Optimize Signal Priority Strategy to Improve Transit Mobility
Bibliographic record
Abstract
In this research the authors studied the impact of Transit Signal Priority (TSP) on the transit bus mobility. For the purpose of experiment the authors have chosen 111Street corridor in Edmonton. Recently the Light Rail Transit (LRT) extension was carried out and the LRT passes through this intersection. What is peculiar to this intersection is that a shopping mall, the LRT and transit bus stations are all located close-by. Moreover, the transit buses have to cross the LRT tracks for entry and exit from the station. For the purpose of analysis and optimizing the LRT crossing signals, the authors used the Ring Barrier Controller (RBC) and Vehicle Actuated Programming (VAP) in VISSIM. The three options explored were (a) LRT preemption, (b) LRT prediction priority (c) LRT/Transit Bus priority. In options (b) and (c), the arrival time of the LRT is predicted in advance and the signal phases are modified to ensure uninterrupted passage of the LRT. This leads to better performance, as there is lesser number of signal changes compared to the first option. In the absence of dedicated bus lanes option (b) was found to be most beneficial.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".