Smart City Digital Twin Edge-Core Deployment: A Case Study on Traffic and Air Quality Management

The increasing demand for smart cities calls for advanced solutions to enhance urban sustainability. Digital twin technology offers transformative potential by synchronizing virtual and physical environments in real time. However, existing approaches struggle with scalability due to the reliance on numerous specialized prediction models for individual urban components, the lack of a unified framework for different use cases limits generalization, and high latencies in real-time synchronization. To address these, this paper presents a comprehensive software architecture for smart city DT and integrates correlation-aware model reduction and dynamic adaptive forecasting to support diverse urban applications to improve generalizability and scalability. This is done while adapting a smart distribution of DT software components between edge and core servers to ensure a low-latency performance. Validated using real-world traffic and air quality data, the system demonstrates significant improvements in traffic flow, emissions reduction, and public transportation efficiency, and enhances air quality monitoring, forecasting, and pollutant management. Key contributions include a scalable and generalizable DT architecture, AI-driven adaptability, edge-core deployment, and extensive validation through predictive analytics. This work establishes a replicable blueprint for metropolitan-scale DTs, balancing computational efficiency with responsive urban analytics.