AI-Enabled Digital Twin Framework for Safe and Sustainable Intelligent Transportation

Keke Long, Chengyuan Ma (), Hangyu Li, Zheng Li, Heye Huang, Haotian Shi, Zilin Huang, Zihao Sheng, Lei Shi, Pei Li, Sikai Chen and Xiaopeng Li
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Keke Long: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Chengyuan Ma: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Hangyu Li: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Zheng Li: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Heye Huang: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Haotian Shi: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Zilin Huang: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Zihao Sheng: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Lei Shi: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Pei Li: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Sikai Chen: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA
Xiaopeng Li: Department of Civil and Environmental Engineering, University of Wisconsin, Madison, WI 53705, USA

Sustainability, 2025, vol. 17, issue 10, 1-17

Abstract: This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability.

Keywords: highway system; Advanced Traffic Management Systems; traffic safety; telematics data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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