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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Keke Long & Chengyuan Ma & Hangyu Li & Zheng Li & Heye Huang & Haotian Shi & Zilin Huang & Zihao Sheng & Lei Shi & Pei Li & Sikai Chen & Xiaopeng Li, 2025. "AI-Enabled Digital Twin Framework for Safe and Sustainable Intelligent Transportation," Sustainability, MDPI, vol. 17(10), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4391-:d:1654135
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