An economic approach to road condition assessment using road user feedback: A new model and its application
Bibliographic record
Abstract
Assessing roadway assets condition is the prerequisite of an efficient road management system. It requires participation from the top management, equipment, trained human resources, and dedicated funding. Newfoundland and Labrador have 13,500 lane kilometers of roads, of which almost 7,700 kilometers belong to the local jurisdictions. Local agencies typically consult the Transportation Association of Canada's pavement management guidelines for managing the road networks. But, municipality roads require more specified guidelines considering issues like lack of human resources, equipment, inadequate funding, environmental factors, and public expectations. To better maintain these roads, evaluation of road conditions is the first step. However, a proper evaluation system needs considerable funding, a trained workforce, and necessary equipment. Hence, the idea of using road users’ feedback is introduced in this paper. Citizens from 108 municipalities of the province participated in a feedback survey where they were asked questions about roadway assets condition. The survey resulted in a significant amount of data. First, an exploratory analysis of the road users’ feedback data was conducted. Then, a simple distress-based pavement performance model was developed. This model can be adopted by the local agencies as a simple decision-making tool. To make the model practical, a smartphone application called MUNPave is also introduced in this paper.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".