  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.
