Transportation Digital Twins

Transportation digital twins are dynamic, digital replicas of physical transportation systems that sense real-time conditions, predict future states, and optimize safety and mobility. They integrate data from various sources, such as connected vehicles, infrastructure sensors, and traffic management systems, to create a comprehensive virtual model of the transportation environment. This allows for real-time monitoring, analysis, and decision-making to enhance traffic flow, reduce congestion, and improve safety. Some questions I aim to answer include:

• How can digital twins be leveraged to proactively identify and mitigate safety risks?
• What are the best practices for replicating infrastructure, vehicles, and road users?
• How can data be efficiently collected, processed, and integrated in digital twins?

Relevant Publications

• Long, K., Ma, C., Li, H., Li, Z., Huang, H., Shi, H., Huang, Z., Sheng, Z., Shi, L., Li, P., Chen, S. and Li, X., 2025. "AI-Enabled Digital Twin Framework for Safe and Sustainable Intelligent Transportation." Sustainability.
• Wu, K., Li, P., Cheng, Y., Parker, S. T., Ran, B., Noyce, D. A. and Ye, X., 2025. "A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors." IEEE Transactions on Intelligent Transportation Systems.
• Li, P., Wu, K., Cheng, Y., Parker, S. and Noyce, D.A., 2023. "How Does C-V2X Perform in Urban Environments? Results From Real-World Experiments on Urban Arterials." IEEE Transactions on Intelligent Vehicles.
• Abdel-Aty, M., Zheng, O., Wu, Y., Abdelraouf, A., Rim, H. and Li, P., 2023. "Real-Time Big Data Analytics and Proactive Traffic Safety Management Visualization System." Journal of Transportation Engineering, Part A: Systems, 149(8), p.04023064.