Spatiotemporal data, and more specifically origin-destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. In this paper, we propose a methodology to infer origin-destination (O-D) matrices based on passively-collected cellular signaling data of millions of anonymized mobile phone users in the Rhône-Alpes region, France. This dataset, which consists of records time-stamped with users' unique identifier and tower locations, is used to first analyze the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analyzed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O-D matrix for the full population. We propose a method to perform this scaling and we show that signaling data-based O-D matrix carries similar estimations as those that can be obtained via travel surveys.