Methodology to Match Distributions of Both Household and Person Attributes in Generation of Synthetic Populations

Abstract

The advent of microsimulation approaches in travel demand modeling, wherein activity-travel patterns of individual travelers are simulated in time and space, has motivated the development of synthetic population generators. These generators typically use census-based marginal distributions on household attributes to generate joint distributions on variables of interest using standard iterative proportional fitting (IPF) procedures. Households are then randomly drawn from an available sample in accordance with the joint distribution such that household-level attributes are matched perfectly. However, these traditional procedures do not control for person-level attributes and joint distributions of personal characteristics. In this paper, a heuristic approach, called the Iterative Proportional Updating (IPU) algorithm, is presented to generate synthetic populations whereby both household-level and person-level characteristics of interest can be matched in a computationally efficient manner. The algorithm involves iteratively adjusting and reallocating weights among households of a certain type (cell in the

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