Architecting Digital Twins for Intelligent Transportation Systems

TL;DR

This paper introduces a scalable Digital Twin architecture for intelligent transportation systems that integrates predictive analytics, adaptive machine learning, and modular design to improve traffic management and operational efficiency.

Contribution

It presents a novel modular architecture for ITS Digital Twins built on a Domain Concept Model, enabling seamless integration of predictive modeling and adaptive machine learning operations.

Findings

Accurate traffic pattern predictions demonstrated.

Enhanced computational efficiency achieved.

Effective integration of predictive analytics in ITS.

Abstract

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.