Abstract: We propose a general framework to study the on-demand shared ride-sourcing transportation systems, and focus on the multi-objective matching between demand and supply. 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. We develop an efficient online matching policy that adaptively balances the trade-offs between multiple objectives in a dynamic setting and provide theoretical performance guarantees for the policy. We prove that the proposed…