  Household and individual-level sociodemographic data are essential for
understanding human-infrastructure interaction and policymaking. However, the
Public Use Microdata Sample (PUMS) offers only a sample at the state level,
while census tract data only provides the marginal distributions of variables
without correlations. Therefore, we need an accurate synthetic population
dataset that maintains consistent variable correlations observed in microdata,
preserves household-individual and individual-individual relationships, adheres
to state-level statistics, and accurately represents the geographic
distribution of the population. We propose a deep generative framework
leveraging the variational autoencoder (VAE) to generate a synthetic population
with the aforementioned features. The methodological contributions include (1)
a new data structure for capturing household-individual and
individual-individual relationships, (2) a transfer learning process with
pre-training and fine-tuning steps to generate households and individuals whose
aggregated distributions align with the census tract marginal distribution, and
(3) decoupled binary cross-entropy (D-BCE) loss function enabling distribution
shift and out-of-sample records generation. Model results for an application in
Delaware, USA demonstrate the ability to ensure the realism of generated
household-individual records and accurately describe population statistics at
the census tract level compared to existing methods. Furthermore, testing in
North Carolina, USA yielded promising results, supporting the transferability
of our method.
