A NEW PROCEDURE FOR EVALUATING TRAFFIC SAFETY ON TWO-LANE RURAL ROADS
Bibliographic record
Abstract
This paper is based on research of the authors emphasizing traffic safety and highway geometric design, which has led to the development of three quantitative safety criteria for distinguishing sound and design practices on both planned and existing two-lane roadway sections. The safety criteria are directed toward the achievement of (1) design consistency (Safety Criterion I), (2) operating speed consistency (Safety Criterion II), and (3) driving dynamic consistency (Safety Criterion III) in highway design. All three criteria are evaluated in terms of three ranges, described as Good, Fair and Poor. Cut-off values between the three ranges are developed. Furthermore, it is dealt with the issues: design speed, operating speed, and sound friction factors. A comparative analysis of the actual accident situation with the results of the Safety Criteria reveals a convincing agreement. It is known, that signs and markings can improve the safety record of a road section. However, the improvement is seldom substantial and certainly not to the level of transforming a poor design to a good design. The developed safety evaluation process is meeting with acceptance in the professional highway engineering community. It has been adopted or referenced in their geometric design guidelines by several Roads Agencies internationally including those in Canada, Greece, Hungary, Italy, Japan, South Africa, and partially in the United States. It is thus reasonable to suggest that the methodology has gained international acceptance. Thirty case studies were analyzed. The results confirm that the classification system agrees well with the outcome of large accident databases.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".