Unobserved heterogeneity produced by spatial and temporal correlations of crashes often needs to be captured in crash frequency modeling. Although many studies have included either spatial or temporal effects in crash frequency modeling, only a limited number of studies have considered both. This study addresses the limitations of existing studies by exploring multiple models that best fit the spatial and temporal correlations. In this study, we used Bayesian spatio-temporal models to investigate regional crash frequency trends, and explored the effects of omitting spatial or temporal trends in spatio-temporal correlated data. The fast Bayesian inference approach, integrated nested Laplace approximation, was used to estimate parameters. It was found that fatal crashes showed decreasing trends in all Iowa counties from 2006 to 2015, but the decreasing rates varied by counties. Among all the covariates investigated, only vehicle miles traveled (VMT) was significant. None of the socio-economic or weather indicators were found to be significant in the presence of VMT. Both spatial and temporal effects were found to be important, and they were responsible for both over dispersion and zero inflation in the crash data. In addition, spatial effects played a more important role than did temporal effects in the studied dataset, but temporal component selection was still important in spatio-temporal modeling.

碰撞频率和时间相关性所产生的未观察到的异质性经常需要在碰撞频率建模中捕获。尽管许多研究都在碰撞频率建模中包括了空间或时间影响，但只有少数研究同时考虑了这两种情况。本研究通过探索最适合空间和时间相关性的多个模型来解决现有研究的局限性。在这项研究中，我们使用贝叶斯时空模型调查区域碰撞频率趋势，并探讨了在时空相关数据中省略时空趋势的影响。快速贝叶斯推理方法，集成嵌套拉普拉斯近似，用于估计参数。研究发现，从2006年到2015年，致命的坠机事故在所有爱荷华州县均呈下降趋势，但下降率因县而异。在所有调查的协变量中，仅行驶的车辆英里数（VMT）是重要的。发现存在VMT时，没有任何社会经济或天气指标是重要的。发现空间和时间上的影响都是重要的，并且它们对碰撞数据中的过度分散和零膨胀都负有责任。此外，在所研究的数据集中，空间效应比时间效应起着更重要的作用，但是时间分量的选择在时空建模中仍然很重要。他们负责崩溃数据中的过度分散和零通胀。此外，在所研究的数据集中，空间效应比时间效应起着更重要的作用，但是时间分量的选择在时空建模中仍然很重要。他们负责崩溃数据中的过度分散和零通胀。此外，在所研究的数据集中，空间效应比时间效应起着更重要的作用，但是时间分量的选择在时空建模中仍然很重要。