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Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

Author

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  • Jia Chaolong
  • Xu Weixiang
  • Wei Lili
  • Wang Hanning

Abstract

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

Suggested Citation

  • Jia Chaolong & Xu Weixiang & Wei Lili & Wang Hanning, 2013. "Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-12, October.
  • Handle: RePEc:hin:jnlmpe:486738
    DOI: 10.1155/2013/486738
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