  Census and Household Travel Survey datasets are regularly collected from
households and individuals and provide information on their daily travel
behavior with demographic and economic characteristics. These datasets have
important applications ranging from travel demand estimation to agent-based
modeling. However, they often represent a limited sample of the population due
to privacy concerns or are given aggregated. Synthetic data augmentation is a
promising avenue in addressing these challenges. In this paper, we propose a
framework to generate a synthetic population that includes both socioeconomic
features (e.g., age, sex, industry) and trip chains (i.e., activity locations).
Our model is tested and compared with other recently proposed models on
multiple assessment metrics.
