Department of Civil and Environmental Engineering, National University of Singapore, 117576, Singapore.
Department of Civil & Environmental Engineering, Old Dominion University, Norfolk, VA 23529, USA.
Accid Anal Prev. 2021 Apr;153:106018. doi: 10.1016/j.aap.2021.106018. Epub 2021 Feb 18.
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.
尽管文献中已经很好地解决了碰撞观测的空间和时间相关性问题，但它们之间的相互作用却很少被研究。本研究提出了一种贝叶斯时空交互（BSTI）方法，用于进行碰撞频率建模，并采用集成嵌套拉普拉斯近似（INLA）方法极大地加快了贝叶斯估计过程。选择曼哈顿作为研究区域，曼哈顿是纽约市人口最密集的城区。采用六边形作为基本地理单位，从 2013 年到 2019 年捕获碰撞、交通、土地利用和人口经济数据。开发并比较了一系列具有不同时空规范的贝叶斯模型。具有 II 型交互作用的 BSTI 模型，假设结构化时间随机效应与非结构化空间随机效应相互作用，在拟合优度和减少残差依赖性方面表现优于其他模型。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，当使用最后一年的数据作为测试集时，BSTI II 型模型的预测误差也最低。所提出的 BSTI 方法可以通过实现高精度预测和计算效率，同时保持对影响因素和未观察到的异质性的解释能力，从而为安全分析提供帮助。
