Real‐Time Safety Evaluation at Signalized Intersections: Hierarchical Bayesian Extreme Value Theory Models Based on Different Conflict Types
Bibliographic record
Abstract
Real‐time safety evaluation of urban signalized intersections is a prerequisite for proactive traffic safety management. Due to its independence from historical data, the traffic conflict technique has gained increasing popularity as a tool for real‐time safety evaluation in transportation systems. However, different types of conflicts (e.g., rear‐end and side‐impact conflicts) may lead to differences in safety evaluation, which has led previous studies to typically analyze crash risk based on various conflict types separately. Therefore, this study develops the hierarchical Bayesian extreme value theory (block maxima (BM)) model based on different conflict types to form a novel real‐time safety evaluation approach. The proposed model is applied to the real‐time safety evaluation of five signalized intersections in Harbin, China. The results show that (1) the proposed BM model exhibits good prediction performance when there is sufficient observation duration and a sufficient number of samples; (2) the proposed model is superior to other baseline models developed based on only one conflict type in terms of prediction accuracy. The empirical findings of this study establish innovative frameworks and theoretical foundations for advancing proactive safety protocols and autonomous mobility systems.
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How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".