Unobserved heterogeneity across space, time, and crash type is often non-negligible in crash frequency modeling. When multiple crash types with spatial and temporal features are analyzed, multivariate spatio-temporal models should be considered. For this study, we analyzed the yearly county-level fatal, major injury, and minor injury crashes in Iowa from 2006 to 2015 using a multivariate spatio-temporal Bayesian model. The model adopted a multivariate spatial structure, a multivariate temporal structure, and a multivariate spatio-temporal interaction structure to account for possible correlations across injury severities over space, time, and spatio-temporal interaction, respectively. Income and weather indicators were found to have no significant effects on crash frequencies in the presence of vehicle miles traveled and unemployment rate. Both spatial and temporal effects were found to be important, and they played nearly the same roles for all three crash types in the studied dataset. Counties located in north and southwest Iowa were found to tend to have fewer crashes than the remaining counties. All three crash types generally showed descending trends from 2006 to 2015. They also had significantly positive correlations between each other in space but not in time. The crude crash rates and predicted crash rates were generally consistent for major injury and minor injury crashes but not for low-count fatal crashes. High-risk counties were identified using the posterior expected rank by the predicted crash cost rate, which was more able to truly represent the underlying traffic safety status than the rank by the crude crash cost rate.

在碰撞频率建模中，跨空间，时间和碰撞类型的未观察到的异质性通常不可忽略。当分析具有空间和时间特征的多种碰撞类型时，应考虑多元时空模型。在本研究中，我们使用多元时空贝叶斯模型分析了2006年至2015年爱荷华州的县级每年致命，重伤和轻伤事故。该模型采用了多元空间结构，多元时间结构和多元时空相互作用结构，以分别说明伤害严重度在空间，时间和时空相互作用上的可能相关性。人们发现，在有行驶的车辆行驶里程和失业率的情况下，收入和天气指标对撞车频率没有重大影响。发现空间和时间影响都很重要，并且在所研究的数据集中，这三种崩溃类型几乎都发挥了相同的作用。发现爱荷华州北部和西南部的县比其他县的坠毁事故少。从2006年到2015年，这三种碰撞类型总体上都呈下降趋势。它们在空间上但在时间上没有显着正相关。对于重伤和轻伤，粗事故率和预计的事故率通常是一致的，但对于低事故致命事故，则不然。高风险县使用预测的碰撞成本率的后验预期等级来识别，这比粗暴的碰撞成本率更能真实地表示潜在的交通安全状况。