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...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.展开更多
This paper critically reviews 'Focus on form through collaborative dialogue: Exploring task effects' composed by Swain and Lapkin. Based on the overview of the study, this paper analyses its findings and valid...This paper critically reviews 'Focus on form through collaborative dialogue: Exploring task effects' composed by Swain and Lapkin. Based on the overview of the study, this paper analyses its findings and validity, and presents some modifications to it and some suggestions for further study to ensure its validity and practicality.展开更多
基金Project(51978082)supported by the National Natural Science Foundation of ChinaProject(19B022)supported by the Outstanding Youth Foundation of Hunan Education Department,ChinaProject(2019QJCZ056)supported by the Young Teacher Development Foundation of Changsha University of Science&Technology,China。
文摘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.
文摘This paper critically reviews 'Focus on form through collaborative dialogue: Exploring task effects' composed by Swain and Lapkin. Based on the overview of the study, this paper analyses its findings and validity, and presents some modifications to it and some suggestions for further study to ensure its validity and practicality.
基金the National Natural Science Foundation of China(Nos.51709240 and 51579144)the High-Tech Ship Research Project Supported by Ministry of Industry and Information Technology(No.K24352)the Equipment Pre-research Project(No.41407010202)
