December 5, 2025SymmetryOpen Access

Enhancing Subway Fire Safety with a Symmetric Framework: From Fault Tree Analysis to Dynamic Bayesian Network Inference

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Dynamic Bayesian network analysis demonstrates a 71.9% reduction in fire occurrence probability, highlighting fire safety in subway systems.

Fire prevention measures reduced fire occurrence probability from 0.000249 to 0.00007, indicating a significant safety improvement.
Fault tree analysis identified 117 cuts and 14 paths, emphasizing critical risk nodes such as flammable materials and fire management deficits.
Dynamic Bayesian network modeling was applied to support probability inference using GeNle Academic 4.1 software, enhancing predictive analytics.
Implementation of a symmetric framework could improve evacuation management and safety in subway operations, indicating broader application potential.

Li et al. (2025) studied this question.

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December 5, 2025SymmetryOpen Access

View Full Paper

Ask AI

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Authors

Dynamic Bayesian network analysis demonstrates a 71.9% reduction in fire occurrence probability, highlighting fire safety in subway systems.

Fire prevention measures reduced fire occurrence probability from 0.000249 to 0.00007, indicating a significant safety improvement.
Fault tree analysis identified 117 cuts and 14 paths, emphasizing critical risk nodes such as flammable materials and fire management deficits.
Dynamic Bayesian network modeling was applied to support probability inference using GeNle Academic 4.1 software, enhancing predictive analytics.
Implementation of a symmetric framework could improve evacuation management and safety in subway operations, indicating broader application potential.

Li et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark