  Uber has recently been introducing novel practices in urban taxi transport.
Journey prices can change dynamically in almost real time and also vary
geographically from one area to another in a city, a strategy known as surge
pricing. In this paper, we explore the power of the new generation of open
datasets towards understanding the impact of the new disruption technologies
that emerge in the area of public transport. With our primary goal being a more
transparent economic landscape for urban commuters, we provide a direct price
comparison between Uber and the Yellow Cab company in New York. We discover
that Uber, despite its lower standard pricing rates, effectively charges higher
fares on average, especially during short in length, but frequent in
occurrence, taxi journeys. Building on this insight, we develop a smartphone
application, OpenStreetCab, that offers a personalized consultation to mobile
users on which taxi provider is cheaper for their journey. Almost five months
after its launch, the app has attracted more than three thousand users in a
single city. Their journey queries have provided additional insights on the
potential savings similar technologies can have for urban commuters, with a
highlight being that on average, a user in New York saves 6 U.S. Dollars per
taxi journey if they pick the cheapest taxi provider. We run extensive
experiments to show how Uber's surge pricing is the driving factor of higher
journey prices and therefore higher potential savings for our application's
users. Finally, motivated by the observation that Uber's surge pricing is
occurring more frequently that intuitively expected, we formulate a prediction
task where the aim becomes to predict a geographic area's tendency to surge.
Using exogenous to Uber datasets we show how it is possible to estimate
customer demand within an area, and by extension surge pricing, with high
accuracy.
