Machine Learning for Smart Cities and Urban Analytics
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P117Keywords:
Machine Learning, Smart Cities, Urban Analytics, Predictive Modeling, Intelligent Transportation Systems, Energy Optimization, Environmental Monitoring, Public Safety, Data-Driven Urban Management, Edge AI, Sustainable Cities, Iot AnalyticsAbstract
The rapid urbanization of the global population has placed unprecedented demands on city infrastructure, resource management, and service delivery. Smart city initiatives aim to leverage technology and data to optimize urban living, enhance sustainability, and improve the quality of life for residents. Machine learning (ML), as a core component of data-driven intelligence, has emerged as a transformative tool for analyzing complex urban systems and enabling predictive, adaptive, and autonomous decision-making. By processing vast volumes of heterogeneous urban data—including traffic flows, energy consumption, environmental sensors, social media activity, and public services usage—ML models can uncover patterns, forecast trends, and support real-time interventions. This article provides a detailed exploration of machine learning applications in smart cities, covering urban analytics, transportation and mobility management, energy and resource optimization, environmental monitoring, public safety, and citizen engagement. It also addresses technical challenges, data privacy concerns, ethical implications, and emerging trends such as federated learning and edge intelligence. The integration of machine learning into urban systems promises to make cities more efficient, resilient, and responsive while fostering sustainable and equitable urban development.
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