Prediction of Track Quality Index (TQI) Using Vehicle Acceleration Data based on Machine Learning

IF 0.4 Q4 ENGINEERING, GEOLOGICAL Journal of the Korean Geosynthetic Society Pub Date : 2020-03-01 DOI:10.12814/JKGSS.2020.19.1.045
C. Choi, Hunki Kim, Young Cheul Kim, Sang-su Kim
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Abstract

There is an increasing tendency to try to make predictive analysis using measurement data based on machine learning techniques in the railway industries. In this paper, it was predicted that Track quality index (TQI) using vehicle acceleration data based on the machine learning method. The XGB (XGBoost) was the most accurate with 85% in the all data sets. Unlike the SVM model with a single algorithm, the RF and XGB model with a ensemble system were considered to be good at the prediction performance. In the case of the Surface TQI, it is shown that the acceleration of the z axis is highly related to the vertical direction and is in good agreement with the previous studies. Therefore, it is appropriate to apply the model with the ensemble algorithm to predict the track quality index using the vehicle vibration acceleration data because the accuracy may vary depending on the applied model in the machine learning methods.
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基于机器学习的车辆加速度数据轨迹质量指数预测
在铁路行业,越来越多的人尝试使用基于机器学习技术的测量数据进行预测分析。本文采用基于机器学习的方法,利用车辆加速度数据预测轨道质量指数(TQI)。XGB (XGBoost)在所有数据集中准确率最高,为85%。与单一算法的SVM模型不同,具有集成系统的RF和XGB模型被认为具有较好的预测性能。在Surface TQI的情况下,可以看出z轴的加速度与垂直方向高度相关,这与之前的研究结果很好地吻合。因此,由于机器学习方法中所应用的模型的精度可能会有所不同,因此将集成算法的模型应用于利用车辆振动加速度数据预测轨道质量指标是合适的。
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