Comprehensive analysis of digital twins in smart cities: a 4200-paper bibliometric study

Abstract This survey paper comprehensively reviews Digital Twin (DT) technology, a virtual representation of a physical object or system, pivotal in Smart Cities for enhanced urban management. It explores DT's integration with Machine Learning for predictive analysis, IoT for real-time data, and its significant role in Smart City development. Addressing the gap in existing literature, this survey analyzes over 4,220 articles from the Web of Science, focusing on unique aspects like datasets, platforms, and performance metrics. Unlike other studies in the field, this research paper distinguishes itself through its comprehensive and bibliometric approach, analyzing over 4,220 articles and focusing on unique aspects like datasets, platforms, and performance metrics. This approach offers an unparalleled depth of analysis, enhancing the understanding of Digital Twin technology in Smart City development and setting a new benchmark in scholarly research in this domain. The study systematically identifies emerging trends and thematic topics, utilizing tools like VOSviewer for data visualization. Key findings include publication trends, prolific authors, and thematic clusters in research. The paper highlights the importance of DT in various urban applications, discusses challenges and limitations, and presents case studies showcasing successful implementations. Distinguishing from prior studies, it offers detailed insights into emerging trends, future research directions, and the evolving role of policy and governance in DT development, thereby making a substantial contribution to the field.

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