Desirable Spiral Length Based on Driver Steering Behavior
Bibliographic record
Abstract
Designing horizontal curves conforming to driver behavior is key to creating better-designed, safer highways. Doing so is assisted by a clear, quantitative understanding of driver behavior in a real highway environment. This study is concerned with collecting driver behavior data pertaining to steering behavior and using it to find desirable spiral lengths for horizontal curves. To realize this objective, a comparison between driver steering behavior and actual geometric alignment was performed. The profiles showed that drivers, in approaching horizontal curves, changed their behavior gradually to follow a natural spiral-curve-spiral path. Desirable spiral lengths were also related to geometric characteristics of the curve and were found to correlate well with the radius of curvature for two-lane highways and freeways with high coefficients of determination. In addition, the desirable spiral lengths were compared with the different controls of spiral length found in the North American design guides. The comparison revealed discrepancies in the design procedure of the spiral length. It was found that the minimum criteria found in these guides should be revised to better describe true driver behavior. In addition, the paper showed how new recommended values for the spiral parameter and rate of change of lateral acceleration would achieve spiral lengths that conform well to driver steering behavior.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".