Dr Jiali Zhou is now a Post-doctoral Fellow in the Department of Urban Planning and Design, the University of Hong Kong. His research interest is in public transportation, simulation, machine learning and multi-modal mobility. He uses a variety of quantitative and qualitative methods, including micro/mesoscopic simulation modeling, machine learning and reinforcement learning.
Jiali’s previous research is in the area of Public Transportation, developing methods to analyze the performance of transit networks, designing and evaluating strategies to improve the level of service (also during COVID-19) they provide to passengers.
He holds a B.E. from South China University of Technology, a M.S. from Carnegie Mellon University (CMU), and a Ph.D. from Northeastern University in Boston, Massachusetts. Before joining the University of Hong Kong, he worked at the CMU Intelligent Coordination and Logistics Lab and MIT Transit Lab.
Zhou, J., & Koutsopoulos, H.N. (2021). Virus transmission risk in urban rail systems: Microscopic simulation-based analysis of spatio-temporal characteristics. Transportation Research Record, 2675(10), 120-132.
Zhou, J., Koutsopoulos, H.N., & Saidi, S. (2020). Evaluation of subway bottleneck mitigation strategies using microscopic, agent-based simulation. Transportation Research Record: Journal of the Transportation Research Board, 2674(5).