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Quasi-Sparsity Based Origin-Destination Demand Estimation- [electronic resource]
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Quasi-Sparsity Based Origin-Destination Demand Estimation- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101655
- ISBN
- 9798380327695
- DDC
- 385
- 저자명
- Wang, Jingxing.
- 서명/저자
- Quasi-Sparsity Based Origin-Destination Demand Estimation - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(131 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Ban, Xuegang Jeff.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약A good knowledge of the Origin-Destination (OD) demand matrix has been always important in various transportation applications, including simulation studies, transportation planning, traffic operations and control, and etc. For a large real network, the OD demand matrix may have certain quasi-sparsity property, i.e., the majority of the OD pairs have small demands while only a small portion of OD pairs have large demands. Inspired by Compressed Sensing technique, this dissertation proposes a Quasi-Sparsity Origin-Destination (QSOD) framework to explore such quasi-sparsity property of large-scale OD demand matrices. Three QSOD models (the fixed-mapping QSOD model, the bi-level QSOD model, and the distributionally robust QSOD model) are established under such QSOD framework. The results theoretically and numerically demonstrate that under certain conditions the estimated OD demands will share the same quasi-sparsity with the prior OD demands, and the estimated demands of most OD pairs (of a large-size network) will be equal to their prior values or zeros (or a very small value). Such findings provide important practical insights for OD estimation: one may only require the prior OD demands can capture the relative magnitude of the true OD demands of the network, which makes it much easier to prepare prior OD matrix in practice. The comparison between QSOD models and other existing OS estimation studies, and the integration of multi-sourced data for OD estimation under the QSOD framework are also discussed in this study.
- 일반주제명
- Transportation.
- 일반주제명
- Civil engineering.
- 기타저자
- University of Washington Civil and Environmental Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101655
■006m o d
■007cr#unu||||||||
■020 ▼a9798380327695
■035 ▼a(MiAaPQ)AAI30634888
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a385
■1001 ▼aWang, Jingxing.
■24510▼aQuasi-Sparsity Based Origin-Destination Demand Estimation▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Washington. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(131 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Ban, Xuegang Jeff.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aA good knowledge of the Origin-Destination (OD) demand matrix has been always important in various transportation applications, including simulation studies, transportation planning, traffic operations and control, and etc. For a large real network, the OD demand matrix may have certain quasi-sparsity property, i.e., the majority of the OD pairs have small demands while only a small portion of OD pairs have large demands. Inspired by Compressed Sensing technique, this dissertation proposes a Quasi-Sparsity Origin-Destination (QSOD) framework to explore such quasi-sparsity property of large-scale OD demand matrices. Three QSOD models (the fixed-mapping QSOD model, the bi-level QSOD model, and the distributionally robust QSOD model) are established under such QSOD framework. The results theoretically and numerically demonstrate that under certain conditions the estimated OD demands will share the same quasi-sparsity with the prior OD demands, and the estimated demands of most OD pairs (of a large-size network) will be equal to their prior values or zeros (or a very small value). Such findings provide important practical insights for OD estimation: one may only require the prior OD demands can capture the relative magnitude of the true OD demands of the network, which makes it much easier to prepare prior OD matrix in practice. The comparison between QSOD models and other existing OS estimation studies, and the integration of multi-sourced data for OD estimation under the QSOD framework are also discussed in this study.
■590 ▼aSchool code: 0250.
■650 4▼aTransportation.
■650 4▼aCivil engineering.
■653 ▼aQuasi-sparsity property
■653 ▼aTransportation network
■653 ▼aOrigin-Destination
■690 ▼a0709
■690 ▼a0543
■690 ▼a0796
■71020▼aUniversity of Washington▼bCivil and Environmental Engineering.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934799▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
