Huang Jeffrey, Bibri Simon Elias, Keel Paul
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, Switzerland.
Environ Sci Ecotechnol. 2025 Jan 15;24:100526. doi: 10.1016/j.ese.2025.100526. eCollection 2025 Mar.
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.
快速城市化，加之资源枯竭和生态退化不断加剧，凸显了对创新型城市发展解决方案的迫切需求。作为回应，可持续智慧城市越来越多地采用前沿技术，如生成式人工智能（GenAI）、基础模型（FMs）和城市数字孪生（UDT）框架，来转变城市规划和设计实践。这些变革性工具具备先进能力，可用于分析复杂的城市系统、优化资源管理并实现基于证据的决策。尽管近期取得了进展，但将GenAI和FMs整合到UDT框架中的研究仍然匮乏，这使得我们在捕捉对实现环境可持续性目标至关重要的复杂城市流动和多模式动态方面存在能力差距。此外，这些工具缺乏坚实的理论基础和实际应用，阻碍了全面建模和实际应用。本研究引入了一种开创性的大流量模型（LFM），它基于一个强大的基础框架构建，并具备GenAI功能。该模型专为集成到UDT系统中而设计，以增强预测分析、自适应学习和复杂数据管理功能。为验证其适用性和相关性，以洛桑市的蓝色城市项目为例进行研究，展示了LFM有效建模和分析城市流动（即交通、货物、能源、废物、材料和生物多样性）的能力，这些流动对于推进环境可持续性至关重要。本研究强调了LFM如何应对当前UDT框架中固有的空间挑战。LFM通过完善公正的城市数据、估计新地点的流量数据、预测流量数据的演变以及提供对城市动态及其相互联系的整体理解，展示了其在全面城市建模和分析方面的新颖性。该模型增强了决策过程，支持基于证据的规划和设计，促进综合发展战略，并推动建设更高效、有韧性和可持续的城市环境。本研究通过在UDT框架内实施GenAI和FMs，推进了人工智能驱动的、环境可持续城市发展的理论和实践维度。它为城市规划者、设计师、政策制定者和研究人员提供了精密工具和宝贵见解，以应对现代城市的复杂性，并加速向可持续城市未来的转型。
