Urban Digital Twins for Megalopolises: Requirements, Challenges and Opportunities

Urban digital twins (UDTs) are emerging as critical tools for integrating heterogeneous data and models to support urban decision-making in areas such as mobility and energy management. However, broader adoption of these systems in large cities is constrained by scientific challenges in their architecture related to three interconnected dimensions: (1) scalability, through multi-modeling and surrogate modeling strategies that balance accuracy and resource efficiency; (2) interoperability, via adaptive and opportunistic workflows that dynamically integrate models and datasets based on context and granularity of decision-making; (3) frugality, by optimizing energy consumption across model and workflow executions. This paper details innovative data science and Urban Digital Twin approaches for collecting and analyzing urban data to simulate complex urban phenomena. By proposing scalable, interoperable, and energy-efficient architectures, this study seeks to advance systems supporting evidence-based public policy, promoting broader sustainable development.