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.
Similar content being viewed by others
References
Angueira, J., Konduri, K.C., Chakour, V., Eluru, N.: Exploring the relationship between vehicle type choice and distance traveled: a latent segmentation approach. Transp. Lett. (2017). https://doi.org/10.1080/19427867.2017.1299346
Allenby, G.: Hypothesis testing with scanner data: the advantage of Bayesian methods. J. Mark. Res. 27, 379–389 (1990)
Bagley, M.N., Mokhtarian, P.L.: The impact of residential neighborhood type on travel behavior: a structural equations modeling approach. Ann. Reg. Sci. 36(2), 279–297 (2002)
Bhat, C.R.: An endogenous segmentation mode choice model with an application to intercity travel. Transp. Sci. 31(1), 34–48 (1997)
Bhat, C.R.: The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models. Transp. Res. B 45(7), 923–939 (2011)
Bhat, C.R.: A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables. Transp. Res. B 79, 50–77 (2015)
Bhat, C.R., Guo, J.Y.: A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transp. Res. B 41(5), 506–526 (2007)
Bhat, C.R., Astroza, S., Bhat, A.C.: On allowing a general form for unobserved heterogeneity in the multiple discrete-continuous probit model: formulation and application to tourism travel. Transp. Res. B 86, 223–249 (2016a)
Bhat, C.R., Astroza, S., Bhat, A.C., Nagel, K.: Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: application to residential self-selection effects analysis in an activity time-use behavior model. Transp. Res. B 91, 52–76 (2016b)
Cao, X., Mokhtarian, P.L., Handy, S.: Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach. Transportation 34(5), 535–556 (2007)
Garikapati, V.M., Pendyala, R.M., Morris, E.A., Mokhtarian, P.L., McDonald, N.: Activity patterns, time use, and travel of millennials: a generation in transition? Transp. Rev. 36(5), 558–584 (2016)
Golob, T.F.: A simultaneous model of household activity participation and trip chain generation. Transp. Res. B 34(5), 355–376 (2000)
Johansson, M.V., Heldt, T., Johansson, P.: The effects of attitudes and personality traits on mode choice. Transp. Res. A 40(6), 507–525 (2006)
Krizek, K., Waddell, P.: Analysis of lifestyle choices: neighborhood type, travel patterns, and activity participation. Transp. Res. Rec. J. Transp. Res. Board 1807, 119–128 (2002)
Lavieri, P., Garikapati, V.M., Bhat, C.R., Pendyala, R.M.: Investigation of heterogeneity in vehicle ownership and usage for the millennial generation. Transp. Res Rec. J. Transp. Res. Board 2664, 91–99 (2017)
Lu, X., Pas, E.I.: Socio-demographics, activity participation, and travel behavior. Transp. Res. A 33(1), 1–18 (1999)
Maddala, G.S.: Limited-Dependent and Qualitative Variables in Econometrics. Cambridge University Press, Cambridge (1983)
McDonald, N.C.: Are millennials really the “gonowhere” generation? J. Am. Plan. Assoc. 81(2), 90–103 (2015)
Mishra, G.S., Mokhtarian, P.L., Clewlow, R.R., Widaman, K.F.: Addressing the joint occurrence of self-selection and simultaneity biases in the estimation of program effects based on cross-sectional observational surveys: case study of travel behavior effects in carsharing. Transportation (2017). https://doi.org/10.1007/s11116-017-9791-1
Pendyala, R.M.: Causal analysis in travel behaviour research: a cautionary note. In: Ortuzar, J.D., Hensher, D. (eds.) Travel Behaviour Research: Updating the State of Play, pp. 35–48. Elsevier Science Publishers, B.V, Amsterdam (1998)
Pendyala, R.M., Bhat, C.R.: An exploration of the relationship between timing and duration of maintenance activities. Transportation 31(4), 429–456 (2004)
Silva, J.D.A., Morency, C., Goulias, K.G.: Using structural equations modeling to unravel the influence of land use patterns on travel behavior of workers in Montreal. Transp. Res. A 46(8), 1252–1264 (2012)
Transit Center: Who’s On Board: 2014 Mobility Attitudes Survey. Transit Center, New York, NY. http://transitcenter.org/wp-content/uploads/2014/08/WhosOnBoard2014-ForWeb.pdf. Accessed 15 July 2017
Waddell, P., Bhat, C.R., Eluru, N., Wang, L., Pendyala, R.M.: Modeling interdependence in household residence and workplace choices. Transp. Res. Rec. J. Transp. Res. Board 2003, 84–92 (2007)
Yarlagadda, A.K., Srinivasan, S.: Modeling children’s school travel mode and parental escort decisions. Transportation 35(2), 201–218 (2008)
Ye, X., Pendyala, R.M., Gottardi, G.: An exploration of the relationship between mode choice and complexity of trip chaining patterns. Transp. Res. B 41(1), 96–113 (2007)
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.
Author information
Authors and Affiliations
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
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11116-018-9882-7