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, where I subsequently worked as a Postdoctoral Researcher. Before joining Zhejiang University, I was a Postdoctoral Associate at the Urban AI Lab, University of Florida. I hold an M.S. (2018) and a B.Eng. (2016) in Transportation Engineering, both from Harbin Institute of Technology.
Graduate student opportunities (Fall 2027). For domestic admissions, I expect to recruit one PhD student and up to two master’s students. International applications follow a separate admissions route and do not count against this domestic quota; I welcome both PhD and master’s applicants and currently plan to recruit at least one international graduate student. Details and application information →
Research Interests
My research investigates how Artificial Intelligence (AI) can help predict and simulate complex transportation systems. Building on my previous work in spatiotemporal data modeling, travel behavior analysis, and public transit operations, my current interests include:
- Generative Mobility and Policy Analysis: Generative models of daily activities and mobility trajectories for travel-demand synthesis, missing-data recovery, and transportation-policy scenarios.
- AI-driven Traffic Simulation: Learning realistic vehicle-pedestrian and vehicle-vehicle interactions from real-world multimodal data, and developing realistic simulation methods for intersections and other fine-grained traffic units.
- Forecasting with Large Language Models and Multimodal Data: Studying how large language models can combine quantitative data and textual evidence to forecast transportation demand, infrastructure performance, and policy outcomes.
News
View all news →- July 2026: Our paper “ProST-CP: Probabilistic Spatiotemporal CP Decomposition for Shared Micro-mobility Origin-Destination Demand Modeling” (authors: Xinghang Zhu, Zhanhong Cheng, Luis Miranda-Moreno, and Lijun Sun) was accepted by Transportation Research Part C: Emerging Technologies!
- July 2026: I attended the Emerging Challenges in Statistical Modeling for Transportation Research workshop at the Banff International Research Station (BIRS), Banff, Canada, where I presented “Graph neural networks for residential location choice: connection to classical logit models.”
- 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!
Selected publications
View all publications →- 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]