Digital twin technology in smart cities: A step toward intelligent urban management

Abstract

Digital twins are emerging as a core enabler of smart cities, where growing populations and increasingly complex infrastructures demand responsive, efficient, and sustainable services. This review examines the principles, design choices, and enabling technologies of urban digital twins, and surveys applications across energy, transportation, public safety, and environmental management. Beyond synthesizing prior work, the paper adopts an implementation-oriented view: it organizes twin capabilities into a practical pipeline ingest, synchronize, simulate, predict, decide, actuate and it linked to measurable targets such as latency, synchronicity error, update rate, availability, recovery time, and cost. A capability use case matrix and a Digital-Twin Implementation Readiness Level (DT-IRL) scale are introduced to align technical requirements with real city needs and to stage deployments from concept to closed-loop operation. The review clarifies the role of IoT, AI, big-data analytics, and edge–cloud architectures in achieving real-time performance, and it specifies engineering expectations for immersive services (for example, latency and throughput budgets for holographic communication and 3D streaming). It also details deployable security and privacy measures, including zero-trust controls, confidential computing, federated learning with differential privacy, and ledger backed provenance. Remaining challenges interoperability, standards, cybersecurity, scalability, and organizational readiness are translated into actionable research directions and a roadmap for validation through city-scale pilots, open datasets, and conformance testing. • Comprehensive review of DT applications in smart cities focusing on energy use. • DT’s role real-time monitoring, predictive maintenance, and energy forecasting. • Integration of DT with solar and wind renewable energy for performance enhancement. • Conceptual framework linking IoT, AI, and Big Data to energy-centric smart city apps.. • Identified challenges and proposed strategies for scalability and interoperability.