Reliability-Based Design of Horizontal Curves on Two-Lane Rural Highways
Bibliographic record
Abstract
Current design guides adopt a deterministic approach to the design of horizontal curves; each factor included in the design is represented by the near-worst-case value. In the context of horizontal curve design, the design procedure is based only on the driver comfort criterion, and data correspond to experiments conducted in the 1930s. Furthermore, current horizontal curve design procedures lack a quantitative evaluation for safety. To overcome those shortcomings, a new design framework is proposed to design horizontal curves; a probabilistic approach is adopted and two criteria are considered: vehicle dynamic stability and driver comfort. Reliability analysis was used to provide a quantitative evaluation for the design in regard to the probability of failure, probability of noncompliance, and reliability index. Outputs of simulation runs in a vehicle dynamics model were used to estimate demand lateral friction and lateral acceleration depending on the geometric characteristics of horizontal curves. In addition, data of an instrumented vehicle experiment were used to develop driver-level models for the distribution of the curve speed and driver comfort threshold. The first-order reliability method was used to estimate the probability of failure, probability of noncompliance, and reliability index. The proposed design framework and developed models were applied in an example to design a horizontal curve for a specific design speed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".