Active transit signal priority for streetcars: experience in Melbourne and Toronto
Bibliographic record
Abstract
Active traffic signal priority (TSP) has been identified as a cost effective way to better manage traffic systems to make on-street public transport more reliable, faster and more cost effective. While the implementation of TSP is growing throughout the world, there are relatively few studies which have examined their application to streetcar based systems. This paper reviews the experiences of TSP in Melbourne, Australia and Toronto, Canada. These cities run some of the world's oldest and largest streetcar based TSP systems. This paper describes the TSP systems adopted in both cities including key experiences. TSP performance is reviewed including an assessment of problems and issues identified. The review established that TSP systems in both cities have many similarities including the configuration of approach/request loop and stop line/cancel loop detection, the degree of priority provided and the targeting of clearance phases for turning traffic at intersections. There are some slight differences in the handling of bunching trams and opposing tram movements, which are better handled in the Toronto case. Both systems see rather different futures for TSP development. Toronto is focussed on full system-wide TSP implementation and advancement of TSP algorithms, while Melbourne aims to make priority more conditional on the degree of lateness of trams and on the degree of traffic congestion experienced.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".