文献类型：期刊文献

英文题名：Multiscenario deduction analysis for railway emergencies using knowledge metatheory and dynamic Bayesian networks

作者：Liu, Guanyi[1];Liu, Shifeng[1];Li, Xuewei[1];Li, Xueyan[2];Gong, Daqing[1]

第一作者：Liu, Guanyi

通讯作者：Gong, DQ[1]

机构：[1]Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China;[2]Beijing Union Univ, Sch Management, Beijing 100101, Peoples R China

第一机构：Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China

通讯机构：[1]corresponding author), Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China.

年份：2025

卷号：255

外文期刊名：RELIABILITY ENGINEERING & SYSTEM SAFETY

收录：;EI(收录号：20244817447925);Scopus(收录号：2-s2.0-85210276318);WOS:【SCI-EXPANDED(收录号:WOS:001371571400001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities (2023YJS107) , China State Railway Group Co., Ltd. (B23D00060, B23D00030) , National Natural Science Foundation of China under Grant 62276020, Beijing Natural Science Foundation under Grant 9222025, Beijing Social Science Foundation under Grant 19JDGLA002, MOE (Ministry of Education in China) Project of Humanities and Social Sciences under Grant 19YJC630043, and was partially supported by Beijing Logistics Informatics Research Base. We appreciate their support very much.

语种：英文

外文关键词：Railway emergency; Scenario deduction; Knowledge metatheory; Dynamic Bayesian network

摘要：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.