Bus and Rail Transit Preferential Treatments in Mixed Traffic
Bibliographic record
Abstract
This synthesis provides a review of the application of a number of different transit preferential treatments in mixed traffic and offers insights into the decision-making process that can be applied in deciding which preferential treatment might be the most applicable in a particular location. The types of preferential treatments covered include median transitways, exclusive transit lanes, stop modifications, transit signal priority, special signal phasing, queue jump lanes, and curb extensions. The synthesis is offered as a primer on the topic area for use by transit agencies, as well as state, local, and metropolitan transportation, traffic, and planning agency staffs. This synthesis is based on the results from a survey of transit and traffic agencies related to transit preferential treatments on urban streets. Survey results were supplemented by a literature review of 23 documents and in-depth case studies of preferential treatments in four cities -- San Francisco, Seattle, Portland (Oregon), and Denver. Eighty urban area transit agencies and traffic engineering jurisdictions in the United States and Canada were contacted for survey information and 64 (80%) responded. One hundred and ninety-seven individual preferential treatments were reported on survey forms. In addition, San Francisco Muni identified 400 treatments just in its jurisdiction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".