The development of mobile internet, smartphones, and location-based services has enabled ridesourcing, which pools vehicles and drivers to provide on-demand travel services. As an alternative transportation option, ridesourcing has significant impacts on urban travel. However, the unique mobility patterns of ridesourcing and their ramifications for vehicle electrification have not been well studied. To address this gap, this paper presents a comparative big-data driven framework to characterize ridesourcing mobility patterns, and evaluate the acceptance potential of electric vehicles for ridesourcing. The patterns of three types of urban travel (private vehicles for household use, vehicles used for ridesourcing, and taxis) extracted from GPS trajectories in Bejing are compared. The electrification potentials of these travel types under different charging levels are then evaluated. The mobility patterns of household, ridesourcing, and taxi drivers are similar when a single trip is considered but differ significantly when total vehicle travel is considered. It is shown that the acceptance ceilings of electric vehicles decreases significantly from household to ridesourcing to taxi use. These findings provide useful insights into the potential for vehicle electrification across a range of drivers.