Comparative Analysis of Specific Speed Estimation Methods: INVIAS and Local Equations in the Ecuadorian Context
Bibliographic record
Abstract
In geometric road design, specific speed serves as a complementary approach to design speed, aiming to enhance alignment consistency and road safety.This study evaluates the applicability of the method proposed by the Colombian Institute of Roads (INVIAS) in Ecuador, comparing it against local equations for operating speed prediction.Differences between the two methods were assessed using the mean absolute error (MAE) to identify speed ranges and geometric conditions where discrepancies are most and least significant.The findings indicate that the smallest differences between the INVIAS procedure and Ecuadorian equations occur within design speed ranges of 50-80 km/h, suggesting that the Colombian method can be more confidently applied under these conditions.Additionally, the local equation corresponding to gradients between -3.99% and 0%, with maximum superelevations of 8% and 10%, showed the closest alignment with the INVIAS procedure.As a result of this analysis, specific speed estimation tables were developed based on gradient, expanding the method's utility in local geometric design.This study highlights the importance of validating and adapting international methodologies to local contexts, fostering more consistent and safer road designs.
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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 | 0.004 | 0.015 |
| 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) | 0.002 | 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".