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
Based on the relationship among the geographic events, spatial changes and the database operations, a new automatic (semi-automatic) incremental updating approach of spatio-temporal database (STDB) named as (event-bas...Based on the relationship among the geographic events, spatial changes and the database operations, a new automatic (semi-automatic) incremental updating approach of spatio-temporal database (STDB) named as (event-based) incremental updating (E-BIU) is proposed in this paper. At first, the relationship among the events, spatial changes and the database operations is analyzed, then a total architecture of E-BIU implementation is designed, which includes an event queue, three managers and two sets of rules, each component is presented in detail. The process of the E-BIU of master STDB is described successively. An example of building’s incremental updating is given to illustrate this approach at the end. The result shows that E-BIU is an efficient automatic updating approach for master STDB.展开更多
DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the alg...DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.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Based on the relationship among the geographic events, spatial changes and the database operations, a new automatic (semi-automatic) incremental updating approach of spatio-temporal database (STDB) named as (event-based) incremental updating (E-BIU) is proposed in this paper. At first, the relationship among the events, spatial changes and the database operations is analyzed, then a total architecture of E-BIU implementation is designed, which includes an event queue, three managers and two sets of rules, each component is presented in detail. The process of the E-BIU of master STDB is described successively. An example of building’s incremental updating is given to illustrate this approach at the end. The result shows that E-BIU is an efficient automatic updating approach for master STDB.
基金Project(61103046) supported in part by the National Natural Science Foundation of ChinaProject(B201312) supported by DHU Distinguished Young Professor Program,China+1 种基金Project(LY14F020007) supported by Zhejiang Provincial Natural Science Funds of ChinaProject(2014A610072) supported by the Natural Science Foundation of Ningbo City,China
文摘DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.
