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Generating a Dynamic Synthetic Population – Using an Age-Structured Two-Sex Model for Household Dynamics

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  • Mohammad-Reza Namazi-Rad
  • Payam Mokhtarian
  • Pascal Perez

Abstract

Generating a reliable computer-simulated synthetic population is necessary for knowledge processing and decision-making analysis in agent-based systems in order to measure, interpret and describe each target area and the human activity patterns within it. In this paper, both synthetic reconstruction (SR) and combinatorial optimisation (CO) techniques are discussed for generating a reliable synthetic population for a certain geographic region (in Australia) using aggregated- and disaggregated-level information available for such an area. A CO algorithm using the quadratic function of population estimators is presented in this paper in order to generate a synthetic population while considering a two-fold nested structure for the individuals and households within the target areas. The baseline population in this study is generated from the confidentialised unit record files (CURFs) and 2006 Australian census tables. The dynamics of the created population is then projected over five years using a dynamic micro-simulation model for individual- and household-level demographic transitions. This projection is then compared with the 2011 Australian census. A prediction interval is provided for the population estimates obtained by the bootstrapping method, by which the variability structure of a predictor can be replicated in a bootstrap distribution.

Suggested Citation

  • Mohammad-Reza Namazi-Rad & Payam Mokhtarian & Pascal Perez, 2014. "Generating a Dynamic Synthetic Population – Using an Age-Structured Two-Sex Model for Household Dynamics," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-16, April.
    • Handle: RePEc:plo:pone00:0094761
    DOI: 10.1371/journal.pone.0094761
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    References listed on IDEAS

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    1. Kirk Harland & Alison Heppenstall & Dianna Smith & Mark Birkin, 2012. "Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-1.
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    Cited by:

    1. Namazi-Rad, Mohammad-Reza & Mokhtarian, Payam & Shukla, Nagesh & Munoz, Albert, 2016. "A data-driven predictive model for residential mobility in Australia – A generalised linear mixed model for repeated measured binary data," Journal of choice modelling, Elsevier, vol. 20(C), pages 49-60.
    2. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.

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