2023
DOI: 10.1016/j.scs.2023.104862
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Urban Digital Twin Challenges: A Systematic Review and Perspectives for Sustainable Smart Cities

Charlotte Weil,
Simon Elias Bibri,
Régis Longchamp
et al.
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Cited by 84 publications
(12 citation statements)
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“…From a technical point of view, although all three cases point, in the long run, to a cyber-physical holistic development of CDTs, currently these projects remain anchored to city information models synthetizing GIS and BIM with some IoT-derived data. Hence, integration and interoperability remain major issues to be tackled, as evidenced also by recent literature (Lei et al, 2023;Weil et al, 2023). In fact, in Dublin and Helsinki it remains to see if/how the coordination across various projects, as a result of their decision to develop multiple CDTs, will be tackled.…”
Section: Discussionmentioning
confidence: 99%
“…From a technical point of view, although all three cases point, in the long run, to a cyber-physical holistic development of CDTs, currently these projects remain anchored to city information models synthetizing GIS and BIM with some IoT-derived data. Hence, integration and interoperability remain major issues to be tackled, as evidenced also by recent literature (Lei et al, 2023;Weil et al, 2023). In fact, in Dublin and Helsinki it remains to see if/how the coordination across various projects, as a result of their decision to develop multiple CDTs, will be tackled.…”
Section: Discussionmentioning
confidence: 99%
“…Im Bauingenieurwesen und der Architektur wird der Begriff "Digitaler Zwilling" derzeit als ein Werkzeug zur Unterstützung der Digitalisierung verwendet. In der Stadtplanung existieren dazu bereits Plattformen, welche die sogenannten Smart Citys unter Verwendung der hinterlegten Informationen als urbanen Digitalen Zwilling abbilden können [11][12][13]. Doch auch für die Verkehrsinfrastruktur werden diese Ansätze verfolgt.…”
Section: Anwendung Digitaler Zwillingeunclassified
“…The first pane of Figure 3 contains names of formal methods and the second one lists the goals of their applications in modeling and describing the indicators. These include traditional machine learning techniques such as factor analysis, principal component analysis, and cluster analysis, and advanced ones such as deep learning networks, as well as powerful AI techniques such as machine reasoning [67], synthetic data generation [68,69], and digital twins [70,71]. Data analytics, including data aggregation, visualization, and correlation analysis, are being extended by contemporary causality analysis and predictive tools.…”
Section: Formal Notions and Framework Of Indicatorsmentioning
confidence: 99%