Risk and Reliability Analysis of Geometric Design Criteria: A Critical Synthesis
Bibliographic record
Abstract
Highway and street designers deal with the challenge of designing for a broad range of driver, vehicle, and roadway characteristics and conditions. There is significant variability in design inputs and design controls that influence design criteria and design decisions. This variability has traditionally been considered implicitly through selected values for geometric design parameters and criteria. Average values are used when the variability in the input design parameters is small. Conservative values are used if the variability is larger, often the case within the highway geometric design context. Previous research has demonstrated that addressing this variability and uncertainty more explicitly as part of design decisions can lead to better-informed and more cost-effective design decisions. Probabilistic design approaches that quantify both risk and reliability have been successfully incorporated into other design disciplines for those reasons. These approaches have also been explored in the highway geometric design literature and have shown promise. They are likely to be central to future performance-based design initiatives, as outlined in a recently published framework on conducting performance-based geometric design analysis. Given the emerging importance of performance-based design and the need to address challenges regarding the current method of handling variability and uncertainty in the input design parameters, this paper presents a collective review and assessment of methodological alternatives for quantifying risk and reliability associated with geometric design criteria and decisions.
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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 | 0.033 | 0.072 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".