13th Asia Pacific Transportation Development Conference
Prediction Model and Method of Train Body Vibration Based on Bagged Regression Tree
Publication: Resilience and Sustainable Transportation Systems
ABSTRACT
The vibration acceleration of train body is a key parameter reflecting the running state of train. It is necessary to obtain the acceleration accurately. But the traditional method has low precision. In this paper, a vibration acceleration prediction model and method of train body based on bagged regression tree is proposed. On the basis of GJ-5 to collect a large number of parameters of Guangzhou works section in Guangzhou-Shenzhen II line, Pearson correlation coefficient, Spearman correlation coefficient, and Kendall correlation coefficient are used to analyze the correlation between train body vibration and other detection parameters. Then, the bagging regression tree algorithm is used to establish the prediction model of train body vibration. Finally, the training results are compared with the outputs of the model with multiple linear regression model, support vector machine, and back propagation neural network. According to the evaluation index, the prediction accuracy of the bagged regression tree model is highest compared other three models, which is over 94%.
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ACKNOWLEDGEMENT
This research was funded by National Natural Science Foundation of China [Grant No.: 51907117], and the Shanghai Committee of Science and Technology [Grant No.: 18030501300].
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Information & Authors
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Published In
Resilience and Sustainable Transportation Systems
Pages: 519 - 529
Editors: Fengxiang Qiao, Ph.D., Texas Southern University, Yong Bai, Ph.D., Marquette University, Pei-Sung Lin, Ph.D., University of South Florida, Steven I Jy Chien, Ph.D., New Jersey Institute of Technology, Yongping Zhang, Ph.D., California State Polytechnic University, and Lin Zhu, Ph.D., Shanghai University of Engineering Science
ISBN (Online): 978-0-7844-8290-2
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© 2020 American Society of Civil Engineers.
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Published online: Jun 29, 2020
Published in print: Jun 29, 2020
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