Enhanced synthetic population generator that accommodates control variables at multiple geographic resolutions

Microsimulation models that simulate travel demand at the level of individual travelers have been gaining increasing interest among practitioners. Transportation planning agencies across the country are steadily migrating to activity-based microsimulation models, which provide considerable flexibility when testing policy scenarios. Generating a synthetic population is the first step in the application of any activity-based model system and has therefore been a topic of extensive research in the activity-based modeling arena. Several researchers have developed population synthesizers that can generate synthetic populations and can match household- and person-level constraints at a specified geographic resolution (e.g., a census block group). However, although information for some control variables may be available at the specified spatial resolution, information for other control variables may be available only at a more aggregate spatial resolution. Ignoring control variables at different levels of spatial resolution could result in the generation of a synthetic population that would not be representative of the underlying population. However, there has been limited progress in the development of synthetic population generators that are capable of accommodating control variables at multiple spatial resolutions. This paper proposes a robust approach to control for constraints at multiple geographic resolutions when generating a synthetic population. The method is an extension of the iterative proportional updating algorithm previously proposed and implemented by the authors. A case study demonstrating the efficacy of the enhanced algorithm is presented.