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Integrating Bayesian network and generalized raking for population synthesis in Greater Jakarta

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  • Anugrah Ilahi
  • Kay W. Axhausen

Abstract

Constructing agent data with detailed information on their sociodemographics is substantially important for agent-based modelling. However, to collect data about the whole population is not efficient, since it requires an expensive and time-consuming survey, especially for a large population. The paper uses a novel approach that integrates Bayesian network (BN) and generalized raking (GR) multilevel iterative proportional fitting (IPF). Furthermore, the approach is applied to construct the population for Greater Jakarta, Indonesia, which consists of 30 million inhabitants. The results show that the BN approach can produce data that represent the probability distribution of sample data and that the IPF can match it against aggregate census data.

Suggested Citation

  • Anugrah Ilahi & Kay W. Axhausen, 2019. "Integrating Bayesian network and generalized raking for population synthesis in Greater Jakarta," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 6(1), pages 623-636, January.
    • Handle: RePEc:taf:rsrsxx:v:6:y:2019:i:1:p:623-636
    DOI: 10.1080/21681376.2019.1687011
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    Cited by:

    1. Mohamed Khachman & Catherine Morency & Francesco Ciari, 2024. "Integrated multiresolution framework for spatialized population synthesis," Transportation, Springer, vol. 51(3), pages 823-852, June.
    2. Ilahi, Anugrah & Belgiawan, Prawira F. & Balac, Milos & Axhausen, Kay W., 2021. "Understanding travel and mode choice with emerging modes; a pooled SP and RP model in Greater Jakarta, Indonesia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 398-422.

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