A Surrogate Safety Analysis at Protected Freeway Ramps Using Cross- Sectional and Before-After Video Data
Bibliographic record
Abstract
This study presents a surrogate approach for safety analysis of freeway facilities using automated trajectory collection and behavioral analysis from surrogate measures of safety (in particular time to collision). This methodology is proposed as a potential alternative or complement to the classical approach based on historical accident data, particularly suited for evaluating the microscopic safety effects of road treatments for which there is a lack of traffic and accident data. A short theoretical discussion of traffic conflicts is followed by a proposed methodology illustrated using as a small sample of freeway ramps as an application environment. From this sample, video data is obtained as part of a safety study to investigate the effectiveness of the “one-way lane-change ban” treatment near urban freeway ramps in Montreal, Canada. To illustrate the applicability of our methodology, two comparative examples are presented: (1) a cross-sectional study and (2) a before-after study involving two sites, one of which had video data available before and after the implementation of the treatment. Various methods of aggregating the data, spatially and temporally, are explored in the applications.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".