Evaluating the resilience and recovery of public transit system using big data: Case study from New Jersey

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dc.contributor.authorMudigonda, Sandeep
dc.contributor.authorÖzbay, Kaan
dc.contributor.authorBartın, Bekir
dc.date.accessioned2021-05-15T12:41:23Z
dc.date.available2021-05-15T12:41:23Z
dc.date.issued2019
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractAnalyzing resilience and vulnerability of public transit networks is extremely important in the context of natural disasters as they are essential for evacuation. In this study, the public transit systems in New Jersey based on their vulnerability, resilience, and efficiency during the recovery period following Hurricane Sandy were analyzed. Diverse traffic, infrastructure, events, and web-based sources of Big Data are applied. Due to the sparsity of public transit performance measures for vulnerability, recovery, and resilience, various measures from existing literature were adapted for public transit. Following Hurricane Sandy, the bus transit network of NJ Transit (NJT) recovered much faster than its rail network. This was observed because the road infrastructure recovered much faster as compared to rail and subway networks. Additionally, the most critical link for the NJT buses remained intact during the hurricane whereas rail and subway systems suffered loss of power for driving and signaling. Performance measures such as critical links identification, change in travel time, friability, and resilience triangles for specific bus routes on the NJT bus network were estimated. Transit agencies can use these measures and methodologies in planning and preparing for disasters to study route vulnerability and transit network resilience and standardize performance measures.en_US
dc.description.sponsorshipMINETA Transportation Institute's National Transportation Finance Center; National Science FoundationNational Science Foundation (NSF) [1541164]; MINETA Transportation Institute [DTRT12-G-UTC21]en_US
dc.description.sponsorshipThis study was performed with support from a grant from MINETA Transportation Institute's National Transportation Finance Center. The authors would like to thank the sponsors for their support. National Science Foundation, 1541164 and MINETA Transportation Institute, DTRT12-G-UTC21.en_US
dc.identifier.doi10.1080/19439962.2018.1436105
dc.identifier.endpage519en_US
dc.identifier.issn1943-9962
dc.identifier.issn1943-9970
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85044463691
dc.identifier.scopusqualityQ1
dc.identifier.startpage491en_US
dc.identifier.urihttps://doi.org/10.1080/19439962.2018.1436105
dc.identifier.urihttps://hdl.handle.net/20.500.12939/795
dc.identifier.volume11en_US
dc.identifier.wosWOS:000478934400003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBartın, Bekir
dc.language.isoen
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofJournal of Transportation Safety & Security
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPublic Transiten_US
dc.subjectNatural Disasteren_US
dc.subjectResilienceen_US
dc.subjectVulnerabilityen_US
dc.subjectRecoveryen_US
dc.subjectBig Dataen_US
dc.titleEvaluating the resilience and recovery of public transit system using big data: Case study from New Jersey
dc.typeArticle

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İnşaat Mühendisliği Bölümü Koleksiyonu
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