Special-Color Pavement Marking for Highway Work Zones: Literature Review of International Practices
Bibliographic record
Abstract
Roadway lanes are often repositioned to accommodate highway work operations; as a result, pavement markings need to be altered. Although there are various methods for removing or obscuring existing pavement markings, “ghost” markings often remain at the locations of the old lane lines. These ghost markings can be quite conspicuous under certain lighting conditions, creating the potential for road user confusion. The Canadian province of Ontario and several European countries routinely use a special marking color (orange or yellow) to increase the salience of temporary lane lines. Special-color markings have also been used experimentally in Australia; New Zealand; Quebec City, Canada; and the United States. As a first step toward identifying the benefits and risks of special-color markings, existing practices from several countries are reviewed and summarized. The review identified a significant policy difference among jurisdictions: in some jurisdictions special-color markings override existing markings (so that the old markings are left in place), whereas other jurisdictions use special-color temporary marking but also attempt to remove old lane lines. The recent special-color marking demonstration projects in Australia, Canada, New Zealand, and the United States have been on major freeways, but European practice suggests that special-color marking could have significant benefit for urban arterial streets.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".