Multilevel Residual Prophet Network Time Series Model for Prediction of Irregularities on High-Speed Railway Track

Author:

Wang Xiaohui1ORCID,Yang Jianwei2ORCID,Yang Fei3,Wang Yanxue4,Liu Fu5

Affiliation:

1. Ph.D. Student, School of Civil and Transportation Engineering, Beijing Univ. of Civil Engineering and Architecture, No. 1 Zhanlanguan Rd., Xicheng District, Beijing 100044, China. ORCID: .

2. Professor, Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing Univ. of Civil Engineering and Architecture, No. 1 Zhanlanguan Rd., Xicheng District, Beijing 100044, China (corresponding author). ORCID: .

3. Senior Engineer, Infrastructure Inspection Research Center, China Academy of Railway Sciences Corporation Ltd., 2 Daliushu Rd., Haidian, Beijing 100081, China.

4. Professor, Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing Univ. of Civil Engineering and Architecture, No. 1 Zhanlanguan Rd., Xicheng District, Beijing 100044, China.

5. Ph.D. Student, School of Civil and Transportation Engineering, Beijing Univ. of Civil Engineering and Architecture, No. 1 Zhanlanguan Rd., Xicheng District, Beijing 100044, China.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Reference32 articles.

1. Adhikari R. and R. Agrawal. 2013. “An introductory study on time series modeling and forecasting Ratnadip Adhikari R. K. Agrawal.” Preprint submitted February 26 2013. http://arxiv.org/abs/1302.6613.

2. Classification-learning-based framework for predicting railway track irregularities

3. Time series analysis: Forecasting and control;Box G. E. P.;J. Time,2010

4. Identification of creep camber state of 32-m box girders on high-speed railway and prediction of geometry of track over girder;Du X.-R.;J. Transp. Eng. Part A Syst.,2022

5. Sum of squared ACF and the ljung–box statistics;Hassani H.;Physica A,2019

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