Big data to tackle Urban Mobility Challenges Marta Gonzalez Massachusetts Institute of Technology Here we present a novel framework that enables the systematic analysis of travel times and congestion in a city. We uncover the role of information to mitigate traffic under existing travel demand and infrastructure conditions. Combining demographic data, road network information and billions of mobile phone records, we infer travel demand profiles and estimate travel times across five different cities. We demonstrate that the percentage of time lost in congestion is a function of the proportion of vehicular travel demand to road infrastructure capacity, and is closely tied to spatial density and selfish choices of drivers. In this context we explore the feasibility of smart routing applications.