Lee Jaeyoung, Abdel-Aty Mohamed, Jiang Ximiao
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States.
Accid Anal Prev. 2015 May;78:146-154. doi: 10.1016/j.aap.2015.03.003. Epub 2015 Mar 16.
Macroscopic traffic crash analyses have been conducted to incorporate traffic safety into long-term transportation planning. This study aims at developing a multivariate Poisson lognormal conditional autoregressive model at the macroscopic level for crashes by different transportation modes such as motor vehicle, bicycle, and pedestrian crashes. Many previous studies have shown the presence of common unobserved factors across different crash types. Thus, it was expected that adopting multivariate model structure would show a better modeling performance since it can capture shared unobserved features across various types. The multivariate model and univariate model were estimated based on traffic analysis zones (TAZs) and compared. It was found that the multivariate model significantly outperforms the univariate model. It is expected that the findings from this study can contribute to more reliable traffic crash modeling, especially when focusing on different modes. Also, variables that are found significant for each mode can be used to guide traffic safety policy decision makers to allocate resources more efficiently for the zones with higher risk of a particular transportation mode.
已经进行了宏观交通碰撞分析，以便将交通安全纳入长期交通规划。本研究旨在开发一种宏观层面的多元泊松对数正态条件自回归模型，用于分析不同交通方式（如机动车、自行车和行人碰撞）的碰撞事故。许多先前的研究表明，不同碰撞类型之间存在共同的未观察到的因素。因此，预计采用多元模型结构将显示出更好的建模性能，因为它可以捕捉各种类型之间共享的未观察到的特征。基于交通分析区（TAZ）对多元模型和单变量模型进行了估计并进行了比较。结果发现，多元模型明显优于单变量模型。预计本研究的结果有助于建立更可靠的交通碰撞模型，特别是在关注不同交通方式时。此外，发现对每种交通方式有显著影响的变量可用于指导交通安全政策制定者更有效地为特定交通方式风险较高的区域分配资源。
