Safety Evaluation and Adjustment of Superelevation Design Guides for Horizontal Curves Based on Reliability Analysis
Bibliographic record
Abstract
In general, the use of superelevation and changes in a road’s transverse slope are typically based on the road’s design speed to provide safety and comfort for a vehicle driving on a horizontal curve. However, due to the difference between operating and design speeds, there has always been uncertainty in determining the margin of safety using superelevation. This paper discusses the assessment of the safety margins obtained from the application of superelevation in horizontal-curve design. The assessment uses geometric design guides and a reliability index to determine any uncertainties in the design parameters. In addition, adjusted design charts of superelevation values related to the radius of horizontal curves at various levels of probability of noncompliance are provided. The results show that the adjusted superelevation values are generally greater than those derived from current standards. The adjusted design charts can help designers select an appropriate superelevation value for a particular horizontal curve for highways that have geometric constraints. These adjusted charts may also aid designers in predicting the consequences and safety margins associated with the selection of superelevation alternatives required to approve geometric designs that involve violations of the standard requirements due to environmental constraints.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".