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.

最近的研究表明，单个冲突指标仅代表交通互动严重程度的一部分。这样，在统一模型中集成多个冲突指标可以改善基于冲突的崩溃估计。本研究开发了一种多元贝叶斯分层高斯copula建模方法，该方法包括多元高斯copula和贝叶斯分层结构。前者概括了极值边际，并捕获了多个冲突指标之间的多变量依赖性，而后者则结合了来自不同站点的交通冲突，并结合了一些协变量和特定于站点的未观察到的异质性。copula方法为建模多元结构提供了灵活性，包括从单变量分布的各种参数族中选择边距以及满足不同类型依存结构的参数copula的构造。提出了用于多元贝叶斯分层高斯copula模型的模型估计方法，并将其用于估计不列颠哥伦比亚省萨里市的四个信号交叉口的追尾事故。修改后的碰撞时间（MTTC），侵入后时间（PET）和避免碰撞的减速率（DRAC）被用作冲突指标。考虑到交通极端情况下的非平稳性，包括交通量，冲击波面积和排比的三个协变量。结果表明，在碰撞估计准确性方面，