Simultaneous Prediction of Vehicle-Body Accelerations and Comfort Index based on Track Geometries

Vibration comfort is one of the most influencing factors on the service quality of high-speed railways, and it is meaningful to relate vehicle vibrational responses to track geometry activation for the purpose of locating adverse track states from comfort point of view. Although a wide variety of mechanical and data-driven models have been put forward for this task, these models are unsatisfactory in computational efficiency or accuracy. In this circumstance, a deep learning model, integrating convolutional neural network, long short-term memory, and multilayer perceptron, is proposed to automatically learn the complex relationship between track geometries and vehicle vibrations. The trained model can predict local vehicle-body accelerations and sectional comfort indices simultaneously by taking track geometries and running velocity as input. According to the evaluation test on the inspection data of a high-speed railway in China, the predictions are in good consistency with the real values, with outperforming efficiency in time cost compared with the running velocity of high-speed vehicles. Moreover, the predictions made by the model can in turn be regarded as assessment indices of track geometric state, for revealing adverse track states or potential track defects. The predicted comfort index is superior to traditional track quality index in revealing vibrational comfort of the running vehicle, promising in acting as a supplement, or even a replacement, to the latter.