A deep generative model for feasible and diverse population synthesis

Abstract

An agent-based model (ABM) simulates actions and interactions of the synthetic agents to understand the system-level behaviour. The synthetic population, the key input to ABM, mimics the distribution of the individual-level attributes in the actual population. Since individual-level attributes of the entire population are unavailable, small-scale samples are generally used for population synthesis. Synthesizing the population by directly sampling from the small-scale samples ignores the possible attribute combinations that are observed in the actual population but do not exist in the small-scale samples, called ‘sampling zeros’. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of falsely generating the infeasible attribute combinations that should be ‘zero’ in the generated data but exist, called ‘structural zeros’. This study proposes a novel method to ensure that the generation of structural zeros is minimal while recovering the ignored sampling zeros. Two loss functions for regularizing the DGMs are devised to customize the training 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 generation probability of structural zeros and lower generation probability of sampling zeros indicate the higher feasibility and the lower diversity, respectively. Results show that the proposed loss functions achieve considerable performance improvement in the 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., the generation ratio of structural zeros and the total samples is 20.8 %), 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. Since synthesizing the population is the first stage of ABM, the proposed approach improves the overall accuracy of the ABM by circumventing the error propagation to later modelling stages.