Design Criteria for Improving Safety Performance of Rural Intersections
Bibliographic record
Abstract
This paper, on the one hand, aims to identify significant crash risk factors at unsignalized three-leg intersections connecting rural two-lane two-way roads and minor roads with a STOP control on the approaches (3ST) and, on the other, to make adjustments to the Highway Safety Manual (HSM) procedure, fine-tuning its Safety Performance Function (SPF) based on observation of the local context. Over an 8-year period of study, a total of 240 crashes on 35 3ST intersections were observed, with no geometric-infrastructure adjustments or changes in the Annual Average Daily Traffic (AADT) and surrounding context noted at the intersections investigated. To obtain reliable results, the study period was divided into two groups: (a) 5 years to calibrate a new SPF, and (b) the remaining 3, not included in the first dataset, were used to validate the results. A negative binomial regression model was adopted to calibrate the new SPF. It was found that the AADT on the major and minor roads, the intersection skew angle, the co-occurrence of left and right-turn lanes on the major roads, and lighting seriously affect the crash scenario.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".