Application of Extreme Value Theory for Before-After Road Safety Analysis
Bibliographic record
Abstract
Because of well-recognized quality and quantity problems associated with historical crash data, traffic conflict techniques have been increasingly used in before-after safety analysis in recent years. This study proposes using an extreme value theory (EVT) approach to conduct traffic conflict-based before-after analysis. The capability of providing confident estimation of extreme events by the EVT approach drives the before-after analysis to shift from normal traffic conflicts to more serious conflicts, which are relatively rare but have more in common with actual crashes. The approach is applied to evaluate the safety effects of converting channelized right-turn lanes into smart channels, based on traffic conflicts defined by time to collision (TTC) and collected from three treatment intersections and one control intersection in the city of Penticton, British Columbia. Odds ratios and treatment effects are calculated from extreme-serious conflicts, the frequencies of which are estimated from the generalized Pareto distributions of traffic conflicts with TTC⩽0.5 s. The results show approximately 34% reduction in total extreme-serious conflicts (i.e., combining merging conflicts and rear-end conflicts), indicating overall a remarkable safety improvement following the smart channel treatment. This finding is consistent with the analysis result based on traffic conflicts with TTC⩽3.0 s. It is also found that the reduction in extreme-serious merging conflicts is small and insignificant. This is caused by the phenomenon that TTC values of merging conflicts become smaller after the treatment, and it is possibly because drivers become more aggressive with the better view of approaching cross-street traffic provided by the smart channel.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".