, Volume 39, Issue 3, pp 685–704 | Cite as

Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously

  • David R. Pritchard
  • Eric J. Miller


Agent-based microsimulation models of transportation, land use or other socioeconomic processes require an initial synthetic population derived from census data, conventionally created using the iterative proportional fitting (IPF) procedure. This paper introduces a novel computational method that allows the synthesis of many more attributes and finer attribute categories than previous approaches, both of which are long-standing limitations discussed in the literature. Additionally, a new approach is used to fit household and person zonal attribute distributions simultaneously. This technique was first adopted to address limitations specific to Canadian census data, but could also be useful in U.S. and other applications. The results of each new method are evaluated empirically in terms of goodness-of-fit.


Iterative proportional fitting Population synthesis Microsimulation Agent-based Census microdata Transportation models Trip forecasting 



This research was supported by funding from an Ontario Graduate Scholarship, the Transportation Association of Canada, and a Transport Canada Transportation Planning and Modal Integration grant. The authors would also like to thank Laine Ruus of the University of Toronto Data Library for her invaluable assistance.


  1. Agresti, A.: Categorical Data Analysis, 2nd edn. John Wiley & Sons, New York (2002). doi: 10.1002/0471249688 CrossRefGoogle Scholar
  2. Arentze, T.A., Timmermans, H.J.: ALBATROSS Version 2: A Learning-Based Transportation Oriented Simulation System, Chapter 2. Eindhoven University of Technology, Eindhoven (2005)Google Scholar
  3. Auld, J.A., Mohammadian, A.K., Wies, K.: Population synthesis with subregion-level control variable aggregation. J. Transp. Eng. 135(9), 632–639 (2009)CrossRefGoogle Scholar
  4. Barrett, C., et al: TRANSIMS 3.0 volume 3 (modules), Chapter 2 (population synthesizer). Unclassified Report LA-UR-00-1725. Los Alamos National Laboratories, Los Alamos (2003)Google Scholar
  5. Beckman, R.J., Baggerly, K.A., McKay, M.D.: Creating synthetic baseline populations. Transp. Res. A. 30(6), 415–435 (1996)CrossRefGoogle Scholar
  6. Bowman, J.L.: A comparison of population synthesizers used in microsimulation models of activity and travel demand. Unpublished working paper. (2004)
  7. Csiszár, I.: I-divergence geometry of probability distributions and minimization problems. Ann. Probab. 3(1), 146–159 (1975)CrossRefGoogle Scholar
  8. Deming, W.E., Stephan, F.F.: On a least square adjustment of a sampled frequency table when the expected marginal totals are known. Ann. Math. Stat. 11(4), 427–444 (1940)CrossRefGoogle Scholar
  9. Guan, J.J.: Synthesizing family relationships between individuals for the ILUTE micro-simulation model. Bachelor’s Thesis, Department of Civil Engineering, University of Toronto (2002)Google Scholar
  10. Guo, J.Y., Bhat, C.R.: Population synthesis for microsimulating travel behavior. Transp. Res. Rec. 2014, 92–101 (2007)CrossRefGoogle Scholar
  11. Huang, Z., Williamson, P.: Comparison of synthetic reconstruction and combinatorial optimisation approaches to the creation of small-area microdata. Working Paper 2001/2. Department of Geography, University of Liverpool (2001)Google Scholar
  12. Ihaka, R., Gentleman, R.: R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5(3), 299–314 (1996)CrossRefGoogle Scholar
  13. Knudsen, D.C., Fotheringham, A.S.: Matrix comparison, goodness-of-fit, and spatial interaction modelling. Int. Reg. Sci. Rev. 10(2), 127–147 (1986)CrossRefGoogle Scholar
  14. Little, R.J., Wu, M.M.: Models for contingency tables with known marginals when target and sampled populations differ. J. Am. Stat. Assoc. 86(413), 87–95 (1991)CrossRefGoogle Scholar
  15. Pritchard, D.R.: Synthesizing agents and relationships for land use/transportation modelling. Master’s Thesis, Department of Civil Engineering, University of Toronto (2008)Google Scholar
  16. Salvini, P.A., Miller, E.J.: ILUTE: an operational prototype of a comprehensive microsimulation model of urban systems. Netw. Spat. Econ. 5(2), 217–234 (2005)CrossRefGoogle Scholar
  17. Stephan, F.F.: Iterative methods of adjusting sample frequency tables when expected margins are known. Ann. Math. Stat. 13(2), 166–178 (1942)CrossRefGoogle Scholar
  18. Wickens, T.D.: Multiway Contingency Tables Analysis for the Social Sciences. Lawrence Erlbaum Associates, Hillsdale (1989)Google Scholar
  19. Williamson, P., Birkin, M., Rees, P.H.: The estimation of population microdata by using data from Small Area Statistics and Samples of Anonymised Records. Environ. Plan. A. 30(5), 785–816 (1998). doi: 10.1068/a300785 CrossRefGoogle Scholar
  20. Ye, X., Konduri, K., Pendyala, R.M., Sana, B., Waddell, P.: A methodology to match distributions of both household and person attributes in the generation of synthetic populations. Presented at the 88th Annual Meeting of the Transportation Research Board, Washington (2009)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  1. 1.MetrolinxTorontoCanada
  2. 2.Cities CentreUniversity of TorontoTorontoCanada

Personalised recommendations