Ride-Sourcing Systems & Multiple-Objective Online Ride Matching
Dr. Hai Wang
School of Computing and Information Systems
Singapore Management University
We propose a general framework to study the on-demand shared ride-sourcing transportation systems and summarize the relevant research problems in four areas, namely, demand, supply, platform operation, and system problems. We then focus on the online matching problem, in which the platforms match passengers and drivers in real-time without observing future information, considering multiple objectives such as pick-up time, platform revenue, and service quality. Given stationary and non-stationary decision scenarios, we develop efficient online matching policies that adaptively balances the trade-offs between multiple objectives in a dynamic setting and provide theoretical performance guarantees for the policy. Through numerical experiments and industrial testing using real data from a ride-sourcing platform, we demonstrate that our approach can arrive at a delicate balance among multiple objectives and bring value to all the stakeholders in the ride-sourcing ecosystem.