# Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis

arXiv: https://arxiv.org/abs/2004.06838 · HTML: https://mlsanity.com/paper/2004.06838/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [A Deep Generative Model for Feasible and Diverse Population Synthesis](https://mlsanity.com/paper/2208.01403/) (2022)
- [A Large Language Model for Feasible and Diverse Population Synthesis](https://mlsanity.com/paper/2505.04196/) (2025)
- [Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus](https://mlsanity.com/paper/2409.13790/) (2024)
- [A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research](https://mlsanity.com/paper/2410.07066/) (2024)
- [A deep learning framework to generate realistic population and mobility data](https://mlsanity.com/paper/2211.07369/) (2022)
- [Generating Feasible and Diverse Synthetic Populations Using Diffusion Models](https://mlsanity.com/paper/2508.09164/) (2025)
- [Robustness Analysis of Deep Learning Models for Population Synthesis](https://mlsanity.com/paper/2211.13339/) (2022)
- [ciDATGAN: Conditional Inputs for Tabular GANs](https://mlsanity.com/paper/2210.02404/) (2022)
- [A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport](https://mlsanity.com/paper/2510.24375/) (2025)
- [Enhancing Diversity and Feasibility: Joint Population Synthesis from Multi-source Data Using Generative Models](https://mlsanity.com/paper/2602.15270/) (2026)
- [Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes](https://mlsanity.com/paper/2608.15867/) (2026)
- [Masked Diffusion for Travel Diaries Synthesis](https://mlsanity.com/paper/2610.17109/) (2026)

## Abstract (arXiv)

Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entire populations, data that are not typically readily available. Various methods have been proposed to synthesize population data for this purpose. We present a Composite Travel Generative Adversarial Network (CTGAN), a novel deep generative model to estimate the underlying joint distribution of a population, that is capable of reconstructing composite synthetic agents having tabular (e.g. age and sex) as well as sequential mobility data (e.g. trip trajectory and sequence). The CTGAN model is compared with other recently proposed methods such as the Variational Autoencoders (VAE) method, which has shown success in high dimensional tabular population synthesis. We evaluate the performance of the synthesized outputs based on distribution similarity, multi-variate correlations and spatio-temporal metrics. The results show the consistent and accurate generation of synthetic populations and their tabular and spatially sequential attributes, generated over varying spatial scales and dimensions.