Description
TitleHeterogeneous mobile data analytics for smart living
Date Created2019
Other Date2019-05 (degree)
Extent1 online resource (xii, 138 pages) : illustrations
DescriptionWith 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.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
LanguageEnglish
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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