Human-Machine Transportation Systems (HMTS) Lab envisions future transportation solutions and utilizes human-machine synergy to turn the visions into reality.
CURRENT FOCUS 1:
Agentic Planning & Management of Transportation Systems
We develop and apply agentic workflows for both the strategic infrastructure planning and the real-time traffic operation under uncertainty.
Highlighted papers:
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Yu, J. (2025). Preparing for an Agentic Era of Human-Machine Transportation Systems: Opportunities, Challenges, and Policy Recommendations. Transport Policy.​
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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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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.
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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.
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Yu, J., & McKinley, G. (2024). Synthetic participatory planning of shared automated electric mobility systems. Sustainability, 16(13), 5618.
CURRENT FOCUS 2:
Agentic Vehicles & Agentic Mobility Services
We design and investigate mobility services powered by agentic AI, aiming to create more human-centered mobility systems.
Highlighted papers:​
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Yu, J. (2025). Agentic Vehicles (AgVs) for Human-Centered Mobility. Arxiv. preprint at: https://arxiv.org/abs/2507.04996
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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.​
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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.​
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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.