Automated Analysis of Pedestrians’ Nonconforming Behavior and Data Collection at an Urban Crossing
Bibliographic record
Abstract
This paper addresses the automated analysis of pedestrians’ conformance behavior and data collection in an intersection with a perceived high rate of traffic conflicts involving pedestrians. The intersection is located in the Downtown Eastside of Vancouver, British Columbia, Canada. Despite implementation of countermeasures such as reducing the speed limit, safety issues linger at the intersection. The intersection is characterized by a high pedestrian volume and considerable crossing violations by pedestrians, which elevate the safety risk and disrupt vehicle traffic flow. A challenge in performing pedestrian road safety analysis is the shortfall of reliable data. Recent advances in automated detection of pedestrians through the use of computer vision expanded the range of applications in traffic safety. In this study, an automated system for identifying pedestrian crossing nonconformance to traffic regulations by using pattern matching was developed and tested. The results show satisfactory accuracy in detecting both spatial and temporal violations, with the detection rate for violations being more than 84% correct. The automated collection of pedestrian data on crossing speed and counts is also demonstrated with high accuracy. The availability of these data is important for diagnosing the safety issues at the intersection and for justifying capital for implementing pedestrian safety countermeasures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".