Urban System Integration and Predictive Modeling for Sustainable City Management
Main Article Content
Abstract
This study investigates the effectiveness of integrated data frameworks and predictive models in urban governance through empirical analysis of 120 city sub-regions. A dual-group experimental design was implemented, with the experimental group deploying a bi-directional recurrent model and cross-domain data integration, while the control group relied on conventional rule-based methods. The results demonstrate that the proposed framework achieved significant improvements: traffic flow prediction accuracy increased with a 34.2% reduction in RMSE (12.3 vs. 18.7 veh/min) and a 31.9% reduction in MAE (9.1 vs. 13.4 veh/min), energy consumption decreased by 13.5%, and public transport punctuality improved by 11.2%. Quality control through redundant sampling, anomaly detection, and five-fold cross-validation ensured the robustness of the results, with performance variance below 2.1%. Compared with existing approaches, the study highlights the advantages of systematic data integration and model design in enhancing both system-level efficiency and task-specific accuracy. These findings provide evidence for scaling predictive frameworks to city-wide applications and underscore the importance of transparent, reliable, and sustainable pathways in urban system development.
Article Details
Issue
Section
How to Cite
References
1. J. Fan, and T. W. Chow, "Non-linear matrix completion," Pattern Recognition, vol. 77, pp. 378-394, 2018, doi: 10.1016/j.patcog.2017.10.014
2. J. Xu, H. Wang, and H. Trimbach, "An OWL ontology representation for machine-learned functions using linked data," In 2016 IEEE International Congress on Big Data (BigData Congress), June, 2016, pp. 319-322.
3. X. Sun, K. Meng, W. Wang, and Q. Wang, "Drone Assisted Freight Transport in Highway Logistics Coordinated Scheduling and Route Planning," In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT), March, 2025, pp. 1254-1257.
4. H. Peng, X. Jin, Q. Huang, and S. Liu, "A Study on Enhancing the Reasoning Efficiency of Generative Recommender Systems Using Deep Model Compression," Available at SSRN 5321642, 2025.
5. G. Wang, "Performance evaluation and optimization of photovoltaic systems in urban environments," International Journal of New Developments in Engineering and Society, vol. 9, pp. 42–49, 2025, doi: 10.25236/IJNDES.2025.090106.
6. S. Srinivasan, Z. Fang, R. Iyer, S. Zhang, M. Espig, D. Newell, and H. Haussecker, "Performance characterization and optimization of mobile augmented reality on handheld platforms," In 2009 IEEE International Symposium on Workload Characterization (IISWC), October, 2009, pp. 128-137, doi: 10.1109/iiswc.2009.5306788.
7. O. Handa, H. Miura, T. Gu, M. Osawa, H. Matsumoto, E. Umegaki, R. Inoue, Y. Naito, and A. Shiotani, "Reduction of butyric acid-producing bacteria in the ileal mucosa-associated microbiota is associated with the history of abdominal surgery in patients with Crohn’s disease," Redox Report, vol. 28, no. 1, p. 2241615, 2023, doi: 10.1080/13510002.2023.2241615.
8. S. Yo, H. Matsumoto, T. Gu, M. Sasahira, M. Oosawa, O. Handa, E. Umegaki, and A. Shiotani, "Exercise affects mucosa-associated microbiota and colonic tumor formation induced by azoxymethane in high-fat-diet-induced obese mice," Microorganisms, vol. 12, no. 5, p. 957, 2024, doi: 10.3390/microorganisms12050957.
9. M. Sasahira, H. Matsumoto, T. T. Go, S. Yo, S. Monden, T. Ninomiya, M. Oosawa, O. Handa, E. Umegaki, R. Inoue, and A. Shiotani, "The relationship between bacterial flora in saliva and esophageal mucus and endoscopic severity in patients with eosinophilic esophagitis," International Journal of Molecular Sciences, vol. 26, no. 7, p. 3026, 2025, doi: 10.3390/ijms26073026.
10. E. Cina, E. Elbasi, G. Elmazi, and Z. AlArnaout, "The Role of AI in Predictive Modelling for Sustainable Urban Development: Challenges and Opportunities," Sustainability (2071-1050), vol. 17, no. 11, 2025.
11. M. Yuan, B. Wang, S. Su, and W. Q. Qin, "Architectural Form Generation Driven by Text-Guided Generative Modeling Based on Intent Image Reconstruction and Multi-Criteria Evaluation," Available at SSRN 5373645, 2025.
12. S. Jing, "Practice of digital construction to improve construction project progress management," Academic Journal of Engineering and Technology Science, vol. 8, no. 2, pp. 36–44, 2025, doi: 10.25236/AJETS.2025.080205.
13. F. Chen, G. Xu, S. Li, L. Yue, and H. Liang, "Optimization study of thermal management of domestic SiC power semiconductor based on improved genetic algorithm," In 2025 2nd International Conference on Electrical Technology and Automation Engineering (ETAE), May, 2025, pp. 510-514.
14. B. Wu, "Market research and product planning in e-commerce projects: A systematic analysis of strategies and methods," Academic Journal of Business and Management, vol. 7, no. 3, pp. 45–53, 2025, doi: 10.25236/AJBM.2025.070307.
15. L. Yun, "Analyzing credit risk management in the digital age: Challenges and solutions," Economics and Management Innovation, vol. 2, no. 2, pp. 81–92, Apr. 2025, doi: 10.71222/ps8sw070.
16. L. Yang, "The evolution of ballet pedagogy: A study of traditional and contemporary approaches," Journal of Literature and Arts Research, vol. 2, no. 2, pp. 1–10, Apr. 2025, doi: 10.71222/2nw5qw82.
17. J. Zhong, X. Fang, Z. Yang, Z. Tian, and C. Li, "Skybound Magic: Enabling Body-Only Drone Piloting Through a Lightweight Vision-Pose Interaction Framework," International Journal of Human-Computer Interaction, pp. 1-31, 2025.
18. J. Yang, Y. Zhang, K. Xu, W. Liu, and S. E. Chan, "Adaptive Modeling and Risk Strategies for Cross-Border Real Estate Investments," 2024.
19. H. Chen, X. Ma, Y. Mao, and P. Ning, "Research on Low Latency Algorithm Optimization and System Stability Enhancement for Intelligent Voice Assistant," Available at SSRN 5321721, 2025.
20. M. Yang, Y. Wang, J. Shi, and L. Tong, "Reinforcement Learning Based Multi-Stage Ad Sorting and Personalized Recommendation System Design," 2025.
21. Z. Li, K. Dey, M. Chowdhury, and P. Bhavsar, "Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment," IET Intelligent Transport Systems, vol. 10, no. 5, pp. 370-377, 2016, doi: 10.1049/iet-its.2015.0154.
22. A. D. Singleton, and S. E. Spielman, "Urban governance," In Urban Informatics, 2021, pp. 229-241, doi: 10.1007/978-981-15-8983-6_15.
23. S. Yang, "The impact of continuous integration and continuous delivery on software development efficiency," Journal of Computer and Signal Systems Research, vol. 2, no. 3, pp. 59–68, Apr. 2025, doi: 10.71222/pzvfqm21.
24. Y. Liu, "Post-pandemic architectural design: A review of global adaptations in public buildings," International Journal of Engineering Advances, vol. 2, no. 1, pp. 91–100, Apr. 2025, doi: 10.71222/1cj1j328.
25. G. Xie, W. Guo, Z. Fang, Z. Duan, X. Lang, D. Liu, G. Mei, Y. Zhai, X. Sun, and X. Lu, "Dual-metal sites drive tandem electrocatalytic CO₂ to C₂⁺ products," Angewandte Chemie, vol. 136, no. 47, p. e202412568, 2024, doi: 10.1002/ange.202412568.
26. Y. Song, X. Zhang, Z. Xiao, Y. Wang, P. Yi, M. Huang, and L. Zhang, "Coupled amorphous NiFeP/crystalline Ni₃S₂ nanosheets enables accelerated reaction kinetics for high current density seawater electrolysis," Applied Catalysis B: Environmental, vol. 352, p. 124028, 2024, doi: 10.1016/j.apcatb.2024.124028.
27. Y. Xiao, L. Tan, and J. Liu, "Application of machine learning model in fraud identification: A comparative study of CatBoost, XGBoost and LightGBM," 2025.
28. T. Yuan, X. Zhang, and X. Chen, "Machine learning based enterprise financial audit framework and high risk identification," arXiv preprint arXiv:2507.06266, 2025, doi: 10.18063/csa.v3i1.918.
29. Z. Zhang, J. Ding, L. Jiang, D. Dai, and G. Xia, "Freepoint: Unsupervised point cloud instance segmentation," In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 28254-28263, doi: 10.1109/cvpr52733.2024.02669.
30. A. Ji, and P. Shang, "Analysis of financial time series through forbidden patterns," Physica A: Statistical Mechanics and its Applications, vol. 534, p. 122038, 2019, doi: 10.1016/j.physa.2019.122038.
31. J. Yang, "Deep learning methods for smart grid data analysis," In 2024 4th International Conference on Smart Grid and Energy Internet (SGEI), December, 2024, pp. 717-720, doi: 10.1109/sgei63936.2024.10914173.
32. H. Peng, L. Ge, X. Zheng, and Y. Wang, "Design of Federated Recommendation Model and Data Privacy Protection Algorithm Based on Graph Convolutional Networks," 2025, doi: 10.20944/preprints202505.2200.v1.
33. J. Zheng, and M. Makar, "Causally motivated multi-shortcut identification and removal," Advances in Neural Information Processing Systems, vol. 35, pp. 12800-12812, 2022.
34. J. Xu, "Building a Structured Reasoning AI Model for Legal Judgment in Telehealth Systems," In RAIS Conf. on Social Sciences and Humanities., August, 2025, doi: 10.20944/preprints202507.0630.v1.
35. H. Kim, M. Ahn, S. Hong, S. Lee, and S. Lee, "Wearable device control platform technology for network application development," Mobile Information Systems, vol. 2016, no. 1, p. 3038515, 2016.