Overview of Shared Use Lane Pavement Markings for Cyclists
Bibliographic record
Abstract
The provision of cyclist markings on roadways is increasingly important as a means of encouraging cycling, which can help achieve greenhouse gas emission reduction goals, improve personal health, and alleviate traffic congestion. While reserved bicycle lanes are a common measure, there are situations where roadway geometry and/or operations do not readily lend themselves to bicycle lane implementation. As an alternative marking option, the shared-use pavement marking symbol, or may be used, and was recently adopted by TAC for use in Canada. The sharrow marking consists of two chevron markings placed in front of a bicycle stencil. The general purpose of the sharrow symbol is to indicate to cyclists the correct positioning on the roadway, and to indicate to drivers the position where cyclists may be expected. There are three general applications of this marking: 1) side-by-side cyclist-motorist operation, 2) single file cyclist-motorist operation, and 3) conflict zones. An overview of the marking design will be given, as well as an overview of the three applications in terms of marking placement, spacing, signage considerations and the range of applicability. Finally, a review of several case studies of actual device implementation will be presented, highlighting emergent issues with the use of this device.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".