Population Synthesis using Discrete Copulas

Synthetic population is one of the most important foundations of disaggregated travel demand forecasting and agent-based traffic simulation. This paper proposes a new sample-based method for synthetic population generation, which can be viewed as an alternative of the traditional Iterative Proportional Fitting. The method introduces bootstrapping techniques to compute a discrete copula function. Based on the copula function, associations among different attributes can be estimated and the population structure can be recovered. Experiments using actual Chinese national population data indicate that the new method can achieve the same level of accuracy as Iterative Proportional Fitting while acquire better results of the partial joint distributions.