< Logistics Management
Transportation big data and transportation artificial intelligence
Smart highway state prediction and control
Dynamic modeling and control of traffic flow
Trajectory prediction and control of autonomous vehicles
Graph neural networks, physics-informed deep learning, reinforcement Learning
Dr. Ting Wang received the Ph.D. degree from Tongji University, China, in 2025. From 2023 to 2024, he was a Visiting Student with the Department of Civil Engineering at Monash University. His awards and honors include the Scholarship of China Scholarship Council and the National Scholar-ship for Doctoral Students. He is the author of more than 40 articles, including Transportation Research Part C, IEEE Transactions on Intelligent Transportation Systems, Information Fusion, Pattern Recognition, Transportmetrica A/B, etc. His research focuses on using Graph Neural Networks, Physics-informed Deep Learning, and Neural Operators for traffic flow modeling and control. He serves as a reviewer for more than twenty journals in the fields of transportation and artificial intelligence.
Master: Transportation system control theory
[1]Wang T, Li Y, Cheng R, et al. Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach[J]. Transportation Research Part C: Emerging Technologies, 2026, 182: 105422.
[2]Wang T, Li Z, Cheng R, et al. Spatiotemporal Fourier neural operator-empowered super-resolution traffic flow field reconstruction from sparse observations[J]. Physica A: Statistical Mechanics and its Applications, 2026: 131742.
[3]Wang T, Ngoduy D, Zou G, et al. PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction[J]. Expert Systems with Applications, 2024, 258: 125144.
[4]Wang T, Ngoduy D, Li Y, et al. Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction[J]. Chaos, Solitons & Fractals, 2024, 187: 115437.
[5]Wang T, Li Y, Lyu H, et al. Multi-scale feature-aware spatiotemporal graph convolutional network for highway traffic flow prediction[J]. Transportmetrica A: Transport Science, 2025: 2550377.
[6]Wang T, Cheng R, Wu Y. Stability analysis of heterogeneous traffic flow influenced by memory feedback control signal[J]. Applied Mathematical Modelling, 2022, 109: 693-708.
