Generating Synthetic Populations for Transportation: A Variational Autoencoder Approach

Synthetic populations are commonly used in transportation analysis to feed traffic simulators. Recently, they have also been used to assess the sensitivity of a territory to factors such as construction noise. However, traditional methods as Iteratif Proportianal Fitting (IPF) for generating synthetic populations, based on sampling and calibration to aggregated data, have limitations. Indeed, they only allow generating individuals similar to those in the initial sample. Machine Learning and Statistical Learning methods, such as Variational Autoencoders (VAE), offer a promising alternative. VAE have already demonstrated their effectiveness in generating realistic images. We present here how to use VAE to generate synthetic populations, allowing for more varied representations of a territory.