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Investigating scalability in population synthesis: a comparative approach

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  • Ismaïl Saadi
  • Hamed Eftekhar
  • Jacques Teller
  • Mario Cools

Abstract

In this paper, we investigate the influence of scalability on the accuracy of different synthetic populations using both fitting and generation-based approaches. Most activity-based models need a base-year synthetic population of agents with various attributes. However, when several attributes need to be synthesized, the accuracy of the synthetic population may decrease due to the mixed effects of scalability and dimensionality. We analyze two population synthesis methods for different levels of scalability, i.e. two to five attributes and different sample sizes – 10%, 25% and 50%. Results reveal that the simulation-based approach is more stable than Iterative Proportional Fitting (IPF) when the number of attributes increases. However, IPF is less sensitive to changes in sample size when compared to the simulation-based approach. We also demonstrate the importance of choosing the appropriate metric to validate the synthetic populations as the trends in terms of RMSE/MAE are different from those of SRMSE.

Suggested Citation

  • Ismaïl Saadi & Hamed Eftekhar & Jacques Teller & Mario Cools, 2018. "Investigating scalability in population synthesis: a comparative approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(7), pages 724-735, October.
    • Handle: RePEc:taf:transp:v:41:y:2018:i:7:p:724-735
    DOI: 10.1080/03081060.2018.1504182
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    Cited by:

    1. Martin Johnsen & Oliver Brandt & Sergio Garrido & Francisco C. Pereira, 2020. "Population synthesis for urban resident modeling using deep generative models," Papers 2011.06851, arXiv.org.

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