At present, artificial intelligence has been rapidly developed and applied in the field of intelligent transportation. Safety risk assessment and prediction of high-speed railway operation has become an important part of intelligent high-speed railway operation management. In this paper, based on chaos theory and artificial neural network, a comprehensive risk prediction model based on the phase space delay reconstruction of chaos theory and the powerful approximation and mapping ability of RBF neural network is established. Accordingly, the high-speed railway operation risk prediction problem is transformed into a chaotic time series prediction problem, and then the RBF neural network is used to predict risk results in the future. 300 days of safety accident case data, from the public data set, are utilized to train the model and some desirable prediction results are obtained. These research results provide important theoretical and technical basis for the safe operation of high-speed railway, and have important guiding significance and reference value for the operation safety of high-speed railway in China.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on Safety Risk Prediction Model of High-Speed Railway Based on Chaotic RBF Neural Network


    Contributors:
    Yi, Saili (author) / Kuang, Jieshuang (author) / Liu, Ran (author)


    Publication date :

    2023-12-16


    Size :

    222168 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A neural network approach for railway safety prediction

    Nefti, S. / Oussalah, M. | Tema Archive | 2004


    Hybrid Prediction Model for High-Speed Railway Embankment Settlement Using Grey Artificial Neural Network

    Zhang, T.G. / Hu, Z.B. / Yang, C.F. et al. | British Library Conference Proceedings | 2012


    Proactive Railway Safety: ML-based Risk Prediction

    Kumar Rapolu, Praveen / Ramya, Pannala / Teja, G.Sai et al. | IEEE | 2025



    Research on risk assessment of high-speed railway operation based on network ANP

    Leyi Cheng / Yinghan Wang / Yichuan Peng | DOAJ | 2021

    Free access