Road traffic accident is a serious threat to human life and safety of living environment. In this paper, a new road traffic accident prediction model (TAP-CNN) is established by using traffic accident influencing factors, such as traffic flow, weather, light to build a state matrix to describe the traffic state and CNN model. This paper uses samples to test the accuracy of the new model. The experimental results show that the TAP-CNN model is more effective than the traditional neural network model to predict the traffic accident. It provides a reference for the forecast of the traffic accident.


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    Titel :

    A model of traffic accident prediction based on convolutional neural network


    Beteiligte:
    Wenqi, Lu (Autor:in) / Dongyu, Luo (Autor:in) / Menghua, Yan (Autor:in)


    Erscheinungsdatum :

    2017-09-01


    Format / Umfang :

    262482 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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