Transit Signal Priority Impact Analysis and Evaluation in the City of Edmonton
Bibliographic record
Abstract
It has been proven that Transit Signal Priority (TSP) strategies can reduce bus travel time as well as increase bus service reliability. Nonetheless, some major issues remain. The performance of TSP strategies may significantly increase traffic control delay if the TSP requests are frequent. Or TSP may further deteriorate the traffic conditions or even cause cycle failures at congested intersections. In this paper, the authors examined and estimated the potential performance of TSP under different scenarios composed of various signal control strategies and traffic demand levels. The authors also conducted a case study using the microscopic traffic simulation software, VISSIM, which contains a fully-functioned signal control emulator with a TSP module. The comparison between TSP and non-TSP strategies in simulation demonstrates that TSP strategies are not necessarily beneficial for all traffic scenarios. The comparison also explores the benefits of TSP with respect to the total traffic volumes, demand distributions and frequency of TSP requests at intersections. Lastly, the authors conducted a simulation study of a bus corridor of the City of Edmonton to identify intersections which can benefit from TSP. The simulation results show that TSP will reduce bus travel times as well as deteriorate the overall traffic performance. Disabling some of the TSP control may result in greater balance for the whole system. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".