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Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice

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  • Bhat, Chandra R.
  • Dubey, Subodh K.
  • Nagel, Kai

Abstract

In the current paper, we propose the use of a multivariate skew-normal (MSN) distribution function for the latent psychological constructs within the context of an integrated choice and latent variable (ICLV) model system. The multivariate skew-normal (MSN) distribution that we use is tractable, parsimonious in parameters that regulate the distribution and its skewness, and includes the normal distribution as a special interior point case (this allows for testing with the traditional ICLV model). Our procedure to accommodate non-normality in the psychological constructs exploits the latent factor structure of the ICLV model, and is a flexible, yet very efficient approach (through dimension-reduction) to accommodate a multivariate non-normal structure across all indicator and outcome variables in a multivariate system through the specification of a much lower-dimensional multivariate skew-normal distribution for the structural errors. Taste variations (i.e., heterogeneity in sensitivity to response variables) can also be introduced efficiently and in a non-normal fashion through interactions of explanatory variables with the latent variables. The resulting model we develop is suitable for estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The proposed model is applied to model bicyclists’ route choice behavior using a web-based survey of Texas bicyclists. The results reveal evidence for non-normality in the latent constructs. From a substantive point of view, the results suggest that the most unattractive features of a bicycle route are long travel times (for commuters), heavy motorized traffic volume, absence of a continuous bicycle facility, and high parking occupancy rates and long lengths of parking zones along the route.

Suggested Citation

  • Bhat, Chandra R. & Dubey, Subodh K. & Nagel, Kai, 2015. "Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 341-363.
  • Handle: RePEc:eee:transb:v:78:y:2015:i:c:p:341-363
    DOI: 10.1016/j.trb.2015.04.005
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Bhat, Chandra R. & Astroza, Sebastian & Hamdi, Amin S., 2017. "A spatial generalized ordered-response model with skew normal kernel error terms with an application to bicycling frequency," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 126-148.
    3. repec:eee:jeborg:v:142:y:2017:i:c:p:47-63 is not listed on IDEAS
    4. repec:eee:transa:v:100:y:2017:i:c:p:53-64 is not listed on IDEAS
    5. repec:eee:transa:v:105:y:2017:i:c:p:66-78 is not listed on IDEAS
    6. Czajkowski, Mikołaj & Vossler, Christian A. & Budziński, Wiktor & Wiśniewska, Aleksandra & Zawojska, Ewa, 2017. "Addressing empirical challenges related to the incentive compatibility of stated preferences methods," Journal of Economic Behavior & Organization, Elsevier, vol. 142(C), pages 47-63.
    7. repec:eee:transb:v:108:y:2018:i:c:p:84-105 is not listed on IDEAS
    8. Kamargianni, Maria, 2015. "Investigating next generation's cycling ridership to promote sustainable mobility in different types of cities," Research in Transportation Economics, Elsevier, vol. 53(C), pages 45-55.
    9. repec:kap:theord:v:84:y:2018:i:2:d:10.1007_s11238-017-9638-4 is not listed on IDEAS
    10. Motoaki, Yutaka & Daziano, Ricardo A., 2015. "A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 217-230.

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