Modified Geometric Design Consistency Criteria for Two-Lane Rural Highways Based On Crash Severity
Bibliographic record
Abstract
The present study focuses on analyzing geometric design consistency (GDC) and modifying GDC criteria and correlating them to the field-reported crash severity, specifically for heterogeneous traffic conditions on two-lane rural highways. Two National Highway (NH) sites—NH-953 (S-1, hilly terrain) and NH-56 (S-2, rolling terrain) with varying roadway conditions—were selected to achieve the study objectives. The vehicle speed data for cars and heavy commercial vehicles (HCVs) were collected using the performance box (P-Box) for the 57 curves. For the selected study curves, firstly, the GDC was analyzed using the available design consistency criteria as the difference between operating and design speed. The results indicate that the proportion of good, fair, and poor GDC for the S-1 study section are 45%, 35%, and 20% and for the S-2 study section, they are 80%, 20%, and 0%, respectively. In addition, in the S-1 study section, the vehicles (64% of cars and 40% of HCVs) and the S-2 study section (65% of cars and 0% of HCVs) moved higher than the design speed. Afterward, some modifications are made to the available design consistency criteria to establish an accurate relationship between GDC results and crash severity. The modified results revealed that fatal, grievous, and minor-injury crashes correlate well with poor, fair, and good design consistency criteria. The study results would be helpful for highway authorities to evaluate a highway’s alignment consistency for its operational and safety aspects, thereby aiding some surrogate safety measures to reduce the crash probability.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".