Pedestrian Safety Prediction Methodology for Urban Signalized Intersections
Bibliographic record
Abstract
A coordinated effort is under way to develop a Highway Safety Manual (HSM) for use in making quantitative estimates of the safety performance of specific highway types and quantitative estimates of proposed improvements to specific highway types. The highway types being addressed in the first edition of HSM are rural two-lane highways, rural multilane highways, and urban and suburban arterials. Explicit consideration of pedestrian safety on urban and suburban arterials is considered critical to implementation of the first edition of HSM. The objective of the present research was to develop a methodology for quantifying the effects of existing site characteristics and proposed improvements on urban and suburban arterials on pedestrian safety. The pedestrian safety prediction methodology has been developed to function as a component of the overall safety prediction methodology for urban and suburban arterials proposed for the first edition of HSM. In the present research, a methodology for prediction of vehicle–pedestrian collisions at signalized intersections was developed. This methodology includes base models for three- and four-leg signalized intersections and several accident modification factors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".