Speaker: KE Jintao, The Hong Kong Polytechnic University

Date: 13:00-14:30, 7th July 2021

Tencent Meeting ID: 622452958, PW：277998

Speaker: Wang Long, Assistant Professor, School of Entrepreneurship and Management at the ShanghaiTech University

Abstract: 

As a symbolic icon for smart 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.