Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method

Large scale activity-based simulation models inform a variety of transportation and planning policies using models that often rely on fixed or flexible workplace location in a synthetic population as input to work related activity, participation, and subsequent destination dependent travel decisions. Although discrete choice models can estimate workplace location with greater flexibility, disaggregate data available (e.g., travel surveys) are often too sparse to estimate workplace location at sufficient spatial detail. Alternatively, aggregated employment data are often readily available at higher spatial resolutions, but are typically only used in separately estimated ad hoc models, which introduces error if the estimations have divergent solutions. This paper’s primary contribution is to reduce error by integrating population synthesis and workplace assignment, yielding a synthetic population with home and work locations included as attributes. The two are integrated using additional variables shared between population and workplace assignment (i.e., industry sector), but this increased matrix size can render conventional multilevel person-household re-weighting methods computational intractable. A secondary contribution is to mitigate this scalability challenge using more efficient optimization-based re-weighting approaches, substantially reducing computation time. The proposed process is applied to the Greater Boston Area, generating a population of 4.6-million persons within 1.7-million households across 965 census tract zones. The integrated process is compared against conventional ad hoc location assignment process, using both classical and contemporary synthesis techniques of Iterative Proportional Fitting, Markov chain Monte Carlo simulation, and Bayesian Network simulation. The integrated approach yielded an improvement in workplace location assignment, with only modest impact on population accuracy.