article · Artificial Intelligence Review
Digital twin technology creates virtual representations of physical systems to improve urban management within smart cities. When combined with machine learning for predictive analysis and the Internet of Things for real-time data collection, these tools support a wide variety of municipal functions. A large-scale bibliometric review of more than 4,220 scientific publications maps the field by examining datasets, software platforms, and performance metrics. Using data visualisation tools such as VOSviewer, the analysis identifies publication patterns, active contributors, and major thematic clusters. In addition to reviewing successful implementation case studies and current operational limitations, the work outlines the developing influence of policy and governance alongside emerging technological directions for future smart city initiatives.
As urban centres grow more complex, city authorities require effective methods to monitor, model, and predict infrastructure needs. Synthesising findings from thousands of studies provides municipal planners, technologists, and policymakers with a clear view of existing platforms, practical case studies, and regulatory considerations essential for modernising city operations.
The underlying technologies apply directly to urban management, offering utility for municipal authorities, systems integrators, and smart city software providers. Because the underlying research synthesises established platforms and working case studies alongside emerging trends, the solutions span applied testing through to operational, near-market deployments in urban infrastructure monitoring and predictive municipal planning.
AI-generated from the published abstract. Always read the original work before citing.
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.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1007/s10462-024-10781-8
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
