Impacts of Transit Priority on Signal Coordination: Case Study of Toronto, Ontario, Canada
Bibliographic record
Abstract
This study introduces the current application of transit signal priority (TSP) in the City of Toronto, Ontario, Canada, and focuses on the following: traffic signal operations, constraints of the current signal control system and TSP technology, evaluation metrics, and models for simulating transit priority operation. In Toronto, 335 traffic signals have transit priority, 321 of which are under the control of the main traffic signal system (MTSS). Active transit priority unconditionally allows an extension of up to 30 s per cycle in addition to the normal signal green time on transit routes. The extension generates complaints about traffic delays for vehicles on side streets, long pedestrian wait times, and poor signal coordination. Field tests were conducted on a section of a downtown Toronto arterial bus route. Actual MTSS logs and signpost data obtained by the transit agency during field tests were used to investigate three scenarios along Bathurst Street at six signalized intersections. An analysis of signal timing changes that encompassed selection and determination of traffic operation performance measures was conducted on a per-cycle basis in the morning peak, afternoon peak, and off-peak periods. Green-band effectiveness was one of the evaluation measures used to assess signal progression. Subsequently, a performance assessment frame based on a model for the analytic hierarchy process was built to determine the best scenario and to help develop simulation models. The analysis shows that the TSP strategy of unconditional extension up to 30 s in signal green time is not recommended for use with the existing system of traffic signal control. The approaches described can apply to different transit routes with variable situations in Toronto.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".