Accurate prediction of freight volume data is directly related to the preparation of railway transport plans. This paper proposes the Informer model to predict freight volume at major stations of railway network. First, the freight volume data is transformed by temporal embedding, which considers whether the day is in the skylight maintenance period, to better explore the time characteristics. Next, the decoder structure containing the ProbSparse self-attention mechanism is applied to capture the long-term correlation from the embedded data. Finally, the decoder structure is used to generate forecast information at once. Using the freight volume data of China Energy Investment, the high-precision railway freight volume prediction of twelve loading stations in the next 14 days is achieved. Experiments show that Informer model exhibits better prediction performance with the growth of the sequence: the RMSE value is improved by 90/0-27.4% over LSTM, GRU, LSTM with Self-Attention and Transformer.


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    Titel :

    Improving the Accuracy of Multi-Step Prediction of Railway Freight Volume Based on Informer Model


    Beteiligte:
    Liu, Jiaqi (Autor:in) / Jing, Yun (Autor:in)


    Erscheinungsdatum :

    12.01.2024


    Format / Umfang :

    1646137 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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