A Comparison of Online and In-Person Activity Engagement: The Case of Shopping and Eating Meals

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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

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

On the Nonlinearity of Response to Level of Service Variables in Travel Mode Choice Models

It is important to accommodate variations in responsiveness (or response heterogeneity) to level of service attributes in travel mode choice models. This response heterogeneity may be disaggregated into a systematic (observed) component and a random (unobserved) component. Earlier studies have typically considered systematic response heterogeneity by examining differences in LOS response sensitivities due to individual demographic and other attributes. In this research, our emphasis is on another element of systematic response heterogeneity - systematic response heterogeneity originating from nonlinear responsiveness to LOS attributes. Specifically, we consider both the components of systematic response heterogeneity (due to individual characteristics and due to nonlinear responsiveness) as well as unobserved response heterogeneity at the same time, and compare the empirical results of models that assume a traditional linear responsiveness to LOS attributes with those that adopt a nonlinear responsiveness to LOS attributes. The empirical analysis uses the Austin Commuter Stated Preference Survey data to examine commute travel mode choice. The nonlinear specifications for travel time and travel time unreliability indicate that commuters place a small value to travel time, and a very high value to travel time reliability, in the first 15 minutes. Beyond 15 minutes, however, the valuation of travel time increases rapidly, while that of travel time reliability drops dramatically. In addition to clearly indicating the nonlinear nature of responsiveness to travel time and travel time unreliability, the results indicate that ignoring nonlinear responsiveness leads, in the current empirical context, to (a) biased parameter estimates, (b) an inflated estimate of unobserved heterogeneity, (c) counterintuitive signs on the LOS variables for a high fraction of individuals, (d) inaccurate estimates of willingness-to-pay measures, and (e) loss in model fit.Civil, Architectural, and Environmental Engineerin

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

A Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) Model: Formulation and Application to Non-Worker Activity Time-Use and Timing Behavior on Weekdays

At the time of publication A.R. Pinjari was at the University of South Florida; and C.R. Bhat was at the University of Texas at Austin.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 (2005 and 2008). 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 timeuse 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.Civil, Architectural, and Environmental Engineerin

Pooling Data From Fatality Analysis Reporting System (Fars) And Generalized Estimates System (Ges) To Explore The Continuum Of Injury Severity Spectrum

Fatality Analysis Reporting System (FARS) and Generalized Estimates System (GES) data are most commonly used datasets to examine motor vehicle occupant injury severity in the United States (US). The FARS dataset focuses exclusively on fatal crashes, but provides detailed information on the continuum of fatality (a spectrum ranging from a death occurring within thirty days of the crash up to instantaneous death). While such data is beneficial for understanding fatal crashes, it inherently excludes crashes without fatalities. Hence, the exogenous factors identified as critical in contributing (or reducing) to fatality in the FARS data might possibly offer different effects on non-fatal crash severity levels when a truly random sample of crashes is considered. The GES data fills this gap by compiling data on a sample of roadway crashes involving all possible severity consequences providing a more representative sample of traffic crashes in the US. FARS data provides a continuous timeline of the fatal occurrences from the time to crash - as opposed to considering all fatalities to be the same. This allows an analysis of the survival time of victims before their death. The GES, on the other hand, does not offer such detailed information except identifying who died in the crash. The challenge in obtaining representative estimates for the crash population is the lack of readily available appropriate data that contains information available in both GES and FARS datasets. One way to address this issue is to replace the fatal crashes in the GES data with fatal crashes from FARS data thus augmenting the GES data sample with a very refined categorization of fatal crashes. The sample thus formed, if statistically valid, will provide us with a reasonable representation of the crash population. This paper focuses on developing a framework for pooling of data from FARS and GES data. The validation of the pooled sample against the original GES sample (unpooled sample) is carried out through two methods: (1) univariate sample comparison and (2) econometric model parameter estimate comparison. The validation exercise indicates that parameter estimates obtained using the pooled data model closely resemble the parameter estimates obtained using the unpooled data. After we confirm that the differences in model estimates obtained using the pooled and unpooled data are within an acceptable margin, we also simultaneously examine the whole spectrum of injury severity on an eleven point ordinal severity scale - no injury, minor injury, severe injury, incapacitating injury, and 7 refined categories of fatalities ranging from fatality after 30 days to instant death - using a nationally representative pooled dataset. The model estimates are augmented by conducting elasticity analysis to illustrate the applicability of the proposed framework

Analyzing The Continuum Of Fatal Crashes: A Generalized Ordered Approach

Abstract In the United States, safety researchers have focused on examining fatal crashes (involving at least one fatally injured vehicle occupant) by using Fatality Analysis Reporting System (FARS) dataset. FARS database compiles crashes if at least one person involved in the crash dies within 30 consecutive days from the time of crash along with the exact timeline of the fatal occurrence. Previous studies using FARS dataset offer many useful insights on what factors affect crash related fatality, particularly in the context of fatal vs. non-fatal injury categorization. However, there is one aspect of fatal crashes that has received scarce attention in the traditional safety analysis. The studies that dichotomize crashes into fatal vs. non-fatal groups assume that all fatal crashes in the FARS dataset are similar. Keeping all else same, a fatal crash that results in an immediate fatality is clearly much more severe than another crash that leads to fatality after several days. Our study contributes to continuing research on fatal crashes. Specifically, rather than homogenizing all fatal crashes as the same, our study analyzes the fatal injury from a new perspective by examining fatality as a continuous spectrum based on survival time ranging from dying within 30 days of crash to dying instantly (as reported in the FARS data). The fatality continuum is represented as a discrete ordered dependent variable and analyzed using the mixed generalized ordered logit (MGOL) model. By doing so, we expect to provide a more accurate estimation of critical crash attributes that contribute to death. In modeling the discretized fatality timeline, the Emergency Medical Service (EMS) response time variable is an important determinant. However, it is possible that the EMS response time and fatality timeline are influenced by the same set of observed and unobserved factors, generating endogeneity in the outcome variable of interest. Hence, we propose to estimate a two equation model that comprises of a regression equation for EMS response time and MGOL for fatality continuum with residuals from the EMS model to correct for endogeneity bias on the effect of exogenous factors on the timeline of death. Such research attempts are useful in determining what factors affect the time between crash occurrence and time of death so that safety measures can be implemented to prolong survival. The model estimates are augmented by conducting elasticity analysis to highlight the important factors affecting time-to-death process

Residential Self-Selection Effects in an Activity Time-use Behavior Model

At the time of publication A.R. Pinjari was at the University of South Florida; C.R. Bhat was at the University of Texas at Austin; and D.A. Hensher was at the University of Sydney.This study presents a joint model system of residential location and activity time-use choices that considers a comprehensive set of activity-travel environment (ATE) variables, as well as sociodemographic variables, as determinants of individual weekday activity time-use choices. The model system takes the form of a joint mixed Multinomial Logit -Multiple Discrete- Continuous Extreme Value (MNL/MDCEV) structure that (a) accommodates differential sensitivity to the ATE attributes due to both observed and unobserved individual-related attributes, and (b) controls for the self selection of individuals into neighborhoods due to both observed and unobserved individual-related factors. The joint model system is estimated on a sample of 2793 households and individuals residing in Alameda County in the San Francisco Bay Area. The model results indicate the significant presence of residential self-selection effects due to both observed and unobserved individual-related factors. For instance, individuals from households with more bicycles are associated with a higher preference for out-of-home physically active pure recreational travel pursuits (such as bicycling around in the neighborhood). These same individuals locate into neighborhoods with good bicycling facilities. This leads to a non-causal association between individuals' time investment in out-of-home physically active pure recreational travel and bicycling facilities in their residential neighborhoods. Thus, ignoring the effect of bicycle ownership in the time-use model, would lead to an inflated estimate of the effect of bicycling facility density on the time invested in physically active pure recreational travel. Similarly, there are significant unobserved individual factors that lead to a high preference for physically active recreational activities and also make individuals locate in areas with good bicycling facilities. When such unobserved factors were controlled by the proposed joint residential location and time-use model, the impact of bicycling facility density on out-of-home physically active recreational activities ceased to be statistically significant (from being statistically significant in the independent time-use model). These results highlight the need to control for residential self-selection effects when estimating the effects of the activity-travel environment on activity time-use choices.Civil, Architectural, and Environmental Engineerin