Innovative Roadside Design Curve of Lateral Clearance: Roadway Spiraled Horizontal Curves
Bibliographic record
Abstract
This paper presents an innovative design method for determining lateral clearance needs on a spiraled horizontal curve to satisfy sight distance requirements. The roadside lateral clearance is represented by a spiraled horizontal curve that is easy to implement in practice. The design parameters were determined. The characteristics of the corresponding lateral offsets were explored for the influential roadway factors, including required sight distance or design speed, curve radius, curve length, spiral curve length, and station location expressed in fraction of sight distance. The results show that ratio of spiral curve length to required sight distance is the major factor that affects the ratio of the lateral offset to the maximum offset at a circular curve. A single design chart and a design table also are provided as alternative design tools to determine the offset for a specific obstruction location. The proposed design method not only greatly improves the AASHTO approximate approach, but also provides an alternative approach to improving design consistency on horizontal alignments. This paper complements another paper on lateral clearance needs for simple horizontal curves.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".