Understanding the Effectiveness of Bus Rapid Transit Systems in Small and Medium-Sized Cities in North America
Bibliographic record
Abstract
In response to a lack of existing academic literature in relation to bus rapid transit (BRT) system success in small and medium-sized cities, this research examines the operational, demographic, and socioeconomic aspects of BRT at the route and system level in 16 small and medium-sized cities across North America. The results are compared with BRTs of large North American metropolitan areas to establish how the determinants of and requirements for BRT success differ. A wide array of factors collected from transit agencies, the Canadian and American 2016 censuses, and General Transit Feed Specification (GTFS) data are analyzed alongside ridership, which represents the primary determinant of BRT system success. The findings suggest that BRT routes of larger cities generally enjoy higher ridership levels compared with smaller and medium-sized cities in North America. Operational variables including service frequency were considerably higher in larger cities, with shorter route lengths, compared to small and medium cities. Higher population density, local accessibility, and percentage of rented households can also be observed in larger cities’ BRT system catchment areas in comparison with smaller cities. However, some BRT routes of smaller and medium-sized cities in North America exhibit comparable ridership levels with those in large cities. These routes have similar levels in relation to rentership, route length, and headway, with good local accessibility, while falling behind in population density. This paper expands on previous research on BRT systems, helping transit planners and policymakers to better understand the relationship between the city size and BRT ridership levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".