Article ID: | iaor20132373 |
Volume: | 47 |
Issue: | 2 |
Start Page Number: | 266 |
End Page Number: | 279 |
Publication Date: | May 2013 |
Journal: | Transportation Science |
Authors: | Toint Philippe L, Barthelemy Johan |
Keywords: | simulation: applications, statistics: sampling |
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