Dynamic discrete choice models (DDCMs) allow us to analyze trip attribute choices within an activity-based framework where the sequence of activities matters. These models have been primarily developed for travel survey data. However, the advent of big data presents new opportunities for mobility analysis. This paper bridges traditional approaches with novel data sources by adapting an established activity-based DDCM to handle anonymized mobile network data. The proposed framework advances methods within transportation planning in two ways. First, dynamic models allow for detailed assessments of sequential travel decisions, for instance related to activity, departure time, and modes. Second, automatically collected data are advantageous as compared to travel surveys that are limited by respondents’ ability and willingness to disclose travel behavior. However, this approach poses methodological challenges, particularly because individuals’ exact locations over time are not directly observed. We address these in a latent-based framework in which the positions of the individuals are treated as latent variables and the location of cell towers as observations. We estimate the model using a stochastic expectation-maximization (StEM) algorithm. The performance is highlighted in a case study of daily travel demand in Stockholm, Sweden. Our paper demonstrates the agility of activity-based DDCMs in adapting to new data sources, by incorporating appropriate modifications within a well-established modeling paradigm.