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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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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.

Microsimulation models that simulate travel demand at the level of individual travelers have been gaining increasing interest among practitioners. Transportation planning agencies across the country are steadily migrating to activity-based microsimulation models which provide considerable flexibility in testing policy scenarios. Generating a synthetic population is the first step in the application of any activity-based model system and hence has been a topic of extensive research in the activity-based modeling arena. Several researchers have developed population synthesizers that are able to generate synthetic populations while matching household-and personlevel constraints at a specified geographical resolution, e.g., census block group. However, information regarding control variables may not always be available at the specified spatial resolution. While information for some control variables may be available at the specified resolution, information on other control variables may be available only at a more aggregate spatial resolution. Ignoring control variables at different levels of spatial resolution could result in the generation of a synthetic population that is not representative of the underlying population. However, there has been limited progress on the development of synthetic population generators that are capable of accommodating control variables at multiple spatial resolutions. This paper proposes a robust approach to control for constraints at multiple geographic resolutions in generating a synthetic population. The methodology is an extension of the Iterative Proportional Updating (IPU) algorithm previously proposed and implemented by the authors. A case study demonstrating the efficacy of the enhanced algorithm is presented.

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.

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.

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.