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Generating synthetic baseline populations from register data

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  • Rich, Jeppe
  • Mulalic, Ismir

Abstract

The paper presents a population synthesiser based on the method of Iterative Proportional Fitting (IPF) algorithm developed for the new Danish national transport model system. The synthesiser is designed for large population matrices and allows target variables to be represented in several target constraints. As a result, constraints for the IPF are cross-linked, which makes it difficult to ensure consistency of targets in a forecast perspective. The paper proposes a new solution strategy to ensure internal consistency of the population targets in order to guarantee proper convergence of the IPF algorithm. The solution strategy consists in establishing a harmonisation process for the population targets, which combined with a linear programming approach, is applied to generate a consistent target representation. The model approach is implemented and tested on Danish administrative register data. A test on historical census data shows that a 2006 population could be predicted by a 1994 population with an overall percentage deviation of 5–6% given that targets were known. It is also indicated that the deviation is approximately a linear function of the length of the forecast period.

Suggested Citation

  • Rich, Jeppe & Mulalic, Ismir, 2012. "Generating synthetic baseline populations from register data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 467-479.
    • Handle: RePEc:eee:transa:v:46:y:2012:i:3:p:467-479
    DOI: 10.1016/j.tra.2011.11.002
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    References listed on IDEAS

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    1. Sherman Robinson & Andrea Cattaneo & Moataz El-Said, 2001. "Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods," Economic Systems Research, Taylor & Francis Journals, vol. 13(1), pages 47-64.
    2. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    3. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    4. McDougall, Robert A., 1999. "Entropy Theory and RAS are Friends," Working papers 283439, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    5. McDougall, Robert, 1999. "Entropy Theory and RAS are Friends," GTAP Working Papers 300, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
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    Cited by:

    1. 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.
    2. Ma, Lu & Srinivasan, Sivaramakrishnan, 2016. "An empirical assessment of factors affecting the accuracy of target-year synthetic populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 247-264.

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