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 Latent-Segmentation Based Approach to Investigating the Spatial Transferability of Activity-Travel Models

At the time of publication C.R Bhat and Z. Wafa were at the University of Texas at Austin, R.M Pendyala at the Georgia Institute of Technology, and V.M. Garikapato at Arizona State University.Spatial transferability of travel demand models has been an issue of considerable interest, particularly for small and medium sized planning areas that often do not have the resources and staff time to collect large scale travel survey data and estimate model components native to the region. With the advent of more sophisticated microsimulation-based activity-travel demand models, the interest in spatial transferability has surged in the recent past as smaller metropolitan planning organizations seek to take advantage of emerging modeling methods within the limited resources they can marshal. Traditional approaches to identifying geographical contexts that may borrow and transfer models between one another involve the exogenous a priori identification of a set of variables or criteria that are used to characterize the similarity between geographic regions. However, this ad hoc procedure presents considerable challenges as it is difficult to identify the most appropriate criteria a priori. To address this issue, this paper proposes a latent segmentation approach whereby the most appropriate criteria for identifying areas with similar profiles are determined endogenously within the model estimation phase. In other words, the relationships embedded in the data set help identify the optimal set of criteria that can be used to cluster regions according to their similarity with respect to activity-travel characteristics of interest. The methodology is demonstrated and its efficacy established through a case study in this paper that utilizes the National Household Travel Survey (NHTS) data set. It is found that the methodology offers a robust mechanism for identifying latent segments and establishing criteria for assessing transferability of models between areas.Civil, Architectural, and Environmental Engineerin

Use of Shared Automated Vehicles for First-Mile Last-Mile Service: Micro-Simulation of Rail-Transit Connections in Austin, Texas

Shared fleets of fully automated vehicles (SAVs) coupled with real-time ride-sharing to and from transit stations are of interest to cities and nations in delivering more sustainable transportation systems. By providing first-mile last-mile (FMLM) connections to key transit stations, SAVs can replace walk-to-transit, drive-to-transit, and drive-only trips. Using the SUMO (Simulation of Urban MObility) toolkit, this paper examines mode splits, wait times, and other system features by micro-simulating two fleets of SAVs providing an FMLM ride-sharing service to 10% of central Austin’s trip-makers near five light-rail transit stations. These trips either start or end within two geofenced areas (called automated mobility districts [AMDs]), and travel time and wait time feedbacks affect mode choices. With rail service headways of 15 min, and 15 SAVs serving FMLM connections to and from each AMD, simulations predict that 3.7% of the person-trip-making will shift from driving alone to transit use in a 3 mi × 6 mi central Austin area. During a 3-h morning peak, 30 SAVs serve about 10 person-trips each (to or from the stations), with 3.4 min average wait time for SAVs, and an average vehicle occupancy of 0.74 persons (per SAV mile-traveled), as a result of empty SAV driving between riders. Sensitivity analysis of transit headways (from 5 to 20 min) and fleet sizes (from 5 to 20 vehicles in each AMD) shows an increase in FMLM mode share with more frequent transit service and larger fleet size, but total travel time served as the biggest determinant in trip-makers’ mode share

Integrating Life-cycle Environmental and Economic Assessment with Transportation and Land Use Planning

The environmental outcomes of urban form changes should couple life-cycle and behavioral assessment methods to better understand urban sustainability policy outcomes. Using Phoenix, Arizona light rail as a case study, an integrated transportation and land use life-cycle assessment (ITLU-LCA) framework is developed to assess the changes to energy consumption and air emissions from transit-oriented neighborhood designs. Residential travel, commercial travel, and building energy use are included and the framework integrates household behavior change assessment to explore the environmental and economic outcomes of policies that affect infrastructure. The results show that upfront environmental and economic investments are needed (through more energy-intense building materials for high-density structures) to produce long run benefits in reduced building energy use and automobile travel. The annualized life-cycle benefits of transit-oriented developments in Phoenix can range from 1.7 to 230 Gg CO₂e depending on the aggressiveness of residential density. Midpoint impact stressors for respiratory effects and photochemical smog formation are also assessed and can be reduced by 1.2–170 Mg PM₁₀e and 41–5200 Mg O₃e annually. These benefits will come at an additional construction cost of up to $410 million resulting in a cost of avoided CO₂e at$16–29 and household cost savings