Patrícia S. Lavieri

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.

A Model of Ridesourcing Demand Generation and Distribution

Ridesourcing has experienced exponential growth in recent years, yet its impact on individual travel are unclear and have not been adequately examined. Recently, an Austin-based ridesourcing company released a large dataset containing disaggregate trip-level information. In this research, we use this new dataset in tandem with several publicly available data sources to estimate two models: a spatially lagged multivariate count model, which is used to describe how many trips are generated in a specific zone on both weekdays and weekend days; and a fractional split model, which helps us identify the characteristics of zones that attract ridesourcing trips. Our results show spatial dependence in ridesourcing trips among proximally located zones, as well as correlation between weekday and weekend day trips originating in a zone. Another interesting finding is the identification of a possible substitution effect between ridesourcing and transit use for weekday trips. Moreover, our results suggest that different income segments in the population may use ridesourcing for different activity purposes. From a travel behavior researcher perspective, the results in this paper identify aggregate area-level variables impacting ridesourcing, which can guide future efforts to better understand the demand for ridesourcing as well as the demand for autonomous and connected vehicles.

Investigation of Heterogeneity in Vehicle Ownership and Usage for the Millennial Generation

This study explored differences in activity travel behavior within the millennial generation to understand better how their choices might shape transportation systems of the future. Through the estimation of a generalized heterogeneous data model on a mobility attitude survey data set targeting millennials, this study investigates heterogeneity among millennials with respect to their driver’s license–holding status, vehicle ownership, and commute mode choice. After self-selection effects are accounted for, age, parenting status, and location of residence have a substantial and statistically significant influence on automobile-oriented mobility choices. Millennials seem to become more automobile-oriented as they age and gain economic resources. Parenthood is associated with an increase in driver’s license holding and personal vehicle ownership; however, in general, it does not seem to have a direct effect on commute mode choice. For all types of millennials, mode choice seems to be strongly correlated with residence location. Thus, the development of a well-connected public transit system and dense, mixed land use are still the key ingredients for reducing the car commute. Planning professionals should explore ways to retain millennials in the city core so that their sustainable patterns of transportation mode use can be preserved.

Introducing latent psychological constructs in injury severity modeling: Multivehicle and multioccupant approach

This paper presents a comprehensive model of injury severity that accounts for unmeasured driver behavior attributes. The results of the model have important implications for the design of safety interventions and advanced vehicular features and technologies. Engineering designs that accommodate the diminished capabilities of older drivers, that include rear-seat safety features, and that alert drivers to potential frontal collisions before they occur (collision warning systems and automated braking systems) would contribute to substantial reductions in injury severity for occupants of vehicles.

Research to Examine Behavioral Responses to Automated Vehicles

This chapter provides a discussion of the important research topics for understanding behavioral responses to highly automated vehicles (AVs) as discussed at a breakout session at the Automated Vehicle Symposium (AVS) 2018. The session, and thus this chapter, highlights the need for valid behavioral data on which to base assumptions, models, forecasts, and impacts to inform AV adoption behaviors, the pathways of AV ownership and use, and the potential impact of AVs on human activity-travel behaviors and longer-term location choices.

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.