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A full Bayes approach for traffic conflict-based before-after safety evaluation using extreme value theory
1School of Transportation Science and Engineering, Harbin Institute of Technology, China; Department of Civil Engineering, The University of British Columbia, Canada.
Accident; Analysis and Prevention
|July 29, 2019
Summary
A new Bayesian method using extreme value theory improves traffic safety analysis. Left-turn bay extensions significantly reduce crash risk, demonstrating effective safety improvements.
Area of Science:
- Traffic safety engineering
- Statistical modeling
- Extreme value theory
Background:
- Traditional before-after safety evaluations face challenges with data heterogeneity and limited statistical power.
- Traffic conflicts offer a surrogate measure for crash prediction, enabling proactive safety assessments.
Purpose of the Study:
- To develop and apply a robust Bayesian approach for traffic safety evaluation using extreme value theory and traffic conflicts.
- To quantitatively assess the safety benefits of a left-turn bay extension project.
Main Methods:
- A full Bayesian approach integrating traffic conflicts from multiple sites and periods.
- Development of a uniform generalized extreme value (GEV) model with a hierarchical structure to incorporate covariates and account for site heterogeneity.
- Application to a left-turn bay extension project in Surrey, Canada, comparing treatment and control sites before and after the intervention.
Main Results:
- The proposed model quantitatively estimated a significant reduction in expected crashes, with an odds ratio (OR) of 0.409.
- Qualitative analysis revealed shifts in the generalized extreme value (GEV) distributions, indicating a reduced risk of crashes post-treatment.
Conclusions:
- The Bayesian extreme value theory approach provides a powerful framework for traffic safety evaluations.
- Left-turn bay extensions demonstrate significant safety improvements, reducing crash likelihood and risk exposure.
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