Bicycle-Specific Traffic Signals: Results from State-of-the-Practice Review
Bibliographic record
Abstract
This poster presents the results of a survey of North American jurisdictions with known installations of bicycle-specific traffic signals and a review of available engineering guidance. Surveys were sent to agencies in 21 jurisdictions (19 in the United States and two in Canada) that requested detailed engineering aspects of the signal design such as placement, mounting height, lens diameter, backplate color, type of actuation, interval times, use of louvers, and performance. We reviewed guidance documents produced by the National Association of City Transportation Officials (NACTO); American Association of State Highway and Transportation Officials (AASHTO); Transportation Association of Canada (TAC); the CROW design manual for bicycle traffic; and the Canadian, U.S. and Californian manuals on uniform traffic control devices. Responses were received for 63 intersections and 149 separate signal heads. The survey results highlight the current treatments and variations of similar designs. A subsequent review of the documents generally revealed consistent guidance with regard to the design of bicycle-specific traffic signals. The guidance on bicycle signals has grown substantially in recent years, and it is likely that there will be less variety in future designs.
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 | 0.028 | 0.200 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".