Architecting Digital Twins for Intelligent Transportation Systems
This work addresses traffic flow, safety, and efficiency problems for transportation systems, but it is incremental as it builds on existing Digital Twin frameworks with modular improvements.
The paper tackles traffic management challenges in Intelligent Transportation Systems by proposing a Digital Twin architecture called DigIT, which uses machine learning for traffic forecasting and adaptive MLOps, achieving accurate predictions and computational efficiency.
Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.