Past Presidents' Award for Merit in Transportation: Bus Rapid Transit Land Development Guidelines
Bibliographic record
Abstract
Transit oriented development (TOD) near bus rapid transit (BRT) stations not only could increase BRT ridership, but could have a positive influence on the community by improving mobility choices, reducing reliance on driving and achieving greater sustainability. This paper investigates the impact of BRT on land development decisions and provides guidelines for public agencies. The paper identifies data sources and analysis tools and presents findings from a survey of developers in Ottawa, Canada. The findings indicate a wide range of opinions, both positive and negative, on TOD and BRT. Developers seem most concerned with the timetable of transit line construction and the amount of right-of-way that developers are required to dedicate to transit routes. Developers feel that BRT is not significantly different than light rail transit in terms of the modes' impacts on TOD project success. Although the local government agencies share several similarities to developers in their perspective of TOD and BRT, a disconnect between transportation professionals, developers and the general public may exist regarding which TOD factors are the most important. The findings from this research are used to develop guidelines to help public agencies assess the potential land development benefits of BRT and to conduct their own surveys of stakeholders in the land development process.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".