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首页> 外文期刊>Journal of transportation safety & security >Direct Spatial Correlation in Crash Frequency Models: Estimation of the Effective Range
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Direct Spatial Correlation in Crash Frequency Models: Estimation of the Effective Range

机译：碰撞频率模型中的直接空间相关性：有效范围的估计

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Spatial correlation has shown to be present in highway safety data, yet the distance at which sites should be considered correlated is largely unknown. The purpose of this research is to explore the effect of direct spatial correlation structures on crashfrequency models at the road segment level and compare them to spatial conditional models. By using direct spatial correlation structures the "effective range" (i.e., the distance at which there is no lingering spatial correlation) is estimated. Full Bayes hierarchical models with direct spatial correlation effects and conditional autoregressive (CAR) spatial effects are estimated. The model of crash, traffic, and roadway inventory data from Pennsylvania shows an average effective range of around 168 m which is in line with previous findings from conditional models. The direct spatial correlation model has a better goodness of fit than the random effects and the CAR model. Furthermore, the proportion of variation in the data explained by the spatial correlation term is almost the same for both spatial models. The standard deviations in coefficient estimates are slightly lower for the direct spatial correlation model compared to the random effects model but significantly lower than the CAR model.
机译：在公路安全数据中已经显示出空间相关性，但是在很大程度上应该未知地点之间的相关性。这项研究的目的是探讨直接空间相关结构对路段水平碰撞频率模型的影响，并将其与空间条件模型进行比较。通过使用直接空间相关结构，可以估计“有效范围”（即，没有持久的空间相关的距离）。估计具有直接空间相关效应和条件自回归（CAR）空间效应的全贝叶斯分层模型。宾夕法尼亚州的事故，交通和道路库存数据模型显示平均有效范围约为168 m，这与条件模型的先前发现相符。直接空间相关性模型比随机效应和CAR模型具有更好的拟合优度。此外，对于两个空间模型，由空间相关项解释的数据变化比例几乎相同。与随机效应模型相比，直接空间相关模型的系数估计值中的标准偏差略低，但显着低于CAR模型。

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