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
为研究不同类型车辆组成的混合交通流的运行模式,假定无人驾驶车辆、先进出行者出行系统(advanced traveler information systems,ATIS)装置车辆和普通驾驶车辆分别遵从系统最优模式、用户均衡模式、随机用户均衡模式选择路径,分别建立...为研究不同类型车辆组成的混合交通流的运行模式,假定无人驾驶车辆、先进出行者出行系统(advanced traveler information systems,ATIS)装置车辆和普通驾驶车辆分别遵从系统最优模式、用户均衡模式、随机用户均衡模式选择路径,分别建立普通车道、专用车道模式下的交通分配模型,给出求解模型的连续平均算法(method of successive averages,MSA)。通过算例确定路段通行能力,分析信息质量水平、出行需求量、市场渗透率对出行时间的影响,在确定模型各项参数取值的基础上,根据专用车道设置情况对混合均衡流状态进行研究,验证模型算法的可行性和收敛性。研究结果表明:通行能力随着行驶速度的增加先提高后下降,选择合适的行驶速度将提高路段通行能力,且无人驾驶专用道的通行能力明显高于普通车道;适当提高信息质量水平,可降低路径选择的随机性,有效减少平均出行时间;随着出行需求量的增加,平均出行时间逐渐提高,其中系统最优模式(无人驾驶专用道)的平均出行时间最小;根据市场渗透率的变化情况选择合适的车道配置模式,既能提高道路资源的使用效率,又能减少出行者的出行成本;不同车道配置模式下的混合交通流均随着迭代次数的增加逐渐达到稳定状态;当无人驾驶车辆的市场渗透率较高时,设置无人驾驶专用道将缩短行驶时间,提高运行效率。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.
基金National Natural Science Foundation of China(51338002)Open Fund of Engineering Research Center of Catastrophic Prophylaxis and Treatment of Road and Traffic Safety of Ministry of Education(Changsha University of Science and Technology)(kfj160402)Educational Commission of Hunan Province of China(17C0058)
文摘为研究不同类型车辆组成的混合交通流的运行模式,假定无人驾驶车辆、先进出行者出行系统(advanced traveler information systems,ATIS)装置车辆和普通驾驶车辆分别遵从系统最优模式、用户均衡模式、随机用户均衡模式选择路径,分别建立普通车道、专用车道模式下的交通分配模型,给出求解模型的连续平均算法(method of successive averages,MSA)。通过算例确定路段通行能力,分析信息质量水平、出行需求量、市场渗透率对出行时间的影响,在确定模型各项参数取值的基础上,根据专用车道设置情况对混合均衡流状态进行研究,验证模型算法的可行性和收敛性。研究结果表明:通行能力随着行驶速度的增加先提高后下降,选择合适的行驶速度将提高路段通行能力,且无人驾驶专用道的通行能力明显高于普通车道;适当提高信息质量水平,可降低路径选择的随机性,有效减少平均出行时间;随着出行需求量的增加,平均出行时间逐渐提高,其中系统最优模式(无人驾驶专用道)的平均出行时间最小;根据市场渗透率的变化情况选择合适的车道配置模式,既能提高道路资源的使用效率,又能减少出行者的出行成本;不同车道配置模式下的混合交通流均随着迭代次数的增加逐渐达到稳定状态;当无人驾驶车辆的市场渗透率较高时,设置无人驾驶专用道将缩短行驶时间,提高运行效率。
