Big data driven multi-tier architecture for electric mobility as a service in smart cities A design science approach

• Published in: International journal of energy sector management Vol. 14; no. 5; pp. 1023 - 1047
• Main Authors: Anthony Jnr, Bokolo, Abbas Petersen, Sobah, Ahlers, Dirk, Krogstie, John
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 19-08-2020
• Subjects: Alternative energy sources; Application programming interface; Architecture; Bicycles; Big Data; Cities; Data collection; Decision making; Electric bicycles; Electric vehicles; Energy industry; Infrastructure; Innovations; Interfaces; Interoperability; Literature reviews; Mobility; Municipalities; Provisioning; Public transportation; Quality of life; R&D; Renewable resources; Research & development; Smart cities; Sustainability; Sustainable transportation; Technological change; Transportation industry; Transportation services; Vehicles
• ISSN: 1750-6220; 1750-6239
• DOI: 10.1108/IJESM-08-2019-0001

Purpose
Electric mobility as a service (eMaaS) is suggested as a possible solution to ease transportation and lessen environmental issues by providing a collaborative transport sharing infrastructure that is based on electric vehicles (EVs) such as electric cars, electric bicycles and so on. Accordingly, this study aims to propose a multi-tier architecture to support the collection, processing, analytics and usage of mobility data in providing eMaaS within smart cities. The architecture uses application programming interfaces to enable interoperability between different infrastructures required for eMaaS and allow multiple partners to exchange and share data for making decision regarding electric mobility services.
Design/methodology/approach
Design science methodology based on a case study by interview was used to collect data from an infrastructure company in Norway to verify the applicability of the proposed multi-tier architecture.
Findings
Findings suggest that the architecture offers an approach for collecting, aggregating, processing and provisioning of data originating from sources to improve electric mobility in smart cities. More importantly, findings from this study provide guidance for municipalities and policymakers in improving electric mobility services. Moreover, the author’s findings provide a practical data-driven mobility use case that can be used by transport companies in deploying eMaaS in smart cities.
Research limitations/implications
Data was collected from a single company in Norway, hence, it is required to further verify the architecture with data collected from other companies.
Practical implications
eMaaS operates on heterogeneous data, which are generated from EVs and used by citizens and stakeholders such as city administration, municipality transport providers, charging station providers and so on. Therefore, the proposed architecture enables the sharing and usage of generated data as openly available data to be used in creating value-added services to improve citizen’s quality of life and viability of businesses.
Social implications
This study proposes the deployment of electric mobility to address increased usage of vehicles, which contributes to pollution of the environment that has a serious effect on citizen’s quality of life.
Originality/value
This study proposes a multi-tier architecture that stores, processes, analyze and provides data and related services to improve electric mobility within smart cities. The multi-tier architecture aims to support and increase eMaaS operation of EVs toward improving transportation services for city transport operators and citizens for sustainable transport and mobility system.