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

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Area of Science:

  • Railway Engineering
  • Operations Research
  • Data Science

Background:

  • High-speed railway operation faces significant safety challenges.
  • Existing safety measurement methods lack comprehensive analysis of influencing factors.
  • A robust method is needed to address safety issues in railway management.

Purpose of the Study:

  • To develop a novel safety measurement model for high-speed railways.
  • To integrate rough set theory and uncertainty measurement for enhanced safety analysis.
  • To provide an effective theoretical method for multiple attribute safety measurement.

Main Methods:

  • Detailed analysis of factors influencing high-speed railway safety.
  • Construction of a rough measurement model incorporating 16 key indexes (staff, vehicle, equipment, environment).
  • Application of the rough set-based model and value function model to analyze operational data from 10 Chinese railway lines.

Main Results:

  • The proposed rough set-based model demonstrated smaller error and greater stability in calculating operation safety values.
  • Comparative analysis showed superior performance of the new model over the value function method.
  • Feasibility and effectiveness of the developed model were verified through empirical data.

Conclusions:

  • The rough set-based measurement model offers a significant advancement in high-speed railway safety assessment.
  • The method provides a reliable approach for managing and improving railway operational safety.
  • This research contributes a valuable tool for ensuring the safety and efficiency of high-speed rail systems.