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Neighborhood-level determining factors of travel distance by trip purpose: Policy implications for the N-minute city

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dc.contributor.authorKim, Yejin-
dc.contributor.authorLee, Sugie-
dc.date.accessioned2025-08-14T07:00:13Z-
dc.date.available2025-08-14T07:00:13Z-
dc.date.issued2025-01-
dc.identifier.issn0264-2751-
dc.identifier.issn1873-6084-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/208530-
dc.description.abstractAs part of efforts to reduce carbon emissions, the concept of n-minute cities has been proposed. Although many global cities have attempted to implement this idea, there is still a lack of quantitative research on n-minute cities. This study aims to analyze factors that influence living radii by utilizing mobile phone location-based mobility big data and spatial regression analysis, thereby contributing to the implementation of n-minute cities. By distinguishing between pre- and post-COVID-19 periods and between commuting and non-commuting travel, this study reveals decreased average travel distances in the post-pandemic period and identifies factors influencing living radii, thereby favoring the concept of n-minute cities. During the post-COVID-19 period, the impact of factors such as population density, commercial facilities, and the job-housing ratio on travel distance has shifted, while neighborhood living and cultural facilities maintain consistent influence. This underscores the importance of prioritizing these aspects in n-minute city planning. Additionally, the mix of residential and business uses consistently affects shorter commuting distances, highlighting its critical role in n-minute city development. This study is significant for identifying urban environmental factors that effectively reduce living radii based on actual travel behaviors.-
dc.format.extent13-
dc.language영어-
dc.language.isoENG-
dc.publisherPergamon Press Ltd.-
dc.titleNeighborhood-level determining factors of travel distance by trip purpose: Policy implications for the N-minute city-
dc.typeArticle-
dc.publisher.location영국-
dc.identifier.doi10.1016/j.cities.2024.105464-
dc.identifier.scopusid2-s2.0-85206975986-
dc.identifier.wosid001344030400001-
dc.identifier.bibliographicCitationCities, v.156, pp 1 - 13-
dc.citation.titleCities-
dc.citation.volume156-
dc.citation.startPage1-
dc.citation.endPage13-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaUrban Studies-
dc.relation.journalWebOfScienceCategoryUrban Studies-
dc.subject.keywordPlusCOMMUTING PATTERNS-
dc.subject.keywordPlusBUILT ENVIRONMENT-
dc.subject.keywordAuthor15-Minute city-
dc.subject.keywordAuthorCOVID-19-
dc.subject.keywordAuthorMobile phone location-based mobility big data-
dc.subject.keywordAuthorN-minute city-
dc.subject.keywordAuthorTravel distance-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0264275124006784?via%3Dihub-
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