Short-term conflict-based crash risk forecasting: A Bayesian conditional peak-over-threshold approach
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Bibliographic record
Abstract
• Conditional peak-over-threshold models are introduced, with Bayesian inference for real-time crash risk forecasting. • Self-exciting and score-driven conditional peak-over-threshold specifications are demonstrated. • Score-driven peak-over-threshold models with Burr hazard function achieve superior crash risk forecasting performance. • Classical peak-over-threshold models underestimate crash risk by ignoring temporal dependence in extreme traffic conflicts. • The models enable uncertainty quantification in crash risk forecasting, supporting proactive safety interventions. Forecasting short-term crash risks is crucial for real-time road safety management, yet this research area remains largely underexplored. Classical Extreme Value Theory (EVT) models assume independent observations, limiting their ability to capture the clustering behavior in occurrence times and magnitudes of extreme traffic conflicts. To overcome this limitation, we introduce conditional peak-over-threshold (POT) models that incorporate time-varying parameters to simultaneously capture the dynamics of extreme traffic conflicts and enable forecasting for crash risk. Within the framework of marked point process (MPP) and EVT, we develop the conditional POT models based on two observation-driven approaches (self-exciting and score-driven) through Bayesian inference. A dynamic risk measure, Value-at-Risk (VaR), is employed to assess the performance of these conditional POT models for crash risk forecasting. Empirical analysis of rear-end conflict data collected from a signalized intersection across two separate days demonstrates that both self-exciting and score-driven POT models effectively characterize the clustering behavior of extreme traffic conflicts. Furthermore, backtesting confirms that conditional POT models provide more accurate crash risk forecasts than classical POT models, which tend to underestimate crash risk by ignoring temporal dependence in extreme traffic conflicts. Among the examined model specifications, score-driven POT models demonstrate superior forecasting performance. Our proposed Bayesian conditional POT approach provides probabilistic forecasting that enables direct uncertainty quantification and dynamic monitoring of crash risk, thereby supporting informed safety decisions.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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