From univariate to bivariate extreme value models: Approaches to integrate traffic conflict indicators for crash estimation

This study develops bivariate extreme value models to integrate traffic conflict indicators for crash estimation. Based on video data collected from four sites of two signalized intersections, the automated traffic conflict analysis system was used to extract the time to collision (TTC) and post-encroachment time (PET) between left-turn vehicles and through vehicles. Bivariate Generalized Extreme Value (BGEV) and Bivariate Generalized Pareto (BGP) models that jointly used the two conflict indicators were then developed, and the number of crashes were derived from the estimated model parameters. Univariate Generalized Extreme Value (UGEV) and Univariate Generalized Pareto (UGP) models were also applied using individual conflict indicators. The developed bivariate models and univariate models were evaluated by comparing model estimated crashes to observed left-turn crashes. The results show that the BGP model performed the best, followed by the BGEV model, UGP model, and UGEV model. It is found that crash estimates from univariate models based on PET and TTC are underestimated and overestimated, respectively. The bivariate models integrating the two indicators improve the crash estimation accuracy and precision. The BGP model outperforming the BGEV model is likely due to the former allowing more efficient use of the collected data. Overall, it is concluded that the bivariate extreme value modeling approach that is capable of integrating traffic conflict indicators with clear boundaries between traffic conflicts and crashes is more promising for crash estimation. Moreover, with the emerging automated traffic conflict analysis and connected vehicle techniques that facilitate the indicators extraction, the bivariate approach can be readily applied to provide accurate crash estimations for proactive road safety analysis.


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  • Accession Number: 01704982
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 24 2019 3:03PM