Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece
This research aims to assess the contribution of artificial intelligence (AI)-driven digital twin technology in improving the predictive planning of European smart cities, particularly in Greece. It considers the effect of specific elements including simulation accuracy, real-time data processing, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Urban Science |
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| Online Access: | https://www.mdpi.com/2413-8851/9/7/267 |
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| author | Dimitrios Kalfas Stavros Kalogiannidis Konstantinos Spinthiropoulos Fotios Chatzitheodoridis Evangelia Ziouziou |
| author_facet | Dimitrios Kalfas Stavros Kalogiannidis Konstantinos Spinthiropoulos Fotios Chatzitheodoridis Evangelia Ziouziou |
| author_sort | Dimitrios Kalfas |
| collection | DOAJ |
| description | This research aims to assess the contribution of artificial intelligence (AI)-driven digital twin technology in improving the predictive planning of European smart cities, particularly in Greece. It considers the effect of specific elements including simulation accuracy, real-time data processing, artificial intelligence tools, and system readiness on the urban planning process. Structured questionnaires were administered to 301 urban professionals working in smart cities across Greece, focusing on their perceptions of the impact of digital twin features on predictive urban planning effectiveness. Respondents were asked how crucial they found the different features of digital twins in actually improving predictive urban planning. Measurement data were described using the arithmetic mean, standard deviation, and coefficient of variation, while categorical data were described using frequency distribution tables and percentages. This study revealed that the simulation fidelity, available real-time data integration, artificial intelligence analytics, and results- oriented monitoring system maturity have a positive impact on the accuracy, speed, and flexibility of urban planning. Some of the respondents noted these features as very useful for the prediction of urban conditions and decision-making purposes. Nevertheless, some drawbacks related to the computational load and data flow were also revealed. AI-driven digital twins are useful for improving the effectiveness of urban planning. However, they encounter technical issues; therefore, seeking to focus on system maturity and data integration is necessary for their successful implementation. Cities should adopt advanced digital twin technologies and enhance the compatibility of data and maintain AI transparency for better urban planning results. |
| format | Article |
| id | doaj-art-91dfecb6514d4b9bbc43e6d5a453d57c |
| institution | DOAJ |
| issn | 2413-8851 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Urban Science |
| spelling | doaj-art-91dfecb6514d4b9bbc43e6d5a453d57c2025-08-20T02:47:14ZengMDPI AGUrban Science2413-88512025-07-019726710.3390/urbansci9070267Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of GreeceDimitrios Kalfas0Stavros Kalogiannidis1Konstantinos Spinthiropoulos2Fotios Chatzitheodoridis3Evangelia Ziouziou4Department of Agriculture, School of Agricultural Sciences, University of Western Macedonia, 53100 Florina, GreeceDepartment of Business Administration, University of Western Macedonia, 51100 Grevena, GreeceDepartment of Management Science and Technology, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Management Science and Technology, University of Western Macedonia, 50100 Kozani, GreeceDepartment of Management Science and Technology, University of Western Macedonia, 50100 Kozani, GreeceThis research aims to assess the contribution of artificial intelligence (AI)-driven digital twin technology in improving the predictive planning of European smart cities, particularly in Greece. It considers the effect of specific elements including simulation accuracy, real-time data processing, artificial intelligence tools, and system readiness on the urban planning process. Structured questionnaires were administered to 301 urban professionals working in smart cities across Greece, focusing on their perceptions of the impact of digital twin features on predictive urban planning effectiveness. Respondents were asked how crucial they found the different features of digital twins in actually improving predictive urban planning. Measurement data were described using the arithmetic mean, standard deviation, and coefficient of variation, while categorical data were described using frequency distribution tables and percentages. This study revealed that the simulation fidelity, available real-time data integration, artificial intelligence analytics, and results- oriented monitoring system maturity have a positive impact on the accuracy, speed, and flexibility of urban planning. Some of the respondents noted these features as very useful for the prediction of urban conditions and decision-making purposes. Nevertheless, some drawbacks related to the computational load and data flow were also revealed. AI-driven digital twins are useful for improving the effectiveness of urban planning. However, they encounter technical issues; therefore, seeking to focus on system maturity and data integration is necessary for their successful implementation. Cities should adopt advanced digital twin technologies and enhance the compatibility of data and maintain AI transparency for better urban planning results.https://www.mdpi.com/2413-8851/9/7/267AI-driven digital twinspredictive urban planningsmart citiessimulation fidelityreal-time data integrationsystem maturity |
| spellingShingle | Dimitrios Kalfas Stavros Kalogiannidis Konstantinos Spinthiropoulos Fotios Chatzitheodoridis Evangelia Ziouziou Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece Urban Science AI-driven digital twins predictive urban planning smart cities simulation fidelity real-time data integration system maturity |
| title | Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece |
| title_full | Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece |
| title_fullStr | Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece |
| title_full_unstemmed | Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece |
| title_short | Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece |
| title_sort | enhancing predictive urban planning in european smart cities through ai driven digital twin technology a case study of greece |
| topic | AI-driven digital twins predictive urban planning smart cities simulation fidelity real-time data integration system maturity |
| url | https://www.mdpi.com/2413-8851/9/7/267 |
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