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Short Biography
Dr. Jintao Ke is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in civil engineering from Zhejiang University, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. Prior to joining HKU, he was a research assistant professor in the Hong Kong Polytechnic University. His research interests include smart transportation, smart city, urban computing, shared mobility, machine learning in transportation, operational management for transportation studies, etc. He has published over 30 SCI/SSCI indexed research papers in in top-tier transportation journals, such as Transportation Research Part A/B/C/D/E, IEEE TITS and IEEE TKDE. He serves as Advisory Board Member of Transportation Research Part C: Emerging Technologies and Transportation Research Part E: Logistics and Transportation Review, guest editor of special issues of Transportation Research Part C and Travel Behavior & Society, and referees for a few top transportation journals.
Academic Positions
Assistant Professor, 10/2021 - present, Department of Civil Engineering, the University of Hong Kong
Research Assistant Professor, 08/2020 - 09/2021, Department of Logistic and Maritime Studies, Hong Kong Polytechnic University
Education
PhD, 2020, Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology (Advisor: Prof. Hai Yang)
Exchange student, 2020, University of Michigan (Advisor: Prof. Yafeng Yin)
B.S., 2016, Department of Civil Engineering, Zhejiang University (Advisor: Prof. Xiqun Chen)
Research Interests
Smart Transportation & Urban Computing
Emerging Mobility Services (e.g., ride-sourcing, taxi, food delivery, last-mile services)
Artificial Intelligence for Transportation
Transportation Economics
Operational Management & Operations Research
A demo of my research
This is a large-scale simulation platform for managing and controlling ride-hailing vehicles. The simulation platform is calibrated by a real dataset in Manhattan NYC. Researchers can make use of this simulation platform to train and test optimization, machine learning and reinforcement learning algorithms for designing better operating strategies, such as order dispatching, vehicle repositions and dynamic pricing. The simulation platform can also assist the government in evaluating and designing policies for ride-hailing markets, including vehicle fleet size control and pricing regulations. The ultimate goal of developing this simulation platform is to help ride-hailing operators and government to better manage and operate ride-hailing vehicles (such as Uber) so as to achieve a sustainable, efficient, and environmentally friendly on-demand transportation systems.