Three-Dimensional Model for Stop-Control Intersection Sight Distance
Bibliographic record
Abstract
Intersection sight distance (ISD) is the sight distance to be provided at intersections between a minor road and a major road. Current AASHTO policy provides an equation and charts for the required at-grade ISD so that a driver on the minor road can depart (crossing, turning left, or turning right) safely, even though an approaching vehicle on the major road comes into view as the stopped vehicle begins to depart. The AASHTO model is based on two assumptions: (1) both minor and major roads are assumed to be straight without any vertical or horizontal curvature; and (2) the intersection angle is assumed to be 90°. In many practical situations, however, sight distance is required to be checked for an existing or proposed 3D intersection alignment where vertical curves (crest or sag) and horizontal curves overlap. This paper presents a new mathematical model for the analysis of stop-controlled ISD on 3D highway alignments that allows the major road to have vertical and horizontal curves with skewed angle, and the minor road to have a longitudinal grade. Design aids are developed to determine the available ISD for different geometric alignment variables (e.g., radius of horizontal curve, lane width, number of lanes, and vertical curve parameters). Application of the methodology is illustrated using numerical examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".