Smart City Gnosys
Smart city article details
| Title | Generative Spatial Artificial Intelligence For Sustainable Smart Cities: A Pioneering Large Flow Model For Urban Digital Twin |
|---|---|
| ID_Doc | 27848 |
| Authors | Huang J.; Bibri S.E.; Keel P. |
| Year | 2025 |
| Published | Environmental Science and Ecotechnology , 24 |
| DOI | http://dx.doi.org/10.1016/j.ese.2025.100526 |
| Abstract | Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the critical need for innovative urban development solutions. In response, sustainable smart cities are increasingly turning to cutting-edge technologies—such as Generative Artificial Intelligence (GenAI), Foundation Models (FMs), and Urban Digital Twin (UDT) frameworks—to transform urban planning and design practices. These transformative tools provide advanced capabilities to analyze complex urban systems, optimize resource management, and enable evidence-based decision-making. Despite recent progress, research on integrating GenAI and FMs into UDT frameworks remains scant, leaving gaps in our ability to capture complex urban flows and multimodal dynamics essential to achieving environmental sustainability goals. Moreover, the lack of a robust theoretical foundation and real-world operationalization of these tools hampers comprehensive modeling and practical adoption. This study introduces a pioneering Large Flow Model (LFM), grounded in a robust foundational framework and designed with GenAI capabilities. It is specifically tailored for integration into UDT systems to enhance predictive analytics, adaptive learning, and complex data management functionalities. To validate its applicability and relevance, the Blue City Project in Lausanne City is examined as a case study, showcasing the ability of the LFM to effectively model and analyze urban flows—namely mobility, goods, energy, waste, materials, and biodiversity—critical to advancing environmental sustainability. This study highlights how the LFM addresses the spatial challenges inherent in current UDT frameworks. The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data, estimating flow data in new locations, predicting the evolution of flow data, and offering a holistic understanding of urban dynamics and their interconnections. The model enhances decision-making processes, supports evidence-based planning and design, fosters integrated development strategies, and enables the development of more efficient, resilient, and sustainable urban environments. This research advances both the theoretical and practical dimensions of AI-driven, environmentally sustainable urban development by operationalizing GenAI and FMs within UDT frameworks. It provides sophisticated tools and valuable insights for urban planners, designers, policymakers, and researchers to address the complexities of modern cities and accelerate the transition towards sustainable urban futures. © 2025 The Authors |
| Author Keywords | Foundation models; Generative artificial intelligence; Generative spatial artificial intelligence; Large flow model; Sustainable smart cities; Urban digital twin; Urban planning and design |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
56986  | 0.945 | Bibri S.E.; Huang J.; Jagatheesaperumal S.K.; Krogstie J. | The Synergistic Interplay Of Artificial Intelligence And Digital Twin In Environmentally Planning Sustainable Smart Cities: A Comprehensive Systematic Review | Environmental Science and Ecotechnology, 20 (2024) |
| 10521 | 0.926 | Bibri S.E.; Huang J. | Artificial Intelligence Of Things For Sustainable Smart City Brain And Digital Twin Systems: Pioneering Environmental Synergies Between Real-Time Management And Predictive Planning | Environmental Science and Ecotechnology , 26 (2025) |
| 56481 | 0.918 | Cina E.; Elbasi E.; Elmazi G.; AlArnaout Z. | The Role Of Ai In Predictive Modelling For Sustainable Urban Development: Challenges And Opportunities | Sustainability (Switzerland), 17, 11 (2025) |
| 27838 | 0.914 | Moreno-Ibarra M.; Saldaña-Perez M.; Rodríguez S.P.; Carbajal E.J. | Generative Ai (Genai) And Smart Cities: Efficiency, Cohesion, And Sustainability | Smart Cities: Lock-in, Path-dependence and Non-linearity of Digitalization and Smartification (2024) |
| 31668 | 0.913 | Baidrakhmanova M.; Karabayev G.; Mamedov S. | Innovative Approaches To Sustainable Urban Planning: Analysing Current Trends | Journal of Studies in Science and Engineering, 5, 1 (2025) |
| 10519 | 0.912 | Bibri S.E. | Artificial Intelligence Of Things For Smarter Eco-Cities: Pioneering The Environmental Synergies Of Urban Brain, Digital Twin, Metabolic Circularity, And Platform | Artificial Intelligence of Things for Smarter Eco-Cities: Pioneering the Environmental Synergies of Urban Brain, Digital Twin, Metabolic Circularity, and Platform (2025) |
| 51841 | 0.912 | Bibri S.E.; Krogstie J.; Kaboli A.; Alahi A. | Smarter Eco-Cities And Their Leading-Edge Artificial Intelligence Of Things Solutions For Environmental Sustainability: A Comprehensive Systematic Review | Environmental Science and Ecotechnology, 19 (2024) |
| 13070 | 0.91 | Razavi H.; Titidezh O.; Asgary A.; Bonakdari H. | Building Resilient Smart Cities: The Role Of Digital Twins And Generative Ai In Disaster Management Strategy | Urban Sustainability, Part F3988 (2024) |
| 24199 | 0.909 | Zehouani N.; Ababou M.; Faquir S.; Rabiai S. | Environmental Ai For Resilient And Sustainable Urban Infrastructures: Rethinking Smart City Transformation | Sustainable Civil Infrastructures (2025) |
| 24071 | 0.907 | Gkontzis A.F.; Kotsiantis S.; Feretzakis G.; Verykios V.S. | Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, And Digital Twin Techniques At The Neighborhood Level | Future Internet, 16, 2 (2024) |