Road users’ perception of roughness and the corresponding IRI threshold values
Bibliographic record
Abstract
One of the primary objectives of highway agencies in Canada is providing a safe and reliable road network with a good level of service. In the Province of Alberta specific International Roughness Index (IRI) threshold values classify pavements into good, fair, and poor condition categories to manage and schedule rehabilitation and maintenance programs. This research investigated the significant factors that affect the perception of road roughness and established IRI threshold values for good, fair, and poor road condition based on public perception. A questionnaire was designed to investigate the road users’ perception and included questions covering gender, age, familiarity with the road, type and model of car, and perception of road roughness. In addition, psychometric scaling analysis was used to develop a set of IRI threshold values for classifying road condition based on public perception in the Province of Alberta. According to the results of the survey, Alberta Transportation threshold values of IRI do not agree with the road users’ opinion and an alternate set of threshold values was developed. The analysis of the survey results identified that trip purpose, driving experience, dry surface, and familiarity with the road are the most significant factors that influence the perception of road roughness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".