Agent-based modeling (ABM) is a wide-spread technique that can be utilized as an artiffcial laboratory for in-silico experiments of real-case studies of different domains such as mobility. To initialize agent/environment attributes and their relationships, disaggregated (individual level) micro-data is required as an input. However, having such data is not often possible due to several reasons such as privacy concerns. To bridge the gap, generating realistic synthetic data (from census/survey data) becomes an initial and essential step of agent-based modeling. In this piece of research, we employ the mobility pro_les of the Swiss population for generating synthetic populations along with their mobility activities. To validate the synthetic data, an agent-based model, which is already calibrated to the empirical data, is re-run with a sample and the generated synthetic data. Accumulated decisions of agents in both cases are compared. In addition, marginal frequencies of control attributes are benchmarked. The first obtained results demonstrate that increasing size of the generated population decreases the difference between simulation results of the synthesized data and the real data.