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Smart city article details

Title	Ai-Enabled Digital Twin Framework For Safe And Sustainable Intelligent Transportation
ID_Doc	7062
Authors	Long K.; Ma C.; Li H.; Li Z.; Huang H.; Shi H.; Huang Z.; Sheng Z.; Shi L.; Li P.; Chen S.; Li X.
Year	2025
Published	Sustainability (Switzerland), 17, 10
DOI	http://dx.doi.org/10.3390/su17104391
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. © 2025 by the authors.
Author Keywords	Advanced Traffic Management Systems; highway system; telematics data; traffic safety

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