Automated Analysis of Pedestrian–Vehicle Conflicts
Bibliographic record
Abstract
This paper presents a novel application of automated video analysis for a before-and-after (BA) safety evaluation of a scramble phase treatment. Data availability has been a common challenge to pedestrian studies, especially for proactive safety analysis. The traditional reliance on collision data has many shortcomings because of the quality and quantity of collision records. Qualitative and quantitative issues with road collision data are more pronounced in pedestrian safety studies. In addition, little information on the mechanism of action implicated can be drawn from collision reports. Traffic conflict techniques have been advocated as supplements or alternatives to collision-based safety analysis. Automated conflict analysis has been advocated as a new safety analysis paradigm that empowers the drawbacks of survey-based and observer-based traffic conflict analysis. One of the areas of focus of pedestrian safety that could greatly benefit from vision-based road user tracking is BA evaluation of safety treatments. This paper demonstrates the feasibility of conducting a BA analysis with video data collected from a commercial-grade camera in Chinatown, Oakland, California. Video sequences for a period of 2 h before and 2 h after scramble were automatically analyzed. The BA results of the automated analysis exhibit a declining pattern of conflict frequency, a reduction in the spatial density of conflicts, and a shift in the spatial distribution of conflicts farther from crosswalks.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".