Assessing quality of crash modification factors estimated by empirical Bayes before-after methods

Abstract

Before-after study with the empirical Bayes (EB) method is the state-of-the-art approach for estimating crash modification factors (CMFs). The EB method not only addresses the regression-to-the-mean bias, but also improves accuracy. However, the performance of the CMFs derived from the EB method has never been fully investigated. This study aims to examine the accuracy of CMFs estimated with the EB method. Artificial realistic data (ARD) and real crash data are used to evaluate the CMFs. The results indicate that: 1) The CMFs derived from the EB before-after method are nearly the same as the true values. 2) The estimated CMF standard errors do not reflect the true values. The estimation remains at the same level regardless of the pre-assumed CMF standard error. The EB before-after study is not sensitive to the variation of CMF among sites. 3) The analyses with real-world traffic and crash data with a dummy treatment indicate that the EB method tends to underestimate the standard error of the CMF. Safety researchers should recognize that the CMF variance may be biased when evaluating safety effectiveness by the EB method. It is necessary to revisit the algorithm for estimating CMF variance with the EB method.

摘要

事故修正系数(措施安全效果)的评估是交通安全管理的重要环节,经验贝叶斯(EB)方法是目前 最先进、首选的方法。该方法能够解决回归到均值的问题并提高评估精度,然而,尚未有学者对EB 方法所得到事故修正系数的精确度进行细致的分析,本论文旨在填补该项空白,并重点针对事故修正 系数的标准差进行分析。论文采用了模拟与实际观测两项数据对EB 方法进行了分析,结果表明:1) EB 方法得到的事故修正系数与理论值比较接近;2) EB 方法所得到事故修正系数的标准差并不能反映真 实值,估计值不随真实值的变化而变化;3) 基于实际数据的分析表明EB 方法往往低估事故修正系数 的标准差。交通安全研究人员应当注意在使用EB 方法评估安全效果时,事故修正系数的方差可能存 在偏差,有必要进一步优化EB 方法中方差的算法。