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Energy Implications of Current Travel and the Adoption of Automated Vehicles
Current travel patterns and energy usage could be dramatically disrupted by new vehicle technologies, specifically in the case of automated vehicle (AV) technology. AV development is rapidly progressing and some experts anticipate it will be widely available to the public within 10 to 30 years (Litman and Litman 2018; Bansal and Kockelman 2017). This transformation in transportation could have a significant impact on the way people travel and potentially on overall energy consumption. Previous studies estimated that the use of AVs could decrease energy consumption by up to 60%, or alternatively increase energy consumption up to 200%, depending on how and where they are used (T.S. Stephens 2016).
AV adoption could have a wide range of potential energy implications, depending on their usage and the efficiency of the AVs. To better understand these implications, a random survey of more than 1,000 adults living in the continental United States was conducted for the National Renewable Energy Laboratory (NREL) using the Opinion Research Corporation (ORC) survey methodology (See A.1). While other survey efforts at the time of the study broadly investigated the barriers to acceptance of AVs, this study uniquely investigated how the technology will affect driving and commuting habits. The results were analyzed to better understand which groups of people will likely adopt AV technology first, how respondents currently travel, and how respondents may change their travel patterns if AVs are widely adopted. It is important to acknowledge that a person’s stated preference in an interview about a hypothetical setting often does not match their revealed preference, which is demonstrated in an actual decision-making situation (Keane and Wasi 2013). However, due to the early stage of AV technology, there is limited opportunity to research the revealed preference. Findings from this study are intended to provide an additional resource for model projections used by researchers to understand how transportation innovations may affect travel behaviors in the coming decades.