About me

I am Zhanhong Cheng (程展鸿), a ZJU 100 Young Professor at the Institute of Intelligent Transportation Systems, Zhejiang University. I received my Ph.D. (2022) in Civil Engineering from McGill University, I continued there as a Postdoctoral Researcher, then joined the Urban AI Lab at the University of Florida as a Postdoctoral Associate. I hold an M.S. (2018) and a B.Eng. (2016) in Transportation Engineering, both from Harbin Institute of Technology.

Research Interests

My research explores the intersection of Artificial Intelligence (AI) and transportation systems, with extensive work in spatiotemporal data modeling, travel behavior analysis, and public transit operations. My current research interests include, but are not limited to:

• Spatiotemporal Data: Integrating AI and statistical learning with multimodal data (e.g., trajectories, traffic flow, text, and networks) for high-fidelity travel demand forecasting and behavior analysis.
• Generative Mobility: Leveraging Generative AI to address fundamental challenges in transportation, including data privacy protection, scenario generation, simulation, and system optimization.
• Emerging Transportation: Exploring, understanding, and optimizing emerging transportation modes, such as Robotaxi, autonomous transit, and Urban Air Mobility (UAM).

• May 2026: Excited to begin my tenure-track position as a ZJU Hundred Talents Program Young Professor at the Institute of Intelligent Transportation Systems, Zhejiang University!
• Apr 2026: I visited TUPA at the Korea Advanced Institute of Science and Technology (KAIST), South Korea, for a one-month research stay.
• Apr 2026: Our paper “Graph neural networks for residential location choice: connection to classical logit models” (authors: Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou, Shenhao Wang) was accepted by Transportation Research Part B: Methodological!
• March 2026: I was pleased to deliver a talk titled “Data, Model, and Intelligence: Travel Behavior Modeling and Demand Prediction” at the Institute of Intelligent Transportation Systems (IITS), Zhejiang University.
• Nov 2025: Our paper “Graph neural networks for residential location choice: connection to classical logit models” was accepted in ISTTT 2026!

• Cheng, Z., Trepanier, M., & Sun, L. (2022). Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition. Transportation Science, 56(4), 904-918. [Full-text] [Code] [Slides] (2nd best paper at CASPT and TransitData 2022🏅)
• Cheng, Z., Hu, L., Bu, Y., Zhou, Y., & Wang, S. (2026). Graph neural networks for residential location choice: connection to classical logit models. Transportation Research Part B: Methodological, 209, 103464. [Full-text]
• Cheng, Z., Wang, J., Trépanier, M., & Sun, L. (2025). Abnormal metro passenger demand is predictable from alighting and boarding correlation. Transportation Research Part C: Emerging Technologies, 178, 105239. [Full-text] [Slides] [Poster] [Code]
• Cheng, Z., Trépanier, M., & Sun, L. (2021). Incorporating travel behavior regularity into passenger flow forecasting. Transportation Research Part C: Emerging Technologies, 128, 103200. [Full-text]
• Cheng, Z., Trépanier, M., & Sun, L. (2021). Probabilistic model for destination inference and travel pattern mining from smart card data. Transportation, 48(4), 2035-2053. [Full-text] [Code]
• Chen, X., Cheng, Z., Jin, J. G., Trépanier, M., & Sun, L. (2023). Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model. Transportation Science, 57(6), 1516-1535. [Full-text] [Slides]
• Chen, X., Cheng, Z., Cai, H., Saunier, N., & Sun, L. (2024). Laplacian convolutional representation for traffic time series imputation. IEEE Transactions on Knowledge and Data Engineering. [Full-text] [Slides] [Code]