Hierarchical IPF: Generating a synthetic population for Switzerland

Agent-based microsimulation models for land use or transportation simulate the behavior of agents over time, although at different time scales and with different goals. For both kinds of models, the initial step is the definition of agents and their relationships. Synthesizing the population of agents often is the only solution, due to privacy and cost constraints. In this paper, we assume that the model simulates persons grouped into households, and a person/household population needs to be synthesized. However, the methodology presented here can be applied to other kinds of agent relationships as well, e.g. persons and jobs/workplaces or persons and activity chains. Generating a synthetic population requires (a) reweighting of an initial population, taken from census or other survey data, with respect to current constraints, and (b) choosing the households that belong to the generated population. The reweighting task can be performed using an Iterative Proportional Fitting (IPF) procedure; however, IPF cannot control for attributes at both person and household levels. A frequently applied pattern is to estimate household-level weights using IPF, so that they match the control totals for the households, and then, using these weights, to generate a population of households that best fits the person-level control totals. We propose an algorithm that estimates household-level weights that fit the control totals at both person and household levels. This eliminates the need to account for person-level control during the generation of synthetic households. The algorithm essentially performs a proportional fitting in the domains of both households and persons, and introduces an entropy-minimizing fitting step to switch between these two domains. We evaluate the performance of our algorithm by generating a synthetic population for Switzerland and checking it against the complete Swiss census.