Advanced driver-assistance systems such as Honda’s collision mitigation brake system(CMBS)can help achieve traffic safety.In this paper,the naturalistic driving study and a series of simulations are combined to bette...Advanced driver-assistance systems such as Honda’s collision mitigation brake system(CMBS)can help achieve traffic safety.In this paper,the naturalistic driving study and a series of simulations are combined to better evaluate the performance of the CMBS in the Chinese traffic environment.First,because safety-critical situations can be diverse especially in the Chinese environment,the Chinese traffic-accident characteristics are analyzed according to accident statistics over the past 17 years.Next,10 Chinese traffic-accident scenarios accounting for more than 80%of traffic accidents are selected.For each typical scenario,353 representative cases are collected from the traffic-management department of Beijing.These real-world accident cases are then reconstructed by the traffic-accident-reconstruction software PC-Crash on the basis of accident-scene diagrams.This study also proposes a systematic analytical process for estimating the effectiveness of the technology using the co-simulation platform of PC-Crash and rateEFFECT,in which 176 simulations are analyzed in detail to assess the accident-avoidance performance of the CMBS.The overall collision-avoidance effectiveness reaches 82.4%,showing that the proposed approach is efficient for avoiding collisions,thereby enhancing traffic safety and improving traffic management.展开更多
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
基金Project(51625503) supported by the National Science Fund for Distinguished Young Scholars,ChinaProject(61790561) supported by the National Natural Science Foundation of China+1 种基金Project(20163000124) supported by Tsinghua-Honda Joint Research,ChinaProject(TTS2017-02) supported by the Open Fund for Jiangsu Key Laboratory of Traffic and Transportation Security,China
文摘Advanced driver-assistance systems such as Honda’s collision mitigation brake system(CMBS)can help achieve traffic safety.In this paper,the naturalistic driving study and a series of simulations are combined to better evaluate the performance of the CMBS in the Chinese traffic environment.First,because safety-critical situations can be diverse especially in the Chinese environment,the Chinese traffic-accident characteristics are analyzed according to accident statistics over the past 17 years.Next,10 Chinese traffic-accident scenarios accounting for more than 80%of traffic accidents are selected.For each typical scenario,353 representative cases are collected from the traffic-management department of Beijing.These real-world accident cases are then reconstructed by the traffic-accident-reconstruction software PC-Crash on the basis of accident-scene diagrams.This study also proposes a systematic analytical process for estimating the effectiveness of the technology using the co-simulation platform of PC-Crash and rateEFFECT,in which 176 simulations are analyzed in detail to assess the accident-avoidance performance of the CMBS.The overall collision-avoidance effectiveness reaches 82.4%,showing that the proposed approach is efficient for avoiding collisions,thereby enhancing traffic safety and improving traffic management.
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.
