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A hierarchical mixture modeling framework for population synthesis

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  • Sun, Lijun
  • Erath, Alexander
  • Cai, Ming

Abstract

Synthetic population is a key input to agent-based urban/transportation microsimulation models. The objective of population synthesis is to reproduce the underlying statistical properties of real population based on available microsamples and marginal distributions. However, characterizing the joint associations among a large set of attributes is challenging because of the curse of dimensionality, in particular when attributes are organized in a hierarchical household-individual structure. In this paper, we use a hierarchical mixture model to characterize the joint distribution of both household and individual attributes. Based on this model, we propose a framework of generating representative household structures in population synthesis. The framework integrates three models: (1) probabilistic tensor factorization, (2) multilevel latent class model, and (3) rejection sampling. With this framework, one can generalize not only the associations of within- and cross-level attributes, but also reproduce structural relationships among household members (e.g., husband-wife). As a case study, we implement this framework based on the household interview travel survey (HITS) data of Singapore, and then use the inferred model to generate a synthetic population pool. This model demonstrates great potential in reproducing the underlying statistical distribution of real population. The generated synthetic population can serve as a replacement for census in developing agent-based models, with privacy and confidentiality being protected and preserved.

Suggested Citation

  • Sun, Lijun & Erath, Alexander & Cai, Ming, 2018. "A hierarchical mixture modeling framework for population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 199-212.
    • Handle: RePEc:eee:transb:v:114:y:2018:i:c:p:199-212
    DOI: 10.1016/j.trb.2018.06.002
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    References listed on IDEAS

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    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. David Pritchard & Eric Miller, 2012. "Advances in population synthesis: fitting many attributes per agent and fitting to household and person margins simultaneously," Transportation, Springer, vol. 39(3), pages 685-704, May.
    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.
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    6. Farooq, Bilal & Bierlaire, Michel & Hurtubia, Ricardo & Flötteröd, Gunnar, 2013. "Simulation based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 243-263.
    7. Johan Barthelemy & Philippe L. Toint, 2013. "Synthetic Population Generation Without a Sample," Transportation Science, INFORMS, vol. 47(2), pages 266-279, May.
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    Cited by:

    1. Nicholas Fournier & Eleni Christofa & Arun Prakash Akkinepally & Carlos Lima Azevedo, 2021. "Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method," Transportation, Springer, vol. 48(2), pages 1061-1087, April.
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    3. Nejad, Mohammad Motalleb & Erdogan, Sevgi & Cirillo, Cinzia, 2021. "A statistical approach to small area synthetic population generation as a basis for carless evacuation planning," Journal of Transport Geography, Elsevier, vol. 90(C).

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