A space-time multivariate Bayesian model to analyse road traffic accidents by severity
A space-time multivariate Bayesian model to analyse road traffic accidents by severity
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DOI：
10.1111/rssa.12178
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发表时间：
2017-01-01
影响因子：
2
通讯作者：
Blangiardo, Marta
中科院分区：
文献类型：
--
作者：
Boulieri, Areti;Liverani, Silvia;Blangiardo, Marta
本文研究了道路交通事故严重程度之间的依赖关系，同时考虑了空间和时间的相关性。该研究分析了2005-2013年英格兰各区的道路交通事故数据。我们在我们的模型中包括多元空间结构化和非结构化效应，以在贝叶斯层次公式中捕获严重性之间的依赖关系。我们还包括一个时间分量来捕捉时间效应，并进行了广泛的模型比较。结果表明，严重程度之间的空间结构效应和非结构效应具有重要的相关性，并且严重程度的高低在时间上呈下降趋势。后验事故率图显示，在大城市发生低严重性事故的风险较高，而在英格兰北部和南部海岸的郊区发生高严重性事故的风险较高。从公共卫生的角度来看，极端率的后验概率用于提示热点的存在。
The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic accidents data at ward level in England over the period 2005-2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective.