Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin
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 (Ge...
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Elsevier
2025-03-01
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| Series: | Environmental Science and Ecotechnology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666498425000043 |
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| author | Jeffrey Huang Simon Elias Bibri Paul Keel |
| author_facet | Jeffrey Huang Simon Elias Bibri Paul Keel |
| author_sort | Jeffrey Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b434f6baf80b49e28643a0becaf76990 |
| institution | Kabale University |
| issn | 2666-4984 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Environmental Science and Ecotechnology |
| spelling | doaj-art-b434f6baf80b49e28643a0becaf769902025-02-07T04:48:23ZengElsevierEnvironmental Science and Ecotechnology2666-49842025-03-0124100526Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twinJeffrey Huang0Simon Elias Bibri1Paul Keel2Institute of Computer and Communication Sciences (IINFCOM), School of Architecture, Civil and Environmental Engineering (ENAC), Media and Design Laboratory (LDM), Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, SwitzerlandCorresponding author.; Institute of Computer and Communication Sciences (IINFCOM), School of Architecture, Civil and Environmental Engineering (ENAC), Media and Design Laboratory (LDM), Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, SwitzerlandInstitute of Computer and Communication Sciences (IINFCOM), School of Architecture, Civil and Environmental Engineering (ENAC), Media and Design Laboratory (LDM), Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, SwitzerlandRapid 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.http://www.sciencedirect.com/science/article/pii/S2666498425000043Sustainable smart citiesGenerative artificial intelligenceGenerative spatial artificial intelligenceFoundation modelsLarge flow modelUrban digital twin |
| spellingShingle | Jeffrey Huang Simon Elias Bibri Paul Keel Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin Environmental Science and Ecotechnology Sustainable smart cities Generative artificial intelligence Generative spatial artificial intelligence Foundation models Large flow model Urban digital twin |
| title | Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin |
| title_full | Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin |
| title_fullStr | Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin |
| title_full_unstemmed | Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin |
| title_short | Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin |
| title_sort | generative spatial artificial intelligence for sustainable smart cities a pioneering large flow model for urban digital twin |
| topic | Sustainable smart cities Generative artificial intelligence Generative spatial artificial intelligence Foundation models Large flow model Urban digital twin |
| url | http://www.sciencedirect.com/science/article/pii/S2666498425000043 |
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