TRANSIT SIGNAL PRIORITY : A COMPARISON OF RECENT AND FUTURE IMPLEMENTATIONS
Bibliographic record
Abstract
The deployment of advanced traffic signal controllers, automatic vehicle location (AVL) systems for bus transit and improved bus detection technology has resulted in more jurisdictions considering and deploying transit signal priority (TSP) for buses. Comparisons among deployments of transit signal priority (TSP) provide useful insights for future deployments. Factors for consideration in designing new TSP systems include the use of AVL, transit vehicle detection, traffic controller functionality, traffic signal priority schemes, and performance measures. This paper provides a comparison of experiences in Toronto, ON, Snohomish County (a county north of Seattle, WA), and Vancouver, BC. For each jurisdiction, an overview of the project is provided including a description of the roadside vehicle, and central systems. Control strategy, operations institutional arrangements and key lessons learned are also presented. The last section contrasts the experiences in each jurisdiction and provides factors to consider in future implementations
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".