An AI-Driven, Scalable, and Modular Digital Twin Framework for Traffic Management

The growing need for intelligent tools to support urban planning and resource management has positioned Digital Twin (DT) technology as a cornerstone of smart city development. DTs, as dynamic virtual replicas of physical systems, offer capabilities that extend beyond mere representation, enabling monitoring, diagnostics, forecasting, and optimization. In the context of urban traffic management, DTs provide a robust solution for real-time traffic monitoring and predictive analytics. However, existing approaches often lack a systematic design methodology, leading to challenges in scalability and adaptability, particularly in heterogeneous environments. This paper presents a novel methodology for developing scalable and adaptive smart city DT architectures, with a focus on real-time traffic management. A modular and unified software framework is proposed, leveraging AI-driven approaches to address the complexity of managing diverse traffic data sources. A sequential learning model is integrated into the architecture to enhance the DT's adaptability to evolving traffic conditions and congestion patterns. The proposed framework is validated using real-world traffic data from an IoT network deployed in Madrid, demonstrating its scalability and low-latency performance. Experimental results highlight the effectiveness of the framework in handling heterogeneous traffic scenarios and its ability to deliver accurate predictions while minimizing resource overhead.