​​​​​​Selected ​Articles

 

Human-Machine Collaborative Planning & Engineering for Future 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.

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

• 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.

• Yu, J. & McKinley, G. (2024). Synthetic Participatory Planning of Shared Automated Electric Mobility Systems. Sustainability. 16, no. 13: 5618. 

• Yu, J., & Hyland, M. F. (2023). Coordinated Flow Model for Strategic Planning of Autonomous Mobility-on-Demand Systems. Transportmetrica A: Transport Science, 1-39. 

• ​Yu, J., Selby, B., Vlahos, N., Yadav, V., & Lemp, J. (2021). A Feature-Oriented Vehicle Trajectory Data Processing Scheme for Data Mining: A Case Study for Statewide Truck Parking Behaviors. Transportation Research Interdisciplinary Perspectives, 11, 100401.

• Yu, J. & Jayakrishnan, R. (2018). A Quantum Cognition Model for Bridging Stated and Revealed Preference. Transportation Research Part B: Methodological, 118, 263-280.

• Yu, J. & Jayakrishnan, R. (2018). A Cognitive Framework for Unifying Human and Artificial Intelligence in Transportation Systems Modeling. IEEE International Conference on Intelligent Transportation Systems (pp. 3767-3772).

• Yu, J. (2018). Paradigms of Identifying and Quantifying Uncertainty and Information in Constructing a Cognition-Modeling Framework of Human-Machine Transportation Systems. University of California, Irvine.​

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​Understanding, Designing, and Managing Human-Machine Interactions in Future Transportation Systems

• 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.

• Liu, S., Jiao, Y., & Yu, J. (2026). Modeling and analyzing phantom crowd stampedes through emotion accumulation, behavioral synchronization, and collision propagation. Chaos, Solitons & Fractals, 208, 118200.

• 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.

• 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.

• Ding, J., Yu, J., & Bansal, P. (2025). Preferences for Electric Vehicles under Uncertain Charging Prices: An Eye-Tracking study. Transportation Research Part D: Transport and Environment, 140, 104608.

• Wang, Z., Yu, J., Fu, X., & Chen, A. (2024). Subsidy policies towards zero-emission bus fleets: A systematic technical-economic analysis. Transport Policy.

• Yu, J., Hyland, M. F., & Chen, A. (2023). Improving Infrastructure and Community Resilience with Shared Autonomous Electric Vehicles (SAEV-R). 2023 IEEE Intelligent Vehicles Symposium (IV). 

• 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. 

• 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., & 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. (2019). Quantifying Traveler Information Provision in Dynamic Heterogeneous Traffic Networks. Transportation Planning and Technology, Vol. 42, No. 4. 339-354.

• Nam, D., Yang, D., An, S., Yu, J., Jayakrishnan, R., & Masoud, N. (2018). Designing a Transit-Feeder System using Multiple Sustainable Modes: Peer-to-Peer (P2P) Ridesharing, Bike Sharing, and Walking. Transportation Research Record: Journal of the Transportation Research Board, 2672(8), 754-763.

• 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.

• Masoud, N., Nam, D., Yu, J., & Jayakrishnan, R. (2017). Promoting Peer-to-Peer Ridesharing Services as Transit System Feeders. Transportation Research Record: Journal of the Transportation Research Board, 2650(1), 74-83.

• Yu, J. & Jayakrishnan, R. (2017). Multiclass, Multicriteria Dynamic Traffic Assignment with Path-Dependent Link Cost and Entropy-Based Risk Preference. Transportation Research Record: Journal of the Transportation Research Board, 2667(1), 108-118.