Critical conflict probability: A novel risk measure for quantifying intensity of crash risk at unsignalized intersections
Bibliographic record
Abstract
A significant number of traffic crashes are reported at unsignalized intersections. However, in developing countries, challenges such as underreporting and limited crash data hinder the direct correlation of traffic conflicts with reported crashes for effective safety analysis. To address this, the study introduces Critical Conflict Probability (CCP) as a novel metric to quantify the intensity of conflict risk at unsignalized intersections. Higher CCP values indicate a greater likelihood of crash risk. CCP is derived from Post-Encroachment Time (PET) using the Generalized Extreme Value (GEV)-based extreme value theory (EVT) modeling framework. The CCP values are modeled as a function of traffic flow and driving behavior variables using three approaches: fixed parameters, random intercept, and grouped random parameters Beta regression models. The results revealed grouped random parameters Beta regression model as the best fit, highlighting the importance of accounting for spatial unobserved heterogeneity. As a practical outcome, the study develops a CCP-based intersection prioritization framework to rank and identify critical intersections within a traffic network, enabling traffic planners to improve safety management in data-scarce environments.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".