Effect of Vertical Alignment on Driver Perception of Horizontal Curves
Bibliographic record
Abstract
The perception of the driver of the road features ahead is an important human factor that can considerably affect traffic safety and design consistency, and should be addressed in road design. An erroneous perception of the road can lead to actions that may compromise traffic safety. Previous studies have shown that combined horizontal and vertical alignments can cause a wrong perception of the horizontal curvature. In this paper, the hypothesis that the perception of the driver of the horizontal curvature is affected by the overlapping vertical alignment is examined analytically. Computer animation was selected as a three-dimensional presentation method of the road perspective, and was found to produce a realistic view of the road. A sample of drivers was interviewed to determine the radius of a horizontal curve on a level grade that would look equal to a radius of a horizontal curve overlapping with a vertical curve. The statistical analysis showed that the horizontal curvature looked consistently sharper when it overlapped with a crest curve and consistently flatter when it overlaps with a sag curve. Field measurements of operating speed profiles on a selected sample of combined alignments confirmed that, for the selected sample of alignments, driver behavior on horizontal curves depended on the overlapping vertical curve rather than the vertical grade of the approach tangent.
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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) | 0.002 | 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".