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A multi-source data fusion framework for joint population, expenditure, and time use synthesis

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  • Jason Hawkins

    (University of Nebraska – Lincoln)

  • Khandker Nurul Habib

    (University of Toronto)

Abstract

Data are important components of any research; however, it is often the case that the required data are not readily available. Researchers often fuse multiple datasets to obtain the data required to complete their work. In urban simulation, spatially referencing data is of paramount importance to capture local variations in travel and preferences. Data fusion typically obfuscates the spatial reference by merging records from different locations. Population synthesis is used to match these fused household records to a plausible location based on aggregate sociodemographic statistics. In some cases, researchers must also synthesize the necessary data. This paper outlines a data fusion workflow for a statistically valid synthetic population for use in urban simulation models. We develop the framework for the case of household-level expenditure and individuals’ time use patterns. The Greater Toronto Area (GTA) in Canada is used as a testbed. The results of the data fusion and synthesis are validated against statistics from a large-sample travel survey conducted in the GTA, showing a good fit with the validation dataset. Finally, we outline how the framework could be applied in other contexts where a single dataset is unavailable.

Suggested Citation

  • Jason Hawkins & Khandker Nurul Habib, 2023. "A multi-source data fusion framework for joint population, expenditure, and time use synthesis," Transportation, Springer, vol. 50(4), pages 1323-1346, August.
    • Handle: RePEc:kap:transp:v:50:y:2023:i:4:d:10.1007_s11116-022-10279-8
    DOI: 10.1007/s11116-022-10279-8
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