Enhancing Predictive Urban Planning in European Smart Cities Through AI-Driven Digital Twin Technology: A Case Study of Greece - Dimitrios Kalfas; Stavros Kalogiannidis; Konstantinos Spinthiropoulos; Fotios Chatzitheodoridis; Evangelia Ziouziou (Urban Science)
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.