Deep and diverse population synthesis for multi-person households using generative models

Abstract: Synthetic population is an increasingly important material used in numerous areas such as urban and transportation analysis. Traditional methods such as iterative proportional fitting (IPF) is not capable of generating high-quality data when facing datasets with high dimension. Latest population synthesis methods using deep learning techniques can resolve such curse of dimensionality. However, few controls are placed when using these methods, and few of the methods are used to generate synthetic population capturing associations among members in one household. In this study, we propose a framework that tackles these issues. The framework uses a novel population synthesis model, called conditional input directed acyclic tabular generative adversarial network (ciDATGAN), as its core, and a basket of methods are employed to enhance the population synthesis performance. We apply the model to generate a synthetic population for the whole New York State as a public resource for researchers and policymakers. The synthetic population includes nearly 20 million individuals and 7.5 million households. The marginals obtained from the synthetic population match the census marginals well while maintaining similar associations among household members to the sample. Compared to the PUMS data, the synthetic population provides data that is 17% more diverse; when compared against a benchmark approach based on Popgen, the proposed method is 13% more diverse. This study provides an approach that encompasses multiple methods to enhance the population synthesis procedure with greater equity- and diversity-awareness.