DC FieldValueLanguage
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorSalmon, Ioannis-
dc.contributor.authorTsotsolas, Nikos-
dc.contributor.authorFedchenkov, Petr-
dc.contributor.authorZaslavsky, Arkady-
dc.date.accessioned2024-03-22T13:27:34Z-
dc.date.available2024-03-22T13:27:34Z-
dc.date.issued2020-07-01-
dc.identifierscopus-85075213113-
dc.identifier.issn14333058-
dc.identifier.issn09410643-
dc.identifier.other85075213113-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/1557-
dc.description.abstractParking in contemporary cities is a time- and fuel-consuming process. It affects daily stress levels of drivers and citizens. To design the future cities, parking process should be handled efficiently to improve drivers’ time comfort and fuel economy toward a green smart city (SC) ecosystem. In this paper, we propose to model smart parking (SP) with multiagent system (MAS) using long short-term memory (LSTM) neural network. Our model outperforms similar approaches as evidenced from the presented results using an online dataset from the SC of Aarhus, Denmark. We use LSTM for stochastic prediction based on periodic data provided by parking sensors. A SP provides such data on daily basis over a short period of time in the SC. We evaluate the proposed MAS with the prediction accuracy metric and compare it with other approaches in the literature. The proposed system achieves higher prediction accuracy per daily basis than the compared approaches due to our stochastic periodic prediction design and input to the proposed MAS and LSTM model. In addition, LSTM is used more efficiently under the proposed architecture of MAS, which enables online scaling thanks to dynamic and distributed nature of MAS.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.subjectCyber-physical systemsen_US
dc.subjectLSTMen_US
dc.subjectMultiagent modelingen_US
dc.subjectSmart parkingen_US
dc.subjectStochastic predictionen_US
dc.titleDistributed modeling of smart parking system using LSTM with stochastic periodic predictionsen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s00521-019-04613-yen_US
dc.identifier.scopus2-s2.0-85075213113-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume32en_US
dc.relation.issue14en_US
dc.identifier.spage10783en_US
dc.identifier.epage10796en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.subject.fieldEngineering and Technologyen_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.orcid0009-0006-9089-8898-
crisitem.author.orcid0000-0003-4173-3780-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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