Development of performance measures for pedestrian sidewalk asset management
Bibliographic record
Abstract
Recognising the importance of pedestrian sidewalks in supporting active transportation, some municipalities have spent millions of dollars in sidewalk condition assessment, resulting in a large amount of defect data. However, due to the lack of overall performance measures, those defect data have not been fully utilised in supporting asset-management planning. To fill the gap, this study developed two corroborating performance indicators – namely, maintenance repair index (MRI) and sidewalk condition index (SCI). Defined as the weighted sum of the numbers of defects that require repair, MRI gauges the need for repair and maintenance of a sidewalk segment and can be used to develop operation and maintenance budgets. In contrast, SCI evaluates the overall physical health of a sidewalk segment. It measures the need for replacing the entire sidewalk segment and thus can be used to assist long-term capital budgeting and planning. This paper discusses in detail the empirical calibration of the weights used in the definition of the indices. A real-life case study is presented to illustrate the technical details and practical significance of sidewalk performance evaluation using the proposed MRI and SCI.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".