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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    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 :

    01.09.2017


    Format / Umfang :

    262482 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Highway tunnel traffic accident prediction method based on convolutional neural network

    YANG YONGHONG / ZHENG TAO / ZHANG YU | Europäisches Patentamt | 2024

    Freier Zugriff

    Traffic accident prediction method based on graph convolutional network

    YANG QIAO / LI RUI / QI TIANJING | Europäisches Patentamt | 2023

    Freier Zugriff

    Research on Traffic Accident Prediction Model Based on Convolutional Neural Networks in VANET

    Zhao, Haitao / Cheng, Huiling / Mao, Tianqi et al. | IEEE | 2019


    Road Traffic Accident Prediction Based on BP Neural Network

    Xing, Yan / Song, Wen-hao / Liu, Wei-dong et al. | Springer Verlag | 2022


    Road Traffic Accident Prediction Based on BP Neural Network

    Xing, Yan / Song, Wen-hao / Liu, Wei-dong et al. | TIBKAT | 2023