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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".