Road safety performance function(SPF) analysis using data-driven and nonparametric methods, especially recent developed deep learning approaches, has gained increasing achievements. However, due to the learning mechan...Road safety performance function(SPF) analysis using data-driven and nonparametric methods, especially recent developed deep learning approaches, has gained increasing achievements. However, due to the learning mechanisms are hidden in a"black box" in deep learning, traffic features extraction and intelligent importance analysis are still unsolved and hard to generate.This paper focuses on this problem using a deciphered version of deep neural networks(DNN), one of the most popular deep learning models. This approach builds on visualization, feature importance and sensitivity analysis, can evaluate the contributions of input variables on model's "black box" feature learning process and output decision. Firstly, a visual feature importance(Vi FI) method that describes the importance of input features is proposed by adopting diagram and numerical-analysis. Secondly,by observing the change of weights using Vi FI on unsupervised training and fine-tuning of DNN, the final contributions of input features are calculated according to importance equations for both steps that we proposed. Sequentially, a case study based on a road SPF analysis is demonstrated, using data collected from a major Canadian highway, Highway 401. The proposed method allows effective deciphering of the model's inner workings and allows the significant features to be identified and the bad features to be eliminated. Finally, the revised dataset is used in crash modeling and vehicle collision prediction, and the testing result verifies that the deciphered and revised model achieves state-of-theart performance.展开更多
The sorts and detection methods of environmental hormone were analyzed firstly,and then the effects of environmental hormone on organisms and humanity were discussed. Finally the control measures of environmental horm...The sorts and detection methods of environmental hormone were analyzed firstly,and then the effects of environmental hormone on organisms and humanity were discussed. Finally the control measures of environmental hormone pollution,such as reducing the use of goods and pesticide and the study of new substitutes and degradation technology are advanced.展开更多
基金supported by the National Science and Engineering Research Council of Canada(NSERC)Ontario Research Fund–Research Excellence(ORF-RE)+3 种基金the Ministry of Transportation Ontario(MTO)through Its Highway Infrastructure Innovation Funding Program(HIIFP)Beijing Postdoctoral Science Foundation(ZZ-2019-65)Beijing Chaoyang District Postdoctoral Science Foundation(2019ZZ-45)Beijing Municipal Education Commission(KM201811232016)。
文摘Road safety performance function(SPF) analysis using data-driven and nonparametric methods, especially recent developed deep learning approaches, has gained increasing achievements. However, due to the learning mechanisms are hidden in a"black box" in deep learning, traffic features extraction and intelligent importance analysis are still unsolved and hard to generate.This paper focuses on this problem using a deciphered version of deep neural networks(DNN), one of the most popular deep learning models. This approach builds on visualization, feature importance and sensitivity analysis, can evaluate the contributions of input variables on model's "black box" feature learning process and output decision. Firstly, a visual feature importance(Vi FI) method that describes the importance of input features is proposed by adopting diagram and numerical-analysis. Secondly,by observing the change of weights using Vi FI on unsupervised training and fine-tuning of DNN, the final contributions of input features are calculated according to importance equations for both steps that we proposed. Sequentially, a case study based on a road SPF analysis is demonstrated, using data collected from a major Canadian highway, Highway 401. The proposed method allows effective deciphering of the model's inner workings and allows the significant features to be identified and the bad features to be eliminated. Finally, the revised dataset is used in crash modeling and vehicle collision prediction, and the testing result verifies that the deciphered and revised model achieves state-of-theart performance.
文摘The sorts and detection methods of environmental hormone were analyzed firstly,and then the effects of environmental hormone on organisms and humanity were discussed. Finally the control measures of environmental hormone pollution,such as reducing the use of goods and pesticide and the study of new substitutes and degradation technology are advanced.
