AI-driven Sustainable Urban Intelligence: Integrating Smart Technologies for Efficient, Resilient, and Inclusive City Management
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Abstract
Artificial intelligence (AI) is increasingly shaping the evolution of urban ecosystems, yet current implementations often optimize isolated objectives—such as efficiency or automation—without integrating environmental and social dimensions. This study proposes a three-layer framework for sustainable urban intelligence, encompassing cognitive, behavioral, and environmental layers to harmonize personalization, mobility optimization, and carbon management. The cognitive layer leverages large language models and knowledge graphs for sustainability-oriented recommendation; the behavioral layer employs reinforcement learning and graph neural networks for adaptive mobility optimization; and the environmental layer integrates AI-enabled carbon forecasting and energy management. Through cross-layer data flow and dynamic feedback loops, the framework establishes an adaptive AI ecosystem that connects human decision-making, technological performance, and ecological feedback. The proposed model advances the conceptual transition from “smart cities” to sustainably intelligent cities, providing a blueprint for future urban AI systems that optimize not only for humans, but with humans—aligning personal actions with collective sustainability goals.
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1. R. Luo, X. Chen, and Z. Ding, “SeqUDA-Rec: Sequential user behavior enhanced recommendation via global unsupervised data augmentation for personalized content marketing,” arXiv preprint arXiv:2509.17361, 2025.
2. E. Rieder, M. Schmuck, and A. Tugui, "A scientific perspective on using artificial intelligence in sustainable urban development," Big Data and Cognitive Computing, vol. 7, no. 1, p. 3, 2022.
3. S. Li, K. Liu, and X. Chen, “A context-aware personalized recommendation framework integrating user clustering and BERT-based sentiment analysis,” 2025.
4. S. E. Bibri, "Data-driven smart sustainable cities: A conceptual framework for urban intelligence functions and related processes, systems, and sciences," in Advances in the Leading Paradigms of Urbanism and their Amalgamation: Compact Cities, Eco–Cities, and Data–Driven Smart Cities, Cham: Springer International Publishing, 2020, pp. 143-173.
5. J. Jin, T. Zhu, and C. Li, “Graph neural network-based prediction framework for protein-ligand binding affinity: A case study on pediatric gastrointestinal disease targets,” Journal of Medicine and Life Sciences, vol. 1, no. 3, pp. 136–142, 2025.
6. Y. Chen, H. Du, and Y. Zhou, “Lightweight network-based semantic segmentation for UAVs and its RISC-V implementation,” Journal of Technology Innovation and Engineering, vol. 1, no. 2, 2025.
7. S. E. Bibri, "Data-driven smart sustainable cities of the future: Urban computing and intelligence for strategic, short-term, and joined-up planning," Computational Urban Science, vol. 1, no. 1, p. 8, 2021.
8. B. Zhang, Z. Lin, and Y. Su, “Design and implementation of code completion system based on LLM and CodeBERT hybrid subsystem,” arXiv preprint arXiv:2509.0821, 2025.
9. A. Ortega-Fernández, R. Martín-Rojas, and V. J. García-Morales, "Artificial intelligence in the urban environment: Smart cities as models for developing innovation and sustainability," Sustainability, vol. 12, no. 19, p. 7860, 2020.
10. C. Zhang, Y. Zhang, and Z. Huang, “Optimal reutilization strategy for a shipbuilder under the carbon quota policy,” Sustainability, vol. 15, no. 10, p. 8311, 2023.
11. M.-L. Marsal-Llacuna, "How to succeed in implementing (smart) sustainable urban Agendas: ‘keep cities smart, make communities intelligent’," Environment, Development and Sustainability, vol. 21, no. 4, pp. 1977-1998, 2019.
12. C. Zhang, X. Liu, J. Ren, H. Yu, J. Huang, and X. Luo, “The IMAGE framework for human mobility science: A comprehensive bibliometric analysis of research trends and frontiers,” Transport Policy, vol. 171, pp. 706–720, 2025.
13. G. Bao, L. Ma, and X. Yi, "Recent advances on cooperative control of heterogeneous multi-agent systems subject to constraints: A survey," Systems Science & Control Engineering, vol. 10, no. 1, pp. 539-551, 2022.
14. C. Zhang, H. Yu, X. Luo, W. Yin, J. Huang, X. Liu, and Z. Liu, “CitySense RAG: Personalized urban mobility recommendations via streetscape perception and multi-source semantics,” in press, 2025.
15. J. Feng et al., "Integration of multi-agent systems and artificial intelligence in self-healing subway power supply systems: Advancements in fault diagnosis, isolation, and recovery," Processes, vol. 13, no. 4, p. 1144, 2025.
16. R. Pan, Q. Yuan, J. Cao, C. Zhang, C. Yu, Q. Liu, and X. Liang, “Sentence-resampled BERT-CRF model for autonomous vehicle crash causality analysis from large-scale accident narrative text data,” Accident Analysis & Prevention, vol. 221, p. 108184, 2025.
17. N. Xu et al., "Event-triggered distributed consensus tracking for nonlinear multi-agent systems: A minimal approximation approach," IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 13, no. 3, pp. 767-779, 2023.
18. Z. Liu, H. Yu, X. Liu, C. Zhang, J. Huang, A. Liu, and X. Luo, “Spatiotemporal potential and economic assessment of highway slope-based photovoltaics: A case study in Jiangxi, China,” Applied Energy, vol. 401, p. 126705, 2025.
19. Q. Wang et al., "Output synchronization of wide-area heterogeneous multi-agent systems over intermittent clustered networks," Information Sciences, vol. 619, pp. 263-275, 2023.
20. X. Liu, Q. Yu, W. Bian, H. Yu, C. Zhang, X. Liu, and X. Luo, “Unraveling spatiotemporal dynamics of ridesharing potential: Nonlinear effects of the built environment,” Transportation Research Part D: Transport and Environment, vol. 139, p. 104594, 2025.
21. X. Wang, C. Zhao, T. Huang, P. Chakrabarti and J. Kurths, "Cooperative Learning of Multi-Agent Systems Via Reinforcement Learning," in IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 13-23, 2023
22. C. Zhang, “Myopic retailer’s cooperative advertising strategies in supply chain based on differential games,” in Proc. Int. Conf. Cyber Security Intelligence and Analytics, Cham, Switzerland: Springer Nature, pp. 46–55, Mar. 2023.
23. B. Zhu et al., "Observer-based reinforcement learning for optimal fault-tolerant consensus control of nonlinear multi-agent systems via a dynamic event-triggered mechanism," Information Sciences, vol. 689, p. 121350, 2025.
24. H. Yu, C. Zhang, X. Luo, W. Yin, Z. Liu, J. Huang, and X. Liu, “Taxi repositioning via LLM-driven multi-agent coordination and experience accumulation,” preprint, 2025.
25. S. Long et al., "A fixed-time consensus control with prescribed performance for multi-agent systems under full-state constraints," IEEE Transactions on Automation Science and Engineering, 2024.
26. W. Yin, C. Zhang, H. Yu, X. Luo, J. Huang, J. Zhang, and S. Qi, “Beyond pixels: Vision-language models for enhanced street environment perception,” preprint, 2025.
27. J. E. Falciani, M. Grigoratou, and A. J. Pershing, "Optimizing fisheries for blue carbon management: Why size matters," Limnology and Oceanography, vol. 67, pp. S171-S179, 2022.
28. Z. C. J. Yao, “Impact of the Ukraine conflict on food security: A comprehensive analysis using propensity score matching and difference in difference,” Journal of Finance Research, vol. 8, no. 1, 2024, doi: 10.26549/jfr.v8i1.16890.
29. I. Ameer et al., "Land degradation resistance potential of a dry, semiarid region in relation to soil organic carbon stocks, carbon management index, and soil aggregate stability," Land Degradation & Development, vol. 34, no. 3, pp. 624-636, 2023.
30. W. Sun, “Integration of market-oriented development models and marketing strategies in real estate,” European Journal of Business, Economics & Management, vol. 1, no. 3, pp. 45–52, 2025.