Urban Digital Twins for Megalopolises: Requirements, Challenges and Opportunities
Resumo
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.Referências
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Voelz, A., Amlashi, D. M., and Lee, M. (2023). Semantic matching through knowledge graphs: a smart city case. In International Conference on Advanced Information Systems Engineering, pages 92–104. Springer.
Wang, Z., Han, F., and Zhao, S. (2024). A survey on knowledge graph related research in smart city domain. ACM Transactions on Knowledge Discovery from Data, 18(9):1–31.
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Ahmadzadeh, A. and Sarbazi-Azad, H. (2024). Performance analysis and modeling for quantum computing simulation on distributed gpu platforms. Quantum Information Processing, 23(11):1–40.
Al-Yadumi, S., Xion, T. E., Wei, S. G. W., and Boursier, P. (2021). Review on integrating geospatial big datasets and open research issues. IEEE Access, 9:10604–10620.
Cheng, K., Puyang, H., Li, X., Lee, P. P., Hu, Y., Li, J., and Wu, T.-Y. (2025). Toward load-balanced redundancy transitioning for erasure-coded storage. IEEE Transactions on Parallel and Distributed Systems.
Chippagiri, S., Ravula, P., and Gangwani, D. (2024). Optimizing load balancing and task scheduling in cloud computing based on nature-inspired optimization algorithms. European Journal of Theoretical and Applied Sciences, 2(6).
Consoli, S., Mongiovic, M., Nuzzolese, A. G., Peroni, S., Presutti, V., Reforgiato Recupero, D., and Spampinato, D. (2015). A smart city data model based on semantics best practice and principles. In Proceedings of the 24th International Conference on World Wide Web, pages 1395–1400.
Consoli, S., Presutti, V., Recupero, D. R., Nuzzolese, A. G., Peroni, S., Gangemi, A., et al. (2017). Producing linked data for smart cities: The case of catania. Big Data Research, 7:1–15.
De Leeuw, B., Mohammadi Ziabari, S. S., and Sharpanskykh, A. (2022). Surrogate modeling of agent-based airport terminal operations. In International Workshop on Multi-Agent Systems and Agent-Based Simulation, pages 82–94. Springer.
Del Esposte, A. d. M., Santana, E. F., Kanashiro, L., Costa, F. M., Braghetto, K. R., Lago, N., and Kon, F. (2019). Design and evaluation of a scalable smart city software platform with large-scale simulations. Future Generation Computer Systems, 93:427–441.
Gaudou, B. et al. (2014). The MAELIA multi-agent platform for integrated analysis of interactions between agricultural land-use and low-water management strategies. Multi-Agent-Based Simulation XIV. MABS 2013. LNCS, 8235.
Goedegebuure, A., Kumara, I., Driessen, S., Van Den Heuvel, W.-J., Monsieur, G., Tamburri, D. A., and Nucci, D. D. (2024). Data mesh: a systematic gray literature review. ACM Computing Surveys, 57(1):1–36.
Harby, A. A. and Zulkernine, F. (2025). Data lakehouse: A survey and experimental study. Information Systems, 127:102460.
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., et al. (2021). Knowledge graphs. ACM Computing Surveys (Csur), 54(4):1–37.
Ilyas, I. F. and Chu, X. (2019). Data cleaning. Morgan & Claypool.
Jeddoub, I., Nys, G.-A., Hajji, R., and Billen, R. (2024). Data integration across urban digital twin lifecycle: a comprehensive review of current initiatives. Annals of GIS, pages 1–20.
Kim, Y. G., Gupta, U., McCrabb, A., Son, Y., Bertacco, V., Brooks, D., and Wu, C.-J. (2023). Greenscale: Carbon-aware systems for edge computing. arXiv preprint arXiv:2304.00404.
Knebel, F. P., Wickboldt, J. A., and de Freitas, E. P. (2020). A cloud-fog computing architecture for real-time digital twins. arXiv preprint arXiv:2012.06118.
Liu, Z., Lin, M., Wierman, A., Low, S. H., and Andrew, L. L. (2011). Geographical load balancing with renewables. ACM SIGMETRICS Performance Evaluation Review, 39(3):62–66.
Llacay, B. and Peffer, G. (2025). Categorical surrogation of agent-based models: A comparative study of machine learning classifiers. Expert Systems, 42(1):e13342.
Malleson, N., Birkin, M., Birks, D., Ge, J., Heppenstall, A., Manley, E., McCulloch, J., and Ternes, P. (2022). Agent-based modelling for urban analytics: State of the art and challenges. AI Communications, 35(4):393–406.
Mastio, M., Zargayouna, M., Scemama, G., and Rana, O. (2017). Distributed agent-based traffic simulations. IEEE Intelligent Transportation Systems Magazine.
Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., and Guizani, S. (2017). Internet-of-things-based smart cities: Recent advances and challenges. IEEE Communications Magazine, 55(9):16–24.
Michel, F., Ferber, J., and Drogoul, A. (2018). Multi-agent systems and simulation: A survey from the agent community’s perspective. In Multi-Agent Systems, pages 17–66. CRC Press.
Nafus, D., Schooler, E. M., and Burch, K. A. (2021). Carbon-responsive computing: Changing the nexus between energy and computing. Energies, 14(21):6917.
Perin, G., Meneghello, F., Carli, R., Schenato, L., and Rossi, M. (2022). Ease: Energy-aware job scheduling for vehicular edge networks with renewable energy resources. IEEE Trans. on Green Communications and Networking, 7(1):339–353.
Reuillon, R., Leclaire, M., and Rey-Coyrehourcq, S. (2013). Openmole, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Generation Computer Systems, 29(8):1981–1990.
Rocha, B., Cavalcante, E., Batista, T., and Silva, J. (2019). A linked data-based semantic information model for smart cities. In 2019 IX Brazilian Symposium on Computing Systems Engineering (SBESC). IEEE.
Rocha, F. W., Fukuda, J. C., Francesquini, E., and Cordeiro, D. (2021). Accelerating smart city simulations. In Latin American High Performance Computing Conference, pages 148–162. Springer.
Schneider, J., Gröger, C., Lutsch, A., Schwarz, H., and Mitschang, B. (2024). The lakehouse: State of the art on concepts and technologies. SN Computer Science, 5(5):449.
Shin, Y., Moul, V., Kang, K., and Lee, B. (2025). Polypal: A parallel microscale virtual specimen generator. Computer Physics Communications, 308:109458.
Souza, A., Bashir, N., Murillo, J., Hanafy, W., Liang, Q., Irwin, D., and Shenoy, P. (2023). Ecovisor: A virtual energy system for carbon-efficient applications. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, pages 252–265.
Ullrich, A., Hunger, F., Stavroulaki, I., Bilock, A., Jareteg, K., Tarakanov, Y., Gösta, A., Quist, J., Berghauser Pont, M., and Edelvik, F. (2024). A hybrid workflow connecting a network and an agent-based model for predictive pedestrian movement modelling. Frontiers in Built Environment, 10:1447377.
United Nations Human (UN) Settlements Programme (2012). Sustainable Urban Energy. Technical report, United Nations Human (UN) Settlements Programme.
Violos, J., Diamanti, K.-C., Kompatsiaris, I., and Papadopoulos, S. (2025). Frugal machine learning for energy-efficient, and resource-aware artificial intelligence. arXiv preprint arXiv:2506.01869.
Voelz, A., Amlashi, D. M., and Lee, M. (2023). Semantic matching through knowledge graphs: a smart city case. In International Conference on Advanced Information Systems Engineering, pages 92–104. Springer.
Wang, Z., Han, F., and Zhao, S. (2024). A survey on knowledge graph related research in smart city domain. ACM Transactions on Knowledge Discovery from Data, 18(9):1–31.
Weil, C., Bibri, S. E., Longchamp, R., Golay, F., and Alahi, A. (2023). Urban digital twin challenges: A systematic review and perspectives for sustainable smart cities. Sustainable Cities and Society, 99:104862.
Zeng, Y., Feng, G., Chen, Z., Lu, Y., and Xiao, N. (2024). Atm: Area-based partition and topology-aware mapping for large-scale snn simulation. In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), pages 1841–1848. IEEE.
Publicado
23/09/2025
Como Citar
CORDEIRO, Daniel; SONG, Jiefu; BRAGHETTO, Kelly R.; KON, Fabio; AMBLARD, Frédéric.
Urban Digital Twins for Megalopolises: Requirements, Challenges and Opportunities. In: WORKSHOP EM ENGENHARIA DE SOFTWARE PARA GÊMEOS DIGITAIS (SEDT), 1. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 7-16.
DOI: https://doi.org/10.5753/sedt.2025.14274.
