# A Deep Generative Model for Feasible and Diverse Population Synthesis

arXiv: https://arxiv.org/abs/2208.01403 · HTML: https://mlsanity.com/paper/2208.01403/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis](https://mlsanity.com/paper/1903.05628/) (2019)
- [DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data](https://mlsanity.com/paper/1706.02071/) (2017)
- [A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability](https://mlsanity.com/paper/2002.00922/) (2020)
- [Prediction of rare feature combinations in population synthesis: Application of deep generative modelling](https://mlsanity.com/paper/1909.07689/) (2019)
- [Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis](https://mlsanity.com/paper/2004.06838/) (2020)
- [Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models](https://mlsanity.com/paper/2109.12422/) (2021)
- [Scalable Population Synthesis with Deep Generative Modeling](https://mlsanity.com/paper/1808.06910/) (2018)

## Built on by (most cited first, 17 of 17)

- [Copula-based transferable models for synthetic population generation](https://mlsanity.com/paper/2302.09193/) (2023)
- [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025)
- [A Large Language Model for Feasible and Diverse Population Synthesis](https://mlsanity.com/paper/2505.04196/) (2025)
- [A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research](https://mlsanity.com/paper/2410.07066/) (2024)
- [Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning](https://mlsanity.com/paper/2501.10221/) (2025)
- [Generating Feasible and Diverse Synthetic Populations Using Diffusion Models](https://mlsanity.com/paper/2508.09164/) (2025)
- [Population Synthesis using Incomplete Information](https://mlsanity.com/paper/2510.00859/) (2025)
- [Target Population Synthesis using CT-GAN](https://mlsanity.com/paper/2510.00871/) (2025)
- [A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport](https://mlsanity.com/paper/2510.24375/) (2025)
- [SemaPop: Semantic-Persona Conditioned and Controllable Population Synthesis](https://mlsanity.com/paper/2602.11569/) (2026)
- [Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models](https://mlsanity.com/paper/2602.15270/) (2026)
- [Scalable Maximum Entropy Population Synthesis via Persistent Contrastive Divergence](https://mlsanity.com/paper/2603.27312/) (2026)
- [A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis](https://mlsanity.com/paper/2607.03190/) (2026)
- [Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes](https://mlsanity.com/paper/2608.15867/) (2026)
- [LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation](https://mlsanity.com/paper/2609.08288/) (2026)
- [Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)](https://mlsanity.com/paper/2609.30417/) (2026)
- [Masked Diffusion for Travel Diaries Synthesis](https://mlsanity.com/paper/2610.17109/) (2026)

## Abstract (arXiv)

An ideal synthetic population, a key input to activity-based models, mimics the distribution of the individual- and household-level attributes in the actual population. Since the entire population's attributes are generally unavailable, household travel survey (HTS) samples are used for population synthesis. Synthesizing population by directly sampling from HTS ignores the attribute combinations that are unobserved in the HTS samples but exist in the population, called 'sampling zeros'. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of generating 'structural zeros' (i.e., the infeasible attribute combinations that do not exist in the population). This study proposes a novel method to minimize structural zeros while preserving sampling zeros. Two regularizations are devised to customize the training of the DGM and applied to a generative adversarial network (GAN) and a variational autoencoder (VAE). The adopted metrics for feasibility and diversity of the synthetic population indicate the capability of generating sampling and structural zeros -- lower structural zeros and lower sampling zeros indicate the higher feasibility and the lower diversity, respectively. Results show that the proposed regularizations achieve considerable performance improvement in feasibility and diversity of the synthesized population over traditional models. The proposed VAE additionally generated 23.5% of the population ignored by the sample with 79.2% precision (i.e., 20.8% structural zeros rates), while the proposed GAN generated 18.3% of the ignored population with 89.0% precision. The proposed improvement in DGM generates a more feasible and diverse synthetic population, which is critical for the accuracy of an activity-based model.