Exploring Evasive Action–Based Indicators for PTW Conflicts in Shared Traffic Facility Environments
Why this work is in the frame
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Bibliographic record
Abstract
Surrogate safety measures such as traffic conflicts are gaining more and more attention for traffic safety analysis. The traffic conflict technique evaluates the frequency and severity of traffic conflicts at a location typically using various time proximity indicators such as the time-to-collision (TTC) and post-encroachment time (PET). However, growing concerns have been raised that time proximity indicators may not be effective measures for measuring conflict severity in less-organized traffic environments. In such environments, mixed road users are likely to share small spaces and take evasive action to prevent conflicts or collisions. The objective of this study was to examine and compare the time proximity (TTC) indicator and evasive action-based (yaw rate and jerk) indicators for evaluating the severity of powered two-wheeler (PTW) conflicts. PTW usage is growing in many developing countries such as China, and there has been concern about their impact on safety. Video data were collected at a middle block shared traffic street in Kunming, China. Traffic conflict analysis was conducted using automated video-based computer vision techniques. Ordered-response models were used to relate the conflict indicators to safety experts’ evaluation of conflict severity. A random effect model was developed to account for the unobserved heterogeneity that affects conflict severity. As well, a random intercept model was developed to assess the effect of incorporating the variation in each expert evaluation. The results showed that the yaw rate ratio was efficient in measuring conflict severity for electric (e)-scooters, motorcycles, and bicycles. The TTC was an efficient indicator in measuring conflict severity for e-bikes and bicycles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it