Ridership Response to Incremental Bus Rapid Transit Upgrades in North America
Bibliographic record
Abstract
This paper explores ridership increases in response to incrementally upgraded bus services in U.S. and Canadian cities. Current guidelines for developing bus rapid transit (BRT) corridors reveal a tension between comprehensive implementation of full-fledged corridors on the one hand and incremental, flexible development on the other. A review of the literature discusses this tension, various BRT elements, and the impact of these elements on performance and ridership. A methodology for comparing high-productivity bus corridors in different contexts using general transit feed specification (GTFS) data and a spatial database framework is described. Longitudinal and cross-sectional sketch models, with corridors as the unit of analysis, offer some insights into the relative impact of BRT features and external factors. Current data limitations allow for suggestive, if not definitive, results. Dedicated lanes and signal priority were positively correlated with increased ridership in some models tested, even when decreased travel time was controlled for and, suggesting that they may have had important perception and reliability benefits beyond improved speeds. While BRT can be a promising mode for a range of contexts, this analysis suggests that service frequency and reliability improvements are the common foundation for successful projects. Building political momentum for sustained improvements in bus networks is a challenge; the use of emerging data sources, such as GTFS, to compare incremental BRT projects allows for a better understanding of projects that can help meet this challenge.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".