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首页> 外文期刊>Accident Analysis & Prevention >Incorporating spatial effects into temporal dynamic of road traffic fatality risks: A case study on 48 lower states of the United States, 1975-2015
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Incorporating spatial effects into temporal dynamic of road traffic fatality risks: A case study on 48 lower states of the United States, 1975-2015

机译：将空间效应纳入道路交通事故死亡风险的时间动态：以美国48个州为例，1975-2015年

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摘要

The rate of road traffic fatalities has long served as a regular indicator to evaluate and compare road safety performance for different administrative divisions. This article introduces a novel method known as the Markov chain spatial model to incorporate the spatial effects into the temporal dynamic of the fatality rates. Compared to the traditional Markov chain model, the proposed spatial Markov chain model can quantify the influence of neighboring sites explicitly in the transition process. A case study using a long duration dataset, from 1975 to 2015 in the 48 lower states of the United Sates, was conducted to illustrate the proposed model. The fatality rates were measured as the number of traffic fatalities per 100 million vehicle miles or per 10,000 residents. The results show that the probability of transition for one state between different levels of traffic fatality risks depends largely on the context of its surrounding neighbors. Another important finding is that relative to the estimates of traditional Markov chain models, states surrounded by neighborhoods with relatively low fatality rates take a longer time to transform to a higher level of fatality risk in the spatial Markov chain model. On the other hand, those with high-risk neighborhoods takes less time to deteriorate. These findings confirm that it is imperative to incorporate spatial effects when modeling the temporal dynamic of safety indicators to assess and monitor the safety trends in the areas of interest.
机译：长期以来，道路交通事故死亡率一直是评估和比较不同行政区划道路安全绩效的常规指标。本文介绍一种称为马尔可夫链空间模型的新颖方法，以将空间效应纳入死亡率的时间动态。与传统的马尔可夫链模型相比，提出的空间马尔可夫链模型可以在过渡过程中明确量化邻近站点的影响。进行了使用长期数据集（从1975年到2015年）在美国48个低州的案例研究，以说明该模型。死亡率是按照每1亿车辆行驶里程或每10,000居民的交通死亡人数计算的。结果表明，一个州在不同级别的交通事故死亡风险之间进行转换的可能性很大程度上取决于其周围邻居的背景。另一个重要发现是，相对于传统马尔可夫链模型的估计而言，死亡率相对较低的社区所包围的州需要更长的时间才能转化为空间马尔可夫链模型中更高的死亡风险。另一方面，那些高风险社区的人花费更少的时间恶化。这些发现证实，在对安全指标的时间动态建模以评估和监视相关区域的安全趋势时，必须纳入空间效应。

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