| dc.contributor.author | Bokolo, Anthony Junior | |
| dc.date.accessioned | 2020-09-21T13:12:17Z | |
| dc.date.available | 2020-09-21T13:12:17Z | |
| dc.date.created | 2020-06-09T11:31:46Z | |
| dc.date.issued | 2020 | |
| dc.identifier.isbn | 978-1-4503-7130-8 | |
| dc.identifier.uri | https://hdl.handle.net/11250/2678842 | |
| dc.description.abstract | Electro Mobility (eMobility) involves deploying Information and Communication Technologies (ICT) and electric technologies in vehicles to enable electric propulsion of vehicles referred to as Electric Vehicles (EVs). EVs are key infrastructure for achieving a sustainable energy future since EV usage can support in achieving CO2 reduction. However, the deployment of EVs for eMobility is highly dependent on data integration of mobility solutions from different stakeholders involved in urban transportation. Respectively, data integration from different mobility services will result to cost reduction and create valued added services to citizens. Therefore, there is need to achieve data integration not only for physical systems but for all domains in providing mobility related services that can be synergically applied to citizens and stakeholders in order to develop innovative solutions at district and urban level. Therefore, this study adopts Enterprise Architecture (EA) for digital transformations of eMobility services for sustainable transportation. Action research methodology was employed and secondary data from the literature was presented in the industrial data space reference architecture to initially validate digital transformation of electro mobility. Findings from this study reveal that EA support digital transformation of eMobility in managing data integration to support cities to implement sustainable transportation services. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | ACM Digital Library | en_US |
| dc.relation.uri | 10.1145/3378539.3393858 | |
| dc.title | Applying Enterprise Architecture for Digital Transformation of Electro Mobility towards Sustainable Transportation | en_US |
| dc.type | Chapter | en_US |
| dc.description.version | acceptedVersion | en_US |
| dc.source.pagenumber | 172 | en_US |
| dc.identifier.doi | 10.1145/3378539.3393858 | |
| dc.identifier.cristin | 1814510 | |
| dc.relation.project | EC/H2020/824260 | en_US |
| dc.description.localcode | Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. SIGMIS-CPR '20, June 19–21, 2020, Nuremberg, Germany © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7130-8/20/06…$15.00 https://doi.org/10.1145/3378539.3393858 | en_US |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
