Traffic is a complex system with great randomness and uncertainty. It is affected by a combination of many factors, including social, environmental and road factors. Therefore, the results of highway traffic forecasts are often unstable. In response to the above problems, a forecasting model based on artificial neural networks is proposed. The model is applied to traffic forecasting on the Xixian section of the Da-Guang Expressway in China, proving its stability and effectiveness. The application of artificial neural networks to predict the traffic volume of highways can greatly improve the efficiency and accuracy of traffic volume prediction. This is of great significance for solving the problem of road traffic congestion and improving the planning of highway network and regional development planning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Application of artificial neural network in highway traffic volume prediction


    Contributors:
    Yao, Xinwei (editor) / Kumar, Neeraj (editor) / Xu, Hongwei (author) / Tong, Wei (author)

    Conference:

    Fourth International Conference on Smart City Engineering and Public Transportation (SCEPT 2024) ; 2024 ; Beijin, China


    Published in:

    Proc. SPIE ; 13160


    Publication date :

    2024-05-16





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Highway traffic prediction with neural network and genetic algorithms

    WangYan, / Wang Hua, / Xia Limin, | IEEE | 2005


    Highway tunnel traffic accident prediction method based on convolutional neural network

    YANG YONGHONG / ZHENG TAO / ZHANG YU | European Patent Office | 2024

    Free access



    Convolutional neural network for recognizing highway traffic congestion

    Cui, Hua / Yuan, Gege / Liu, Ni et al. | Taylor & Francis Verlag | 2020