Automated measuring of cyclist – motor vehicle post encroachment time at signalized intersections
Bibliographic record
Abstract
Conflicts between motor vehicle and cyclists at a signalized intersection were characterized in this study using an objective conflict indicator; post encroachment time (PET). A total of 384 conflict events for PET (0, 3] seconds between cyclists and vehicles were analyzed in this study. An automated video analysis technique was developed to measure the PET between cyclists and motor vehicles. The results of the conflict analysis showed that the average absolute error of PET between the frame count measurement (MFCM) and automated measurement (AM) methods was 0.12 s and the standard deviation was 0.10 s. The evaluation result showed that the coefficient of determination between the AM and MFCM methods was found to be 0.938 and there was a very good agreement in the PET classification of individual conflicts between the MFCM and AM methods. This study includes procedures to better interpret the conflict point of the motor vehicle and the cyclist in an automated manner (based on the geometry of the bounding box and direction of the travel), which appears to be a contribution for the analysis of cyclist – motor vehicle collisions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".