Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19-64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.

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Fully driverless automated vehicles (AVs) could considerably alter the proximity value of parking, due to an AV's ability to drop passengers off at their destination, search for cheaper parking, and return to pick up their occupants when needed. This study estimates the potential impact of privately-owned driverless vehicles on vehicle miles traveled (VMT), energy use, emissions, parking revenue, and daily parking cost savings in the city of Seattle, Washington from changes in parking decisions using an agent-based simulation model. Each AV is assumed to consider the cost to drive to each parking spot, the associated daily parking cost, and the parking availability at each location, and the AV ranks each choice in terms of economic cost. The simulation results indicate at the low penetration rates (5 to 25 percent AV penetration) AVs in downtown Seattle would travel an additional 3.5-4.0 miles per day on average, and high penetration rates (50 to 100 percent AV penetration) would travel an additional 5.6-8.4 miles per day on average. The results also suggest that as AV penetration rates increase, parking lot revenues decrease significantly and could likely decline to the point where operating a lot is unsustainable economically, if no parking demand management policies are implemented. This could lead to changes in land use as the amount of parking needed in urban areas is reduced and cars move away from the downtown area for cheaper parking. This analysis provides an illustration of the first-order effects of AVs on the built environment and could help inform near and long-term policy and infrastructure decisions during the transition to automation.