AI-Driven Urban Planning: Enhancing Efficiency and Sustainability in Smart Cities
Abstract
Urban planning in smart cities is increasingly leveraging artificial intelligence (AI) to enhance efficiency and sustainability. This article explores the integration of AI-driven technologies to optimize various aspects of urban development and management. Smart cities are characterized by their use of advanced technologies to improve quality of life, resource management, and infrastructure efficiency. Traditional urban planning methods often face challenges in adapting to rapid urbanization and dynamic environmental changes. AI presents opportunities to address these challenges by providing data-driven insights and predictive capabilities. This research employs a case study approach, analyzing the implementation of AI in urban planning processes across several smart cities globally. Key methodologies include data analytics, machine learning algorithms, and predictive modeling techniques applied to diverse urban datasets. The study evaluates how AI-driven decision support systems aid in infrastructure planning, traffic management, energy consumption optimization, and environmental sustainability. The findings demonstrate that AI-enabled urban planning significantly enhances efficiency and sustainability in smart cities. AI algorithms optimize traffic flow, reduce energy consumption through predictive maintenance of infrastructure, and facilitate adaptive urban design based on real-time data analytics. Moreover, AI-driven approaches improve decision-making processes by providing stakeholders with actionable insights for informed policy formulation and resource allocation. This article contributes to the evolving field of smart city technologies by showcasing the transformative potential of AI in urban planning. By harnessing AI capabilities, cities can effectively address complex urban challenges and pave the way for more resilient and sustainable urban environments.
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References
M. Agbali, C. Trillo, T. Fernando, L. Oyedele, I. A. Ibrahim, dan V. O. Olatunji, “Towards a refined conceptual framework model for a smart and sustainable city assessment,†5th IEEE Int. Smart Cities Conf. ISC2 2019, hal. 658–664, 2019, doi: 10.1109/ISC246665.2019.9071697.
O. Gurova, T. R. Merritt, E. Papachristos, dan J. Vaajakari, “Sustainable solutions for wearable technologies: Mapping the product development life cycle,†Sustain., vol. 12, no. 20, hal. 1 – 26, 2020, doi: 10.3390/su12208444.
P. Girardi dan A. Temporelli, “Smartainability: A Methodology for Assessing the Sustainability of the Smart City,†Energy Procedia, vol. 111, no. September 2016, hal. 810–816, 2017, doi: 10.1016/j.egypro.2017.03.243.
AHFE Virtual Conferences on Software and Systems Engineering, and Artificial Intelligence and Social Computing, 2020, vol. 1213 AISC. 2021.
V. Wong dan K. Law, “Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces,†Algorithms, vol. 16, no. 3, hal. 154, Mar 2023, doi: 10.3390/a16030154.
W. Sheng, J. Shen, Q. Huang, Z. Liu, dan Z. Ding, “Multi-objective pedestrian tracking method based on YOLOv8 and improved DeepSORT,†Math. Biosci. Eng., vol. 21, no. 2, hal. 1791–1805, 2024, doi: 10.3934/mbe.2024077.
D. Baroni, S. Ancora, J. Franzaring, S. Loppi, dan F. Monaci, “Tree-rings analysis to reconstruct atmospheric mercury contamination at a historical mining site,†Front. Plant Sci., vol. 14, 2023, doi: 10.3389/fpls.2023.1260431.
I. Torre dan I. Celik, “A Model for Adaptive Accessibility of Everyday Objects in Smart Cities,†2016 Ieee 27Th Annu. Int. Symp. Pers. Indoor, Mob. Radio Commun., hal. 176–181, 2016.
G. Wang, M. Zhou, X. Wei, dan G. Yang, “Vehicular Abandoned Object Detection Based on VANET and Edge AI in Road Scenes,†IEEE Trans. Intell. Transp. Syst., vol. 24, no. 12, hal. 14254–14266, 2023, doi: 10.1109/TITS.2023.3296508.
A. Sheludkov dan A. Starikova, “Nighttime-lights satellite imagery reveals hotspots of second home mobility in rural Russia (a case study of Yaroslavl Oblast),†Reg. Sci. Policy Pract., 2021, doi: 10.1111/rsp3.12441.
S. Lee, C. Lee, J. Won Nam, A. Vernez Moudon, dan J. A. Mendoza, “Street environments and crime around low-income and minority schools: Adopting an environmental audit tool to assess crime prevention through environmental design (CPTED),†Landsc. Urban Plan., vol. 232, hal. 104676, Apr 2023, doi: 10.1016/j.landurbplan.2022.104676.
S. T. Kouyoumdjieva, P. Danielis, dan G. Karlsson, “Survey of Non-Image-Based Approaches for Counting People,†IEEE Commun. Surv. Tutorials, vol. 22, no. 2, hal. 1305–1336, 2020, doi: 10.1109/COMST.2019.2902824.
H. S. Firmansyah, S. H. Supangkat, A. A. Arman, dan P. J. Giabbanelli, “Identifying the Components and Interrelationships of Smart Cities in Indonesia : Supporting Policymaking via Fuzzy Cognitive Systems,†IEEE Access, vol. 7, hal. 46136–46151, 2019, doi: 10.1109/ACCESS.2019.2908622.
M. Perera, “Automatic Video Descriptor for Human Action Recognition,†hal. 13–15, 2017.
M. Wischow, G. Gallego, I. Ernst, dan A. Borner, “Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles,†IEEE Trans. Intell. Transp. Syst., hal. 1–14, 2023, doi: 10.1109/TITS.2023.3328811.
P. S. Koutsourelakis, N. Zabaras, dan M. Girolami, “Special Issue: Big data and predictive computational modeling,†J. Comput. Phys., vol. 321, no. March, hal. 1252–1254, 2016, doi: 10.1016/j.jcp.2016.03.028.
D. C. Marinescu, Complex Systems and Clouds. 2017.
K. Seemanthini, S. S. Manjunath, G. Srinivasa, dan B. Kiran, Video Synchronization and Alignment Using Motion Detection and Contour Filtering, vol. 165. 2020.
F. Monaci, S. Ancora, L. Paoli, S. Loppi, dan J. Franzaring, “Air quality in post-mining towns: tracking potentially toxic elements using tree leaves,†Environ. Geochem. Health, vol. 45, no. 3, hal. 843 – 859, 2023, doi: 10.1007/s10653-022-01252-6.
M. M. Rathore, A. Ahmad, A. Paul, dan S. Rho, “Urban planning and building smart cities based on the Internet of Things using Big Data analytics,†Comput. Networks, vol. 101, no. 2016, hal. 63–80, 2016, doi: 10.1016/j.comnet.2015.12.023.
I. El Naqa, “Perspectives on making big data analytics work for oncology,†Methods, vol. 111, hal. 32–44, 2016, doi: 10.1016/j.ymeth.2016.08.010.
A. A. P. Cattaneo, E. Boldrini, dan F. Lubinu, “‘Take a look at this!’. Video annotation as a means to foster evidence-based and reflective external and self-given feedback: A preliminary study in operati on room technician training,†Nurse Educ. Pract., vol. 44, no. March, hal. 102770, 2020, doi: 10.1016/j.nepr.2020.102770.
