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

Montreal

FOCUS 1: Demand Forecasting and Infrastructure Planning in Agentic Transport Systems (AgTS)

Key Position & Methodology: 

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

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Key Research Development & Applicaitons:

  • 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., & Chen, A. (2021). Differentiating and modeling the installation and the usage of autonomous vehicle technologies: A system dynamics approach for policy impact studies. Transportation Research Part C: Emerging Technologies, 127, 103089.​

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FOCUS 2: Operation and Management of Agentic Vehicles (AgVs) and Agentic Mobility Services (AgMS) 

Key Position & Methodology: 

  • Yu, J. (2026). Agentic Vehicles for Human-Centered Mobility: Definition, Prospects, and Synergistic Co-Development with Vehicle Autonomy. 2026 International Conference on Intelligent Transportation Systems.  IEEE.

  • ​​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.​

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

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Key Research Development & Applications:

  • Wang, J., Wang, Y., Jiao, Y., Yang, X., He, D., Jafarnejad, S., Miranda-Moreno, L., Frank, R., & Yu, J. (2026 In Press). MILD: Mediator agentic system with bidirectional perception and multi-layered alignment for human-vehicle collaboration. Communications in Transportation Research. 

  • Shi, T., Mo, Y., Liu, Y., Hao, Z., Wang, Y., Hu, W., Yu, N., Zhou, M., & Yu, J. (2026). Organizational Control Layer: Governance Infrastructure at the Execution Boundary of LLM Agent Systems. ACM Conference on AI in Society (ACM CAIS 2026). 

  • 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., 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.

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