Dr. Hao Liu joined PATH as a Postdoctoral Researcher in 2016 with a primary research interest in traffic flow modeling and simulation for traffic streams affected by Connected Automated Vehicles (CAV). As an Assistant Research Engineer, he remains focused on using modeling tools to learn about the best traffic operation and management strategies to maximize the benefits from CAV technologies. He is enthusiastic about sharing PATH’s simulation models with other researchers in their tailored studies to aid in CAV development, evaluation, and deployment. He continues to extend traffic flow modeling capability by incorporating the latest technology advancements in CAV control, communication, and machine learning into his research.
Past research includes a sponsored project from the U.S. Department of Energy, “Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities”, where the PATH team developed a cooperative signal control algorithm that adopts CACC datasets and datasets collected by traditional fixed traffic sensors to predict future traffic conditions. The prediction allows the signal controller to assign signal priority, which ultimately improves the overall intersection performance. In follow-up research, Dr. Liu and his colleagues developed a CAV controller that optimizes vehicle arrival and departure trajectories based on the control commands from the signal control algorithm. The team built a hardware-in-the-loop testbed to evaluate the combined signal control and vehicle trajectory planning algorithm under repeatable and nearly real-world traffic conditions.
Currently, Dr. Liu is working on “Truck Platooning Early Deployment Assessment: Phase 2” sponsored by Federal Highway Administration. He is responsible for developing a real-time monitoring system that checks the system health of test trucks over a one-year data collection period. He is the key researcher managing and evaluating the rich data sets generated by the project that determines the performance of the truck platooning system. These results are essential for refining and prompting the advanced platooning technology.
Dr. Liu’s other research interests include freeway and arterial traffic management, hardware-in-the-loop simulation, and vehicle energy consumption estimation. Dr. Liu received his B.A. in Transportation Engineering from Sun Yat-sen University in China, M.Sc. in Transportation Engineering from the Research Institute of Highway in China, and his Ph.D. in Civil Engineering from the University of Cincinnati.
Kan, Y., Liu, H., Lu, X., & Chen, Q. (2021). Development of a novel engine power model to estimate heavy-duty truck fuel consumption. Transportmetrica A: Transport Science, 1-23.
Liu, H., Flores, C. E., Spring, J., Shladover, S. E., & Lu, X. Y. (2021). Field Assessment of Intersection Performance Enhanced by Traffic Signal Optimization and Vehicle Trajectory Planning. IEEE Transactions on Intelligent Transportation Systems.
Liu, H., Lu, X. Y., & Shladover, S. E. (2020). Mobility and energy consumption impacts of cooperative adaptive cruise control vehicle strings on freeway corridors. Transportation Research Record, 2674(9), 111-123.
Liu, H., Shladover, S. E., Lu, X. Y., & Kan, X. (2020). Freeway vehicle fuel efficiency improvement via cooperative adaptive cruise control. Journal of Intelligent Transportation Systems, 1-13.
Liu, H., Lu, X. Y., & Shladover, S. E. (2019). Traffic signal control by leveraging Cooperative Adaptive Cruise Control (CACC) vehicle platooning capabilities. Transportation Research Part C: Emerging Technologies, 104, 390-407.
Kan, X. D., Xiao, L., Liu, H., Wang, M., Schakel, W. J., Lu, X. Y., ... & Ferlis, R. A. (2019). Cross-Comparison and Calibration of Two Microscopic Traffic Simulation Models for Complex Freeway Corridors with Dedicated Lanes. Journal of Advanced Transportation, 2019.
Zuo, T., Wei, H., Liu, H., & Yang, Y. J. (2019). Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand. Journal of Transport Geography, 74, 161-172.
Liu, H., Kan, X. D., Shladover, S. E., Lu, X. Y., & Ferlis, R. E. (2018). Modeling impacts of Cooperative Adaptive Cruise Control on mixed traffic flow in multi-lane freeway facilities. Transportation Research Part C: Emerging Technologies, 95, 261-279.
Liu, H., Kan, X., Shladover, S. E., Lu, X. Y., & Ferlis, R. E. (2018). Impact of cooperative adaptive cruise control on multilane freeway merge capacity. Journal of Intelligent Transportation Systems, 22(3), 263-275.
Wei, H., Zuo, T., Liu, H., & Yang, Y. J. (2017). Integrating land use and socioeconomic factors into scenario-based travel demand and carbon emission impact study. Urban Rail Transit, 3(1), 3-14.
Liu, H., Wei, H., Zuo, T., Li, Z., & Yang, Y. J. (2017). Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment. Transportation research part C: emerging technologies, 76, 132-149.
Yao, Z., Wei, H., Li, Z., Ma, T., Liu, H., & Yang, Y. J. (2013). Developing Operating Mode Distribution Inputs for MOVES with a Computer Vision–Based Vehicle Data Collector. Transportation Research Record, 2340(1), 49-58.
Wei, H., Liu, H., Ai, Q., Li, Z., Xiong, H., & Coifman, B. (2013). Empirical innovation of computational dual‐loop models for identifying vehicle classifications against varied traffic conditions. Computer‐Aided Civil and Infrastructure Engineering, 28(8), 621-634.