In order to predict road traffic accident indicators scientifically and accurately, this paper established a road traffic accident prediction model based on the basic theory of BP neural network. The 14 main influencing factors of traffic accidents were selected by using correlation analysis theory as the input variable of the prediction model and traffic accident deaths was taken as the output variable of the prediction model. Data related to road traffic accidents in China from 2000 to 2017 were selected as training samples of the model, MATLAB nntool was used to train the prediction model by using traingdm, traingda, trainlm and traingd training functions, and predict the deaths of road traffic accidents in 2018 in China. It is verified that the relative error of the constructed road traffic accident prediction model is within 1%, which can be used for road traffic accident prediction.


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    Titel :

    Road Traffic Accident Prediction Based on BP Neural Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Wu, Jianping (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Li, Ruimin (Herausgeber:in) / Zhang, Haodong (Herausgeber:in) / Xing, Yan (Autor:in) / Song, Wen-hao (Autor:in) / Liu, Wei-dong (Autor:in) / Gao, Shu-shida (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021



    Erscheinungsdatum :

    2022-10-28


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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