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Despite the ever-increasing demand, controversies have been surrounding the ride-hailing industry since the day of its rise. Tighter government regulation or even banning is called around the world. In this paper, we address the issue by designing a quasi-experiment and estimate how much Uber benefits consumers in a creative way. Using three datasets created before and after Uber service availability, and dividing San Francisco the studied area into grids of 4km2 each, we are able to investigate consumer commuting behavior at an individual level and find out Uber brings out at least $0.76 gains per commuter per trip and generates an annual consumer surplus of $100 million in San Francisco. The three datasets include the National Household Travel Survey Data from 2008 to 2009 when Uber service was not yet available, the origin-destination level Uber itinerary data and Google map data of 2017. We first use NHTS data to identify consumer preference in 2008 under a discrete choice framework. We then construct counterfactual scenarios in which Uber becomes an option with Uber and Google data, and find out the consumer surplus changes Uber brought.