Bibliographic record
Abstract
The Traffic Operations and Management Standing Committee (TOMSC) of the Transportation Association of Canada (TAC) wanted to develop a national warrant for adding pedestrian signals to existing traffic signals or to determine whether pedestrian heads should be included for new signal installations. This paper discusses the development of that national warrant and consists of three components: (1) an environmental scan; (2) the warrant methodology process; and (3) updating the Manual of Uniform Traffic Control Devices and Traffic Signal Warrant (TSW) user handbook. This environmental scan comprises two elements: (1) a comprehensive review of literature on existing international warrants practices and a review of policies, by-laws and municipal initiatives for pedestrian warrants; and (2) extensive jurisdictional survey on current pedestrian signal head warrant practices. The warrant methodology is based on a 'cumulative factors method' that results in a point score that takes into account pedestrian volumes, pedestrian-vehicle conflict, pedestrian demographics, signal operations, and crossing distance. Parameters used in the warrant calculation are based on the environmental scan, the traffic signal warrant, and recommendations from the project steering committee and the consulting team. The existing TSW handbook is updated to provide traffic operations practitioners with instructions on how to use the pedestrian signal head warrant matrix in a consistent and comprehensive way. The handbook identifies the input data required for the warrant analysis and describes how each of the warrant components is calculated. Important notes, additional information or warnings are also provided in support of specific considerations. (A) For the covering abstract of thsi conference see ITRD record 201310RT334E.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.017 |
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".