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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Guangyuan Pan, Liping Fu, Qili Chen, Ming Yu and Matthew Muresan, "Road Safety Performance Function Analysis With Visual Feature Importance of Deep Neural Nets," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 735-744, May 2020. doi: 10.1109/JAS.2020.1003108
Citation: Guangyuan Pan, Liping Fu, Qili Chen, Ming Yu and Matthew Muresan, "Road Safety Performance Function Analysis With Visual Feature Importance of Deep Neural Nets," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 735-744, May 2020. doi: 10.1109/JAS.2020.1003108

Road Safety Performance Function Analysis With Visual Feature Importance of Deep Neural Nets

doi: 10.1109/JAS.2020.1003108
Funds:  This work was supported by the National Science and Engineering Research Council of Canada (NSERC), Ontario Research Fund – Research Excellence (ORF-RE), 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), and Beijing Municipal Education Commission (KM201811232016)
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  • 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 (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’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.


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    • This is the first to study Explainable AI in both unsupervised learning and supervised learning, and feature importance equations are proposed for both stages. We demonstrate a diagram and numerical-analysis based method, called visual feature importance (ViFI), to understand the black box feature learning process.
    • Two popular techniques, namely visualization and sensitive analysis, are combined to optimize the input and assist to decide model’s structure. This method intuitively highlights which area responds positively or negatively to the inputs. Through this method, it highlights how a DBN model, especially in unsupervised learning, studies differently from other methods.
    • Explainable AI is applied in traffic engineering for the first time. Specifically, ViFI method is applied as a tool to describe the feature importance and establish a more reasonable road safety performance function, achieving state-of-the-art accuracy on road safety analysis.


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