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Welcome to the website for the WholeTraveler Transportation Behavior Study. On this website you can see background information on the study, download a copy of the participant Consent Form for your records, find contact information, and find links to the data and results from the study.

This project is funded by the Department of Energy (DOE) Vehicle Technologies Office (VTO). It is a part of the SMART Mobility Initiative. In particular this research is a three-year project that is a part of the Mobility Decision Science Pillar of the SMART Mobility Initiative.

As part of this initiative an LBNL-lead research team conducted a survey on transportation behaviors, attitudes, and preferences of San Francisco Bay Area residents. The goal of our research is to uncover a comprehensive understanding of travel choice patterns, preferences, and decision-making processes over time and across different time scales. In addition we are interested in how these patterns interrelate with other personality characteristics or circumstantial constraints.

Our results provide insights that can help more accurately predict short- and long-term future shifts in travel patterns in the face of technological innovations and scaling up of recent transportation mega-trends, like electric vehicles, ride-sharing services, ride-hailing apps (like Uber and Lyft), and connected and automated vehicles. These more accurate predictions can inform policy decision-makers and support an efficient transportation system. The knowledge gained can inform decision-makers designing technological, policy and incentive-programmatic pathways to an efficient, petroleum-independent, and energy-secure transportation future.

Research Questions:

Specifically, our research questions focused on the following four areas:

  1. What is the full geo-spatial and temporal picture of transportation mode use patterns in the San Francisco Bay Area, including underlying barriers and drivers of energy efficient choices?

  2. What are the barriers to, as well as drivers and motivations for, adoption and use of alternative transportation options, such as conventional vehicles, public transit options, Uber/Lyft, electric vehicles, and automated driving technologies (e.g., intergenerational impacts, peer effects, personality characteristics, lifecycle phases, choice to have children, commute characteristics)?

  3. What is the relationship between dynamic lifecycle trajectory patterns (e.g., timing of education, residential relocation, employment relocation, having children) and choices surrounding transportation options?

  4. What are the patterns of use, preferences around, and transportation system efficiency implications of e-commerce and associated home-delivery?

  5. Can information regarding all of the above help to improve the prediction accuracy or flexibility of large-scale transportation system simulations?

The overall goal of this study is to improve our understanding of the behavioral drivers underlying transportation choices. This can improve our understanding of how those choices are likely to change in the face of new circumstances, options, and technologies. The objective is to improve our ability to predict impacts of such shifts on the transportation system, and to provide relevant regional decision-makers with information useful in designing the most effective plans and policies to improve transportation system efficiency, and thereby improve U.S. energy security.