Multiscenario deduction analysis for railway emergencies using knowledge metatheory and dynamic Bayesian networks

Abstract

Railway emergencies exhibit uncertainty and complex evolutionary processes during their development. Scenario deduction analysis plays a critical role in identifying the progression of railway emergencies, which is essential for effective response. This paper adopts a “scenario‒response” decision-making approach and proposes a multiscenario deduction model based on knowledge metatheory and dynamic Bayesian networks. First, through an in-depth analysis of railway accident cases, a scenario knowledge metarepresentation model is constructed on the basis of knowledge metatheory. On this basis, a scenario deduction model based on dynamic Bayesian networks is constructed, which is capable of analyzing the evolutionary trajectories of various scenarios. Additionally, an evidence conflict calculation method based on the Tanimoto measure is proposed to reduce the subjectivity of expert evaluations. Finally, the empirical part of this study focuses on a case analysis of a train derailment accident, with the experimental results demonstrating the effectiveness of the proposed model. Furthermore, this study validates the feasibility and utility of the proposed methods, providing valuable insights and guidance for enhancing railway operational safety.