Big Data Predictive Analytics for Smart Cities: Energy, Mobility, and Public Safety Use Cases

Abstract

Smart cities operate through interconnected digital infrastructures that produce continuous streams of high velocity data. Predictive analytics plays an essential role in converting this data into meaningful insights for energy management, mobility optimization, and public safety operations. This article explores a unified framework for applying big data predictive models across multiple smart city domains. The study examines the characteristics of urban data, proposes a multi layer predictive analytics architecture, and evaluates its performance using domain driven use cases. Results demonstrate that the integration of scalable data processing, machine learning pipelines, and domain specific features provides substantial benefits in forecasting energy demand, reducing congestion, and supporting risk aware safety interventions.