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

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.