Yuanyuan Wu

Yuanyuan Wu

she/her

Transport researcher with expertise in AI-driven mobility modelling, behavioural analysis, and the digital transformation of transport systems.

Yuanyuan Wu - PhD

I am a Postdoctoral Researcher in the Division of Transport Planning, Department of Civil and Architectural Engineering, at KTH Royal Institute of Technology, Sweden.

I am an AI-driven urban mobility researcher with expertise in reinforcement learning, synthetic data generation, and mobility analytics. My work sits at the intersection of transport planning and management, travel behavioural analysis and machine learning, and I have research and professional experience spanning China, Singapore, Hong Kong, and Europe.

Education

  • Ph.D., Intelligent Transport Systems
    Nanyang Technological University, Singapore (2017–2021)
    Dissertation: Intersection management strategies to realise the benefits of new generation vehicles

  • M.Sc., Urban Transportation Planning and Management
    Southeast University, China (2014–2016)

  • B.Sc., Surveying and Mapping Engineering
    Southeast University, China (2008–2012)

Academic Appointments

  • Postdoctoral Researcher
    Division of Transport Planning, KTH Royal Institute of Technology, Sweden (2023–Present)
    • Project lead/co-investigator in synthetic trip generation, AI-based policy evaluation, and behavioral modeling under fare incentives.
    • Supervising PhD and Master’s theses, contributing to project management and funding proposals.
  • Research Fellow
    Nanyang Technological University, Singapore (2021–2023)
    • Conducted research on ethics of autonomous driving (AI Singapore TrustFUL project).
    • Coordinated simulation studies, surveys, and lab experiments.
  • Doctoral Researcher
    Nanyang Technological University, Singapore (2017–2021)
    • Research on autonomous intersection management, connected vehicles, and reinforcement learning.

Research Grants & Projects

  • PI, Trafik och Region 2026 (Stockholm) (2026–2027, SEK 1.6M)
    Privacy-preserving synthetic trip generation for public transit from smart card data.

  • Co-Investigator, Digital Futures “cAIMBER” Project (2023–2025, SEK 2.0M)
    Causal AI for mobility behavior modeling under fare incentives.

  • Co-Investigator, AI Singapore – TrustFUL Project (2021–2025, S$6.9M)
    Ethical dilemma of autonomous driving.

  • Co-Investigator, NTU–WeBank Joint Research Centre on FinTech (2020–2022, S$200k)
    Optimization of autonomous driving.
  • PhD Researcher, Singapore Ministry of Education Tier 2 Grant (MOE2017-T2-1-029) (2017–2021)
    Autonomous intersection management.

Teaching & Supervision

  • KTH Royal Institute of Technology
    • Applied AI in Transportation (course design, TA, student supervision)
    • Research Methods & Communication Skills (module teacher: Data analysis with Python)
  • Harbour Education, 2022
    • Traffic Modelling in the Era of Big Data (course design, lectures, examiner)
    • Artificial Intelligence: Cryptography, Cybersecurity and Bitcoin (module leader for assignments)
  • Supervision
    • 2 co-supervised PhD thesis
    • 5 Master’s theses
    • 10 undergraduate final year projects

Skills & Tools

  • Urban Informatics & Analytics: Smart card AFC analysis, GIS & spatial analysis, urban mobility modeling, transport system evaluation, privacy-preserving synthetic data generation
  • AI & Data Science: Deep learning, reinforcement learning, generative models (GAN, VAE, Diffusion, Normalizing Flows), causal inference, behavioral experiments
  • Policy Applications: Public transport pricing, incentive evaluation, sustainable mobility strategies, ethics of autonomous driving
  • Technical Proficiency: Python (PyTorch, TensorFlow, scikit-learn), MATLAB, SUMO, VISSIM

Selected Publications

  • Wu, Y., Yu, H., Yan, Z., & Xu, H. (2026). Dissociating ethical dilemma preferences and actions in autonomous driving by survey and driving experiment. Transportation Research Part A: Policy and Practice, 206, 104891.
  • Wu, Y., Qin, Z., Wang, L., Ma, X., Ma, Z. (2025). Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives. arXiv:2509.06656 (under revision, TRC).
  • Wu, Y., Qin, Z., Ma, Z. (2025). Toward a comprehensive framework for evaluating synthetic trips generated from public transit smart card data. (manuscript completed).
  • Wu, Y., Zhang, Y., Yu, H., Xu, H. (2025). Dissociating Ethical Dilemma Preferences and Actions in Autonomous Driving by Survey and Driving Experiment. (under revision, Transportation Research Part A).
  • Wu, Y., Markham, A., Wang, L., Solus, L., Ma, Z. (2025). Data-driven causal behaviour modelling from trajectory data: A case for fare incentives in public transport. Journal of Public Transportation, 27:100114.
  • Zhang, Q., Ma, Z., Wu, Y., Liu, Y., Qu, X. (2025). Quantifying variable contributions to bus operation delays considering causal relationships. Transportation Research Part E, 194:103881.
  • Wu, Y., Chen, H., Zhu, F. (2019). DCL-AIM: Decentralized coordination learning of autonomous intersection management for CAVs. TRC, 103:246–260.
  • Wu, Y., Wang, D.Z.W., Zhu, F. (2025). Traffic efficiency and fairness optimisation for autonomous intersection management based on reinforcement learning. Transportmetrica A.