Effect of geometric design consistency on road safety
Bibliographic record
Abstract
Geometric design consistency is emerging as an important rule in highway design. Identifying and treating any inconsistency on a highway can significantly improve its safety performance. Considerable research has been undertaken to explore this concept including identifying potential consistency measures and developing models to estimate them. However, little work has been carried out to quantify the safety benefits of geometric design consistency. The objectives of this study are to investigate and quantify the relationship between design consistency and road safety. A comprehensive accident and geometric design database of two-lane rural highways is used to investigate the effect of several design consistency measures on road safety. Several accident prediction models that incorporate design consistency measures are developed. The generalized linear regression approach is used for model development. The models can be used as a quantitative tool for the evaluation of the impact of design consistency on road safety. An application is presented where the ability of accident prediction models that incorporate design consistency measures is compared with those that rely on geometric design characteristics. It is found that models that explicitly consider design consistency may identify the inconsistencies more effectively and reflect the resulting impacts on safety more accurately than those that do not.Key words: geometric design consistency, road safety, quantification, accident prediction models.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".