MOTIF: Mobility Optimized by Traditional Ideas and Frontier technologies: Our Philosophy to Reduce Traffic Congestion
PhD students and Postdoc openings:
Currently, our lab has job openings for fully-funded Ph.D. students/postdoc positions. (Information valid until deleted)
Hi, I'm Hao Zhou, an Assistant Professor at the University of South Florida.
Research statement: In this new era of transportation featuring self-driving and machine learning, challenges of reducing traffic congestion are still out there, if not getting worse. We believe such limitations are due to the lack of a good combination of new technologies and old theories. To fill the gap, we are particularly interested in teaching new technologies old tricks, i.e., incorporating traffic flow/network knowledge into self-driving/machine learning for traffic control.
Research areas:
Traffic flow theory
Network science
CAV modeling & testing
Deep reinforcement learning
Education:
2018-2022: Ph.D., Transportation Engineering, Georgia Institute of Technology
2015-2018: Master, Transportation Engineering, Southeast University
2011-2015: Bachelor, Transportation Engineering, Southeast University
Research Porfolio:
traffic flow theory & percolation theory
self-driving design and road test
Percolation-based signal control
Metering signal at perimeter of clusters
DRL training for fractal signal control
Selected publications:
Laval, J., & Zhou, H. (2022). Congested urban networks tend to be insensitive to signal settings: Implications for learning-based control. IEEE Transactions on Intelligent Transportation Systems.
Li, T., Chen, D., Zhou, H., Xie, Y., & Laval, J. (2022). Fundamental diagrams of commercial adaptive cruise control: Worldwide experimental evidence. Transportation Research Part C: Emerging Technologies, 134, 103458.
Zhou, H., Toth, C., Guensler, R., & Laval, J. (2022). Hybrid modeling of lane changes near freeway diverges. Transportation Research Part B: Methodological, 165.
Zhou, H., Zhou, A., Laval, J., Liu, Y., & Peeta, S. (2022). Incorporating driver relaxation into factory adaptive cruise control to reduce lane-change disruptions. Transportation Research Record, 03611981221085517.
Zhou, H., Zhou, A., Li, T., Chen, D., Peeta, S., & Laval, J. (2022a). Congestion-mitigating mpc design for adaptive cruise control based on Newell’s car following model: History outperforms prediction. Transportation Research Part C: Emerging Technologies, 142.
Zhou, H., Zhou, A., Li, T., Chen, D., Peeta, S., & Laval, J. (2022b). Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory, and experiments. Transportation Research Part C: Emerging Technologies, 140, 103697.
Li, T., Chen, D., Zhou, H., Laval, J., & Xie, Y. (2021). Car-following behavior characteristics of adaptive cruise control vehicles based on empirical experiments. Transportation Research Part B: Methodological, 147, 67–91.
Zhou, H., Laval, J., Zhou, A., Wang, Y., Wu, W., Qing, Z., & Peeta, S. (2021). Review of learning-based longitudinal motion planning for autonomous vehicles: Research gaps between self-driving and traffic congestion. Transportation Research Record, 03611981211035764.
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