Topic: Optimization for on-demand matching process in ride-sourcing systems 

Speaker: Dr. Ke Jintao

Time:10:00-12:00 a.m., July 5th

Place: Room A322, Anzhong Building

Abstract:

As a symbolic icon for shared mobility in recent years, ride-sourcing service, provided by digital platforms like DiDi, Uber and Lyft, has been playing an increasingly important role in meeting mobility needs by efficiently connecting passengers and dedicated drivers through online platforms. Despite its great success in business, ride-sourcing service has also aroused many challenging issues in operations, management, and regulations, from the perspective of different stakeholders, including both private sectors (ride-sourcing platforms) and public sectors (governments). One critical issue in platform operations for ride-sourcing service is the optimizations for on-demand matching process between idle drivers (supply) and waiting passengers (demand). In particular, this talk will present a novel model to jointly optimize two key decision variables in on-demand ride matching process⸺matching time interval and matching radius⸺under different supply and demand levels. The model will enable ride-sourcing platforms to dynamically adjust their matching time interval and matching radius in response to changing supply and demand in a dynamic system. Additionally, this talk will discuss how to integrate advances of reinforcement learning approaches into traditional optimization models to further improve the efficiency of the driver-passenger matching procedure. It is shown that pure optimization models are generally myopic and only focus on immediate feedbacks during the current time interval without considering future system rewards. In contrast, the combination of reinforcement learning and optimization can be more far-sighted by considering the interactions between platform operations and system dynamics, and thus achieve better performance over a long horizon.

Speaker's Bio:

Dr. Jintao Ke is a Research Assistant Professor in Hong Kong Polytechnic University. He received the B.E. and Ph.D. degrees from the Department of Civil Engineering, Zhejiang University, and the Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology (supervisor: Prof. Hai Yang), in 2016 and 2020, respectively.  His research interests include shared mobility on demand, transportation big data analytics, multimodal intelligent transportation systems, transportation pricing, short-term travel demand forecasting, etc. The vision of his research is to develop novel models, algorithms, and conduct data-driven quantitative analyses to better manage, operate, and regulate shared mobility and other emerging mobility services. He has published around 20 SCI/SSCI indexed research papers in top-tier journals in the field of transportation research and data mining, such as Transportation Research Part A/B/C/E, IEEE Transactions on Intelligence Transportation System, IEEE Transactions on Knowledge and Data Engineering. He is in the Early Career Editorial Advisory Board (EAB) of Transportation Research Part C.