AI Thrust Seminar| Control Meets Learning: PDE State Estimation of Phantom Traffic Jam

1:30pm - 2:30pm
Meeting ID:934 4955 9597, Passcode:AIT123

Studies on transportation management systems have undergone several waves of advancement in both theory and practice, led by revolutions taking place in parallel, including automation, machine learning electrification, and sharing economy. My research focuses on a combination of two of these revolutions, control and learning methodologies and their applications in intelligent traffic systems. In this talk, I will discuss traffic state estimation problem of freeway stop-and-go traffic, also known as phantom traffic jam, a common phenomenon that has drawn a lot of research interests over the years. The congestion leads to acceleration-deceleration traffic oscillations on freeway, causing increased fuel consumption, and driving risk. Traffic state estimation problem refers to a process of inferring traffic state variables from partially observed traffic data. I will first show a methodological PDE model-based solution to predict traffic state values from boundary sensing data. Inspired by physics-informed machine learning, we develop observer-informed deep learning which integrates the PDE observer with deep learning paradigm. The observer-informed neural network forms a novel class of data-efficient function approximators that encode PDE observer as theoretical guarantee and improves the accuracy and convergence speed of spatial-temporal traffic state estimation.

Event Format
Speakers / Performers:
Dr. Huan Yu

Dr. Huan Yu is an Assistant Professor in the Intelligent Transportation Thrust of the Systems Hub, an affiliated Assistant Professor in the Department of Civil and Environmental Engineering at the Hong Kong University of Science and Technology (HKUST). Yu received her B.Eng. degree in Aerospace Engineering from the Honor School (Elite program in engineering) of Northwestern Polytechnical University, and the M.Sc. and Ph.D. degrees in Aerospace Engineering from the Department of Mechanical and Aerospace Engineering, University of California, San Diego. She was a visiting scholar at University of California, Berkeley in 2018 and Massachusetts Institute of Technology in 2019. She was a postdoc researcher at University of California, San Diego before joining the HKUST(Guangzhou) in 2021. Dr. Yu is broadly interested in control, optimization, and learning methodologies and their applications in intelligent transportation systems.

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PG students
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Artificial Intelligence Thrust, HKUST(GZ)
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