Real-time crash risk estimation with autonomous vehicle data: a comparative analysis of extreme value models
Bibliographic record
Abstract
In the era of autonomous vehicles (AVs), accurately predicting extreme traffic conflicts is vital for improving road safety. This study leverages Extreme Value Theory (EVT) to analyse AV-generated conflict data, focusing on Modified Time-to-Collision (MTTC) and Post-Encroachment Time (PET) indicators. We compare univariate and bivariate EVT models using Peak-Over-Threshold (POT) and Block Maxima (BM) methods, addressing data’s spatiotemporal gaps. A novel model validation criterion is introduced, applicable across modeling approaches and sample sizes, independent of crash records. Results show bivariate POT models outperform univariate models by up to 20% lower Mean Absolute Error (MAE) and offer greater temporal stability. Univariate BM models are reliable only for short intervals (∼5–7 min), while POT models maintain or improve accuracy over time. Covariate selection significantly impacts model performance, varying by structure. Overall, bivariate POT models prove most effective, offering practical, adaptive tools for AV-based traffic conflict analysis in diverse urban environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".