Comparisons of observed and unobserved parameter heterogeneity in modeling vehicle-miles driven

Sung Hoo Kim and Patricia Mokhtarian

Transportation Research Part A: Policy and Practice, 2023, vol. 172, issue C

Abstract: This study examines potential observed and unobserved parameter heterogeneity in modeling vehicle-miles driven (VMD). This application is both important in its own right, and a vehicle for exploring conceptual, mathematical, and empirical differences among three finite-segmentation ways of addressing such heterogeneity: deterministic segmentation and (endogenous) switching for observed heterogeneity, and latent class models for unobserved heterogeneity. Based on empirical data from about 3,000 Georgia residents, we model weekly VMD, and identify key explanatory variables as well as the different sensitivities to those variables exhibited by various population segments. The study posits that people have different sensitivities to explanatory variables by residence type (urban versus “less urban”), and supporting evidence was found in the deterministic segmentation and endogenous switching models. The switching regression model characterized those who are more likely to live in urban areas, e.g. workers, pro-environmental, and those who are less favorable to driving. In addition, the estimated error correlations in the endogenous switching model corroborated that there are unobserved factors common to the joint decisions of residential choice and VMD generation. The latent class model identified lower and higher VMD-inclined segments and had a better goodness of fit than the other two models. The results confirm that (1) there is notable heterogeneity in the population with respect to the weight given to relevant factors that influence VMD, and (2) accounting for both observed and unobserved heterogeneities is useful in modeling VMD.

Keywords: Mixture modeling; Latent class model; Switching regression; Heterogeneity; Market segmentation; Vehicle-miles driven (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.tra.2023.103614

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