  Nowadays, travel surveys provide rich information about urban mobility and
commuting patterns. But, at the same time, they have drawbacks: they are static
pictures of a dynamic phenomena, are expensive to make, and take prolonged
periods of time to finish. However, the availability of mobile usage data (Call
Detail Records) makes the study of urban mobility possible at levels not known
before. This has been done in the past with good results--mobile data makes
possible to find and understand aggregated mobility patterns. In this paper, we
propose to analyze mobile data at individual level by estimating daily
journeys, and use those journeys to build Origin-Destiny matrices to understand
urban flow. We evaluate this approach with large anonymized CDRs from Santiago,
Chile, and find that our method has a high correlation ($\rho = 0.89$) with the
current travel survey, and that it captures external anomalies in daily travel
patterns, making our method suitable for inclusion into urban computing
applications.
