Investigation of Different Geometric Representations of Road User Volume and Their Impact on Postencroachment Time
Bibliographic record
Abstract
Surrogate safety analysis refers to a large class of methods to evaluate safety at any site without waiting for collisions. A core requirement of any safety analysis method is the calculation of reliable safety indicators, which are mostly computed at a given time with the use of the positions and speeds of the two interacting road users. These positional data can be obtained automatically with video sensors and computer vision techniques. Therefore, automated safety analysis based on video data has gained the interest of safety analysts. However, automatic, video-based methods for surrogate safety analysis generally consider road users as points. Hence, the geometric representation of the road user volume (the outline) can affect the accuracy of the calculated indicators and consequently the safety evaluation. This study investigated geometric representations of road users for the automated computation of safety indicators. In particular, they were compared in the measurement of postencroachment time. This investigation relied on a real-world case study at two sites with two camera configurations, the first with an almost overhead view and the second with a much lower camera angle, which was more common in practice. The results show that representing the road user as a convex hull with extracted features is most accurate, with a root mean square error equal to 0.25 s for computation at the first site with an overhead view. With a lower camera angle, the advantage of that method disappeared and a simpler circular representation with a fixed radius showed the best results.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".