Investigating the Heterogeneity of Postencroachment Time Thresholds Determined by Peak over Threshold Approach
Bibliographic record
Abstract
Inconsistency in the definition of conflict is a fundamental issue of the traffic conflict technique and raises questions about the validity of this technique. A significant aspect of inconsistency is that thresholds to distinguish traffic conflicts from normal events have not been clearly determined. The study presented in this paper proposed a peak-over-threshold (POT) approach to determine postencroachment time thresholds between traffic conflicts and normal events. The determined thresholds were evaluated by testing the correlation between the defined conflicts and observed crashes. A further regression analysis was conducted to explain heterogeneity of POT-determined thresholds. The results show that traffic conflicts defined by POT-determined thresholds have a relatively strong relationship with observed crashes, and the Pearson’s correlation coefficient is .66. Moreover, the threshold heterogeneity in this study mainly stems from the variety of exposure (i.e., traffic volume). This study also implies that the POT approach, which can account for possible heterogeneity in thresholds, is promising for improving the validity of the traffic conflict technique.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".