TY - GEN
T1 - Digital Twin Cities
T2 - Construction Research Congress 2022: Infrastructure Sustainability and Resilience, CRC 2022
AU - Jacobellis, Michael
AU - Ilbeigi, Mohammad
N1 - Publisher Copyright:
© 2022 ASCE.
PY - 2022
Y1 - 2022
N2 - Recent studies have shown potential benefits of digital twin cities for smart urban management. However, a major challenge for full-scale implementation of this concept is data availability. A comprehensive digital twin model that represents urban systems, their functionality, and interdependencies requires a wide range of data. However, little is known about systematic approaches for large-scale urban data collection. This study aims to address these gaps in knowledge in two steps to provide a more accurate depiction of available data. First, we conduct a synthesis study using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify current data collection methods in smart cities from technical articles. Second, we review publicly available databases to identify the types of available data that potentially can be used for digital twin cities models. This study identified and classified various types of available data including datasets related to infrastructure, healthcare, education, government, and environment.
AB - Recent studies have shown potential benefits of digital twin cities for smart urban management. However, a major challenge for full-scale implementation of this concept is data availability. A comprehensive digital twin model that represents urban systems, their functionality, and interdependencies requires a wide range of data. However, little is known about systematic approaches for large-scale urban data collection. This study aims to address these gaps in knowledge in two steps to provide a more accurate depiction of available data. First, we conduct a synthesis study using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify current data collection methods in smart cities from technical articles. Second, we review publicly available databases to identify the types of available data that potentially can be used for digital twin cities models. This study identified and classified various types of available data including datasets related to infrastructure, healthcare, education, government, and environment.
UR - http://www.scopus.com/inward/record.url?scp=85128932960&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128932960&partnerID=8YFLogxK
U2 - 10.1061/9780784483954.045
DO - 10.1061/9780784483954.045
M3 - Conference contribution
AN - SCOPUS:85128932960
T3 - Construction Research Congress 2022: Infrastructure Sustainability and Resilience - Selected Papers from Construction Research Congress 2022
SP - 437
EP - 444
BT - Construction Research Congress 2022
A2 - Jazizadeh, Farrokh
A2 - Shealy, Tripp
A2 - Garvin, Michael J.
Y2 - 9 March 2022 through 12 March 2022
ER -