Comprehensive Safety Diagnosis Using Automated Video Analysis: Applications to an Urban Intersection in Edmonton, Alberta, Canada
Bibliographic record
Abstract
This study conducted an automated safety diagnosis for a major signalized intersection in the city of Edmonton, Alberta, Canada. The study was motivated by concerns raised about the potential safety implications of increasing the speed limit across the major road corridor of the intersection to 60 km/h from the current posted speed of 50 km/h. The diagnosis was performed with video data collected at the intersection for two consecutive days. Traffic conflicts at the location were identified, analyzed, and categorized according to such criteria as severity and road user type. Temporal and spatial violations were automatically identified. Automated data collection for traffic counts and travel speeds was performed and validated. It was observed that the high frequency of conflicts between vehicles and vulnerable road users (i.e., pedestrians and cyclists) and the presence of heavy vehicles could lead to more severe conflicts and possible collisions if the speed limit is raised. According to the outcome of the performed analysis, it was recommended that the existing speed limit not be raised, but kept at the current level. Several safety countermeasures were suggested to improve the safety at the intersection. This study demonstrated the practical application of automated traffic conflict analysis technology and its ability to help traffic engineers conduct a comprehensive safety assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".