IMPACTS OF VARIOUS TRAFFIC PARAMETERS ON TRANSIT SIGNAL PRIORITY EFFECTIVENESS
Bibliographic record
Abstract
This research examines the impacts of a number of traffic parameters on the effectiveness of a Transit Signal Priority (TSP) application. TSP is tested with the 98 B-line rapid buses along Granville Street in the City of Vancouver as a case study. VISSIM, a micro-simulation software, is used to simulate TSP operation on the corridor. The traffic parameters studied include: bus approach volume, cross street volume/capacity (v/c) ratio, bus headway, bus stop location, bus check-in detector location, left turn condition, and signal coordination. Based on results from these experiments, recommendations are provided for TSP application on Granville Street. In general, it is found that a TSP application would be most effective under a traffic condition that has moderate to heavy bus approach volume, little or no turning volume hindering bus movement, slight to moderate cross street v/c ratio, farside bus stop, and signal coordination for traffic running in peak direction.
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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 | 0.003 | 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) | 0.000 | 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".