Predictive Analytics and Simulation for Digital Twin-enabled Decision Support in Smart Cities
Aisha Tan
Universiti Malaysia Sarawak (UNIMAS)
Abstract
The concept of Smart Cities has gained significant traction in recent years, driven by the rapid urbanization and growing global population. Smart Cities leverage advanced technologies, such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI), to optimize resource allocation, enhance citizen services, and promote sustainable development. Digital Twins, a virtual representation of physical assets, systems, or processes, have emerged as a powerful tool for enabling data-driven decision-making in Smart Cities. This research article explores the role of predictive analytics and simulation in Digital Twin-enabled decision support systems for Smart Cities. It delves into the fundamental concepts, computational models, and real-world applications of predictive analytics and simulation in urban planning, infrastructure management, energy optimization, and citizen-centric services. The article also discusses the challenges and future research directions in this rapidly evolving field.