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Inverse discrete choice modelling: theoretical and practical considerations for imputing respondent attributes from the patterns of observed choices

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  • Yuanying Zhao
  • Jacek Pawlak
  • John W. Polak

Abstract

The growing availability of geotagged big data has stimulated substantial discussion regarding their usability in detailed travel behaviour analysis. Whilst providing a large amount of spatio-temporal information about travel behaviour, these data typically lack semantic content characterising travellers and choice alternatives. The inverse discrete choice modelling (IDCM) approach presented in this paper proposes that discrete choice models (DCMs) can be statistically inverted and used to attach additional variables from observations of travel choices. Suitability of the approach for inferring socioeconomic attributes of travellers is explored using mode choice decisions observed in London Travel Demand Survey. Performance of the IDCM is investigated with respect to the type of variable, the explanatory power of the imputed variable, and the type of estimator used. This method is a significant contribution towards establishing the extent to which DCMs can be credibly applied for semantic enrichment of passively collected big data sets while preserving privacy.

Suggested Citation

  • Yuanying Zhao & Jacek Pawlak & John W. Polak, 2018. "Inverse discrete choice modelling: theoretical and practical considerations for imputing respondent attributes from the patterns of observed choices," Transportation Planning and Technology, Taylor & Francis Journals, vol. 41(1), pages 58-79, January.
    • Handle: RePEc:taf:transp:v:41:y:2018:i:1:p:58-79
    DOI: 10.1080/03081060.2018.1402745
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    Cited by:

    1. Zhao, Yuanying & Pawlak, Jacek & Sivakumar, Aruna, 2022. "Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 101-134.
    2. Mejía, Gonzalo & Aránguiz, Raúl & Espejo-Díaz, Julián Alberto & Granados-Rivera, Daniela & Mejía-Argueta, Christopher, 2023. "Can street markets be a sustainable strategy to mitigate food insecurity in emerging countries? Insights from a competitive facility location model," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    3. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.

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