Assessment of speed-flow-density functions under adverse pavement condition
Bibliographic record
Abstract
Many of the speed, flow and density relationships postulated in different literatures are based on empirical evidences collected under favourable conditions.Those that veered into comparative analysis under contrasting conditions often use forced curves to describe the relationship between speed and flow mainly because the graph is not a function.However, the paper is an attempt to postulate that dynamic speed-flow, speed-density and flow-density functions have similar behavioural pattern.In the study, speed and flow relationship under adverse road surface condition depicted with potholes and edge subsidence among others was investigated.The study was carried out in Nigeria where adverse road surface condition on principal roads is prevalent under daylight, dry weather and off-peak conditions.It is based on the hypothesis that adverse road surface condition has significant impact on otherwise uninterrupted traffic stream.The paper compared empirical survey data from 11 locations on roadway segments with control and adverse sections.Optimum speeds for control and adverse road sections were estimated and compared.The study found 50% reduction in optimum speed and concluded that significant speed reduction will occur under adverse road surface condition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".