Bibliographic record
Abstract
Several methods for distributing highway superelevation (e) and side friction (f ) have been presented by the American Association of State Highway and Transportation Officials (AASHTO). Based on a subjective analysis, AASHTO has recommended the curvilinear distribution method. This paper presents an objective method that distributes superelevation using mathematical optimization. A safety margin is defined as the difference between the maximum limiting speed (corresponding to fmax) and the design speed. The objective function of the model minimizes the overall variation of the safety margin along the highway (aggregate analysis) or the individual variations of the safety margin between adjacent curves (disaggregate analysis). Both objectives maximize highway design consistency. The model includes constraints related to the maximum side friction, minimum and maximum superelevations, centripetal ratio, and safety margin. The e and f distributions can be discrete values with no specific mathematical shape or follow a general quadratic curve with a parameter determined by the optimization model. Application of the model was illustrated using two examples, and the results show that the design consistency obtained by the model is considerably better than that obtained by the AASHTO methods.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".