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The rise of Uber as the global alternative taxi operator has attracted a lotof interest recently. Aside from the media headlines which discuss the newphenomenon, e.g. on how it has disrupted the traditional transportationindustry, policy makers, economists, citizens and scientists have engaged in adiscussion that is centred around the means to integrate the new generation ofthe sharing economy services in urban ecosystems. In this work, we aim to shednew light on the discussion, by taking advantage of a publicly availablelongitudinal dataset that describes the mobility of yellow taxis in New YorkCity. In addition to movement, this data contains information on the fares paidby the taxi customers for each trip. As a result we are given the opportunityto provide a first head to head comparison between the iconic yellow taxi andits modern competitor, Uber, in one of the world's largest metropolitancentres. We identify situations when Uber X, the cheapest version of the Ubertaxi service, tends to be more expensive than yellow taxis for the samejourney. We also demonstrate how Uber's economic model effectively takesadvantage of well known patterns in human movement. Finally, we take ouranalysis a step further by proposing a new mobile application that comparestaxi prices in the city to facilitate traveller's taxi choices, hoping toultimately to lead to a reduction of commuter costs. Our study provides a caseon how big datasets that become public can improve urban services for consumersby offering the opportunity for transparency in economic sectors that lack upto date regulations.

OpenStreetCab: Exploiting Taxi Mobility Patterns in New York City to Reduce Commuter Costs