Bibliographic record
Abstract
Intersection sight distance(ISD) is an important design element. Each intersection has a potential for several different types of vehicular conflicts that can be greatly reduced through the provision of proper sight distance. Current guidelines do not adequately address sight distance requirements for intersections located on horizontal curves alone or horizontal curves combined with vertical alignments. In many practical situations, however, sight distance is required to be checked for an existing or proposed three-dimensional(3D) intersection alignments. In this thesis, models were developed to check sight (2001) were considered on 3D alignment: (1)Departure from stop-control minor-road and (2) Left-turns from major-road. For stop-control intersections, several cases were addressed. These include Case 1(a): Intersection and approaching vehicle (object) lie on the curve, Case 2: Intersection lies on the tangent and object lies on the curve. For both cases (1) and (2), obstruction may lie inside or outside the horizontal curve and the intersection and object can be anywhere with respect to the vertical alignment. In many practical situations, however, sight distance is required to be checked for an existing or proposed three-dimensional(3D) intersection alignments. In this thesis, models were developed to check sight (2001) were considered on 3D alignment: (1)Departure from stop-control minor-road and (2) Left-turns from major-road. For stop-control intersections, several cases were addressed. These include Case 1(a): Intersection and approaching vehicle (object) lie on the curve, Case 2: Intersection lies on the tangent and object lies on the curve. For both cases (1) and (2), obstruction may lie inside or outside the horizontal curve and the intersection and object can be anywhere with respect to the vertical alignment. Design aids for required minimum lateral clearance (from the minor and major roads) are presented for different radii of intersections located on horizontal curves, guidelines are presented for offsetting opposing left-turn lanes to provide unobstructed required sight distance. Applications of the methodologies are illustrated using numerical examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".