Cited 0 time in
수도권 지역 장시간 통근통행 영향요인 분석
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 김재훈 | - |
| dc.contributor.author | 이수기 | - |
| dc.date.accessioned | 2026-03-26T02:00:56Z | - |
| dc.date.available | 2026-03-26T02:00:56Z | - |
| dc.date.issued | 2025-12 | - |
| dc.identifier.issn | 1226-7147 | - |
| dc.identifier.issn | 2383-9171 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/211605 | - |
| dc.description.abstract | 본 연구는 통행 데이터를 활용하여 장시간 통근 기준을 정의하고, 이를 바탕으로 장시간 통근에 영향을 미치는 요인의 변화를분석하는 것을 목적으로 한다. 이를 위해 2010년과 2021년 데이터를 활용하여, 시대적·지역적 특성을 반영한 장시간 통근 기준을 설정하고, 통근 시간 변화의 동태적 특성을 규명하였다. 본 연구에서는 2010년 가구통행실태조사와 2021년 개인통행실태조사데이터를 사용하였으며, 가우시안 혼합 모델(Gaussian Mixture Model, GMM)을 적용하여 장시간 통근 기준을 도출하였다. 또한 개인 수준과 지역 수준의 요인을 동시에 고려한 다수준 로지스틱 회귀분석을 통해 장시간통근에 영향을 미치는 주요 요인을 분석하였다. 본 연구는 통근에 대한 명확한 기준을 제시하고, 통근시간 변화와 그 영향요인을 심층적으로 분석함으로써 통근자의삶의 질 개선과 교통 정책 수립에 기여할 것으로 기대된다. | - |
| dc.description.abstract | This study aims to establish criteria for long-time commuting in the Seoul Metropolitan Area (SMA) of Korea and analyze the changes in the factors influencing this trend, with the goal of deriving policy implications to reduce such commutes. In the SMA, long-time commuting has increased due to the expansion of commuting zones, which is driven by large-scale housing development and the enhancement of transportation infrastructure. However, research specifically addressing long-time commuting and its determinants remains limited. Additionally, the definition and outcomes of long-time commuting vary depending on the study area, as well as demographic, social, economic, and cultural contexts. Using data from the 2010 Household Commuting Survey and the 2021 Individual Commuting Survey, this study identifies long-time commuting using a Gaussian Mixture Model and investigates its influencing factors through multilevel logistic regression analysis. The results show that the threshold defining long-time commuting increased by approximately five minutes between 2010 and 2021, alongside a rise in the number of long-time commuters. The likelihood of long-time commuting was higher among individuals aged 20-39, men, and high-income earners. Additionally, housing costs and public transportation were found to significantly influence commuting patterns. This study presents a clear criterion for long-time commuting, analyzing its determinants, and highlighting temporal changes, thereby contributing meaningful policy insights for reducing long commutes in the SMA. | - |
| dc.format.extent | 19 | - |
| dc.language | 한국어 | - |
| dc.language.iso | KOR | - |
| dc.publisher | 대한국토·도시계획학회 | - |
| dc.title | 수도권 지역 장시간 통근통행 영향요인 분석 | - |
| dc.title.alternative | Analysis of Factors Influencing Long-Time Commutes in the Seoul Metropolitan Area | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.doi | 10.17208/jkpa.2025.12.60.7.92 | - |
| dc.identifier.bibliographicCitation | 국토계획, v.60, no.7, pp 92 - 110 | - |
| dc.citation.title | 국토계획 | - |
| dc.citation.volume | 60 | - |
| dc.citation.number | 7 | - |
| dc.citation.startPage | 92 | - |
| dc.citation.endPage | 110 | - |
| dc.type.docType | Y | - |
| dc.identifier.kciid | ART003283517 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.subject.keywordAuthor | Commuting | - |
| dc.subject.keywordAuthor | Long Commute | - |
| dc.subject.keywordAuthor | Gaussian Mixture Model | - |
| dc.subject.keywordAuthor | Multilevel Logistic Regression | - |
| dc.subject.keywordAuthor | 통근통행 | - |
| dc.subject.keywordAuthor | 장시간 통근 | - |
| dc.subject.keywordAuthor | 가우시안 혼합 모델 | - |
| dc.subject.keywordAuthor | 다수준 로지스틱 회귀모형 | - |
| dc.identifier.url | https://kpaj.or.kr/_common/do.php?a=full&b=12&bidx=4316&aidx=47893 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1366
COPYRIGHT © 2024 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
