The establishment of freight volume forecasting model is very important to the development of logistics industry. The traditional freight volume forecasting model can not deal with nonlinear problems effectively and can not accurately predict the freight volume. In view of the above problems, GDP, the lump sum of retail of consumer goods and employment are taken as input variables, and railway freight volume is taken as output variable to forecast the monthly national volume of railway freight. In this paper, GA-BP neural network is established to predict the national railway freight volume based on BP network, which is optimised by genetic algorithm. Based on genetic algorithm and BP network, this model can further optimize the weights and thresholds, which can realize more powerful nonlinear mapping ability and better deal with complex nonlinear problems. The experimental results show that GA-BP neural network model’s prediction results have better stability and accuracy, which testifies the model’s feasibility and provides a reference for freight volume prediction.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Research on prediction model of National Railway Freight Volume based on GA-BP network


    Beteiligte:
    Gai, Shiqiang (Autor:in) / Xie, Haiyan (Autor:in) / Jia, Chenxing (Autor:in)


    Erscheinungsdatum :

    01.07.2022


    Format / Umfang :

    1061642 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Research on Railway Freight Volume Prediction Based on Neural Network

    Yang Can / Li Xuemei | DOAJ | 2020

    Freier Zugriff

    Research on Railway Freight Volume Prediction Based on ARIMA Model

    Zhao, Jianyou / Cai, Jing / Zheng, Wenjie | ASCE | 2018


    Railway Freight Volume Prediction Based on Support Vector Regression (SVR)

    Liu, Yan ;Lang, Mao Xiang | Trans Tech Publications | 2014


    Prediction Models for Railway Freight Volume Based on Artificial Neural Networks

    Sun, Yan ;Lang, Mao Xiang ;Wang, Dan Zhu | Trans Tech Publications | 2014