{"title":"A full domain decision model for robust risk control based on minimum linkage space and copula Bayesian networks","authors":"Pei Zhang, Zhen-Ji Zhang, Da-Qing Gong","doi":"10.1016/j.ress.2025.111046","DOIUrl":null,"url":null,"abstract":"<div><div>To effectively manage the complexity and risks inherent in rail transit operations, we propose a robust three-stage decision model. This model integrates a full-domain decision system, minimum linkage space, three-way clustering, and a Copula-Bayesian approach to create a comprehensive framework for data analysis and risk management. In the first stage, we establish a full-domain decision system that maps operational processes to specific risk characteristics, facilitating a unified approach to data interlinkages. The second stage combines minimum linkage space with a three-way clustering algorithm to identify the major risk factors from 25 potential risks, focusing on those crucial to system integrity. The final stage combines Copula theory and Bayesian networks to model and analyze in detail the dependencies and interrelationships among the 13 major risk factors identified. By utilizing advanced analytical tools, such as scatter plots, percentile spider charts, and correlation coefficients, we identify critical risk factors that significantly affect rail transit safety. This enables precise, predictive, and diagnostic interventions to enhance real-time risk assessments, ultimately reducing system risks and preventing accidents. The model provides actionable insights for managing complex risks in rail transit, offering a valuable tool for decision-makers to ensure safer operations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111046"},"PeriodicalIF":9.4000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832025002479","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
To effectively manage the complexity and risks inherent in rail transit operations, we propose a robust three-stage decision model. This model integrates a full-domain decision system, minimum linkage space, three-way clustering, and a Copula-Bayesian approach to create a comprehensive framework for data analysis and risk management. In the first stage, we establish a full-domain decision system that maps operational processes to specific risk characteristics, facilitating a unified approach to data interlinkages. The second stage combines minimum linkage space with a three-way clustering algorithm to identify the major risk factors from 25 potential risks, focusing on those crucial to system integrity. The final stage combines Copula theory and Bayesian networks to model and analyze in detail the dependencies and interrelationships among the 13 major risk factors identified. By utilizing advanced analytical tools, such as scatter plots, percentile spider charts, and correlation coefficients, we identify critical risk factors that significantly affect rail transit safety. This enables precise, predictive, and diagnostic interventions to enhance real-time risk assessments, ultimately reducing system risks and preventing accidents. The model provides actionable insights for managing complex risks in rail transit, offering a valuable tool for decision-makers to ensure safer operations.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.