{"title":"A method of characterizing the impact of traffic load on metro system from the control centrality","authors":"Nuo Yong , Shunjiang Ni , Shifei Shen","doi":"10.1016/j.jnlssr.2025.01.001","DOIUrl":null,"url":null,"abstract":"<div><div>This paper explores the challenges of controlling complex metro systems, which are influenced by uncertain and uncontrollable large passenger flow impacts. Traditionally, flow-limiting measures during peak periods have been based on experience rather than scientific theory. To bridge this gap, we introduce a novel network analysis method inspired by control centrality theory. This approach assesses the impact of traffic loads from single or multiple sources on any node within the metro network. Our method provides a scientific basis for operators to develop policies for managing overloaded traffic, enhancing both safety and efficiency in metro system operations.</div></div>","PeriodicalId":62710,"journal":{"name":"安全科学与韧性(英文)","volume":"6 3","pages":"Article 100192"},"PeriodicalIF":3.7000,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"安全科学与韧性(英文)","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666449625000180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0
Abstract
This paper explores the challenges of controlling complex metro systems, which are influenced by uncertain and uncontrollable large passenger flow impacts. Traditionally, flow-limiting measures during peak periods have been based on experience rather than scientific theory. To bridge this gap, we introduce a novel network analysis method inspired by control centrality theory. This approach assesses the impact of traffic loads from single or multiple sources on any node within the metro network. Our method provides a scientific basis for operators to develop policies for managing overloaded traffic, enhancing both safety and efficiency in metro system operations.