Influence of vertical alignment on horizontal curve perception: effect of spirals and position of vertical curve
Bibliographic record
Abstract
Several studies have shown that the perception of horizontal curves can be influenced by an overlapping vertical alignment. A previous two-phase study investigated the hypothesis that a horizontal curve appears flatter when overlapping with a vertical sag curve and sharper when overlapping with a vertical crest curve. The study concluded that the hypothesis was valid. The study also developed several statistical models to estimate the perceived radius of horizontal curves in a combined alignment. This study extends the earlier work by investigating the effect of additional geometric parameters on the perception. The parameters examined include the presence of spiral curves, the length of the spirals, and the position of the vertical curve midpoint relative to the horizontal curve. It was found that (1) driver misperception of the horizontal curvature increases as the radius of the horizontal curve increases, (2) the presence of a spiral curve affects driver perception of the horizontal curvature in the case of crest combination only, (3) the length of the spiral curve has no effect on the perception whether on crest or sag combinations, and (4) while the effect of the position of the vertical curve midpoint relative to the horizontal curve is not statistically significant, it seems that the perception problem appears to diminish as the positive offsets increases.Key words: highway geometric design, visual perception, combined alignment.
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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 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".