Yuankai Wu

Ph.D. / Postdoc

Yuankai Wu received his Ph.D. degree in vehicle operation engineering from Beijing Institute of Technology, Beijing, China, in Jun. 2019. He was a visiting PhD student with Department of Civil and Environmental Engineering, University of Wisconsin-Madison, from Nov. 2016 to Dec. 2017. During this time, he was doing research on connected & automated vehicles. His research has focused on various machine learning technologies, e.g., tensor decomposition, graph neural networks, deep reinforcement learning, with the aim to facilitate the applications on intelligent transportation systems.

Research Interests

Spatiotemporal Data Modeling
Data-driven Transportation System Control
Connected & Automated Vehicle

Selected Publications

Wu Y, Tan H, Qin L, et al. A hybrid deep learning based traffic flow prediction method and its understanding[J]. Transportation Research Part C: Emerging Technologies, 90, 166-180
Wu Y, Zhuang D, Labbe A, et al. Inductive graph neural networks for spatiotemporal kriging[J]. arXiv preprint arXiv:2006.07527, 2020.
Wu Y, Tan H, Li Y, et al. A fused CP factorization method for incomplete tensors[J]. IEEE transactions on neural networks and learning systems, 2018, 30(3): 751-764.
Wu Y, Tan H, Peng J, et al. Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus[J]. Applied energy, 2019, 247: 454-466.
Wu Y, Tan H, Qin L, et al. Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm[J]. Transportation research part C: emerging technologies, 2020, 117: 102649.