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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.

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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.

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The rise of ride-hailing services has presented a number of challenges and opportunities in the urban mobility sphere. On the one hand, they allow travelers to summon and pay for a ride through their smartphones while tracking the vehicle’s location. This helps provide mobility for many who are traditionally transportation disadvantaged and not well served by public transit. Given the convenience and pricing of these mobility-on-demand services, their tremendous growth in the past few years is not at all surprising. However, this growth comes with the risk of increased vehicular travel and reduced public transit use, increased congestion, and shifts in mobility patterns which are difficult to predict. Unfortunately, data about ride-hailing service usage are hard to find; service providers typically do not share data and traditional survey data sets include too few trips for these new modes to develop significant behavioral models. As a result, transport planners have been unable to adequately account for these services in their models and forecasting processes. In an effort to better understand the use of these services, this study employs a data fusion process to gain deeper insights about the characteristics of ride-hailing trips and their users. Trip data made publicly available by RideAustin is fused with census and parcel data to infer trip purpose, origin/destination information, and user demographics. The fused data is then used to estimate a model of frequency of ride-hailing trips by multiple purposes.

This paper explores differences in activity-travel behavior within the millennial generation with a view to better understand how their choices might shape transportation systems of the future. Through the estimation of a Generalized Heterogeneous Data Model on a special millennial mobility attitudes survey data set, this study investigates heterogeneity among millennials with respect to their driver's license holding status, vehicle ownership, and commute mode choice. After accounting for self-selection effects, age, parenting status, and location of residence have a substantial and statistically significant influence on auto-oriented mobility choices. Millennials seem to become more auto-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 impact on commute mode choice. For all types of millennials, mode choice seems to be strongly related with residential location. Thus, the development of a well-connected public transit system and dense, mixed land-use are still the key ingredients to reducing car commute. Planning professionals should explore ways to retain millennials in the city core so that their sustainable transportation mode use patterns can be preserved into the future.