The Impact of a Single Bus Rapid Transit Corridor on Transit Ridership: The Winnipeg Example
Bibliographic record
Abstract
This research explores how the implementation of a single bus rapid transit (BRT) corridor affected transit ridership change in Winnipeg, Manitoba, Canada. Key issues in measuring ridership change resulting from BRT include (1) understanding stop-level- rather than corridor-level change; (2) examining the ridership impacts of new infrastructure where there is no comparable pre-BRT infrastructure; and (3) assessing piecemeal implementation of BRT. To address these issues, we undertook a quasi-experimental study using agglomerative hierarchical clustering (AHC), propensity score matching (PSM), and t-tests with Cohen’s d to determine BRT’s causal ridership impact. The use of AHC and PSM in what we refer to as cluster-level modeling provided an improved method for measuring causal ridership change at the stop cluster level in areas with no pre-BRT stations. The results revealed no statistical evidence that BRT caused increased transit ridership for stop clusters directly along the BRT corridor. However, the results did indicate that stop clusters for routes connecting to a BRT station experienced an increase in transit ridership. The importance of such findings is grounded in understanding that a limited number of stops along a single corridor may not be enough to affect transit ridership, yet BRT’s flexibility in being able to operate off the BRT corridor does enhance transit ridership.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".