Population Synthesis Using Incomplete Microsample

This paper presents a population synthesis model based on the Wasserstein Generative-Adversarial Network with Gradient Penalty (WGAN-GP) for training on incomplete microsamples. The proposed method aims to address the challenge of missing information in microsamples on one or more attributes due to privacy concerns or data collection constraints. By using a mask matrix to represent missing values, the study proposes a WGAN-GP training algorithm that lets the model learn from a training dataset that has some missing information. The paper compares the ability of WGAN-GP models trained on incomplete microsamples to those trained on complete microsamples to create a synthetic population. We conducted a series of evaluations of the proposed method using a Swedish national travel survey. We validate the efficacy of the proposed method by generating synthetic populations from all the models and comparing them to the actual population dataset. The results from the experiments showed that the proposed methodology successfully generates synthetic data that closely resembles a model trained with complete data as well as the actual population. The paper makes a contribution to the field by giving a strong solution for population synthesis using incomplete microsamples. It also opens up new research areas and shows how deep generative models can be used to improve population synthesis.