ENHANCING SAFETY ON HORIZONTAL CURVES WITH LIMITED SIGHT DISTANCE: A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK
Bibliographic record
Abstract
This study introduces a multi-objective optimization framework for the re-dimensioning of the cross-section elements of rural horizontal curves with limited sight distance. The optimization aims at minimizing both the risk of collision associated with the limited sight distance and the expected collision frequency corresponding to the cross-section elements’ dimensions. The risk component was assessed using an index known as (Pnc), which is developed based on the reliability theory. The change in collision frequency corresponding to the change of the cross-section elements was extracted from the literature. The risk and the safety components were then combined into one measure (CMFcombined) to develop a direct measure of the safety impacts of the optimization. The proposed framework was applied to five restricted curves in British Columbia, Canada, considering various scenarios. The results showed a considerable reduction in the Pnc value (ranging from 12% to 73%) and the expected collision frequency (ranging from 10% to 31%) after optimization. The estimated combined reduction in collision frequency (CMFcombined) was estimated to vary between 48% and 76%. The results showed that the optimization of cross-section elements can improve the safety of horizontal curves significantly. The framework presented in this study would support transportation engineers in selecting optimal dimensions of cross-section elements of restricted horizontal curves, understanding the safety consequences of selecting a specific cross-section configuration, and assessing the economic viability of different design options.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".