Condition Rating Models for Sustainable Pavement Marking
Bibliographic record
Abstract
The latest available statistics for road casualty collisions (2003) in Canada reported that the average annual cost of motor vehicle traffic crashes was $10-$25 billion. The number of fatal collisions is increasing randomly and that reduces road and public safety. A previous study concluded that inadequate and poorly maintained pavement markings are often cited as the most contributing factor to fatal crashes. There are numerous types of pavement marking materials available that complicate managing the application and replacement of these markings. Canadian municipalities face a great challenge of managing the striping and re-striping activities of these markings. One of these challenges is how to assess the condition of pavement marking materials. Therefore, present research considers several factors which influence the condition of the pavement marking materials, such as age of marking material, traffic conditions, road surface, and environmental conditions and their effect on different pavement marking materials. Condition rating models are developed to assess the effectiveness of striping and re-striping actions for pavement marking. These models target only alkyd and epoxy pavement marking materials but they draw a framework which can be used for other marking materials. A condition rating scale is developed, which numerically ranges from “1” to “5” and linguistically from “Excellent” to “Critical”, respectively. The developed models were based on data collected from the province of Quebec, Canada. Results show that 96% to 99% of pavement marking condition can be explained through the developed regression models. The percentage average validity of the developed models varies from 87% to 99%, which are satisfactory results.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".