Abstract
Recent studies have demonstrated that single conflict indicators represent only a fractional aspect of the severity of a traffic interaction. As such, integrating several conflict indicators in a unified model can improve conflict-based crash estimation. This study develops a multivariate Bayesian hierarchical Gaussian copula modeling approach, which comprises a multivariate Gaussian copula and a Bayesian hierarchical structure. The former has generalized extreme value marginals and captures multivariate dependence among several conflict indicators, while the latter combines traffic conflicts from different sites, incorporating several covariates and site-specific unobserved heterogeneity. The copula approach offers the flexibility in modeling multivariate structures including the selection of margins from various parametric families of univariate distribution and the construction of parametric copulas which satisfies different types of dependence structure. A model estimation approach for the multivariate Bayesian hierarchical Gaussian copula model is proposed and applied to estimate rear-end crashes from four signalized intersections in the city of Surrey, British Columbia. The modified time to collision (MTTC), post encroachment time (PET), and deceleration rate to avoid a crash (DRAC) were employed as conflict indicators. Three covariates including traffic volume, shock wave area, and platoon ratio were considered to account for non-stationarity in conflict extremes. The results show that in terms of crash estimation accuracy, the multivariate Bayesian hierarchical Gaussian copula model outperforms both the bivariate Bayesian hierarchical Gaussian copula models and the recently proposed multivariate Bayesian hierarchical model in which multiple traffic conflict indicators were combined by one dependence parameter.
Published Version
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