Department of Civil and Natural Resources Engineering, University of Canterbury, 20 Kirkwood Ave, Christchurch, 8041, New Zealand.
Department of Civil & Urban Engineering, Center for Urban Science and Progress (CUSP), C2SMART Center, New York University (NYU), 6 MetroTech Center, 4th Floor, Brooklyn, NY, 11201, USA.
Accid Anal Prev. 2019 Jan;122:189-198. doi: 10.1016/j.aap.2018.10.009. Epub 2018 Oct 31.
Conventional safety models rely on the assumption of independence of crash data, which is frequently violated. This study develops a novel multivariate conditional autoregressive (MVCAR) model to account for the spatial autocorrelation of neighboring sites and the inherent correlation across different crash types. Manhattan, which is the most densely populated urban area of New York City, is used as the study area. Census tracts are used as the basic geographic units to capture crash, transportation, land use, and demo-economic data. The specification of the proposed multivariate model allows for jointly modeling counts of various crash types that are classified according to injury severity. Results of Moran's I tests show the ability of the MVCAR model to capture the multivariate spatial autocorrelation among different crash types. The MVCAR model is found to outperform the others by presenting the lowest deviance information criterion (DIC) value. It is also found that the unobserved heterogeneity was mostly attributed to spatial factors instead of non-spatial ones and there is a strong shared geographical pattern of risk among different crash types.
传统的安全模型依赖于崩溃数据独立性的假设，但这种假设经常被违反。本研究开发了一种新的多元条件自回归（MVCAR）模型，以考虑相邻地点的空间自相关和不同崩溃类型之间的固有相关性。曼哈顿是纽约市人口最稠密的城区，被用作研究区域。人口普查区被用作基本地理单位，以捕获崩溃、交通、土地利用和人口经济数据。所提出的多元模型的规范允许联合建模各种根据伤害严重程度分类的崩溃类型的计数。Moran's I 检验的结果表明，MVCAR 模型能够捕捉不同崩溃类型之间的多元空间自相关。通过呈现最低的偏差信息准则（DIC）值，MVCAR 模型被发现优于其他模型。还发现，未观察到的异质性主要归因于空间因素而不是非空间因素，并且不同崩溃类型之间存在强烈的共享地理风险模式。
