Sebastian Astroza

Joint Analysis of Time Use and Consumer Expenditure Data: Examination of Two Approaches to Deriving Values of Time

Estimating the value of time is of considerable interest to transportation professionals charged with evaluating infrastructure investments. Two approaches used to calculate the value of time are the microeconomic utility theory approach and the structural equations modeling method. In an effort to clarify the interpretation and the relationship between the values of time derived from these two approaches, both models were applied to a synthesized data set of one-person, one-worker households created by merging records from the 2008 American Time Use Survey data set with records from the 2008 Consumer Expenditure Survey data set of the United States. The microeconomic model results show that people in the sample data set work until the marginal utility of work is nearly zero. This finding implies that the value of leisure is nearly equal to the wage rate. Comparisons of model parameter estimates between the microeconomic model and the structural equations model suggest that the models offer vastly different measures of the value of leisure. Although the microeconomic model offers a utilitarian measure suitable for computing user benefits, the structural equations model provides a much smaller value of leisure, implying that it is a measure of the willingness to pay as represented by the average relationship between monetary expenditure and time allocation.

Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies

Considerable interest exists in modeling and forecasting the effects of autonomous vehicles on travel behavior and transportation network performance. In an autonomous vehicle (AV) future, individuals may privately own such vehicles, use mobility-on-demand services provided by transportation network companies that operate shared AV fleets, or adopt a combination of those two options. This paper presents a comprehensive model system of AV adoption and use. A generalized, heterogeneous data model system was estimated with data collected as part of the Puget Sound, Washington, Regional Travel Study. The results showed that lifestyle factors play an important role in shaping AV usage. Younger, urban residents who are more educated and technologically savvy are more likely to be early adopters of AV technologies than are older, suburban and rural individuals, a fact that favors a sharing-based service model over private ownership. Models such as the one presented in this paper can be used to predict the adoption of AV technologies, and such predictions will, in turn, help forecast the effects of AVs under alternative future scenarios.

Revealed Willingness to Pay for Leisure: Link Between Structural and Microeconomic Models of Time Use

In this paper, a new theoretical construct, the revealed willingness to pay for leisure, is presented as a link between two of the most powerful time use models that are aimed at understanding time allocation and valuing time: the microeconomic utility theory approach and the structural equations modeling method. This link is achieved by formulating a structural equations model (SEM) that keeps the generic features of the approach as applied to time use modeling but improves the incorporation of expenses in addition to activities and socioeconomic variables. The new equation on expenses in a leisure activity permits the calculation of a revealed willingness to pay for leisure, which the authors show to differ theoretically from the value of leisure that emerges from a micro-economic formulation (value of time as a resource). Using the same data from Santiago, Chile, the authors estimate both the SEM and the system of equations for working time, for the time allocated to a leisure activity, and for the expenses associated to a leisure activity, by following a micro-economic formulation. The authors confirm the theoretical derivations and conclude that the SEM does not permit the calculation of the full value of leisure and that the microeconomic approach needs an explicit constraint relating goods consumption and time use for an improved estimation of a most important component of the value of travel time savings.

Transportation Planning to Accommodate Needs of Wind Energy Projects

There is an upward trend in wind energy production in Texas and a growing need to transport wind turbine components in that state. This paper proposes a methodology and an associated operational planning tool that can be used to develop optimal route plans for the transportation of wind turbine components on Texas roadways. The paper also recommends transportation infrastructure maintenance and improvement strategies, as well as more general multisector infrastructure improvements that will be needed to respond to the predicted growth of wind energy over time. Specifically, researchers predicted the amount of energy that will be installed in Texas from 2015 to 2025 and used the new planning tool, along with detailed knowledge of the wind energy production industry and the related supply chain, to find optimal routes for wind turbine components (minimizing both potential for road damage and driver delay). A methodology in which the planning tool is used for the analysis of several what-if scenarios is also proposed. Although the planning tool and the associated methodology were developed for Texas, they can be generalized to any other state after the underlying databases are updated.

A Microeconomic Theory–Based Latent Class Multiple Discrete–Continuous Choice Model of Time Use and Goods Consumption

A microeconomic theory–based multiple discrete–continuous choice model was developed to accommodate (a) both time allocation and goods consumption as decision variables in the utility function, (b) both time and money budget constraints governing the activity participation and goods consumption decisions, (c) a finite probability of zero consumption and zero time allocation (i.e., corner solutions), and (d) technical constraints in the form of minimum consumption levels for any good that would be consumed and minimum time allocation for any activity conducted. The proposed model was applied in the form of a latent class model (to consider heterogeneity) on a Dutch data set to understand the determinants of weekly time use and goods consumption behavior.

Analysis of the Impact of Technology Use on Multimodality and Activity Travel Characteristics

Smartphone ownership and use are becoming increasingly prevalent around the world. However, little is known about the impacts of this technology on activity travel choices. The objective of this study was to determine the extent to which smartphone ownership and use influence activity travel demand, after controlling for other lifestyle and demographic attributes. The impacts of smartphone ownership and use on multiple travel dimensions were examined through the specification and estimation of a joint model system that explicitly accounts for self-selection effects arising from lifestyle preferences (such as green lifestyle propensity and technology savviness). Specifically, the impacts of smartphone ownership and use on the following choice dimensions were estimated: (a) use of multiple modes of transportation, (b) pursuit of complex trip chains with a large number of intermediate stops, (c) engagement in tours that have a recreational activity, and (d) participation in joint tours that involve an accompanying person. Travel survey data from the 2014–2015 Puget Sound (Washington) Regional Travel Study were used. The results show substantial and statistically significant effects of smartphone ownership and use on activity travel patterns, even after controlling for lifestyle preferences. Smartphone ownership and use were found to increase the likelihood of using multiple modes of transportation and participating in complex tours, joint tours, and recreational tours.