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Analyzing Subway Operation Accidents Causations: Apriori Algorithm and Network Approaches.

Yongliang Deng1, Ying Zhang2, Zhenmin Yuan3

  • 1School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

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Summary
This summary is machine-generated.

This study introduces a subway operation accident causation network (SOACN) to map complex safety risks. Findings highlight focusing on fire and passenger falls for efficient subway safety management.

Keywords:
network theorysafety risksubway operationvulnerability evaluation

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Area of Science:

  • Transportation Safety
  • Network Science
  • Risk Management

Background:

  • Subway operations face significant safety challenges due to complex accident interrelationships.
  • Effective safety management requires understanding the dynamic links between causes and accidents.

Purpose of the Study:

  • To develop and apply a subway operation accident causation network (SOACN) for analyzing safety risks.
  • To identify critical factors and provide data-driven recommendations for enhancing subway safety management.

Main Methods:

  • Constructed the SOACN model using literature review, grounded theory, and association rule analysis.
  • Analyzed network topological features (degree distribution, centrality, etc.) using network theory.
  • Evaluated network vulnerability based on efficiency metrics.

Main Results:

  • The SOACN model integrates 13 accident types, 29 causations, and 84 relationships.
  • The network exhibits small-world and scale-free properties, indicating rapid propagation of issues.
  • Vulnerability analysis identified fire accidents and passenger falls as key risks requiring focused management.

Conclusions:

  • The SOACN effectively captures complex safety-risk-causation relationships in subway operations.
  • Results inform optimized safety decision-making, causation reduction, and efficient accident control.
  • Prioritizing specific accident types can significantly improve overall subway safety.