Evaluating the Safety Risk of Narrow Medians Using Reliability Analysis
Bibliographic record
Abstract
In British Columbia, many highways are located in mountainous terrain and the costs of highway construction in these areas are high. One method of controlling construction costs is narrow medians but the safety consequences of using narrow medians were never determined. Recent research on safety in geometric design has focused on establishing quantitative relationships between collisions and cross-sectional elements using collision prediction models (CPMs) and collision modification factors (CMFs). In some situations, such as the use of narrow medians, it is difficult to find CPMs and CMFs that adequately describe the design scenario. In other instances, it is difficult to measure the safety in terms of collision reduction because of a lack of data or difficulty isolating the impact of a single design element on collision frequency. In these situations, reliability analysis can be used to evaluate the risk associated with a particular design feature. Reliability analysis is not intended as an alternative to quantify safety using collision frequency but represents a complementary method of measuring safety in terms of risk. In this paper, reliability analysis was completed on a series of horizontal curves with varying horizontal sight distance restrictions and the probability of being unable to stop within the available sight distance was calculated. The results of the study found that narrow medians combined with tight horizontal curves did not provide sufficient sight distance that vehicles would be expected to stop if an object was in a vehicle’s path. The analysis was applied to a case study of two constrained alignments in mountainous terrain of British Columbia.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".