Development of an Updated "Traffic Signal Operations Policies and Strategies" Document for the City of Toronto
Bibliographic record
Abstract
Congestion of road networks is a persistent problem in almost all large and growing metropolitan areas. In the City of Toronto, a combination of transportation policies and strategies are applied to address congestion issues. One way of mitigating the impacts of congestion is through consistent, safe, and efficient control of traffic signals. Historically, the City’s traffic signal operations were guided by Standard Operating Practices (SOPs) that were inconsistent, incomplete, or out-of-date. A consolidated document to provide guidance to City staff and consultants was not available. To address this gap, a three-member Working Group was established to develop the City's Traffic Signal Operations Policies and Strategies document. The policies and strategies in this document complement and support the broader vision, goals and objectives of the Toronto Official Plan and Transportation Services Division Strategic Agenda, providing guidelines for the City's signal operations while promoting the consistent, safe, and efficient control of traffic signals for all road users. This paper discusses the policy developmental framework and the policy-making process used to develop the policies and strategies of traffic signal operations in Toronto. It also addresses the challenges and sensitive issues that arose during the process.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".