Prediction of Operating Speed on Three-Dimensional Highway Alignments
Bibliographic record
Abstract
Achieving consistent geometric design is an important goal in highway design to ensure obtaining safe, economical, and smooth traffic operation. Existing operating speed models for design consistency in North America and Europe are mainly based on two-dimensional (2D) analysis of highway horizontal alignments. This paper develops operating speed models for two-lane rural highways that account for the three-dimensional (3D) nature of highways. The models will help highway designers to predict operating speed and evaluate design consistency more accurately, and thus aid highway safety. Two types of 3D combinations were considered: a horizontal curve combined with a sag vertical curve and a horizontal curve combined with a crest vertical curve. Regression analysis was used to develop the operating speed models based on data collected on Highway 61 and Highway 102 in Ontario. The results show that there is a significant difference between the predicted operating speed using the 2D and 3D models. Therefore, it is recommended that the developed 3D models be used in highway consistency analysis and evaluation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".