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Title: 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 distinctmore » 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.« less

Authors:
; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
U.S. Department of Transportation (DOT)
OSTI Identifier:
1441173
Report Number(s):
NREL/JA-5400-71683
Journal ID: ISSN 0049-4488
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Transportation; Journal Volume: none; Journal Issue: none
Country of Publication:
United States
Language:
English
Subject:
30 DIRECT ENERGY CONVERSION; causal relationships; structural heterogeneity; simultaneous equations models; latent segmentation; joint estimation; vehicle ownership; residential location choice; mobility service usage

Citation Formats

Astroza, Sebastian, Garikapati, Venu M., Pendyala, Ram M., Bhat, Chandra R., and Mokhtarian, Patricia L.. Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach. United States: N. p., 2018. Web. doi:10.1007/s11116-018-9882-7.
Astroza, Sebastian, Garikapati, Venu M., Pendyala, Ram M., Bhat, Chandra R., & Mokhtarian, Patricia L.. Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach. United States. doi:10.1007/s11116-018-9882-7.
Astroza, Sebastian, Garikapati, Venu M., Pendyala, Ram M., Bhat, Chandra R., and Mokhtarian, Patricia L.. Sat . "Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach". United States. doi:10.1007/s11116-018-9882-7.
@article{osti_1441173,
title = {Representing heterogeneity in structural relationships among multiple choice variables using a latent segmentation approach},
author = {Astroza, Sebastian and Garikapati, Venu M. and Pendyala, Ram M. and Bhat, Chandra R. and Mokhtarian, Patricia L.},
abstractNote = {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.},
doi = {10.1007/s11116-018-9882-7},
journal = {Transportation},
number = none,
volume = none,
place = {United States},
year = {Sat May 19 00:00:00 EDT 2018},
month = {Sat May 19 00:00:00 EDT 2018}
}