This paper proposes a framework to extract dynamic trip flows and travel demand patterns from large-scale 2 G and 3 G cellular signaling data. Novel data pre-processing techniques based on cell phone activity metrics are presented. The trip extraction method relies on the detection of stationary activities to form trip sequences related to resident users. A probabilistic solution is introduced to estimate the trip starting time, allowing to aggregate trips by time of the day and reconstruct hourly travel flows. To better characterize these flows, a spatial clustering process combined with land-use data is proposed based on the temporal demand profile of each zone. Empirical comparisons have been performed showing that the resulting dynamic travel demand patterns are consistent with those obtained from travel survey data with high correlation coefficients of about 0.9. The results prove the potential of signaling data to generate low-cost valuable information for large-scale travel demand modeling.