VTechWorks staff will be away for the Thanksgiving holiday starting Wednesday afternoon, Nov. 25, through Sunday Nov. 29, and will not be replying to requests during this time. Thank you for your patience.

Show simple item record

dc.contributor.authorMomtazpour, Marjanen_US
dc.date.accessioned2016-04-17T08:00:27Z
dc.date.available2016-04-17T08:00:27Z
dc.date.issued2016-04-16en_US
dc.identifier.othervt_gsexam:7642en_US
dc.identifier.urihttp://hdl.handle.net/10919/65157
dc.description.abstractDue to the rapid growth of urban areas, sustainable urbanization is an inevitable task for city planners to address major challenges in resource management across different sectors. Sustainable approaches of energy production, distribution, and consumption must take the place of traditional methods to reduce the negative impacts of urbanization such as global warming and fast consumption of fossil fuels. In order to enable the transition of cities to sustainable ones, we need to have a precise understanding of the city dynamics. The prevalence of big data has highlighted the importance of data-driven analysis on different parts of the city including human movement, physical infrastructure, and economic activities. Sustainable urban mobility (SUM) is the problem domain that addresses the sustainability issues in urban areas with respect to city dynamics and people movements in the city. Hence, to realize an integrated solution for SUM, we need to study the problems that lie at the intersection of energy systems and mobility. For instance, electric vehicle invention is a promising shift toward smart cities, however, the impact of high adoption of electric vehicles on different units such as electricity grid should be precisely addressed. In this dissertation, we use data analytics methods in order to tackle major issues in SUM. We focus on mobility and energy issues of SUM by characterizing transportation networks and energy networks. Data-driven methods are proposed to characterize the energy systems as well as the city dynamics. Moreover, we propose anomaly detection algorithms for control and management purposes in smart grids and in cities. In terms of applications, we specifically investigate the use of electrical vehicles for personal use and also for public transportation (i.e. electric taxis). We provide a data-driven framework to propose optimal locations for charging and storage installation for electric vehicles. Furthermore, adoption of electric taxi fleet in dense urban areas is investigated using multiple data sources.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectData miningen_US
dc.subjectUrban computingen_US
dc.subjectSmart gridsen_US
dc.subjectElectric vehicles.en_US
dc.titleKnowledge Discovery for Sustainable Urban Mobilityen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairRamakrishnan, Narenen_US
dc.contributor.committeememberMarathe, Madhav Vishnuen_US
dc.contributor.committeememberPrakash, Bodicherla Adityaen_US
dc.contributor.committeememberLu, Chang Tienen_US
dc.contributor.committeememberSharma, Ratnesh K.en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record