An Analysis of Household Vehicle Ownership and Utilization Patterns in the United States Using the 2001 National Household Travel Survey

Vehicle ownership and utilization have a profound influence on activity-travel patterns of individuals, vehicle emissions, fuel consumption, highway capacity, congestion and traffic safety. The influence could be further skewed by the diversity of the vehicle fleet. This thesis presents a detailed analysis of the 2001 National Household Travel Survey data to understand the vehicle ownership patterns, fleet mix, allocation and utilization in the context of household and person socio-demographic characteristics. Along with a rich descriptive analysis, models of vehicle ownership and utilization are estimated to distinguish four vehicle types; cars, SUVs (sport utility vehicles), vans and pickup trucks based on their ownership by households and utilization patterns by household members. The primary driver level vehicle utilization analysis provides insights into the extent of allocation of a vehicle to a single person. In addition to confirming many perceptions about the ownership, acquisition and utilization patterns of different types of vehicles, this analysis brings out some subtle differences and similarities among the vehicle types. The analysis results indicate a greater propensity to acquire and use larger vehicles such as minivans, sports utility vehicles and pickup trucks among certain socio-demographic segments of population. Increased ownership and use of vans and SUVs, and their usage as personal vehicles rather than just work vehicles warrants a need to revise vehicle type specific policies, transportation planning and control measures

Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models

This paper formally derives the class of multiple discrete-continuous generalized extreme value (MDCGEV) models, a general class of multiple discrete-continuous choice models based on generalized extreme value (GEV) error specifications. Specifically, the paper proves the existence of, and derives the general form of, closed-form consumption probability expressions for multiple discrete-continuous choice models with GEV-based error structures. In addition to deriving the general form, the paper derives a compact and readily usable form of consumption probability expressions that can be used to estimate multiple discrete-continuous choice models with general cross-nested error structures. The cross-nested version of the MDCGEV model is applied to analyze household annual expenditure patterns in various transportation-related expenses using data from a Consumer Expenditure Survey in the United States. Model estimation results and predictive log-likelihood based validation tests indicate the superiority of the cross-nested model over the mutually exclusively nested and non-nested model specifications. Further, the cross-nested model was amenable to the accommodation of socio-demographic heterogeneity in inter-alternative covariance across decision-makers through a parameterization of the allocation parameters.Discrete-continuous models Kuhn-Tucker (KT) demand systems Multiple discreteness MDCEV GEV Cross-nested error structure

Allowing for non-additively separable and flexible utility forms in multiple discrete-continuous models

At the time of publication Chandra R. Bhat and Marisol Castro were at the University of Texas at Austin, and Abdul Rawoof Pinjari was at the University of South Florida.Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another, along with a continuous quantity dimension for each chosen alternative. To model such multiple discrete-continuous choices, most multiple discrete-continuous models in the literature use an additively-separable utility function, with the assumption that the marginal utility of one good is independent of the consumption of another good. In this paper, we develop model formulations for multiple discrete-continuous choices that allow a non-additive utility structure, and accommodate rich substitution structures and complementarity effects in the consumption patterns. Specifically, three different nonadditive utility formulations are proposed based on alternative specifications and interpretations of stochasticity: (1) The deterministic utility random maximization (DU-RM) formulation, which considers stochasticity due to the random mistakes consumers make during utility maximization; (2) The random utility deterministic maximization (RU-DM) formulation, which considers stochasticity due to the analyst’s errors in characterizing the consumer’s utility function; and (3) The random utility random maximization (RU-RM) formulation, which considers both analyst’s errors and consumer’s mistakes within a unified framework. When applied to the consumer expenditure survey data in the United States, the proposed non-additively separable utility formulations perform better than the additively separable counterparts, and suggest the presence of substitution and complementarity patterns in consumption.Civil, Architectural, and Environmental Engineerin

A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

This paper develops a multiple discrete-continuous nested extreme value (MDCNEV) model that relaxes the independently distributed (or uncorrelated) error terms assumption of the multiple discrete-continuous extreme value (MDCEV) model proposed by Bhat [Bhat, C.R., 2005. A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B 39 (8), 679-707; Bhat, C.R., 2008. The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B 42 (3), 274-303]. The MDCNEV model captures inter-alternative correlations among alternatives in mutually exclusive subsets (or nests) of the choice set, while maintaining the closed-form of probability expressions for any (and all) consumption pattern(s). The MDCNEV model is applied to analyze non-worker out-of-home discretionary activity time-use and activity timing decisions on weekdays using data from the 2000 San Francisco Bay Area data. This empirical application contributes to the literature on activity time-use and activity timing analysis by considering daily activity time-use behavior and activity timing preferences in a unified utility maximization-based framework. The model estimation results provide several insights into the determinants of non-workers' activity time-use and timing decisions. The MDCNEV model performs better than the MDCEV model in terms of goodness of fit. However, the nesting parameters are very close to 1, indicating low levels of correlation. Nonetheless, even with such low correlation levels, empirical policy simulations indicate non-negligible differences in policy predictions and substitution patterns exhibited by the two models. Experiments conducted using simulated data also corroborate this result.Multiple discrete-continuous choices Random utility maximization Kuhn-Tucker demand model systems Flexible substitution patterns Activity time-use Activity timing

Computationally efficient forecasting procedures for Kuhn-Tucker consumer demand model systems: Application to residential energy consumption analysis

At the time of publication, A.R. Pinjari was at the University of South Florida, and C. Bhat was at the University of Texas at Austin.This paper proposes simple and computationally efficient forecasting algorithms for a Kuhn- Tucker (KT) consumer demand model system called the Multiple Discrete-Continuous Extreme Value (MDCEV) model. The algorithms build on simple, yet insightful, analytical explorations with the Kuhn-Tucker conditions of optimality that shed new light on the properties of the model. Although developed for the MDCEV model, the proposed algorithm can be easily modified to be used for other KT demand model systems in the literature with additively separable utility functions. The MDCEV model and the forecasting algorithms proposed in this paper are applied to a household-level energy consumption dataset to analyze residential energy consumption patterns in the United States. Further, simulation experiments are undertaken to assess the computational performance of the propose d (and existing) KT demand forecasting algorithms for a range of choice situations with small and large choice sets.Civil, Architectural, and Environmental Engineerin

Modeling the Choice Continuum: An Integrated Model of Residential Location, Auto Ownership, Bicycle Ownership, and Commute Tour Mode Choice Decisions

At the time of publication A.R. Pinjari was at the University of South Florida; R.M. Pendyala was at Arizona State University; C.R. Bhat was at the University of Texas at Austin; and P.A. Waddell was at the University of California Berkeley.The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people's lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the "true" causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.Civil, Architectural, and Environmental Engineerin

Allowing for Complementarity and Rich Substitution Patterns in Multiple Discrete- Continuous Models

ABSTRACT Many consumer choice situations are characterized by the simultaneous demand for multiple alternatives that are imperfect substitutes for one another, along with a continuous quantity dimension for each chosen alternative. To model such multiple discrete-continuous choices, most multiple discrete-continuous models in the literature use an additively-separable utility function, with the assumption that the marginal utility of one good is independent of the consumption of another good. In this paper, we develop model formulations for multiple discrete-continuous choices that accommodate rich substitution structures and complementarity effects in the consumption patterns, and demonstrate an application of the model to transportation-related expenditures using data drawn from the 2002 Consumer Expenditure (CEX) Survey

Comprehensive Model of Worker Nonwork-Activity Time Use and Timing Behavior

A comprehensive, high-resolution model for out-of-home nonwork-activity generation is developed; it considers daily activity time-use behavior and activity timing preferences in a unified random utility framework. The empirical analysis is undertaken with data from the 2000 San Francisco, California, Bay Area Travel Survey. Several important household and commuter demographics, commute characteristics, and activity travel environment attributes are found to be significant determinants of workers’ nonwork-activity time use and timing behavior. The developed comprehensive model can serve as an activity-generation module in an activity-based travel demand microsimulation framework