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A rough set-based measurement model study on high-speed railway safety operation

Author

Listed:
  • Qizhou Hu
  • Minjia Tan
  • Huapu Lu
  • Yun Zhu

Abstract

Aiming to solve the safety problems of high-speed railway operation and management, one new method is urgently needed to construct on the basis of the rough set theory and the uncertainty measurement theory. The method should carefully consider every factor of high-speed railway operation that realizes the measurement indexes of its safety operation. After analyzing the factors that influence high-speed railway safety operation in detail, a rough measurement model is finally constructed to describe the operation process. Based on the above considerations, this paper redistricts the safety influence factors of high-speed railway operation as 16 measurement indexes which include staff index, vehicle index, equipment index and environment. And the paper also provides another reasonable and effective theoretical method to solve the safety problems of multiple attribute measurement in high-speed railway operation. As while as analyzing the operation data of 10 pivotal railway lines in China, this paper respectively uses the rough set-based measurement model and value function model (one model for calculating the safety value) for calculating the operation safety value. The calculation result shows that the curve of safety value with the proposed method has smaller error and greater stability than the value function method’s, which verifies the feasibility and effectiveness.

Suggested Citation

  • Qizhou Hu & Minjia Tan & Huapu Lu & Yun Zhu, 2018. "A rough set-based measurement model study on high-speed railway safety operation," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
  • Handle: RePEc:plo:pone00:0197918
    DOI: 10.1371/journal.pone.0197918
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    Cited by:

    1. Xiaoying Yu & Hongsheng Su & Zeyuan Fan & Yu Dong, 2019. "Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.

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