Consumer Preferences and Willingness to Pay for Advanced Vehicle Technology Options and Fuel Types

At the time of publication J. Shin and C.R. Bhat were at the University of Texas at Ausitn. V.M. Garikapati and D. You at Arizona State University, and R.M. Pendyala at Georgia Institute of Technology.The automotive industry is witnessing a revolution with the advent of advanced vehicular technologies, smart vehicle options, and fuel alternatives. However, there is very limited research on consumer preferences for these types of vehicles. But the deployment and penetration of advanced vehicular technologies in the marketplace, and planning for possible market adoption scenarios, calls for collection and analysis of consumer preference data related to these emerging technologies. This study aims to address this gap, offering a detailed analysis of consumer preference for alternative fuel types and technology options using data collected in choice experiments conducted on a sample of consumers in South Korea. The results indicate that there is considerable heterogeneity in consumer preferences for various smart technology options such as wireless internet, vehicle connectivity, and voice command features, but relatively little heterogeneity in the preference for smart vehicle applications such as real-time traveler information on parking and traffic conditions.Civil, Architectural, and Environmental Engineerin

Understanding the Multiple Dimensions of Residential Choice

At the time of publication, X. Fu was at the Shanghai Jiao Tong University, C.R. Bhat at the University of Texas at Austin, R.M. Pendyala at Georgia Institute of Technology, S. Vladlamani and V.M Garikapati at Arizona State University.Residential choice may be characterized as a household’s simultaneous decisions of location, neighborhood, and dwelling. Traditional models do not account for the latent unmeasured constructs which capture individuals’ preferences for and attitudes towards residence and mode choice. This paper employs Bhat’s (2014) Generalized Heterogeneous Data Model (GHMD) to accommodate five inter-related residential choice dimensions, including residential location, neighborhood land-use pattern, public transportation availability, housing type, and dwelling ownership. Four latent variables including pro-driving, pro-public transportation, facility availability, and residential spaciousness are constructed to capture individuals’ attitudes towards travel modes and preferences for residential features. The inclusion of these latent constructs helps account for self-selection effects in residential choice processes. The determination of relationships among multiple dimensions of residential choice behavior, socio-demographics, and latent attitudes and preferences is critical to integrated land use – transport modeling and the formulation of policies as well as urban residential and neighborhood environments that cater to individual preferences and enhance quality of life.Civil, Architectural, and Environmental Engineerin

Activity patterns, time use, and travel of millennials: a generation in transition?

Millennials, defined in this study as those born between 1979 and 2000, became the largest population segment in the United States in 2015. Compared to recent previous generations, they have been found to travel less, own fewer cars, have lower driver’s licensure rates, and use alternative modes more. But to what extent will these differences in behaviour persist as millennials move through various phases of the lifecycle? To address this question, this paper presents the results of a longitudinal analysis of the 2003--2013 American Time Use Survey data series. In early adulthood, younger millennials (born 1988--1994) are found to spend significantly more time in-home than older millennials (born 1979--1985), which indicates that there are substantial differences in activity-time use patterns across generations in early adulthood. Older millennials are, however, showing activity-time use patterns similar to their prior generation counterparts as they age, although some differences -- particularly in time spent as a car driver -- persist. Millennials appear to exhibit a lag in adopting the activity patterns of predecessor generations due to delayed lifecycle milestones (e.g. completing their education, getting jobs, marrying, and having children) and lingering effects of the economic recession, suggesting that travel demand will resume growth in the future

An Integrated Latent Construct Modeling Framework for Predicting Physical Activity Engagement and Health Outcomes

At the time of publication M.M Hoklas, S.K. Dubey, and C.R. Bhat were at the University of Texas at Austin. V.M Garikapati at Arizona State University, R.M. Pendyala at Georgia Institute of Technology, and D. Hyun You at Arizona State University.The health and well-being of individuals is related to their activity-travel patterns. Individuals who undertake physically active episodes such as walking and bicycling are likely to have improved health outcomes compared to individuals with sedentary auto-centric lifestyles. Activity-based travel demand models are able to predict activity-travel patterns of individuals at a high degree of fidelity, thus providing rich information for transportation and public health professionals to infer health outcomes that may be experienced by individuals in various geographic and demographic market segments. However, models of activity-travel demand do not account for the attitudinal factors and lifestyle preferences that affect activity-travel and mode use patterns. Such attitude and preference variables are virtually never collected explicitly in travel surveys, rendering it difficult to include them in model specifications. This paper applies Bhat’s (2014) Generalized Heterogeneous Data Model (GHDM) approach, whereby latent constructs representing the degree to which individuals are health conscious and inclined to pursue physical activities may be modeled as a function of observed socio-economic and demographic variables and then included as explanatory factors in models of activity-travel outcomes and walk and bicycle use. The model system is estimated on the 2005-2006 National Health and Nutrition Examination Survey (NHANES) sample, demonstrating the efficacy of the approach and the importance of including such latent constructs in model specifications that purport to forecast activity and time use patterns.Civil, Architectural, and Environmental Engineerin

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

You're viewing a past Journal from the Good Systems Grand Challenge team at The University of Texas at Austin from February 2020.Office of the VP for Researc

How Will Use of Autonomous Vehicles for Running Errands Affect Future Autonomous Vehicle Adoption and Ownership?

69A3551747116Transportation is experiencing disruptive forces in recent years. One key disruption is the development of autonomous vehicles (AVs) that will be capable of navigating roadways on their own without the need for human presence in the vehicle. In a utopian scenario, AVs may enter the transportation landscape and foster a more sustainable and livable ecosystem with shared automated electric vehicles (SAEV) serving mobility needs and eliminating the need for private ownership. In a more dystopian scenario, AVs would be personally owned by households \u2013 enabling people to live farther away from destinations, inducing additional travel, and roaming roadways with zero occupants. Concerned with the potential deleterious effects of having personal AVs running errands autonomously, this report aims to shed light on the level of interest in sending AVs to run errands and how that variable affects the intent to own an AV. Using data from a survey conducted in 2019 in four automobile-oriented metropolitan regions in the United States, the relationship is explored through a joint model system estimated using the Generalized Heterogeneous Data Model (GHDM) methodology. Results show that, even after accounting for socio-economic and demographic variables as well as latent attitudinal constructs, the level of interest in having AVs run errands has a positive and significant effect on AV ownership intent. The findings point to the need for policies that would steer the entry and use of AVs in the marketplace in ways that avoid a dystopian future

Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit

The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of a new normal for the future of transit. This paper addresses this challenge using a novel panel survey data set collected from a representative sample of individuals across the United States. The study involved the estimation of a panel multinomial probit model of mode choice to capture both socio-economic effects and period (pre-, during-, and post-COVID) effects that contribute to changes in mode choice. This paper provides rich insights into the evolution of commute mode use as a result of the pandemic, with a particular focus on public transit. Through a rigorous modeling approach, this paper provides a deep understanding of how transit use has evolved, how it is likely to evolve into the future, and the socio-economic and demographic characteristics that affect the evolution (and expected future use) of public transit. Results suggest that transit patronage is likely to remain depressed by about 30% for the foreseeable future, in the absence of substantial changes in service configurations. This study also shows that minority groups and those living in higher density regions are more likely to exhibit a return to transit use in the post-pandemic period

Attitudes and Behaviors Causal Relationships: Uncovering Latent Segments Within a Heterogeneous Population

69A3551747116This project aimed at unraveling the contemporaneous relationship that exists between attitudes and choice behaviors. Attitudes, perceptions, and preferences may shape behaviors; likewise, behavioral choices exercised by individuals may offer experiences that shape attitudes. While it is likely that these relationships play out over time, the question of whether attitudes affect behaviors or behaviors affect attitudes at a specific cross-section in time remains unanswered and a fruitful area of inquiry. Various studies in the literature have explored this question, but have done so without explicitly recognizing the heterogeneity that may exist in the population. In other words, the causal structure at play at any point in time may differ across individuals, thus motivating the development of an approach that can account for the presence of multiple segments in the population, each following a different causal structure. Results suggest that there is considerable heterogeneity in the population with the contemporaneous causal structures in which behaviors shape attitudes more prevalent than those in which attitudes affect choice behaviors. These findings have important implications for transport modeling and policy development

The Influence of Mode Use on Level of Satisfaction with Daily Travel Routine: A Focus on Automobile Driving in the United States

69A3551747116How does the extent of automobile use affect the level of satisfaction that people derive from their daily travel routine, after controlling for many other attributes including socio-economic and demographic characteristics, attitudinal factors, and lifestyle proclivities and preferences? This is the research question addressed by this study. In this study, data collected from four automobile-dominated metropolitan regions in the United States (Phoenix, Austin, Atlanta, and Tampa) are used to assess the impact of the amount of driving that individuals undertake on the level of satisfaction that they derive from their daily travel routine. This research effort recognizes the presence of endogeneity when modeling multiple behavioral phenomena of interest and the role that latent attitudinal constructs reflecting lifestyle preferences play in shaping the association between behavioral mobility choices and degree of satisfaction. The model is estimated using the generalized heterogeneous data model (GHDM) methodology. Results show that latent attitudinal factors representing an environmentally friendly lifestyle, a proclivity toward car ownership and driving, and a desire to live close to transit and in diverse land use patterns affect the relative frequency of auto-driving mode use for non-commute trips and level of satisfaction with daily travel routine. Additionally, the amount of driving positively affects satisfaction with daily travel routine, implying that bringing about mode shifts toward more sustainable alternatives remains a formidable challenge\u2014particularly in automobile-centric contexts

Practical Measures for Advancing Public Transit Equity and Access

In partnership with the Federal Transit Administration, researchers at the University of Texas at Austin, Arizona State University, and Dunbar Transportation Consulting, LLC, identified a set of replicable measures that public transportation providers and their partners can use to advance equity for those who have been historically underserved, marginalized, and adversely affected by persistent poverty and/or inequality. These include practical strategies such as advisory committees and intergovernmental partnerships as well as analytical techniques that quantify how public transit links people to opportunities