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Digital Twin Algorithms, Smart City Technologies, and 3D Spatio-Temporal Simulations in Virtual Urban Environments

Digital Twin Algorithms, Smart City Technologies, and 3D Spatio-Temporal Simulations in Virtual... In this article, we cumulate previous research findings indicating that virtual process optimization requires augmented reality tools, data-driven predictive modeling techniques and maintenance algorithms, and cloud-edge computing systems. We contribute to the literature on digital twin algorithms, smart city technologies, and 3D spatio-temporal simulations in virtual urban environments by showing that urban sensing data necessitate machine learning and digital twin algorithms and convolutional neural networks. Throughout March 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “virtual urban environments” + “digital twin algorithms,” “smart city technologies,” and “3D spatio-temporal simulations.” As we inspected research published in 2022, only 177 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 32, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR. Keywords: virtual; urban; environment; digital twin; smart city; simulation http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geopolitics, History, and International Relations Addleton Academic Publishers

Digital Twin Algorithms, Smart City Technologies, and 3D Spatio-Temporal Simulations in Virtual Urban Environments

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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1948-9145
eISSN
2374-4383
Publisher site
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Abstract

In this article, we cumulate previous research findings indicating that virtual process optimization requires augmented reality tools, data-driven predictive modeling techniques and maintenance algorithms, and cloud-edge computing systems. We contribute to the literature on digital twin algorithms, smart city technologies, and 3D spatio-temporal simulations in virtual urban environments by showing that urban sensing data necessitate machine learning and digital twin algorithms and convolutional neural networks. Throughout March 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “virtual urban environments” + “digital twin algorithms,” “smart city technologies,” and “3D spatio-temporal simulations.” As we inspected research published in 2022, only 177 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 32, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR. Keywords: virtual; urban; environment; digital twin; smart city; simulation

Journal

Geopolitics, History, and International Relations – Addleton Academic Publishers

Published: Jan 1, 2022

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