Feasibility of Computer Vision-Based Safety Evaluations
Bibliographic record
Abstract
Traditional road safety analysis has often been undertaken with historical collision records. However, limitations on the quality and completeness of collision data gave rise to surrogate ways of measuring safety, especially the traffic conflict technique. Traditionally, traffic conflict techniques have relied on field observations, which have some reliability and repeatability problems. Therefore, successfully automating conflict detection with data extracted from video sensors could have considerable benefits for traffic safety studies. Before-and-after safety evaluations could greatly benefit from automated analysis of traffic conflicts, and the main objective of this paper is to demonstrate the use of this analysis technique for such evaluations. A right-turn safety improvement was implemented at an intersection in Edmonton, Alberta, Canada, in 2009 to mitigate the high rate of rear-end and merging collisions. The right-turn ramp was closed, and all right-turning vehicles were brought to the right-turn lane at the intersection, where a “No-Right-Turn-on-Red” sign was installed. In this study, video sensors were the primary source of conflict data. The video data were analyzed and traffic conflicts were measured with an automated traffic safety tool. The distributions of the calculated conflict indicators before and after the treatment showed a considerable reduction in the frequency and severity of traffic conflicts. This result suggests significant positive changes in rear-end, merging, and total conflicts. The results of this study show the potential benefit of adopting automated conflict analysis for before-and-after safety studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".