Regional Smart City Development Focus: The South Korean National Strategic Smart City Program

Yang, Jeyun; Kwon, Young-Sang; Kim, Dae-Hwan

doi:10.1109/access.2020.3047139

Cited by 64 publications

(35 citation statements)

References 103 publications

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“…This definition was cited and discussed by multiple researchers in the following years [5][6][7][8]. Similarly, Yang et al [9] conclude that there are six application domains for smart cities: Natural Resources and Energy, Transportation and Mobility, Building and Infrastructure, Living, Governance, Industry and Human Resources.…”

Section: Related Workmentioning

confidence: 99%

Towards a Digital Ecosystem for a Smart City District: Procedure, Results, and Lessons Learned

Elberzhager¹,

Mennig²,

Polst³

et al. 2021

Smart Cities

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The digital transformation supports many cities on the way to becoming smarter cities, enabling them to enhance digital processes, care about climate-friendly goals, or improve the quality of life of their citizens. However, such changes usually take place step by step and not in a big-bang approach. In order for the direction of the digital transformation to be defined, it is necessary to know and understand the needs and requirements of all relevant stakeholders who will be affected or are intended to use the new digital solutions. As our environment, a smart city district, is currently under construction, we do not know most of the future stakeholders yet. Therefore, we had to find new ways of eliciting the needs and requirements for digital solutions without knowing, e.g., the citizens who will live in the future district. We show a framework of the procedures we followed, classified into (a) vision and concepts, (b) smart city district digital ecosystem, and (c) dissemination and events. We substantiate the processes with example results and provide a discussion on how we evaluate our solutions with respect to future applicability. Because evaluations are only very limited in our setting right now, we focus on four lead questions to argue why the procedures and results are adequate and share the lessons we learned on this path towards a digital smart city district.

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Section: Related Workmentioning

confidence: 99%

Towards a Digital Ecosystem for a Smart City District: Procedure, Results, and Lessons Learned

Elberzhager¹,

Mennig²,

Polst³

et al. 2021

Smart Cities

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show abstract

“…Recently, smart cities have become an interesting research topic for academics, industry, and government [26,27,28,29,30]. However, there is relatively limited research on using new technologies such as big data, AI, and data-driven techniques for microscopic vehicle emission modelling.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Carbon Dioxide Emissions From Road Transport Using Taxi Trips

Ghahramani

Pilla

2021

IEEE Access

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Transport emissions, including road, rail, air, and marine transportation, account for a large part of the overall emissions; hence, there is a need to review strategies for managing associated issues and coping with negative impacts. A simultaneous improvement in economic efficiency can help us achieve our desired objectives in the concerned context. Sharing economy, i.e., a peer-to-peer-based sharing of access to assets, can help reduce the total resources required and consequently reduce carbon footprints. In line with this objective, we propose an intelligent model to study carbon dioxide emissions from road transport using taxi trips in Dublin, Ireland. The proposed method is a hybrid unsupervised learning approach tailored for the particular structure of the problem. We present how an intelligent approach can be implemented to model CO2 emissions from road transport. The model categorizes taxis based on different features related to the emissions they release. Different clusters are detected. Those vehicles labeled as highest emitters can be targeted for further improvements in reducing CO2, i.e., replacing pollutant cars with electric cars or including them in the taxi fleet as sharing ones only.

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“…The current implementation model for the integration of a smart city has several shortcomings [23] , such as focusing on the provision of various city services, rather than hard infrastructure. Smart services are driven without any integrated system thus smart city initiatives can hardly be sustained [24] .…”

Section: ) the Integration Ability Of Software And Hardware Is Insufficientmentioning

confidence: 99%

“…No two cities are the same. Smart city challenges vary by city and region due to a variety of factors, including the local urban characteristics, social needs, economy, governmental structures, culture, technological settings and geographical contexts [11,24,60] . In smart city policy making, the focus should be on the significant diversity of the cities, considering the needs and challenges and formulating policies according to local conditions.…”

Section: A Characterization Of the Casementioning

confidence: 99%

Modeling and Key Technologies of a Data-Driven Smart City System

Fang

Shan

Wang³

2021

IEEE Access

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The smart city operation and management center with a hierarchical data-driven architecture has already become one of the most widely used solutions for smart cities in practice, solving the problems associated with data acquisition, data gathering and storage, data processing, and data application. At present, the construction of smart city operation and management center faces bottlenecks such as incomplete top-level design theory, the insufficient integration capability of software and hardware, the low efficiency of data collection and aggregation, and the lack of intelligence in data analysis and application. Aiming to address the above problems, this paper proposes a 'two-dimension, three-layer, and six-goal' top-level design model for a smart city, with six principles for a smart city operational pattern, and focuses on three key technologies: (1) infrastructure integration and application, (2) multidimensional perception data collection and aggregation, and (3) intelligent data analysis and data service. Following the guidance of this model, Longgang District of Shenzhen has constructed a smart city operation and management center including integrated ICT infrastructure, as urban fine management system, and an intelligent urban data analysis and service system. Practice shows that the top-level design model of a smart city proposed in this paper has achieved successful results, and it thereby offers a model of a smart city that can be referenced and replicated.INDEX TERMS Data-driven, urban governance, smart city, top-level design.

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Regional Smart City Development Focus: The South Korean National Strategic Smart City Program

Cited by 64 publications

References 103 publications

Towards a Digital Ecosystem for a Smart City District: Procedure, Results, and Lessons Learned

Towards a Digital Ecosystem for a Smart City District: Procedure, Results, and Lessons Learned

Analysis of Carbon Dioxide Emissions From Road Transport Using Taxi Trips

Modeling and Key Technologies of a Data-Driven Smart City System

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