# A deep learning framework to generate realistic population and mobility data

arXiv: https://arxiv.org/abs/2211.07369 · HTML: https://mlsanity.com/paper/2211.07369/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Coupled Generative Adversarial Networks](https://mlsanity.com/paper/1606.07536/) (2016)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [PacGAN: The power of two samples in generative adversarial networks](https://mlsanity.com/paper/1712.04086/) (2017)
- [Language GANs Falling Short](https://mlsanity.com/paper/1811.02549/) (2018)
- [Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis](https://mlsanity.com/paper/2004.06838/) (2020)
- [DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data](https://mlsanity.com/paper/2203.03489/) (2022)
- [Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy](https://mlsanity.com/paper/2201.01139/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Deep Generative Framework for Joint Households and Individuals Population Synthesis](https://mlsanity.com/paper/2407.01643/) (2024)
- [Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes](https://mlsanity.com/paper/2608.15867/) (2026)

## Abstract (arXiv)

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.