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Technical University of Munich

Ningkang Yang

Ningkang Yang is a Ph.D. candidate at Technical University of Munich (TUM) since October 2023. His research focuses on traffic prediction, spatial-temporal data mining and the application of advanced machine learning methods in intelligent transportation systems. He is currently working on the Transport MOde Detection and Analysis (MODA) project.

Ningkang Yang holds a bachelor’s degree in Road, Bridge and River-Crossing Engineering from Jilin University (JLU), China, and a Master of Science in Transportation Systems from Technical University of Munich (TUM), Germany.

Education

Oct. 2023 – PresentPh.D. Candidate
Technical University of Munich (TUM)
Oct. 2021 – Mar. 2023Master of Science in Transportation Systems
Technical University of Munich (TUM) 
Thesis: Transfer learning for transportation systems resilience estimation using floating car data
Sep. 2017 – Jun. 2021Bachlor of engineering in Road, Bridge and River-Crossing Engineering
Jilin University (JLU) Changchun, China

Teaching

  • Cross-Cutting Fundamentals and Methods
  • Special Topic on Model Calibration
  • Optimization for Transportation Systems
  • Urban Operations Research for Transportation Systems

Publications

  • Yang, N., Lu, Q.-L., Yamnenko, I., & Antoniou, C. (2025). Efficient Cloud-Sourced Transport Mode Detection Using Trajectory Data: A Semi-Supervised Asynchronous Federated Learning Approach. IEEE Internet of Things Journal, 12(9), 11841–11857. https://doi.org/10.1109/JIOT.2024.3516695
  • Yang, N., Lu, Q.-L., Lyu, C., & Antoniou, C. (2024). Transfer Learning for Transportation Demand Resilience Pattern Prediction Using Floating Car Data. Transportation Research Record: Journal of the Transportation Research Board, 2678(11), 1622-1638. https://doi.org/10.1177/03611981241245681
  • Hu, W., Lu, Q.-L., Yang, N., & Antoniou, C. (2025). Modeling Crowdedness at Public Transport Stations During Special Events: A Comparative Study of Eleven Cities. Transportation Research Record: Journal of the Transportation Research Board, 0(0). https://doi.org/10.1177/03611981251362140
  • Yang, Ningkang and Al Haddad, Christelle and Al Haddad, Christelle and Yamnenko, Iuliia and Antoniou, Constantinos, Machine Learning for Data-Centric Transport Mode Detection: A Systematic Review. Available at SSRN: https://ssrn.com/abstract=4960556 or http://dx.doi.org/10.2139/ssrn.4960556
  • Huang, C., Guo, T., Yang, N., & Antoniou, C. (2025). Augmenting Spatio-Temporal Dependencies for GNN-Based Short-Term Traffic Flow Prediction. Euro Working Group of Transportation Annual Conference.

 

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