dc.contributor.author
Trasanidis, Orestis
en
dc.date.accessioned
2022-07-25T06:53:36Z
dc.date.available
2022-07-25T06:53:36Z
dc.date.issued
2022-07-25
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/30010
dc.rights
Default License
dc.subject
Smart Cities
en
dc.subject
City Intelligence
en
dc.subject
Machine Learning
en
dc.subject
Decision-making
en
dc.subject
Sustainability
en
dc.title
Decision Making Tool for Smart Cities
en
heal.type
masterThesis
en_US
heal.creatorID.email
orestistra@gmail.com
heal.contributorName
Tzanidaki, Johanna
en
heal.dateAvailable
2022-05
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSC in Smart Cities and Communities
en_US
heal.publicationDate
2021-06
heal.abstract
Smart cities and their link to sustainability especially in the mobility sector has become a major concern on a global scale. Nonetheless, the challenges of this link must be first dealt at the local scale. Urbanization is rapidly transforming the cities and creates new urban behaviors pushing for a holistic assessment of cities. Furthermore, the complexity of the resulting urban problems ne- cessitates an evidence based decision and policy making approach. This study aspires to propose a methodological framework for enabling an exploratory analysis of the smart mobility charac- teristics of European cities by utilizing the capabilities of ICT tools in tackling the obstacles of large scale assessment. This work involves the combination of a qualitative survey (questionnaire) and the creation of new quantitative datasets in order to cluster cities according to a proposed system of indicators. This will serve as a basis for developing an interactive tool that will dynam- ically visualize the correlations between sustainability assessment indicators, cities and clusters allowing the identification of key influencers, city profiles and characteristics. The methodology is tested on 57 European cities and incorporates the use of composite indicators, data mining techniques and state-of-the-art analysis of the theoretical background and a detailed review of relevant past studies.
el
heal.sponsor
European Commision
en
heal.advisorName
Tjortjis, Christos
el
heal.committeeMemberName
Tzanidaki, Johanna
en
heal.academicPublisher
SMACCs
en
heal.academicPublisherID
ihu
en_US
heal.numberOfPages
79
en_US