The WholeTraveler Transportation Behavior Study explores the energy implications of behavioral factors associated with the adoption and use of emerging transportation technologies and services, including connected and automated vehicles, mobility-on-demand, electric vehicles, and e-commerce.

Berkeley Lab researchers analyze behavioral factors, including underlying barriers to adoption, drivers of adoption, and use of these emerging technologies and more traditional transportation modes — such as personal vehicles, public transit, and walking/biking — informed by the travelers’ life contexts.

The project uses an innovative, regionally focused survey designed to reveal the relationships between pivotal population characteristics, attitudes, and preferences. The survey data, available through the Livewire Data Platform (https://livewire.energy.gov/), will indicate how people will likely use those technologies and services, the resulting impacts on the transportation system, and implications for energy. The WholeTraveler team tackled several research topics.

Life-Course Patterns and Mobility Choices

This project considers how individuals’ phases in life, and the patterns in which they progress through those phases, impact their mobility behaviors and decisions. Researchers identified focused on these key phases: being in school, being employed, living with a partner, and having children. The study applied machine learning techniques to classify survey respondents into five patterns characterizing how they progress through these key phases.

These patterns were related to their concurrent mobility choices to generate insights regarding the underlying forces influencing their mobility behaviors. The study found that the timing and order of key life events can impact mobility choices and that events happening relatively earlier in life have more significant effects. For example, the study found that having children can drive a faster transition to more vehicle-dependence for some people, but not necessarily for others.

The following set of publications describe results from this research effort:

• Jin, Ling, Alina Lazar, James Sears, Annika Todd, Alex Sim, Kesheng Wu, Hung-Chia Yang, and C. Anna Spurlock. "Clustering Life Course to Understand the Heterogeneous Effects of Life Events, Gender, and Generation on Habitual Travel Modes." IEEE Access (2020) 1-17. eta.lbl.gov/publications/clustering-life-course-understand
• Lazar, Alina, Alexandra Ballow, Ling Jin, C. Anna Spurlock, Alex Sim, and Kesheng Wu. "Machine Learning for Prediction of Mid to Long Term Habitual Transportation Mode Use." 2019 IEEE International Conference on Big Data (Big Data)2019 IEEE International Conference on Big Data (Big Data). Los Angeles, CA, USA: IEEE, 2019.  eta.lbl.gov/publications/machine-learning-prediction-mid-long

The following set of publications describe work on the methodology used in this research effort:

• Lazar, Alina, Ling Jin, C. Anna Spurlock, Kesheng Wu, Alex Sim, and Annika Todd. "Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization." Journal of Data and Information Quality 11.2 (2019) 1 - 22. eta.lbl.gov/publications/evaluating-effects-missing-values
• Lazar, Alina, Ling Jin, C. Anna Spurlock, Annika Todd, Kesheng Wu, and Alex Sim. "Data quality challenges with missing values and mixed types in joint sequence analysis." 2017 IEEE International Conference on Big Data (Big Data)2017 IEEE International Conference on Big Data (Big Data). Boston, MA, USA: IEEE, 2017. eta.lbl.gov/publications/data-quality-challenges-missing

Describing the Users: Understanding Adoption of and Interest in Shared, Electrified, and Automated Transportation 

Another study identified traits that indicate likely adoption of technology. Berkeley Lab researchers correlated individual and household characteristics with interest in and adoption of multiple emerging technologies and services.

They leveraged data from a novel survey of San Francisco Bay Area residents to analyze adoption patterns for shared mobility, electrified vehicle technologies, and vehicle automation. They found that ride-hailing and adaptive cruise control have penetrated the market more extensively than have electrified vehicles or carsharing services. Over half of respondents have adopted or expressed interest in adopting all levels of vehicle automation. Overall, there is substantial potential for market growth for the technologies and services they analyzed.

Using county fixed effects regressions, they investigated which individual and location-level factors correlate to adoption and interest. They found that, although higher-income people are disproportionately represented among current adopters of most new technologies and services, low- to middle-income people are just as likely to have adopted pooled ride-hailing.

Younger generations have high interest in automated and electrified vehicles relative to their current adoption of these technologies, suggesting that young people could contribute substantially to future market growth — as they are doing for ride-hailing. They found no evidence that longer commutes present a barrier to plug-in electric vehicle adoption.

Finally, women are less likely than men to adopt and/or be interested in adopting most new transportation technologies, with the exception of ride-hailing. There was a significant gender gap in adopting plug-in electric vehicles. A mediation analysis demonstrated that over 20% of the gender gap could be explained by women’s mobility needs and the lower household income of female survey respondents compared to male participants. Specifically, the study found women need to make multiple stops, need more cargo and seating space, and need to transport children. Designing or marketing technologies with women’s preferences in mind could contribute to future market expansion.

See a preprint of the journal article that summarizes this research,  "Describing the users: Understanding adoption of and interest in shared, electrified, and automated transportation in the San Francisco Bay Area." Transportation Research Part D: Transport and Environment 71 (2019) 283-301.

Impact of Home Delivery on Shopping Trips

Berkeley Lab researchers conducted an in-depth analysis of e-commerce behavior and the degree to which receiving home delivery of items replaced or added to household shopping trips. The study, based on survey data collected in 2018, found a given delivery was 1.7 times as likely to replace a shopping trip as not, and 1.3 times as likely to replace a vehicle shopping trip than add on to existing trips.

For more information, please see the following journal article: "Children, Income and the Impact of Home Delivery on Household Shopping Trips."

The WholeTraveler Transportation Behavior Study is a pivotal project of the Mobility Decision Science pillar of the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium, funded by the Energy Efficient Mobility Systems (EEMS) program of the U.S. Department of Energy (DOE), Vehicle Technologies Office.

Impacts on Energy Use

Berkeley Lab researchers analyzed detailed data from a ride-sourcing or Transportation Network Company (TNC) to determine the energy use of these services. The RideAustin program in Austin, Texas provided detailed data on approximately 1.5 million individual rides. The study quantified the “deadheading” miles driven by TNC drivers: before beginning and after ending their shifts, reaching a passenger once a ride has been requested, and between consecutive rides. It also compared the fuel efficiency of the RideAustin drivers’ vehicles with the average vehicle registered in Austin.

The analysis found that TNC driver commutes to and from their service areas accounted for 19% of total ride-sourcing vehicle miles traveled (VMT). TNC drivers drove 55% more miles between ride requests within 60 minutes of each other, accounting for 25% of total ride-sourcing VMT. Vehicles used for ride-sourcing are, on average, 3.2 miles per gallon more fuel-efficient than comparable light-duty vehicles registered in Austin, with twice as many hybrid-electric vehicles.

The net effect of ride-sourcing on energy use is a 41% to 90% increase compared to baseline pre-TNC personal travel.

To read the paper published on this study, see: "Travel and energy implications of ride-sourcing service in Austin, Texas."

Impact of TNCs on Vehicle Ownership in U.S. Cities

Berkeley Lab researchers estimated the effects of on-demand ride-sourcing services such as Uber and Lyft on vehicle ownership and transit outcomes using a set of difference-in-difference propensity score-weighted regression models that exploit staggered market entry across the U.S. from 2010 to 2017. TNC entry causes a 0.7% increase in vehicle registrations on average, with the effect ranging from a 10% decrease to a 15% increase in specific cities. TNC entry appears to increase vehicle ownership in urban areas with fewer vehicle registrations and slower growth rates. At the same time, TNC entry also appears to increase average vehicle fuel economy in slower-growth areas. Researchers conclude that, while these services induce some households to dispose of their current vehicles, on average, this reduction in vehicle ownership is overwhelmed by other households purchasing new vehicles to drive for the services.

To read the paper published on this study, see: "The Impact of Uber and Lyft on Vehicle Ownership, Fuel Economy, and Transit Across U.S. Cities."

Impact of TNCs on Vehicle Ownership and Use in Texas Cities

In another study, the ongoing analysis of a unique dataset of annual miles of travel of individual vehicles over many years in Texas will provide further insights. Researchers will learn whether providing TNC services has led not only to changes in vehicle registrations, but also average and total miles driven and average vehicle fuel economy in different service areas throughout the state.

Researchers from Berkeley Lab and the Transportation Sustainability Research Center collaborated on a project analyzing the spatial allocation of impacts from a one-way carsharing service provided by car2go. The analysis focused on changes in travel mode, the shedding of existing personal vehicles, and suppressing the purchase of a new personal vehicle. The analysis was based on 10,000 respondents to a survey in five North American cities: San Diego, Seattle, Washington D.C., Calgary, and Vancouver.

To read a press release about the study, visit "UC Berkeley Study Illustrates car2go's Growing One-Way Carsharing Service Helps Cut Traffic, Improves Urban Mobility."

TSRC was formed to study the economic, social, environmental, and technological aspects of sustainable transportation. It is housed at the Institute of Transportation Studies at the University of California, Berkeley.

TSRC does extensive research on the intersection of transportation and other key infrastructure systems. This includes utility electrical grids and intelligent transportation systems (ITS), such as eco-driving, real-time traveler and refueling information, the future of ITS (20 to 40 years), and augmented speed enforcement.

The SMART Mobility Consortium is a project of the DOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office under their Energy Efficient Mobility Systems (EEMS) Program.

This multi-year, multi-laboratory collaborative is dedicated to further understanding the energy implications and opportunities of advanced mobility solutions. Berkeley Lab researchers join colleagues from the Argonne, Idaho, and Oak Ridge national labs, and from the National Renewable Energy Lab (NREL).

EEMS work operates in a continuous feedback loop between research and development (R&D), analysis and modeling, and real-world living labs. R&D activities are focused on scalable smart mobility projects that identify system-level opportunities to significantly increase the energy efficiency of the movement of people and goods. The consortium aims to deliver new EEMS data, analysis, and modeling tools, and create new knowledge to support smarter mobility systems. Living labs projects demonstrate and assess the return on investment of mobility systems that reduce energy consumption while delivering the benefits of new mobility technology and provide critical real-world data to inform EEMS R&D efforts. EEMS also coordinates with other programs within the Department of Energy such as Clean Cities, and Advanced Research Projects Agency–Energy (ARPA-E), as well as the Department of Transportation, and the Department of Commerce.

BEAM extends the Multi-Agent Transportation Simulation Framework (MATSim)
to enable powerful and scalable analysis of urban transportation systems.

BEAM is maintained by Lawrence Berkeley National Laboratory and is a U.S. Department of Energy Core Capability.