Bibliographic record
Abstract
Poor coordination of horizontal and vertical alignments can create locations where the available sight distance drops below the required sight distance. Therefore, current design guides have recommended a number of guidelines to enhance the alignment coordination. A better and more quantified approach for alignment coordination can be achieved using a concept, called sight distance red zones, based on 3D analysis. A red zone, based on preview sight distance (PVSD), is defined as a section of the road where a horizontal curve should not start relative to a vertical curve. This paper presents a framework to estimate the required PVSD, which is the sight distance required to see, perceive, and react to a horizontal curve before its beginning. The required PVSD consists of two parts: PVSD on tangent and PVSD on curve. A simple analytical model of PVSD on tangent is presented based on the laws of kinematics. The PVSD on curve was investigated empirically using physical modeling and computer animation. Curves with different radii (500–2,000 m), turning directions (left and right), and configurations (with and without spirals) were simulated. Using the collected data, the effect of curve parameters was examined, regression models for the required PVSD on curve were developed, and preliminary design values for the required PVSD are presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".