Generating Synthetic Populations using Iterative Proportional Fitting (IPF) and Monte Carlo Techniques Martin Frick, IVT, ETH Zürich Kay W. Axhausen, IVT ETH Zürich Conference paper STRC 2003 Session Model & Generation Swiss Transport Research Conference 3 rd STRC Monte Verità / Ascona, March 19-21, 2003 Swiss Transport Research Conference _______________________________________________________________________________ March 19-21, 2003 Generating Synthetic Populations using Itera- tive Proportional Fitting (IPF) and Monte Carlo Techniques Martin Frick IVT ETH Zürich 8093 Zürich Phone: 01-633 37 13 Fax: 01-633 10 57 e-Mail:
[email protected] I Swiss Transport Research Conference _______________________________________________________________________________ March 19-21, 2003 Abstract The generation of synthetic populations represents a substantial contribution to the acquisition of useful data for large scale multi agent based microsimulations in the field of transport plan- ning. Basically, the observed data is available from censuses (microcensus) in terms of simple summary tables of demographics, such as the number of persons per household for census block group sized areas. Nevertheless, there is a need of more disaggregated personal data and thus another data source is considered. The Public Use Sample (PUS), often used in transportation studies, is a 5% representative sample of almost complete census records for each individual, missing addresses and unique identifiers, including missing items. The problem is, to generate a large number of individual agents (~ 1Mio.) with appropriate characteristic values of the demo- graphic variables for each agent, interacting in the microsimulation. Due to the fact that one is faced with incomplete multivariate data it is useful to consider multiple data imputation tech- niques. In this paper an overview is given over existing methods such as Iterative Proportional Fitting (IPF), the Maximum Likelihood method (MLE) and Monte-Carlo (MC) techniques.