Synthetic Population Generation Without a Sample

Johan Barthélemy

doi:10.1287/trsc.1120.0408

2013-05-01

PRE

Johan Barthélemy *

Philippe L. Toint

DOI：10.1287/trsc.1120.0408

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摘要

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The advent of microsimulation in the transportation sector has created the need for extensive disaggregated data concerning the population whose behavior is modeled. Because of the cost of collecting this data and the existing privacy regulations, this need is often met by the creation of a synthetic population on the basis of aggregate data. Although several techniques for generating such a population are known, they suffer from a number of limitations. The first is the need for a sample of the population for which fully disaggregated data must be collected, although such samples may not exist or may not be financially feasible. The second limiting assumption is that the aggregate data used must be consistent, a situation that is most unusual because these data often come from different sources and are collected, possibly at different moments, using different protocols. The paper presents a new synthetic population generator in the class of the Synthetic Reconstruction methods, whose objective is to obviate these limitations. It proceeds in three main successive steps: generation of individuals, generation of household type's joint distributions, and generation of households by gathering individuals. The main idea in these generation steps is to use data at the most disaggregated level possible to define joint distributions, from which individuals and households are randomly drawn. The method also makes explicit use of both continuous and discrete optimization and uses the chi(2) metric to estimate distances between estimated and generated distributions. The new generator is applied for constructing a synthetic population of approximately 10,000,000 individuals and 4,350,000 households localized in the 589 municipalities of Belgium. The statistical quality of the generated population is discussed using criteria extracted from the literature, and it is shown that the new population generator produces excellent results.

Keyword:

synthetic population

microsimulation

limitations of iterative proportional fitting based procedures

sample-free generator

nonexisting sample