DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.date.accessioned2024-07-05T14:17:48Z-
dc.date.available2024-07-05T14:17:48Z-
dc.date.issued2021-03-01-
dc.identifierscopus-85104017030-
dc.identifier.issn2624-6511-
dc.identifier.other85104017030-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2686-
dc.description.abstractSmart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, trans-port commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Ar-tificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.en_US
dc.language.isoenen_US
dc.relation.ispartofSmart Citiesen_US
dc.subjectArtificial intelligenceen_US
dc.subjectPredictionen_US
dc.subjectRecommendationen_US
dc.subjectSmart citiesen_US
dc.subjectUser commutingen_US
dc.subjectVehicle ride sharingen_US
dc.titleA predictive vehicle ride sharing recommendation system for smart cities commutingen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/smartcities4010010en_US
dc.identifier.scopus2-s2.0-85104017030-
dcterms.accessRights1en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume4en_US
dc.relation.issue1en_US
dc.identifier.spage177en_US
dc.identifier.epage191en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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