Establishing highway horizontal alignment to maximize design consistency
Bibliographic record
Abstract
Highway design consistency is one of the important criteria in selecting the geometric features of proposed or existing alignments of two-lane rural highways. Operating-speed (OS) profile models have been used to evaluate design consistency by trial and error. For a proposed new highway, however, there may be geometric and physical constraints, and selection of these elements by trial and error to achieve optimal design consistency would be difficult, if not impossible. This paper presents an optimization model that establishes highway horizontal alignment to achieve maximum design consistency based on the OS profile. The decision variables of the model include radius of horizontal curves, spiral curve lengths, length of speed-change (SC) segments, and acceleration and deceleration rates. The objective function of the model minimizes the mean OS difference or the maximum OS difference for successive geometric features along the highway section. Application examples and sensitivity analysis are presented to illustrate the capabilities of the model in evaluating improvement strategies and to ensure that the model produces sound optimum alignments. The proposed model, which complements existing optimization models that mainly address highway construction cost, should be of interest to highway practitioners and engineers.Key words: design consistency, highway, geometric, horizontal alignment, optimization modeling, speed profile.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".