Yantao Huang

Computational Transportation Engineer

Argonne National Laboratory

Yantao Huang is a computational transportation engineer and data scientist with expertise in agent-based simulation, machine learning, and statistical and optimization modeling. He develops scalable analytical models and simulation frameworks for forecasting, optimization, and decision-making in complex systems. His work integrates large-scale geospatial and behavioral data to generate actionable insights, with applications in transportation, logistics, and infrastructure planning. At Argonne National Laboratory, he leads the development tasks of agent-based simulation tools. He has authored 20+ peer-reviewed publications.

Google Scholar

Research Projects

Research Interests

Connected and Automated Vehicles
Shared and Automated Mobility System
Transportation Simulation
Travel Demand Modeling and Forecasts
Smart Cities and Communities
Network Optimization and Analysis
Sustainable Transportation

Education

Ph.D. in Civil Engineering, 2021

The University of Texas at Austin, United States

Master of Science in Civil Engineering, 2015

Imperial College London, United Kingdom

Bachelor of Engineering in Civil Engineering, 2013

Central South University, China

Publications

Huang, Y., and Kockelman, K.M. 2021. Can SAVs Beat Buses? Travel Time Impacts of Using Shared Automated Vehicles Along a Fixed-Route Transit Corridor. Transport Findings, November. https://doi.org/10.32866/001c.29147.

Huang, Y., Kockelman, K.M., and Garikapati, V., 2021. Shared Autonomous Vehicle Fleet Operations for First-Mile Last-Mile Transit Connections. Computers, Environment, and Urban Systems, 92, p.101730. https://doi.org/10.1016/j.compenvurbsys.2021.101730.

Huang, Y., Kockelman, K.M. and Truong, L.T., 2021. SAV Operations on a Bus Line Corridor: Travel Demand, Service Frequency, and Vehicle Size. Journal of Advanced Transportation, 2021. doi: https://doi.org/10.1155/2021/5577500.

Huang, Y., Kockelman, K., Garikapati, V., Zhu, L. and Young S., 2020. Use of Shared Automated Vehicles for First-Mile Last-Mile Service: Micro-Simulation of Rail-Transit Connections in Austin, Texas. Transportation Research Record, 2675(2), pp. 135-149. doi: https://doi.org/10.1177/0361198120962491.

Li, W., Kockelman, K. and Huang, Y., 2020. Traffic and Welfare Impacts of Credit-Based Congestion Pricing Applications: An Austin Case Study. Transportation Research Record, 2675(1), pp. 10-24. doi: https://doi.org/10.1177/0361198120960139.

Huang, Y., Kockelman, K. and Quarles, N., 2020. How Will Self-Driving Vehicles Affect U.S. Megaregion Traffic? The Case of the Texas Triangle. Research in Transportation Economics, 84. doi: https://doi.org/10.1016/j.retrec.2020.101003.

Huang, Y. and Kockelman, K.M., 2020. Electric vehicle charging station locations: Elastic demand, station congestion, and network equilibrium. Transportation Research Part D: Transport and Environment, 78. doi: https://doi.org/10.1016/j.trd.2019.11.008.

Perrine, K.A., Kockelman, K.M. and Huang, Y., 2020. Anticipating long-distance travel shifts due to self-driving vehicles. Journal of Transport Geography, 82. doi: https://10.1016/j.jtrangeo.2019.102547.

Huang, Y. and Kockelman, K., 2019. What Will Autonomous Trucking Do to U.S. Trade Flows? Application of the Random-Utility-Based Multi-Regional Input-Output Model. Transportation, 47(5), pp. 2529-2556. doi: https://10.1007/s11116-019-10027-5.