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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    Title :

    A model of traffic accident prediction based on convolutional neural network


    Contributors:
    Wenqi, Lu (author) / Dongyu, Luo (author) / Menghua, Yan (author)


    Publication date :

    2017-09-01


    Size :

    262482 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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