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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.  

 

As HMTS quickly enter the agentic era, a major lab effort is to study the design, evaluation, and governance of machine agency to ensure that mobility services remain human-centered and societally-benefitial. ​​​​​Please find the position article at : Yu, J. (2025). Preparing for an Agentic Era of Human-Machine Transportation Systems: Opportunities, Challenges, and Policy Recommendations. Transport Policy.​ 171: 78-97.​

 

We pursue this research direction through two main tracks, shown below. 

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

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Conceptual/Methodological 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.​​​

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

  • 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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Example Tools, Applications, & Impact Studies: 

  • ​Fares, A., Yu, J., Taiwo, R., Faris, N., Zayed, T., & Miranda-Moreno, L. (2026). Leveraging Large Language Models as Synthetic Experts: A Methodology and Validation Framework for Pavement Performance Assessment. Results in Engineering, 111630.

  • 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., & 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.​​​​​

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

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Conceptual/Methodological Foundation: 

  • 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. Naples, Italy.

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

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

 

Example Tools, Applications, & Impact Studies: 

  • Jiao, Y., Miranda-Moreno, L., & Yu, J. (2026). Can Large Language Models Infer Driver Mental State Dynamics? A Fatigue-Focused Case Study. IEEE Transactions on Intelligent Transportation Systems.

  • Wang, J., Wang, Y., Jiao, Y., Yang, X., He, D., Jafarnejad, S., Miranda-Moreno, L., Frank, R., & Yu, J. (2026). 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., & 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). Alaska, USA.

  • 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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