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Aesthetic considerations of tap water do not necessarily indicate public health threats. If consumers notice the taste or odor of their tap water, however, they are likely to think differently of the water provider. The source of the problem is often nuisance chemicals, and although these chemicals can be measured, their detection is relative and may vary based on a person's sociodemographics. This study aims to identify the sociodemographic and geographic parameters that influence users' recognition of select tap water aesthetic issues. To do so, a bivariate binary probit model is used. Enabling this study is a household survey conducted in 2016 in Austin, Texas. Results indicate that the drivers of noticing odor and taste occur at the individual, household, and regional levels. By identifying factors that influence people's perceptions of aesthetic considerations, water providers may develop outreach campaigns and make decisions that address the groups revealed in this analysis (e.g., users who pay their water bill). Additionally, this study demonstrates how joint modeling can be used to more accurately capture relationships in the water sector. [ABSTRACT FROM AUTHOR]

Abstract: This paper uses revealed preference and stated preference data collected from a web-based commuter survey in Austin, Texas, to estimate a commute mode choice model. This model accommodates weekly and daily commute and midday stop-making behavior, as well as travel time reliability. A mixed logit framework is used in estimation. The results emphasize the effects of commute and midday stop-making on commute mode choice. The results also indicate that travel time reliability is an important variable in commute mode choice decisions. The paper applies the estimated model to predict the potential mode usage of a proposed commuter rail option as well as to examine the impact of highway tolls. More generally, the mode choice model can be used to examine a whole range of travel mode-related policy actions for the Austin metropolitan region. [Copyright &y& Elsevier]

• Investigates the effects of AV availability on multiple activity-travel choices. • Considers built environment, demographics, and psycho-social variables. • Estimates a multivariate ordered-response model system. • Extracts policy insights from the results. This paper develops an analytic system to investigate the effects of AV availability on multiple dimensions of activity-travel behavior at once, based on a direct survey-based modeling approach. In particular, the model uses individual socio-demographics, built environment variables, as well as psycho-social variables (in the form of latent psychological constructs) as determinant variables to explain likely AV impacts on five dimensions of short-term activity-travel choices: (1) Additional local area trips (that is, those that would not characterized as long distance trips; a long distance trip was defined in the survey as a trip more than 75 miles one-way), (2) Trip distance to shop or eat-out activities in the local area, (3) Trip distance to leisure activities in the local area, (4) Additional long distance road trips beyond the local area, and (5) Commute travel time. The model system includes a confirmatory factor analysis step, a multivariate linear regression model for the latent constructs, and a multivariate ordered-response model for the five main outcomes just listed. Data from a 2019 Austin area survey of new mobility service adoption and use forms the basis for our empirical analysis. Our results, when aggregated across all respondents, does suggest that AVs may not after all have a substantial impact on overall trip-making levels, although local area trips are likely to become longer (for all purposes, including the commute). The highest impact of AVs will, it appears, be on the number of long distance trips (with such trips increasing). Our in-depth examination of the variations in AV activity-travel responses across population segments and geographies underscores the importance of modeling multiple activity-travel dimensions all at once. In addition, our results highlight the value of using psycho-social latent constructs in studies related to the adoption/use of current and emerging mobility services, both in terms of improved prediction fit as well as proactive strategies to design equitable, safe, and community-driven AV systems. There is likely to be considerable heterogeneity in how different population groups view and respond to AVs, and it is imperative that AV campaigns and AV design consider such heterogeneity so as to not "leave anyone behind". [ABSTRACT FROM AUTHOR]

• A ranked stated preference question is used to examine AV adoption. • A joint model of ranked, nominal and continuous dependent variables is estimated. • Mediated (through psycho-social variables) demographic effects are considered. • Driving and mobility control are introduced in the mix of psycho-social variables. Understanding the " if " and " when " of autonomous vehicle (AV) adoption is of clear interest to car manufacturers in their positioning of business processes, but also to transportation planners and traffic engineers. In this paper, we examine the individual-level AV adoption and timing process, considering the psycho-social factors of driving control, mobility control, safety concerns, and tech-savviness. A ranked choice stated preference design is used to elicit responses from Austin area residents regarding AV adoption. Our results underscore the need to examine the adoption of technology through a psycho-social lens. In particular, technology developments and design should not be divorced from careful investigations of habits and consumption motivations of different groups of individuals in the population. The findings from our analysis are translated to specific policy actions to promote AV adoption and accelerate the adoption time frame. [ABSTRACT FROM AUTHOR]

• Adoption of travel-based activities in an AV is studied. • Impact of AVs on activity-travel is investigated. • A mixed ranked-ordered response model is formulated. • Policy implications are identified. This paper undertakes a deep dive into the kinds of activities that individuals are likely to pursue when freed from the task of driving in the fully automated vehicle (AV) era. We refer to such activities as travel-based activities (TBAs) and examine the potential effects of TBA participation on activity-based travel (ABT). Two aggregate ABT characteristics are considered: additional local area travel (ALT) and additional long distance travel (ALDT). TBAs and the two ABTs are jointly modeled in a parsimonious fashion using psycho-social latent constructs, individual characteristics, and built environment (BE) attributes. The data used in this study is drawn from a 2019 "emerging mobility" survey administered in the Austin, Texas metropolitan area by the research group. Our study indicates that "productive use of time" is not necessarily always tied with activities such as work and study; rather, being able to partake in relatively chill activities (such as sleeping, relaxing, and gazing out the window) is also considered as good use of time. This suggests caution in the interpretation of what are traditionally referred to as "productive" activities and also a need for scholarly restraint in the use of the label "multitasking" to exclusively refer to non-passive activities. We suggest that the field move away from subjective/ambiguous terms such as multitasking and "productive" activities, and adopt the more neutral label of "travel-based activity". The results also support the notion that the option of opening up travel to pursue work/study activities may itself be engendering stress in individuals; that is, as the option to pursue "non-chill" activities increases in an AV environment, that itself may produce angst in individuals and lead to less enjoyment in travel. This also highlights a need to examine TBAs in the broader context of emotional well-being and quality of life. Indeed, AVs may further erode into our time of tuning-out from the "chatter" of routine life and make it less possible to partake in "calm and mindless" activities. Finally, our study cautions against the use of simple and uniform (across individuals) value of travel time savings (VTTS) factor modifications to study AV impacts on ABT. [ABSTRACT FROM AUTHOR]

• A joint first use/use frequency approach is used to model micromobility. • E-Scooter and bike sharing systems are jointly considered. • Mediated (through psycho-social variables) demographic effects are considered. • Average treatment effects and policy implications are identified. Shared micromobility modes have increasingly penetrated the mobility environment of cities in the U.S. and the world over. At the same time, to best integrate these emerging modes within the fabric of the existing (and larger) transportation ecosystem, it is critical to understand how individuals may respond and "who" the likely users of these relatively new modes may be. In this paper, we develop a model to analyze first-use and use frequency of two micromobility modes: E-scooter sharing systems (ESS) and Bike sharing systems (BSS). The model employs psycho-social constructs, built environment attributes, as well as individual-level demographics as determinants. In doing so, we explicitly recognize the role played by awareness/first-use of new technologies as a cognitive antecedent to subsequent frequency decisions. The main data source for this analysis is drawn from a 2019 survey of Austin, Texas area residents. Our results highlight the importance of considering psycho-social attitudes to both gain better insights into the behavioral process leading up to ESS/BSS adoption/use as well as ensure an accurate data fit. In particular, there are distinctive pathways of adoption/use frequency for each of the ESS and BSS modes, but also complementary processes and behavioral spillover effects at play that warrant a joint modeling of the ESS and BSS modes. Our results suggest that addressing safety concerns of micromobility modes should be the top priority of providers and public agencies. Efforts solely directed toward extoling the "green" virtues of micromobility modes is likely to have limited returns. [ABSTRACT FROM AUTHOR]

• A joint revealed-stated preference model for pooled ride-hailing is estimated. • Pooled ride-hailing awareness is considered as an endogenous variable. • Individual-level and trip-level variables are used in the model. • An approach is developed to anchor joint model to revealed choice. • Implications for policy are derived from the model results. Pooled mobility services hold substantial promise as a means to provide better accessibility to those who may find it difficult to drive themselves, while also promoting sustainable transportation efforts. In this paper, we develop a joint revealed preference-stated preference model for the choice between pooled versus private ride-hailing that (a) accommodates a suite of individual-level socio-demographics, individual-level psycho-social attributes, built environment variables, and trip-level variables, and (b) explicitly recognizes the importance of considering familiarity with pooled ride-hailing (RH) as an integral element of the pooled RH choice process. The primary source of data for the analysis is drawn from a 2019 survey of Austin, Texas residents. Our results underscore the value of using psycho-social latent constructs in the adoption of current and emerging mobility services, both in terms of improved prediction fit as well as in terms of designing proactive strategies to promote pooled RH service adoption. Women, older adults, and non-Hispanic/non-Latino Whites have a low propensity to choose the pooled RH mode, while employed individuals, highly educated individuals, and those living in high density urban areas have a high propensity. Overall, the average VTT estimate is $27.80 per hour for commute travel, $19.40 per hour for shopping travel, and $10.70 per hour for leisure travel, while the willingness to pool (that is, the willingness to pay to not pool a ride or WTS) averages about 62 cents for commute travel, $1.70 for shopping travel and $1.32 for leisure travel. These estimates can be used by TNCs and cities to consider new integrated pooled RH-fixed transit service designs, position traffic congestion alleviation strategies and new mobility services, and customize information campaigns to promote pooled RH mode use. [ABSTRACT FROM AUTHOR]