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• W3013822684 abstract "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 ʼ s “ black box ” feature learning process and output decision. Firstly, a visual feature importance ( ViFI ) method that describes the importance of input features is proposed by adopting diagram and numerical-analysis. Secondly, by observing the change of weights using ViFI 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 ʼ 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-the-art performance." @default.
• W3013822684 created "2020-04-03" @default.
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• W3013822684 date "2020-05-01" @default.
• W3013822684 modified "2023-09-29" @default.
• W3013822684 title "Road safety performance function analysis with visual feature importance of deep neural nets" @default.
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• W3013822684 doi "https://doi.org/10.1109/jas.2020.1003108" @default.
• W3013822684 hasPublicationYear "2020" @default.
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