Data-driven risk assessment framework for railway operation accidents based on an improved Bayesian network

As a cornerstone national infrastructure, railway systems have faced increasingly intensive operational demands, escalating accident risks that threat socio-economic stability and public welfare. Therefore, ensuring operational safety is of paramount importance. This study develops an effective framework for assessing railway operational accident risks by integrating an improved Bayesian network. Firstly, to mitigate data imbalance, the Adaptive Synthetic Sampling algorithm is introduced to establish a reliable and representative dataset. Subsequently, the AcciMap theory is introduced to form a multi-level risk assessment system. Secondly, to address the epistemic uncertainty within unstructured data, the Bayesian network is combined with hesitant cloud model. Finally, the performance of the proposed model is validated through different methods. The results demonstrate that the proposed model scientifically identifies the critical risk influencing factors of railway operation and can provide reliable and accurate risk assessment results. This study provides valuable insights for railway operation decision-makers to develop reasonable accident prevention measures and valuable support for railway operation risk management.