Evaluation of Transit Signal Priority Strategies and the Impact of Background Signal Timing
Bibliographic record
Abstract
A recent Transit Cooperative Research Program (TCRP) study that surveyed the transit Signal priority (TSP) practices of 31 transit agencies in the US and Canada showed that despite the widespread adoption of TSP, there is no universal agreement in the selection of TSP strategies and parameters or the quantification of TSP benefits and impacts. To help address this uncertainty, the current study develops simulation experiments to assess the performance of different TSP strategies, identify critical factors and conditions that affect TSP performance, and propose strategies for improved consideration of transit vehicles in signal timing development. The results indicate that TSP performance is most favorable in lower v/c conditions. Compared to Early Green (EG), Green Extension (GE) provides a greater benefit to individual buses. However, as congestion increases, the effectiveness of GE decreases. On a highly congested corridor, with v/c ratios approaching or exceeding 1.0, it is possible that TSP may become infeasible as the conflicting non-TSP movements may have insufficient slack in available capacity to recover from the TSP-related green truncation. The results show that the use of a cycle length slightly longer than the traffic-demand-based optimal cycle could lead to lower impacts to conflicting non-transit vehicles during TSP events.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".