About Me

Dr. Brian Yueshuai He has extensive research experience in urban mobility modeling, artificial intelligence, data synthesis for complex systems, travel behavior analysis, transportation economics, and sustainable urban planning. In the scope of cyber-physical systems, his research enables interactions between the physical infrastructure and virtual cyber systems by adopting data-driven techniques to support multi-scale urban system planning, management, and decision-making.

His research interests include Mobility Decision Science, Complex Systems, Multi-Scale System Modeling, Computational Simulation, and Sustainable and Equitable Transportation Planning.

RESEARCH

Cities are experiencing a surge in emerging technologies and services with the rise of the Internet of Things (IoT). As complex systems par excellence, transportation systems are poised due to the emergence of extensive data and innovative and disruptive technologies. My research develops a forward-thinking Digital Twin Technology for Mobility Systems to support research in New Mobility Solutions, Energy & Environment Analysis, Data Science Technology, and Infrastructure Management.

D:aisy-T | Decision-making: automated and integrated system of Transportation

D:aisy-T comprehensively simulates the dynamic interactions between travelers and the physical environment to capture the highly resolved system dynamics and envisions a next-generation mobility system in smart cities. Enabled by machine learning models, economic theory, computational simulation, and advanced transportation modeling techniques, it explores the potential of the transportation system digital twin as a living laboratory for technology innovations, system operation and management, policy evaluation, and urban sustainability and resilience analysis. 

See Research

Highlighted Publications

  • He, Y. B., Jiang, Q., & Ma, J. (2022). Impact evaluation of connected and automated vehicles in Southern California with an activity-based approach part I: Travel behavior and demand analysis. Transportation Research Part D: Transport and Environment, special issue: Pathway into Full Autonomy by Leveraging Infrastructure Enabled Automation. doi: 10.1016/j.trd.2022.103329.

  • Yu, Q., He, Y. B., Zhu, Y., & Ma, J. (2023). Environmental justice implications of distributive equity and near-roadway air quality from zero-emission vehicle adoption in California. Nature Communications. doi: 10.1038/s41467-023-43309-9

  • Jiang, Q., Zhang, N., He, Y. B., & Ma, J. (2022). Public charging demand prediction for electric vehicles in large-scale transportation systems with a scenario-and activity-based approach. Transportation Research Part A: Practice and Policy. doi: 10.1016/j.tra.2023.103935

  • He, Y. B., & Chow, J. Y. (2019). Optimal privacy control for transport network data sharing. Transportation Research Part C: Emerging Technologies. doi: 10.1016/j.trc.2019.07.010.

Two Fully Funded Ph.D. Positions at UofL

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Two Fully Funded Ph.D. Positions at UofL ⋅

Prof. Brian Yueshuai He of the Department of Civil and Environmental Engineering at the University of Louisville is actively looking for two new Ph.D. students to join his lab. Selected candidates will work on the following areas: transportation system modeling, artificial intelligence and deep learning, agent-based simulation, human mobility and trajectories, spatial-temporal data analysis, and electric vehicles. Specifically, the candidates may work with agent-based modeling, large-scale microsimulation, or perform analysis, modeling, simulation, control, and deployment of future mobility systems. Candidates with backgrounds in agent-based modeling, artificial intelligence, data science, statistics, and/or any related areas are encouraged to contact Dr. He directly at yueshuai.he@louisville.edu. Strong computer programming skills (e.g., Java, Python with provable experiences) are preferred.

About UofL and Louisville: The University of Louisville is a public university located in Kentucky’s largest metropolitan area. As one of only 80 universities in the United States to earn recognition by the Carnegie Foundation as both a Research 1 (R1: Doctoral Universities – Very high research activity) and a Community Engaged university, UofL is uniquely positioned to impact lives in areas of student success and research and innovation.

Lab highlights:

1. Interdisciplinary research environment interacting with computer science, data science, transportation policy/planning, environmental engineering, and public health, etc.

2. Cutting-edge research opportunities with large-scale real-world and synthetic datasets, AI and deep learning models, and spatial-temporal data analysis.

3. State-of-the-art facility: access to high-performance computing server and driving simulator.