Although spatial and temporal correlations of crash observations have been well addressed in the literature, the interactions between them are rarely studied. This study proposes a Bayesian spatiotemporal interaction (BSTI) approach for crash frequency modeling with an integrated nested Laplace approximation (INLA) method to greatly expedite the Bayesian estimation process. Manhattan, which is the most densely populated urban area of New York City, is selected as the study area. Hexagons are used as the basic geographic units to capture crash, transportation, land use, and demo-economic data from 2013 to 2019. A series of Bayesian models with various spatiotemporal specifications are developed and compared. The BSTI model with Type II interaction, which assumes that the structured temporal random effect interacts with the unstructured spatial random effect is found to outperform the others in terms of goodness-of-fit and the ability to reduce the dependency of residuals. It is also found that the unobserved heterogeneity is mostly attributed to the spatial effects instead of temporal effects. In addition, the BSTI Type II model also yields the lowest predictive error when the last year’s data are used as the test set. The proposed BSTI approach can potentially advance safety analytics by achieving high prediction accuracy and computational efficiency while maintaining its interpretability on the effects of contributing factors and the unobserved heterogeneity.

尽管在文献中已经很好地解决了碰撞观测的时空相关性，但很少研究它们之间的相互作用。这项研究提出了一种贝叶斯时空相互作用（BSTI）方法，该方法使用集成的嵌套拉普拉斯逼近（INLA）方法对碰撞频率进行建模，以大大加快贝叶斯估计过程。曼哈顿是纽约市人口最稠密的市区，被选为研究区域。六边形被用作捕获2013年至2019年崩溃，运输，土地使用和演示经济数据的基本地理单位。开发并比较了一系列具有各种时空规格的贝叶斯模型。具有II型互动的BSTI模型，假设结构化的时间随机效应与非结构化的空间随机效应相互作用，在拟合优度和减少残差依存性的能力方面优于其他结构。还发现未观察到的异质性主要归因于空间效应而不是时间效应。此外，当将去年的数据用作测试集时，BSTI II型模型的预测误差也最低。所提出的BSTI方法可以通过实现较高的预测准确性和计算效率来潜在地推进安全分析，同时保持其对影响因素和未观察到的异质性影响的可解释性。还发现未观察到的异质性主要归因于空间效应而不是时间效应。此外，当将去年的数据用作测试集时，BSTI II型模型的预测误差也最低。所提出的BSTI方法可以通过实现较高的预测准确性和计算效率来潜在地推进安全分析，同时保持其对影响因素和未观察到的异质性影响的可解释性。还发现未观察到的异质性主要归因于空间效应而不是时间效应。此外，当将去年的数据用作测试集时，BSTI II型模型的预测误差也最低。所提出的BSTI方法可以通过实现较高的预测准确性和计算效率来潜在地推进安全分析，同时保持其对影响因素和未观察到的异质性影响的可解释性。