Existing Problems of Transit Signal Priority on Streetcar Routes
Bibliographic record
Abstract
Transit signal priority (TSP) is a traffic control strategy that gives priority to transit vehicles by adjusting intersection signals in real time. The technology is implemented in many major cities and has proved to benefit transit routes in reducing the overall passenger travel time. Unfortunately, there are several problems with TSP that are commonly ignored. These problems are predominantly operational, such as misjudgment of the arrival time at the intersection and insensitivity to the TSP activation time. It is worsened with streetcars, which have shorter headways and thus are more prone to bunching delays. In this paper, five delay problems that streetcars experience are identified. Data are collected from a TSP-equipped intersection in the City of Toronto to provide a statistical analysis of the issues. It was found that 25.8% of times, TSP is inadequate at prioritizing public transit. From the problems raised, the most frequent one is the late arrival of a streetcar during a green interval, representing 72.5% of TSP issue cases. It was also found that TSP has a bias toward different failure cases, and favors early arrivals to the intersection. Several remedies for the defined issues are recommended. The suggestions include implementing green truncations, avoiding late TSP activation in each cycle, and introducing a prediction-based TSP system.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".