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用于前后对比观测性道路安全研究的全贝叶斯方法的验证及其在农村信号灯转换评估中的应用。

Validation of a Full Bayes methodology for observational before-after road safety studies and application to evaluation of rural signal conversions.

作者信息

Lan Bo, Persaud Bhagwant, Lyon Craig, Bhim Ravi

机构信息

Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.

出版信息

Accid Anal Prev. 2009 May;41(3):574-80. doi: 10.1016/j.aap.2009.02.010. Epub 2009 Mar 4.

Abstract

The objective of the study on which the paper is based was to explore the application of fully Bayesian methods for before-after road safety studies. Several variations of the methodology were evaluated with a simulated dataset in which hypothetical treatments with no safety effect were randomly assigned to high accident locations to mimic the common site selection process in road jurisdictions. It was confirmed that the fully Bayesian method by estimating no safety effect can account for the regression-to-the-mean that results from this biased site selection process. The fully Bayesian method was then applied to California rural intersection data to evaluate the safety effect of conversion from stop to signalized control. The results were then compared with those from the empirical Bayesian method, currently the accepted approach for conducting unbiased before-after evaluations. This comparison was generally favorable in that FB can provide similar results as EB.

摘要

本文所基于的研究目的是探索全贝叶斯方法在前后道路安全研究中的应用。使用一个模拟数据集对该方法的几种变体进行了评估,在该数据集中,将无安全效果的假设处理随机分配到高事故地点,以模拟道路辖区常见的选址过程。结果证实,通过估计无安全效果的全贝叶斯方法可以解释这种有偏选址过程导致的均值回归现象。然后将全贝叶斯方法应用于加利福尼亚州农村交叉路口数据,以评估从停车控制转换为信号灯控制的安全效果。然后将结果与经验贝叶斯方法的结果进行比较,经验贝叶斯方法是目前进行无偏前后评估的公认方法。这种比较总体上是有利的,因为全贝叶斯方法可以提供与经验贝叶斯方法类似的结果。

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