Bibliographic record
Abstract
The main objective of this study was to calculate the required sight distance corresponding to the various probabilities of failure by considering two methods of reliability analysis. This paper presents a probabilistic approach based on such random variables as major road vehicle speed, walking speed, pedestrian observation-reaction time, the length of the crossing unit, pedestrian setback from the nearest curb of the major road. A safety margin is defined as the difference between available and required sight distances. By using the first-order second moment (FOSM) method, relationships for the mean and standard deviation of the safety margin were developed. The advance first-order second-moment (AFOSM) was also used to find the supplied sight distance corresponding to reliability index. Comparison of two methods was done. Obtained results from the two methods were almost similar to a low coefficient of variation. Different design graphs were developed to calculate the required sight distance at a different coefficient of variation corresponding to the probability of failure and different vehicle design. Sensitivity analysis was performed to obtain the most sensitive variable to the pedestrian crossing sight distance. It was found that vehicle speed is more sensitive to required sight distance and perception-reaction time has least effect on supplied (required) sight distance. Application of these methods is presented with two examples. This probabilistic method is valuable in designing pedestrian crossing sight distance for any preferred reliability level.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".