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

2025-08-13

AI Summary

Traditional population synthesis methods (e.g., Iterative Proportional Fitting) suffer from degraded fidelity in high-dimensional settings and fail to capture structured intra-household dependencies; existing deep learning approaches lack controllability and explicit modeling of household-level correlations. To address these limitations, we propose the Conditional Input Directed Acyclic Table Generative Adversarial Network (ciDATGAN)—the first table-GAN framework integrating conditional generation with directed acyclic graph (DAG)-structured embeddings to explicitly model heterogeneous, asymmetric dependencies among household members. Leveraging data augmentation and multi-stage optimization, ciDATGAN synthesizes a high-fidelity population dataset comprising 20 million individuals across 7.5 million households. The synthetic data strictly preserves marginal distributions aligned with census statistics, achieves 17% higher diversity than U.S. Public Use Microdata Samples (PUMS) and 13% over the Popgen baseline, and significantly improves representational capacity and fairness in urban and transportation simulation tasks.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data generation and simulation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Generating high-dimensional synthetic household population data
• Capturing associations among household members accurately
• Improving diversity and equity in population synthesis

Innovation

• Uses ciDATGAN for synthetic population generation
• Enhances diversity with multi-method framework
• Matches census data while preserving household associations