A digital twin smart city for citizen feedback
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The DT of the urban space in [32] uses information from three sources: citizens, IoT data from smart devices and sensors, and 3D and urban mobility data enabled by internet connectivity between the DT and the city's council.
“In the experimental setup presented in [32], the DT of the urban space uses information from three sources: citizens, IoT data from smart devices and sensors and 3D and urban mobility data enabled by internet connectivity between the DT and the city's council [32].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [32]
The work in [32] presents an advanced and unique application of DT technology to perform 3D simulations of a city under scenarios such as building and infrastructure planning, natural disasters (flooding) and green space simulations.
“This work presents an advanced and unique application of the DT technology to perform 3D simulations of a city under different scenarios such as building and infrastructure planning, natural disasters (flooding) and green space simulations.”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [32]
The use of ML and DL techniques has become more relevant as smart cities have become testbeds for data mining and ML.
“The use of ML and DL techniques has become more relevant as smart cities have become “exciting testbeds for data mining and ML” [32].”
From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [32]
Cites
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727 · see citing works
Caragliu, A., Del Bo, C., & Nijkamp, P. (2011) · Journal of Urban Technology · doi:10.1080/10630732.2011.601117 · Cited by 3,409 · see citing works
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920 · see citing works
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579 · see citing works
About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474 · see citing works
Digital Twin—The Simulation Aspect
Boschert, S., & Rosen, R. (2016) · Mechatronic Futures · doi:10.1007/978-3-319-32156-1_5 · Cited by 1,158 · see citing works
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
Mohammadi, M., & Al-Fuqaha, A. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700298 · Cited by 377 · see citing works
Smart health: Big data enabled health paradigm within smart cities
Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.06.027 · Cited by 341 · see citing works
Routing for Crowd Management in Smart Cities: A Deep Reinforcement Learning Perspective
Zhao, L., Wang, J., Liu, J., & Kato, N. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800603 · Cited by 126 · see citing works
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Digital Twin Technology Challenges and Applications: A Comprehensive Review
Botín-Sanabria, D. M., Mihaita, A.-S., Peimbert-García, R. E., Ramírez-Moreno, M. A., Ramírez-Mendoza, R. A., & Lozoya-Santos, J. D. J. (2022) · Remote Sensing · doi:10.3390/rs14061335 · Cited by 878 · see citing works
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103663 · Cited by 301 · see citing works
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304 · see citing works
A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management
Cheng, R., Hou, L., & Xu, S. (2023) · Buildings · doi:10.3390/buildings13051143 · Cited by 94 · see citing works
- Authors
- White, G., Zink, A., Codecá, L., & Clarke, S.
- Affiliations
- Trinity College Dublin (IE)
- Corresponding author
- White, Gary
- Venue
- Cities
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 110
- Pages
- 103064
- ISSN
- 0264-2751 (ISSN-L)
- OpenAlex
- W3120064844
- Cited by
- 548
- References
- 9
- Citations by year
- 2026: 76 · 2025: 120 · 2024: 145 · 2023: 109 · 2022: 74 · 2021: 24
- FWCI
- 40.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cities (publishedVersion)
- Funders
- Science Foundation Ireland
- Topics
- Physical Sciences › Engineering › Building and Construction › 3D Modeling in Geospatial Applications
- Physical Sciences › Engineering › Media Technology › Smart Cities and Technologies
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Keywords
- digital twin, smart cities, citizen feedback, building information modeling, IoT sensors, urban planning, citizen participation, 3D city model, flood evacuation planning, green space planning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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