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A quantification method of dynamic miscommunication based on the Markov process within subway teams.

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Area of Science:

  • Cognitive Psychology
  • Systems Engineering
  • Human Factors Analysis

Background:

  • Effective communication is critical for subway team operations.
  • Existing human error prediction models may not fully capture dynamic communication nuances.

Purpose of the Study:

  • To analyze subway team communication using cognitive psychology principles.
  • To develop a novel model for calculating miscommunication probability in subway teams.
  • To validate the model's effectiveness in a real-world scenario.

Main Methods:

  • Qualitative analysis of subway team communication.
  • Application of Markov process theory to model communication flow.
  • Derivation of a dynamic, two-way communication miscommunication probability model.
  • Case study validation using a screen door fault event.

Main Results:

  • Subway team communication aligns with Markov process characteristics.
  • The derived model accurately calculates dynamic miscommunication probability.
  • The proposed model showed significantly lower miscommunication probability than the THERP manual.
  • Dynamic two-way communication effectively reduces miscommunication.

Conclusions:

  • The developed Markov-based model is feasible and effective for assessing subway team miscommunication.
  • Dynamic two-way communication strategies enhance operational safety by minimizing errors.
  • This research provides a quantitative approach to understanding and improving team communication in critical infrastructure.