Constructing Bayesian networks from knowledge graphs for risk assessment and causal inference of urban rail transit...

The safety risks associated with urban rail transit equipment are characterized by multi-source heterogeneity and dynamic evolution. Traditional expert-driven static management models often fail to meet the proactive prevention demands in complex scenarios, leading to critical issues such as ambiguous risk identification and insufficiently targeted prevention measures. This study proposes a novel risk assessment and inference method that integrates knowledge graphs with Bayesian networks. First, a safety risk knowledge graph is constructed based on historical accident case reports. Then, a mapping method is proposed to convert the knowledge graph into a Bayesian network. Subsequently, data-driven statistical approaches are employed to estimate the network parameters. Finally, a case study involving equipment failures in urban rail transit is conducted to validate the proposed method. Experimental results demonstrate that the proposed method effectively identifies key risk factors and accurately traces accident causes through backward inference. The method also significantly outperforms traditional approaches in terms of practical accuracy. The findings provide intelligent decision support for the risk management of urban rail transit equipment.