Browsing by Subject "vehicle miles of travel"
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Item A Copula-Based Approach to Accommodate Residential Self-Selection Effects in Travel Behavior Modeling(Elsevier, 2009) Bhat, Chandra R.; Eluru, NaveenThe dominant approach in the literature to dealing with sample selection is to assume a bivariate normality assumption directly on the error terms, or on transformed error terms, in the discrete and continuous equations. Such an assumption can be restrictive and inappropriate, since the implication is a linear and symmetrical dependency structure between the error terms. In this paper, we introduce and apply a flexible approach to sample selection in the context of built environment effects on travel behavior. The approach is based on the concept of a copula, which is a multivariate functional form for the joint distribution of random variables derived purely from pre-specified parametric marginal distributions of each random variable. The copula concept has been recognized in the statistics field for several decades now, but it is only recently that it has been explicitly recognized and employed in the econometrics field. The copula-based approach retains a parametric specification for the bivariate dependency, but allows testing of several parametric structures to characterize the dependency. The empirical context in the current paper is a model of residential neighborhood choice and daily household vehicle miles of travel (VMT), using the 2000 San Francisco Bay Area Household Travel Survey (BATS). The sample selection hypothesis is that households select their residence locations based on their travel needs, which implies that observed VMT differences between households residing in neo-urbanist and conventional neighborhoods cannot be attributed entirely to the built environment variations between the two neighborhoods types. The results indicate that, in the empirical context of the current study, the VMT differences between households in different neighborhood types may be attributed to both built environment effects and residential self-selection effects. As importantly, the study indicates that use of a traditional Gaussian bivariate distribution to characterize the relationship in errors between residential choice and VMT can lead to misleading implications about built environment effects.Item A Copula-Based Joint Multinomial Discrete-Continuous Model of Vehicle Type Choice and Miles of Travel(Springer, 2009) Spissu, Erika; Pinjari, Abdul R.; Pendyala, Ram M.; Bhat, Chandra R.In this paper, a joint model of vehicle type choice and utilization is formulated and estimated on a data set of vehicles drawn from the 2000 San Francisco Bay Area Travel Survey. The joint discrete-continuous model system formulated in this study explicitly accounts for common unobserved factors that may affect the choice and utilization of a certain vehicle type (i.e., self-selection effects). A new copula-based methodology is adopted to facilitate model estimation without imposing restrictive distribution assumptions on the dependency structures between the errors in the discrete and continuous choice components. The copula-based methodology is found to provide statistically superior goodness-of-fit when compared with previous estimation approaches for joint discrete-continuous model systems. The model system, when applied to simulate the impacts of a doubling in fuel price, shows that individuals are more likely to shift vehicle type choices than vehicle usage patterns.Item A Joint Flexible Econometric Model System of Household Residential Location and Vehicle Fleet Composition/Usage Choices(Springer, 2010) Eluru, Naveen; Bhat, Chandra R.; Pendyala, Ram M.; Konduri, Karthik C.Modeling the interaction between the built environment and travel behavior is of much interest totransportation planning professionals due to the desire to curb vehicular travel demand throughmodifications to built environment attributes. However, such models need to take into accountself-selection effects in residential location choice, wherein households choose to reside inneighborhoods and built environments that are conducive to their lifestyle preferences andattitudes. This phenomenon, well-recognized in the literature, calls for the specification andestimation of joint models of multi-dimensional land use and travel choice processes. However,the estimation of such model systems that explicitly account for the presence of unobservedfactors that jointly impact multiple choice dimensions is extremely complex and computationallyintensive. This paper presents a joint GEV-based logit regression model of residential locationchoice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copulabased framework that facilitates the estimation of joint equations systems with error dependencestructures within a simple and flexible closed-form analytic framework. The model system isestimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey.Estimation results show that there is significant dependency among the choice dimensions andthat self-selection effects cannot be ignored when modeling land use-travel behavior interactions.