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Human-Machine Transportation Systems (HMTS) Lab envisions future mobility services and develops human-machine synergy to turn the visions into reality.

Montreal

FOCUS 1:
Agentic Demand Forecasting & Infrastructure Planning

We develop AI-aided decision-support tools and workflows for transportation systems planning

​Selected Recent Papers: 

  • Paquette-Greenbaum, S., & Yu, J. (2026). LLM Agents for Combinatorial Efficient Frontiers: Investment Portfolio Optimization. arXiv preprint arXiv:2601.00770.

  • ​​Yu, J. (2025). Preparing for an Agentic Era of Human-Machine Transportation Systems: Opportunities, Challenges, and Policy Recommendations. Transport Policy.​​

  • Yu, J., & Hyland, M. F. (2025). Interpretable state-space model of urban dynamics for human-machine collaborative transportation planning. Transportation Research Part B: Methodological, 192, 103134.​​

  • Yu, J., Zhao, J., Miranda-Moreno, L., & Korp, M. (2025). Modular AI agents for transportation surveys and interviews: Advancing engagement, transparency, and cost efficiency. Communications in Transportation Research, 5, 100172.​​

  • Manzolli, J. A., Yu, J., & Miranda-Moreno, L. (2025). Synthetic multi-criteria decision analysis (S-MCDA): A new framework for participatory transportation planning. Transportation Research Interdisciplinary Perspectives, 31, 101463.​​​

  • Liang, Y., Wang, S., Yu, J., Zhao, Z., Zhao, J., & Pentland, S. (2026). Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning. Travel Behaviour and Society, 43, 101171.

  • Yu, J., & McKinley, G. (2024). Synthetic participatory planning of shared automated electric mobility systems. Sustainability, 16(13), 5618.​​

  • Yu, J., & Jayakrishnan, R. (2018). A quantum cognition model for bridging stated and revealed preference. Transportation Research Part B: Methodological, 118, 263-280.​

Montreal

FOCUS 2:
Agentic Mobility Services & Traffic Operation

We design AI-powered mobility services and synergistically integrate them into broader mobility systems

​Selected Recent Papers: 

  • Eslami, A., & Yu, J. (2026). A Control-Theoretic Foundation for Agentic Systems. arXiv preprint arXiv:2603.10779.

  • Eslami, A., & Yu, J. (2025). Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats. arXiv preprint arXiv:2512.17041.

  • ​​Yu, J. (2025). Agentic Vehicles (AgVs) for Human-Centered Mobility. Arxiv. preprint at: https://arxiv.org/abs/2507.04996​​

  • Yu, J., & Hyland, M. F. (2025). Coordinated flow model for strategic planning of autonomous mobility-on-demand systems. Transportmetrica A: Transport Science, 21(2), 2253474.​​

  • Wu, H., Jiao, Y., Jiang, C., Wang, T., & Yu, J. (2025). Fatigue state evaluation of urban railway transit drivers using psychological, biological, and physical response signals. IEEE Access.

  • Yu, J., Hyland, M. F., & Chen, A. (2023). Improving infrastructure and community resilience with shared autonomous electric vehicles (SAEV-R). In 2023 IEEE Intelligent Vehicles Symposium (IV) (pp. 1-6). IEEE.​

  • Yu, J., & Hyland, M. F. (2020). A generalized diffusion model for preference and response time: Application to ordering mobility-on-demand services. Transportation Research Part C: Emerging Technologies, 121, 102854.

  • Yu, J., & Jayakrishnan, R. (2018). A cognitive framework for unifying human and artificial intelligence in transportation systems modeling. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE.

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