Unobserved heterogeneity, which has been recognized as a critical issue in crash frequency modelling, generates from multiple sources, including observable and unobservable factors, space and time instability, crash severities, etc. However, only a very limited body of research is dedicated to distinguish and simultaneously address all these sources of unobserved heterogeneity. In this study, hierarchical Bayesian random parameters models with various spatiotemporal interactions are developed to address this issue. Selected for analysis are the yearly county-level alcohol/drug impaired-driving related crash counts data of three different injury severities including minor injury, major injury, and fatal injury in Idaho from 2010 to 2015. The variables, including daily vehicle miles traveled (DVMT), the proportion of male (MALE), unemployment rate (UR), and the percentage of drivers of 25 years and older with a bachelor's degree or higher (BD), are found to have significant impacts on crash frequency and be normally distributed in certain crash severities. Significant temporal and spatial heterogenous effects are also detected in all three crash severities. These empirical results support the incorporation of temporal and spatial heterogeneity in random parameters models.

未观察到的异质性已被认为是碰撞频率建模中的一个关键问题，它是由多种来源产生的，包括可观察和不可观察的因素，时空不稳定性，碰撞严重性等。但是，只有非常有限的研究机构专门用于区分同时解决所有这些未观察到的异质性来源。在这项研究中，具有各种时空相互作用的分层贝叶斯随机参数模型被开发来解决这个问题。选择进行分析的是爱达荷州从2010年到2015年的县级酒精/药物驾驶不便相关的年度撞车计数数据，其中包括轻伤，重伤和致命伤的三种不同损伤严重程度。这些变量包括每日行驶的车辆行驶里程（ DVMT），男性比例（MALE），失业率（UR）以及25岁及以上学士学位或更高学历的驾驶员（BD）的百分比被发现会对撞车频率产生重大影响，并且在某些撞车严重程度中呈正态分布。在所有三种碰撞严重性中也检测到明显的时间和空间异质性影响。这些经验结果支持将时空异质性纳入随机参数模型中。