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Integrated choice and latent variable models: A literature review on mode choice

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  • Hélène Bouscasse

    (GAEL - Laboratoire d'Economie Appliquée de Grenoble - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - INRA - Institut National de la Recherche Agronomique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes)

Abstract

Mode choice depends on observable characteristics of the transport modes and of the decision maker, but also on unobservable characteristics, known as latent variables. By means of an integrated choice and latent variable (ICLV) model, which is a combination of structural equation model and discrete choice model, it is theoretically possible to integrate both types of variables in a psychologically and economically sound mode choice model. To achieve such a goal requires clear positioning on the four dimensions covered by ICLV models: survey methods, econometrics, psychology and economics. This article presents a comprehensive survey of the ICLV literature applied to mode choice modelling. I review how latent variables are measured and incorporated in the ICLV models, how they contribute to explaining mode choice and how they are used to derive economic outputs. The main results are: 1) the latent variables used to explain mode choice are linked to individual mental states, perceptions of transport modes, or an actual performed behaviour; 2) the richness of structural equation models still needs to be explored to fully embody the psychological theories explaining mode choice; 3) the integration of latent variables helps to improve our understanding of mode choice and to adapt public policies.

Suggested Citation

  • Hélène Bouscasse, 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers hal-01795630, HAL.
  • Handle: RePEc:hal:wpaper:hal-01795630
    Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01795630
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    References listed on IDEAS

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    1. Ricardo Daziano & Denis Bolduc, 2013. "Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes," Transportation, Springer, vol. 40(3), pages 647-670, May.
    2. Marcel Paulssen & Dirk Temme & Akshay Vij & Joan Walker, 2014. "Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice," Transportation, Springer, vol. 41(4), pages 873-888, July.
    3. Rafael Maldonado-Hinarejos & Aruna Sivakumar & John Polak, 2014. "Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach," Transportation, Springer, vol. 41(6), pages 1287-1304, November.
    4. Khandker M. Nurul Habib & Md. Hamid Zaman, 2012. "Effects of incorporating latent and attitudinal information in mode choice models," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(5), pages 561-576, June.
    5. Jennifer Roberts & Gurleen Popli & Rosemary J. Harris, 2018. "Do environmental concerns affect commuting choices?: hybrid choice modelling with household survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 299-320, January.
    6. Glerum, Aurélie & Atasoy, Bilge & Bierlaire, Michel, 2014. "Using semi-open questions to integrate perceptions in choice models," Journal of choice modelling, Elsevier, vol. 10(C), pages 11-33.
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    8. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    9. Chorus, Caspar G. & Kroesen, Maarten, 2014. "On the (im-)possibility of deriving transport policy implications from hybrid choice models," Transport Policy, Elsevier, vol. 36(C), pages 217-222.
    10. Akshay Vij & Joan L. Walker, 2014. "Hybrid choice models: the identification problem," Chapters,in: Handbook of Choice Modelling, chapter 22, pages 519-564 Edward Elgar Publishing.
    11. Khandker Nurul Habib & Yuan Tian & Hamid Zaman, 2011. "Modelling commuting mode choice with explicit consideration of carpool in the choice set formation," Transportation, Springer, vol. 38(4), pages 587-604, July.
    12. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    13. Fernández-Antolín, Anna & Guevara, C. Angelo & de Lapparent, Matthieu & Bierlaire, Michel, 2016. "Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data," Journal of choice modelling, Elsevier, vol. 20(C), pages 1-15.
    14. Bhat, Chandra R. & Dubey, Subodh K., 2014. "A new estimation approach to integrate latent psychological constructs in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 68-85.
    15. Maria Kamargianni & Moshe Ben-Akiva & Amalia Polydoropoulou, 2014. "Incorporating social interaction into hybrid choice models," Transportation, Springer, vol. 41(6), pages 1263-1285, November.
    16. Dirk Temme & Marcel Paulssen & Till Dannewald, 2007. "Integrating latent variables in discrete choice models – How higher-order values and attitudes determine consumer choice," SFB 649 Discussion Papers SFB649DP2007-065, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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    Keywords

    Mode choice; Survey; Integrated choice and latent variable model; Structural equation modelling; Behavioural theories; Economic outputs;

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