In safety literature, there are two ways to incorporate the potential correlation between multiple crash frequency variables: (1) simulation-based approach and (2) analytical closed-form approach. The current research effort undertakes a comparison between simulation-based multivariate model and copula based closed-form approach to analyze zonal level crash counts for different crash types. Further, the research builds on earlier copula based models by incorporating random parameters thus proposing a hybrid (combination of analytical and simulation based system) approach to incorporating unobserved heterogeneity. Within the proposed hybrid copula model, the empirical analysis involves estimation of count models using four different copula structures which cover a wide range of dependency structures, including radial symmetry and asymmetry, and asymptotic tail independence and dependence. Further, to the best of authors’ knowledge, this study is the first of its kind to incorporate attribute variability (random parameters) effect within the copula framework. The empirical analysis is based on traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. A comprehensive set of exogenous variables including roadway, built environment, land-use, traffic, socio-demographic and spatial spillover characteristics are considered for the analysis. The resulting data fit and prediction performance offered by the proposed approach clearly highlights the hybrid model – Random Parameter Copula based approach’s superiority over the purely simulation-based multivariate model in our study context. The comparison exercise is further augmented by undertaking an in-depth comparison for different count events across different crash types and a correct classification analysis. The estimated results further reinforce the improved performance of the Random Parameter Copula-based multivariate approach. The applicability of the model for hot spot identification is illustrated by generating plots identifying high-crash and low- crash zones by crash type in the Central Florida region.

在安全文献中，有两种方法可以将多个碰撞频率变量之间的潜在相关性纳入考虑：（1）基于仿真的方法和（2）解析封闭式方法。当前的研究工作是在基于仿真的多元模型与基于copula的封闭形式方法之间进行比较，以分析不同碰撞类型的区域级别碰撞计数。此外，该研究建立在早期基于copula的模型上，方法是合并随机参数，从而提出了一种混合（基于分析和模拟的系统组合）方法来合并未观察到的异质性。在提出的混合语系模型中，经验分析涉及使用四种不同的语系结构估算计数模型，该四种语系结构涵盖了广泛的依存关系结构，包括径向对称性和非对称性，和渐近的尾巴独立性和依赖性。此外，据作者所知，该研究是将属性可变性（随机参数）效应纳入copula框架内的同类研究中的第一项。经验分析基于2016年佛罗里达州中部机动车和非机动车事故的交通分析区（TAZ）级事故计数数据。一组全面的外生变量，包括道路，建筑环境，土地使用，交通，分析时要考虑社会人口和空间溢出特征。所提出的方法提供的结果数据拟合和预测性能清楚地突出了混合模型-在我们的研究背景下，基于随机参数Copula的方法优于纯粹基于模拟的多元模型。通过对不同碰撞类型之间的不同计数事件进行深入比较并进行正确的分类分析，进一步增强了比较操作。估计的结果进一步增强了基于随机参数Copula的多元方法的改进性能。通过在佛罗里达州中部地区生成按碰撞类型识别高碰撞区和低碰撞区的图，说明了该模型在热点识别中的适用性。