The heavy reliance of many of the previously researched data-driven models on experimental data significantly limits their generalizability and robustness to the prediction of passenger ride comfort under similar vehicle-track system conditions. Consequently, this study attempts to demonstrate that rich data obtained from validated Multi-Body System (MBS) simulation models can achieve better generalizability to accurately predict passenger ride comfort under different vehicle-track system conditions. By varying the railway vehicle-track parameters iteratively, rich data is obtained and used for training and testing of a CNN-LSTM-SVM hybrid Deep Learning (DL) model which is proposed for the evaluation and prediction of railway passenger ride comfort. The proposed model is compared with CNN and CNN-LSTM models to demonstrate its robustness and high accuracy in the prediction of railway passenger ride comfort.


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    Title :

    Railway Passenger Ride Comfort Evaluation Based on Deep Learning and Multibody Dynamics Simulation


    Contributors:

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 2437-2446


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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