This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using...This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.展开更多
Taking the Hubble parameter directly as a function of the scalar field instead of as a function of time,H = H( ), we present a new exact solution in the new inflation model with induced gravity. This includes solution...Taking the Hubble parameter directly as a function of the scalar field instead of as a function of time,H = H( ), we present a new exact solution in the new inflation model with induced gravity. This includes solution which is inflation for < > end, and develops smoothly towards radiation-like evolution for ≥ end. The inflation is driven by the evolution of the field with inflation potential, V( ) = λ 2 v2)2.density, ns, is computed and ns lies well inside the limits set by the cosmic background explorer (COBE) satellite.the dex of the scalar effective cosmological constant Aeff tends to zero when inflation ends.展开更多
Taking the cosmological expansion rate directly as a function of field , H = H( ), we present a new exact solution to Einstein's equations that describe the evolution of cosmological chaotic inflation model. The i...Taking the cosmological expansion rate directly as a function of field , H = H( ), we present a new exact solution to Einstein's equations that describe the evolution of cosmological chaotic inflation model. The inflation is driven by the evolution of scalar field with inflation potential V( ) = λ 2 v2)2.8 ( 2- 2)2.The spectral indices of the scalar density ns and gravitational wave fluctuations ng are computed. The value of ns lies well inside the limits set by the cosmic background explorer satellite.展开更多
基金Project(BK20160685)supported by the Science Foundation of Jiangsu Province,ChinaProject(61620106002)supported by the National Natural Science Foundation of China
文摘This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.
文摘Taking the Hubble parameter directly as a function of the scalar field instead of as a function of time,H = H( ), we present a new exact solution in the new inflation model with induced gravity. This includes solution which is inflation for < > end, and develops smoothly towards radiation-like evolution for ≥ end. The inflation is driven by the evolution of the field with inflation potential, V( ) = λ 2 v2)2.density, ns, is computed and ns lies well inside the limits set by the cosmic background explorer (COBE) satellite.the dex of the scalar effective cosmological constant Aeff tends to zero when inflation ends.
文摘Taking the cosmological expansion rate directly as a function of field , H = H( ), we present a new exact solution to Einstein's equations that describe the evolution of cosmological chaotic inflation model. The inflation is driven by the evolution of scalar field with inflation potential V( ) = λ 2 v2)2.8 ( 2- 2)2.The spectral indices of the scalar density ns and gravitational wave fluctuations ng are computed. The value of ns lies well inside the limits set by the cosmic background explorer satellite.
