasing the span-of-service, or the hours of operation for transit routes, affects ridership patterns. The research addresses a key question: "How does transit ridership change when additional hours of transit service are provided?" Additionally, the thesis analyzes the demographic characteristics of the neighborhoods where service enhancements occur, assessing the correlation between these characteristics and ridership changes. This work is rooted in the broader context of public transit's role in urban mobility. Public transportation provides essential access to employment, education, and services while reducing dependence on private automobiles and contributing to environmental sustainability. The thesis highlights the importance of bus services within an integrated transit network, especially in a major city like Toronto, where buses serve diverse neighborhoods and play a crucial role in ensuring mobility for residents. The study utilizes ridership data before and after two significant service increase initiatives in Toronto, in 2008 and 2015, to analyze changes in ridership across different routes. The research investigates the degree to which increased hours of operation lead to corresponding changes in ridership. The findings suggest that expanding the span-of-service can positively impact ridership, particularly in areas with specific demographic characteristics, such as neighborhoods with higher populations of low-income residents, recent immigrants, and renters. Overall, this thesis contributes to the understanding of how public transit service improvements, particularly in bus operations, can enhance urban mobility and support equitable access to transportation in metropolitan areas like Toronto.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".