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A flexible model structure approach for discrete choice models

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  • Robert Ishaq

  • Shlomo Bekhor
  • Yoram Shiftan

Abstract

Multi-dimensional discrete choice problems are usually estimated by assuming a single-choice hierarchical order for the entire study population or for pre-defined segments representing the behavior of an “average” person and by indicating either limited differences or a variety in choices among the study population. This study develops an integral methodological framework, termed the flexible model structure (FMS), which enhances the application of the discrete choice model by developing an optimization algorithm that segment given data and searches for the best model structure for each segment simultaneously. The approach is demonstrated here through three models that conceptualize the multi-dimensional discrete choice problem. The first two are Nested Logit models with a two-choice dimension of destination and mode; they represent the estimation of a fixed-structure model using pre-segmented data as is mostly common in multi-dimensional discrete choice model implementation. The third model, the FMS, includes a fuzzy segmentation method with weighted variables, as well as a combination of more than one model structure estimated simultaneously. The FMS model significantly improves estimation results, using fewer variables than do segmented NL models, thus supporting the hypothesis that different model structures may best describe the behavior of different groups of people in multi-dimensional choice models. The implementation of FMS involves presenting the travel behavior of an individual as a mix of travel behaviors represented by a number of segments. The choice model for each segment comprises a combination of different choice model structures. The FMS model thus breaks the consensus that an individual belongs to only one segment and that a segment can take only one structure. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Robert Ishaq & Shlomo Bekhor & Yoram Shiftan, 2013. "A flexible model structure approach for discrete choice models," Transportation, Springer, vol. 40(3), pages 609-624, May.
    • Handle: RePEc:kap:transp:v:40:y:2013:i:3:p:609-624
    DOI: 10.1007/s11116-012-9431-8
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    References listed on IDEAS

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    1. Stephane Hess & Amanda Stathopoulos & Andrew Daly, 2012. "Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies," Transportation, Springer, vol. 39(3), pages 565-591, May.
    2. Yoram Shiftan & Moshe Ben-Akiva, 2011. "A practical policy-sensitive, activity-based, travel-demand model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 47(3), pages 517-541, December.
    3. Mark Bradley & Peter Vovsha, 2005. "A model for joint choice of daily activity pattern types of household members," Transportation, Springer, vol. 32(5), pages 545-571, September.
    4. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    5. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
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    1. Mahmud, Asif & Gayah, Vikash V. & Paleti, Rajesh, 2022. "A latent choice model to analyze the role of preliminary preferences in shaping observed choices," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 95-108.
    2. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    3. Gonçalves, Tânia & Pinto, Lígia M. Costa & Lourenço-Gomes, Lina, 2020. "Attribute non-attendance in wine choice: Contrasts between stated and inferred approaches," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 262-275.
    4. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    5. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.

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