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Heterogeneous mobile data analytics for smart living

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TitleInfo
Title
Heterogeneous mobile data analytics for smart living
Name (type = personal)
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Liu
NamePart (type = given)
Yanchi
NamePart (type = date)
1986-
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Yanchi Liu
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author
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Xiong
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Hui
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Hui Xiong
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Advisory Committee
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chair
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Lin
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Xiaodong
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Xiaodong Lin
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Advisory Committee
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internal member
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Lidbetter
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Thomas
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Thomas Lidbetter
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Advisory Committee
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internal member
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Wang
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Guiling
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Guiling Wang
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Advisory Committee
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Rutgers University
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degree grantor
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Graduate School - Newark
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theses
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2019
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2019-05
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2019
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English
Abstract
With the development of mobile, sensing, and positioning technologies, large-scale urban geographic data and human mobility data have been accumulated recently. The availability of heterogeneous mobile data and the emergence of big data technology provide unparalleled opportunities on understanding user behaviors and enabling smart living, e.g., developing livable and vibrant communities, improving energy efficiency in transportation, and enhancing urban planning. To this end, the objective of this dissertation is to exploit heterogeneous mobile data for developing data-driven solutions to enable smart living.
Along this line, we first provide a data driven solution to recommend Points-of- Interest (POIs) for the purpose of improving people’s experiences for urban living. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, we propose a unified recommender system to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the proposed system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the proposed system by setting this time period long enough.
In addition, we study the Point-of-Interest (POI) demand modeling issue in urban regions for urban planning. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data.
Finally, we investigate intelligent bus routing to facilitate urban traveling. Optimal planning for public transportation is one of the keys helping to bring a sustain- able development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. We focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people’s real demand for public transportation.
Subject (authority = local)
Topic
Smart living
Subject (authority = RUETD)
Topic
Management
Subject (authority = LCSH)
Topic
Mobile computing
Subject (authority = LCSH)
Topic
Residential mobility
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Rutgers University Electronic Theses and Dissertations
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ETD_9911
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1 online resource (xii, 138 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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Identifier (type = doi)
doi:10.7282/t3-70vc-bd14
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
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Name
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Liu
GivenName
Yanchi
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Permission or license
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2019-04-25 20:49:25
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Name
Yanchi Liu
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Affiliation
Rutgers University. Graduate School - Newark
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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
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Copyright protected
Availability
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Open
Reason
Permission or license
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2019-04-25T01:41:20
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