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.
Railway Passenger Ride Comfort Evaluation Based on Deep Learning and Multibody Dynamics Simulation
24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China
CICTP 2024 ; 2437-2446
11.12.2024
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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