Effect of speed prediction models and perceived radius on design consistency
Bibliographic record
Abstract
The most frequently used criteria to evaluate design consistency are the difference between design speed and operating speed, operating speeds on successive elements, and assumed side friction of the road and that demanded by the driver. The operating speed, often obtained using speed prediction models, influences each design consistency criteria. There has been considerable research in developing design consistency measures and evaluation tools, but little research has been done on the influence that different speed prediction models have on design consistency criteria. In addition, there has been little research as to how design factors such as driver perception of combined horizontal and vertical curves influence speed prediction models. This paper investigates how the selection of speed prediction models and driver perception of combined horizontal and vertical curves influence design consistency evaluation. The selection of the speed prediction model has a significant impact on design consistency evaluation. The use of perceived radius, which accounts for driver perception of combined alignments, slightly influenced the outcome of the design consistency evaluation.Key words: design consistency, speed prediction models, driver perception, combined horizontal and vertical curves, perceived radius, geometric design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".