This study applies mixture components in a multivariate random parameters spatial model for zonal crash counts. Three different modeling formulations are employed to demonstrate the effects of mixture components and spatial heterogeneity in the goodness-of-fit in a multivariate random parameter model. The models are built for injury (i.e., possible, non-incapacitating, incapacitating, and fatal injury) and non-injury crashes using the data from 738 traffic analysis zones (TAZs) in Hillsborough County of Florida during a three-year period. The Deviance Information Criteria (DIC) is used to evaluate the performances of these models indicate the proposed model outperforms the rests. According to the estimated results, various traffic-related, demographics, and socioeconomic factors affect the occurrences of crashes for different severity levels. With regard to the effect of mixture components, it identifies two homogeneous sub-classes labeled as “stable pattern” and “unstable pattern” to better capture the heterogeneity. The standard deviation (SD) and correlation across injury and non-injury crashes are both very high in the “stable pattern” compared with its “unstable pattern” counterpart. On the other hand, the results of model comparison reveal that: (i) adding one more mixture component has no significant influences on the spatial heterogeneity and spatial correlation of different kinds of crash frequency and (ii) the consideration of spatial effects improves the accuracy of estimate results. Moreover, the multivariate random parameters spatial model with mixture components was compared with its univariate form to highlight the validity of applying multivariate structure.

这项研究将混合成分应用于多元随机参数空间模型中，用于区域碰撞计数。三种不同的建模公式用于证明多元随机参数模型中拟合优度的混合成分和空间异质性的影响。该模型使用三年内来自佛罗里达州希尔斯伯勒县的738个交通分析区（TAZ）的数据构建，用于伤害（即，可能的，非致残的，致残的和致命的伤害）和非伤害性碰撞。偏差信息标准（DIC）用于评估这些模型的性能，表明所提出的模型优于其他模型。根据估算结果，各种与交通有关的，人口统计学和社会经济因素都会影响不同严重程度的事故的发生。关于混合物成分的影响，它确定了标记为“稳定模式”和“不稳定模式”的两个同质子类，以更好地捕获异质性。与“不稳定模式”相比，“稳定模式”中的标准偏差（SD）以及跨伤害和非伤害碰撞的相关性都非常高。另一方面，模型比较的结果表明：（i）添加一个混合成分对不同种类的碰撞频率的空间异质性和空间相关性没有显着影响；（ii）考虑空间效应可提高准确性估计结果。此外，将具有混合成分的多元随机参数空间模型与其单变量形式进行了比较，以突出应用多元结构的有效性。