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Dynamic origin-destination estimation with location-based social networking data

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TitleInfo
Title
Dynamic origin-destination estimation with location-based social networking data
SubTitle
exploring urban travel demand sensor
Name (type = personal)
NamePart (type = family)
Hu
NamePart (type = given)
Wangsu
NamePart (type = date)
1989-
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Wangsu Hu
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Jin
NamePart (type = given)
Jing
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Jing Jin
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Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Liu
NamePart (type = given)
Xiang
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Xiang Liu
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Gong
NamePart (type = given)
Jie
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Jie Gong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Di
NamePart (type = given)
Xuan
DisplayForm
Xuan Di
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = personal)
NamePart (type = family)
Xiong
NamePart (type = given)
Hui
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Hui Xiong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
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NamePart
School of Graduate Studies
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school
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Text
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theses
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DateCreated (qualifier = exact)
2019
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2019-01
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2019
Place
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xx
Language
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eng
Abstract
The emergence of Transportation Big Data provides rich information for estimating and predicting urban travel demand patterns. The traditional travel demand sensors involve labor-intensive survey data, traffic detector data for assignment model calibration, vehicle re-identification data from scattered Bluetooth, Wifi, or License plate readers, or aggregated cellphone activity data used in existing dynamic Origin-Destination estimation models or applications. With the growing number of mobile devices with GPS units and improvement in WLT technologies, the Location-Based Social Network (LBSN) data is an emerging travel demand data source. LBSN data recorded check-in or tweeting activities of massive users at different points of interests (POIs). The wide-range of POIs ensure the dense coverage of the main urban areas and the user-confirmed POI information provides the much-needed trip purpose information not available in other data sources. Meanwhile, the LBSN data has the advantages of passive secondary data collection usually not for the purpose of travel surveys, and anonymization. Despite the above advantages, LBSN data is not without its limitations for estimating urban travel demand, as well as dynamic OD estimation for proactive urban congestion mitigation and operations. First, LBSN can have a systematic temporal error for estimating travel demand. The LBSN activities do not always mimic travel activities throughout the day. Second, LBSN data includes a sampling bias for different population groups and venue types. Third, the stochastic nature of human activities, especially the POI arriving patterns are critical for travel demand estimation. The existing approaches to the LBSN-based travel demand analysis have suffered from those limitations on deriving the dynamic travel demand patterns.
Recent development in spatial-temporal characteristics provides the opportunities to identify and quantify the correlation between LSBN-based travel activity and urban travel demand pattern. In this dissertation, a novel set of travel demand models based on the LBSN data is proposed and tested. The research starts with a comprehensive review of the existing travel demand data collection methods and the travel demand modeling. Then we introduce a profiling method to infer the functionality of city zones based on the POI categorical distribution and local mobility patterns. By classifying zones by these zone topics, we can now analyze interactions between zones of different functionality. Thirdly, by conducting zonal time-of-day variation modeling on the LBSN check-in arrivals, a new stochastic point process based trip arrivals estimation is developed. The output is applied to the input of a temporal delay based trip distribution model for deriving dynamic OD patterns. And the model calibration and applications are also provided and discussed. The evaluation results illustrate the promising benefits of applying LBSN Data in urban travel demand modeling.
Subject (authority = RUETD)
Topic
Civil and Environmental Engineering
Subject (authority = ETD-LCSH)
Topic
Location-based services
Subject (authority = ETD-LCSH)
Topic
Multiscale modeling
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9509
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (110 pages : illustrations)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Wangsu Hu
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-10p8-ra87
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Hu
GivenName
Wangsu
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-01-09 02:46:18
AssociatedEntity
Name
Wangsu Hu
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
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Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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