构建可扩展交通数字孪生平台，实现实时预测与自适应调控
Architecting Digital Twins for Intelligent Transportation Systems
- 基于领域概念模型，模块化建模交通系统核心组件
- 融合历史与实时数据，实现高精度交通流量预测
- 集成自适应MLOps，支持模型自动部署与持续更新
现代交通系统在动态交通模式下面临流量管理、安全维护和运行效率的多重挑战。本文提出DigIT架构，一种面向智能交通系统的数字孪生平台，旨在克服现有框架的局限性，提供模块化、可扩展的交通管理解决方案。该架构基于领域概念模型（DCM），系统性地建模关键智能交通系统组件，实现预测模型与仿真的无缝集成。通过机器学习模型，利用历史与实时数据预测交通模式变化；为应对动态演变的交通状况，架构引入自适应机器学习运维（MLOps），自动化完成预测模型的部署与生命周期管理。评估结果表明，该架构在预测精度与计算效率方面均表现优异。
原文摘要 · Abstract (English)
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.
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