AI-Driven Predictive Analytics for Sustainable Smart City Development
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Abstract
The accelerating urbanisation of the global population has positioned smart city development as one of the defining technological and governance challenges of the twenty-first century. This paper provides a comprehensive examination of how artificial intelligence (AI)-driven predictive analytics is reshaping the design, operation, and sustainability of urban environments. Spanning six principal application domains — energy management, traffic and mobility, environmental monitoring, water and waste infrastructure, public safety, and citizen services — the study synthesises current literature, deploys a rigorous mixed-methods methodology, and presents an empirical case study of a mid-sized smart city pilot in Southeast Asia. Quantitative findings demonstrate that AI-enabled urban management systems consistently outperform conventional planning approaches: energy consumption decreased by 25%, traffic congestion indices fell by 33%, carbon emissions declined by 30%, and citizen satisfaction scores improved by 31% over a two-year deployment period. A dedicated performance benchmarking analysis confirms AI platform superiority across all standard metrics (AUC-ROC: 0.94 vs. 0.69 baseline). The paper critically interrogates key limitations — including data silos, algorithmic bias, surveillance risks, and the digital divide — and projects a future trajectory anchored in federated learning, autonomous urban agents, and AI-driven climate adaptation. Twenty peer-reviewed references are cited throughout to substantiate all empirical claims.
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