Cited 0 time in
스마트카드 자료를 활용한 통근통행 추정과 통근네트워크구조 분석 - 가구통행실태조사자료와 비교검증을 통한 활용가능성을 중심으로 -
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 하재현 | - |
| dc.contributor.author | Lee, Su gie | - |
| dc.date.accessioned | 2022-07-15T19:30:48Z | - |
| dc.date.available | 2022-07-15T19:30:48Z | - |
| dc.date.issued | 2016-00 | - |
| dc.identifier.issn | 1226-7147 | - |
| dc.identifier.issn | 2383-9171 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/155438 | - |
| dc.description.abstract | 본 연구에서는 스마트카드(smart card, SC) 자료를 활용하여 통근통행을 추정하고, 통근네트워크의구조를 분석하고자 한다. 더 나아가, 분석된 결과를가구통행실태조사(household travel survey, HTS) 의 자료와 비교함으로써 스마트카드 자료를 활용한통근 OD 및 통근 네트워크의 추정이 어느 정도 수준에서 가능한지, 어떠한 방식으로 추정하는 것이가장 적합한지를 논의하고자 한다. 이뿐만 아니라, 본 연구는 대중교통 수단에 대한 통행 자료만으로전체수단에 대한 통행량을 추정할 수 있는지 확인하고자 한다. 본 연구의 결과는 향후 스마트카드자료를 활용하여 통근통행을 추정하고 통근네트워크의 구조를 분석하는 데 있어, 스마트카드 자료의활용방안 확립에 크게 기여할 것으로 기대된다. This study examines the possibility of application of smart card data for analyses of commuting patterns and the commuting network structure through the validation process using the household travel survey data. First, this study develops four different methods to extract journey-to-work trips from the smart card data that does not include trip purposes. Second, this study identifies the best method to extract journey-to-work trips from smart card data from the validation process. Lastly, this study investigates to what extent smart card data is able to estimate the commuting network structure using the prestige centrality index. The results confirm that estimated commuting patterns from the smart card database are reasonable comparing to the results from the traditional household travel survey data. This finding indicates that smart card data could be used to analyze commuting patterns and commuting network structure. However, since smart card data include public transportation only, it should be used with the traditional household travel survey data for an analysis of commuting patterns including other transportation modes. Despite a few limitations, this study points out that smart card data has substantial benefits for an analysis of commuting patterns and the commuting network structure using the real-time travel information. | - |
| dc.format.extent | 21 | - |
| dc.language | 한국어 | - |
| dc.language.iso | KOR | - |
| dc.publisher | 대한국토·도시계획학회 | - |
| dc.title | 스마트카드 자료를 활용한 통근통행 추정과 통근네트워크구조 분석 - 가구통행실태조사자료와 비교검증을 통한 활용가능성을 중심으로 - | - |
| dc.title.alternative | The estimation of commuting pattern and the analysis of the commuting network structure using smart card data - Focused on the possibility of application through the validation process with household travel survey data - | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.bibliographicCitation | 국토계획, v.51, no.4, pp 123 - 143 | - |
| dc.citation.title | 국토계획 | - |
| dc.citation.volume | 51 | - |
| dc.citation.number | 4 | - |
| dc.citation.startPage | 123 | - |
| dc.citation.endPage | 143 | - |
| dc.identifier.kciid | ART002140864 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.subject.keywordAuthor | Big Data | - |
| dc.subject.keywordAuthor | Smart Card Data | - |
| dc.subject.keywordAuthor | Commuting Pattern | - |
| dc.subject.keywordAuthor | Commuting Network | - |
| dc.subject.keywordAuthor | Public Transportation | - |
| dc.subject.keywordAuthor | 빅데이터 | - |
| dc.subject.keywordAuthor | 스마트카드 자료 | - |
| dc.subject.keywordAuthor | 통근통행패턴 | - |
| dc.subject.keywordAuthor | 통근 네트워크 | - |
| dc.subject.keywordAuthor | 대중교통 | - |
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.
