Performance Assessment Methodology for Sustainable Pavement Marking
Bibliographic record
Abstract
One of the most contributing factors to fatal motor vehicles crashes is the inadequate and poorly maintained pavement marking. The cost of these crashes is estimated to range between 10-25 billion Canadian dollars annually. Consequently, the different companies/authorities that manage pavement marking should set a target for themselves to be more cost efficient through building a strategic plan to renew and restripe pavement marking. The objective of this paper is to develop a methodology and model(s) that predict the performance of the pavement marking at different average annual daily traffic, percentage of trucks, age, and type of road. Due to the typical scarcity of data, particularly for performance of pavement marking, there is an essential need to predict this performance. In order to achieve this objective, a sound technique, such as unsupervised neural network, is used. Therefore, the developed models are designed using unsupervised neural network in conjunction with regression analysis. Data for this research were collected from the city of Ottawa, Ontario, Canada for the Alkyd paint material. The developed model is validated and the results show that the percentage average validity is 73%. Similarly, the fitness function value is found to be 789; which means the developed model is a sound fit. Marking performance is assessed using a performance scale, which numerically ranges from “1” to “5” and linguistically from “Excellent” to “Critical”, respectively.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".