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Имитационное моделирование для прогнозирования развития автомобильного электротранспорта на уровне региона
[journal article]

dc.contributor.authorKatalevsky, D. Yu.de
dc.contributor.authorGareev, Timur R.de
dc.date.accessioned2020-10-26T09:41:30Z
dc.date.available2020-10-26T09:41:30Z
dc.date.issued2020de
dc.identifier.issn2079-8555de
dc.identifier.urihttps://www.ssoar.info/ssoar/handle/document/70249
dc.description.abstractElectric transport is rapidly gaining popularity across the world. It is an example of technological advancement that has multiple consequences for regional economies, both in terms of the adaptation of production, transport and energy systems and their spatial optimization. The experience of leading economic regions, including countries of the Baltic Sea region, shows that electric transport can potentially substitute traditional transport technologies. Based on an authentic model of system dynamics, the authors propose a new approach to simulation modelling of the dissemination of electric vehicles in a given region. The proposed model allows the authors to take into account the key systemic feedback loops between the pool of electric vehicles and the charging infrastructure. In the absence of data required for the econometric methods of demand forecasting, the proposed model can be used for the identification of policies stimulating the consumer demand for electric vehicles in regions and facilitating the development of the electric transport infrastructure. The proposed model has been tested using real and simulated data for the Kaliningrad region, which due to its specific geographical location, is a convenient test-bed for developing simulation models of a regional scale. The proposed simulation model was built via the AnyLogic software. The authors explored the capacity of the model, its assumptions, further development and application. The proposed approach to demand forecasting can be further applied for building hybrid models that include elements of agent modelling and spatial optimization.de
dc.languageende
dc.subject.ddcSociology & anthropologyen
dc.subject.ddcSoziologie, Anthropologiede
dc.subject.othersystem dynamics; electric transport; charging stations infrastructure; demand stimulation; Bass model; AnyLogicde
dc.titleDevelopment of electric road transport: simulation modellingde
dc.title.alternativeИмитационное моделирование для прогнозирования развития автомобильного электротранспорта на уровне регионаde
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalBaltic Region
dc.source.volume12de
dc.publisher.countryRUS
dc.source.issue2de
dc.subject.classozWissenschaftssoziologie, Wissenschaftsforschung, Technikforschung, Techniksoziologiede
dc.subject.classozSociology of Science, Sociology of Technology, Research on Science and Technologyen
dc.subject.thesozprognosisen
dc.subject.thesozPlanungde
dc.subject.thesozNachfragelenkungde
dc.subject.thesozRusslandde
dc.subject.thesozSimulationde
dc.subject.thesozRussiaen
dc.subject.thesozElektrofahrzeugde
dc.subject.thesozInfrastrukturde
dc.subject.thesozelectric vehicleen
dc.subject.thesozdemand developmenten
dc.subject.thesozdemand managementen
dc.subject.thesoztransportationen
dc.subject.thesozTransportde
dc.subject.thesozsimulationen
dc.subject.thesoztrafficen
dc.subject.thesozVerkehrde
dc.subject.thesozregionale Faktorende
dc.subject.thesozneue Technologiede
dc.subject.thesozmodelen
dc.subject.thesozregional factorsen
dc.subject.thesozplanningen
dc.subject.thesozinfrastructureen
dc.subject.thesozPrognosede
dc.subject.thesozNachfrageentwicklungde
dc.subject.thesozModellde
dc.subject.thesoznew technologyen
dc.identifier.urnurn:nbn:de:0168-ssoar-70249-0
dc.rights.licenceCreative Commons - Attribution-NonCommercial 4.0en
dc.rights.licenceCreative Commons - Namensnennung, Nicht-kommerz. 4.0de
ssoar.contributor.institutionSkolkovo Institute of Science and Technologyde
internal.statusformal und inhaltlich fertig erschlossende
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dc.type.stockarticlede
dc.type.documentjournal articleen
dc.type.documentZeitschriftenartikelde
dc.source.pageinfo118-139de
internal.identifier.classoz10220
internal.identifier.journal38
internal.identifier.document32
internal.identifier.ddc301
dc.identifier.doihttps://doi.org/10.5922/2079-8555-2020-2-8de
dc.description.pubstatusPublished Versionen
dc.description.pubstatusVeröffentlichungsversionde
internal.identifier.licence32
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