Kukic_STRC2022 TRANSP-OR TALK Kukic, Marija Bierlaire, Michel One-step simulator for synthetic household generation 2022 STRC 2022 Monte Veritá, Ascona, Switzerland May 19, 2022 Transportation science today is tasked with predicting the complex mobility needs of individuals, which necessitates the use of advanced mobility and travel demand models. However, the quality of the model outputs depends on the data quality. In transport, data privacy and data availability are two limitations. Therefore, transportation scientists rely increasingly on the usage of synthetic populations. Typically, a synthetic population is generated either on the level of individuals or on the level of households using simulation or machine learning approaches. This paper presents a follow-up work on the existing simulation techniques for the generation of synthetic households, addressing several literature gaps. In existing methodologies, the generation of individuals and their matching into households is done separately, through two sequential processes. Although the marginal distributions of key generated attributes might show a perfect fit, the “twostep” household generator produces unrealistic households. The generation of illogical observations is caused by neglecting the dependencies between individuals while grouping them into households. In order to create realistic households, this paper suggests a “single-step” household simulator where relationships between individuals are considered simultaneously within the generation process by imposing various statistical constraints. However, as shown in the past, the simulation methods struggle to deliver accurate results in a reasonable time while dealing with high-dimensional datasets. We propose the so-called “Divide and Conquer Gibbs Sampler” that solves this problem by decomposing and parallelizing the generation process based on the level of correlation. This approach increases accuracy and efficiency, as highly correlated areas are isolated, enabling a better representation of less probable values. The case study compares the developed approach with state-of-the-art methodologies based on 2015 Swiss census data.