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Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach

Abstract

Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict activity-travel choices, with those downstream in the decision-making chain influenced by those upstream in the sequence. The adoption of a singular sequential causal structure to depict relationships among activity-travel choices in travel demand model systems ignores the possibility that some choices are made jointly as a bundle as well as the possible presence of structural heterogeneity in the population with respect to decision-making processes. As different segments in the population may adopt and follow different causal decision-making mechanisms when making selected choices jointly, it would be of value to develop simultaneous equations model systems relating multiple endogenous choice variables that are able to identify population subgroups following alternative causal decision structures. Because the segments are not known a priori, they are considered latent and determined endogenously within a joint modeling framework proposed in this paper. The methodology is applied to a national mobility survey data set to identify population segments that follow different causal structures relating residential location choice, vehicle ownership, and car-share and mobility service usage. It is found that the model revealing three distinct latent segments best describes the data, confirming the efficacy of the modeling approach and the existence of structural heterogeneity in decision-making in the population. Future versions of activity-travel model systems should strive to incorporate such structural heterogeneity to better reflect varying decision processes across population subgroups.

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Acknowledgements

This research was partially supported by the Center for Teaching Old Models New Tricks (TOMNET) as well as the Data-Supported Transportation Operations and Planning (D-STOP) Center, both of which are Tier 1 University Transportation Centers sponsored by the US Department of Transportation (Grant Nos. 69A3551747116 and DTRT13-G-UTC58). The authors are grateful to Lisa Macias for her help in formatting this document. The authors thank three anonymous reviewers for their valuable comments and input that greatly improved the paper.

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Contributions

SA: Literature review, variable specification development, model estimation and coding. VMG: Literature review, manuscript writing, editing. RMP: Conceptual development, manuscript writing, variable specification development. CRB: Conceptual development, methodology development, manuscript writing. PLM: Literature synthesis, editing, variable specification development.

Corresponding author

Correspondence to Chandra R. Bhat.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Astroza, S., Garikapati, V.M., Pendyala, R.M. et al. Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach. Transportation 46, 1755–1784 (2019). https://doi.org/10.1007/s11116-018-9882-7

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Keywords

  • Causal relationships
  • Structural heterogeneity
  • Simultaneous equations models
  • Latent segmentation
  • Joint estimation
  • Vehicle ownership
  • Residential location choice
  • Mobility service usage