Jingran Sun , Kyle Bathgate , Shidong Pan , Zhanmin Zhang
{"title":"Network-based method for assessing multi-modal transportation network vulnerability to cascading failures","authors":"Jingran Sun , Kyle Bathgate , Shidong Pan , Zhanmin Zhang","doi":"10.1016/j.samod.2024.100034","DOIUrl":null,"url":null,"abstract":"<div><div>Transportation systems are vulnerable to disruptive events such as natural disasters, industrial accidents, terrorist attacks, and climate change. Vulnerability assessment is necessary to understand the impacts of these disruptive events, identify underlying deficiencies within the network, and improve transportation system resilience. Multi-modal transportation networks are often interdependent and form a “system of systems”, which creates a susceptibility to cascading indirect impacts within the integrated transportation network. Accurately modeling these interdependencies typically requires a large amount of data, such as traffic flow and travel demand information for transportation systems, which may not be available or accessible. This paper proposes a network topology-based framework to conduct an interdependent transportation network vulnerability analysis by introducing an algorithm to simulate cascading failures across transport systems. The proposed framework estimates the vulnerability of the network with respect to a specific hazard, combining the network topology and the functional attributes of the transportation infrastructure components. A case study with real-world data is conducted to demonstrate the applicability of the framework to the Houston freight transportation network, and to understand the network performance under different scenarios. This study presents an alternate method that stakeholders may use to assess interdependent transportation network vulnerability when more detailed flow-based data is not available.</div></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"4 ","pages":"Article 100034"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainability Analytics and Modeling","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667259624000067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Transportation systems are vulnerable to disruptive events such as natural disasters, industrial accidents, terrorist attacks, and climate change. Vulnerability assessment is necessary to understand the impacts of these disruptive events, identify underlying deficiencies within the network, and improve transportation system resilience. Multi-modal transportation networks are often interdependent and form a “system of systems”, which creates a susceptibility to cascading indirect impacts within the integrated transportation network. Accurately modeling these interdependencies typically requires a large amount of data, such as traffic flow and travel demand information for transportation systems, which may not be available or accessible. This paper proposes a network topology-based framework to conduct an interdependent transportation network vulnerability analysis by introducing an algorithm to simulate cascading failures across transport systems. The proposed framework estimates the vulnerability of the network with respect to a specific hazard, combining the network topology and the functional attributes of the transportation infrastructure components. A case study with real-world data is conducted to demonstrate the applicability of the framework to the Houston freight transportation network, and to understand the network performance under different scenarios. This study presents an alternate method that stakeholders may use to assess interdependent transportation network vulnerability when more detailed flow-based data is not available.