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Validating the bivariate extreme value modeling approach for road safety estimation with different traffic conflict
Lai Zheng1, Tarek Sayed2, Mohamed Essa2
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
|December 19, 2018
Summary
Combining traffic conflict indicators like time to collision (TTC) and post encroachment time (PET) with bivariate extreme value models improves road safety analysis and crash estimation accuracy.
Area of Science:
- Traffic Engineering
- Road Safety Analysis
- Statistical Modeling
Background:
- Various traffic conflict indicators exist, but their independent nature necessitates integration for comprehensive safety analysis.
- Reliable road safety estimation requires understanding the severity of traffic events through combined indicators.
Purpose of the Study:
- To propose and validate a bivariate extreme value model for integrating multiple traffic conflict indicators.
- To enhance the accuracy of road safety estimation by combining different conflict indicators.
Main Methods:
- Utilized computer vision to identify rear-end traffic conflicts at signalized intersections.
- Applied bivariate extreme value models to combinations of Time to Collision (TTC), Modified Time to Collision (MTTC), Post Encroachment Time (PET), and Deceleration to Avoid Crash (DRAC).
- Validated model-estimated crashes against actual crash data.
Main Results:
- Bivariate extreme value models demonstrated promising results, with most estimated crashes falling within the 95% Poisson confidence interval of observed crashes.
- The combination of TTC and PET yielded the most accurate crash estimations.
- Independent conflict indicators, when combined, showed improved crash estimation performance.
Conclusions:
- The bivariate extreme value model is an effective tool for integrating traffic conflict indicators for more reliable road safety estimation.
- Combining independent indicators, particularly TTC and PET, significantly enhances crash prediction accuracy.
- This integrated approach offers a more robust method for traffic safety analysis.
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