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Description:
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. 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. Recently, three multi-level fitting algorithms have been proposed, all of which aim at reweighting the initial population so that all constraints at both household and person levels are satisfied. The main contribution of this paper is twofold. First, we propose an algorithmic framework for the three algorithms in which the implementation of each algorithm consists only of subtle changes, implying inherent similarity of the three algorithms. Second, we demonstrate formal equivalence of one of the multi-level fitting algorithms to a special case of generalized raking, a procedure known and used in the field of survey statistics for almost 20 years but largely ignored by transportation planners. This allows for the first time to benefit from an enormous amount of theoretical results from a field that focuses primarily on analyzing data from different sources.