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The Human–Machine Transportation Systems (HMTS) Lab envisions the future of mobility and develops human–machine collaborative tools and methods to translate this vision into reality. A major portion of our current research focuses on designing, evaluating, and governing machine agency (from agentic AI systems) in existing and future mobility services and their underpinning infrastructure. 

Track 1: Demand Forecasting and Infrastructure Planning for Agentic Transport Systems (AgTS)

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Conceptual Framework:  

  • ​​Yu, J. (2025). Preparing for an Agentic Era of Human-Machine Transportation Systems: Opportunities, Challenges, and Policy Recommendations. Transport Policy.​171: 78-97.​

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Mathematical Foundation: 

  • 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.​​​

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Example Tools, Applications, & Impact Studies: 

  • ​​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.

  • 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.

  • Ding, J., Yu, J. G., & Bansal, P. (2025). Preferences for electric vehicles under uncertain charging prices: An eye-tracking study. Transportation Research Part D: Transport and Environment, 140, 104608.

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

  • Wang, Z., Yu, J., Li, G., Zhuge, C., & Chen, A. (2023). Time for hydrogen buses? Dynamic analysis of the Hong Kong bus market. Transportation Research Part D: Transport and Environment, 115, 103602.

  • ​Yu, J. (2022). An elementary mechanism for simultaneously modeling discrete decisions and decision times. System Dynamics Review, 38(3), 215-245.

  • 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.

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

Track 2: Operation & Control of Agentic Vehicles (AgVs) and Agentic Mobility Services (AgMS) 

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Conceptual Framework: 

  • Yu, J., Frank, R., Miranda-Moreno, L., Jafarnejad, S., Manzolli, J. A., Liu, F., Wang, J., Chernakov P., & Eslami, A. (2026). Agentic Vehicles for Human-Centered Mobility: Definition, Prospects, and Synergistic Co-Development with Vehicle Autonomy. 2026 IEEE International Conference on Intelligent Transportation Systems. 

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Mathematical Foundation: 

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

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Example Tools, Applications, & Impact Studies: 

  • 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.

  • Jiao, Y., & Yu, J. (2026). An Online–Offline Machine Learning Approach for Estimating Driver States Using Multi-Sensory Data. IEEE Transactions on Intelligent Transportation Systems.

  • Augusto Manzolli, J., Yu, J., D’Apice, A. V., & Miranda-Moreno, L. (2026). Balancing energy resilience and mobility: a multi-objective strategy for deploying shared autonomous electric vehicles during power outages. npj Sustainable Mobility and Transport, 3(1), 13.

  • 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. (2023). Coordinated flow model for strategic planning of autonomous mobility-on-demand systems. Transportmetrica A: Transport Science, 21(2), 2253474.​

  • ​​​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. 2018 International Conference on Intelligent Transportation Systems. IEEE.

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