Synthetic Population Generation at Disaggregated Spatial Scales for Land Use and Transportation Microsimulation

Abstract

The execution of agent-based microsimulation requires an initial set of agents with detailed socioeconomic and demographic attributes to support subsequent behavioral and market models. Data limitations and privacy reasons often restrict the scope and detail with which a synthetic population can be generated by the traditional population synthesis approach. For the accommodation of the growing requirement of microsimulation on spatial resolution and variety, considering new data sources that overcome the data limitations and support population synthesis at more disaggregated levels is necessary. This paper presents a two-stage population synthesis approach not only to improve the accuracy of population generation with imperfect microdata and marginal data, but also to use additional data sets when the spatial details of the synthetic population are interpolated. A general iterative proportional fitting (IPF) method is used in the first stage to estimate the joint distribution of household and individual characteristics under multiple levels of constraints. Additional building information is collected from multiple sources and used to estimate spatial patterns of housing and household characteristics that are then preserved through a second IPF procedure. Preliminary tests of the proposed two-stage IPF-based approach with Singapore data show that the method yields better fitted population realizations at more fine-grained levels than do traditional one-step population synthesis methods.