Thinking Local About Self-Driving Cars: a Local Framework for Autonomous Vehicle Development in the United States

Thesis (Master's)--University of Washington, 2016-06

Once a feature from science-fiction movies and books, self-driving cars are now a reality on public roads throughout the United States. I argue that until extensive data and research on self-driving cars is made available to the public, a flexible, place-based framework should drive local development of autonomous vehicles. Through existing literature, I highlight how autonomous vehicles will create different benefits and costs in safety, energy use/emissions, employment, congestion, and the built environment. However, variation in spatial patterns will lead to different outcomes with self-driving cars across urban, suburban, and rural areas in the United States. I created a flexible local policy framework to analyze case studies in King County, Washington through demographic, geographic, and transportation data. These case studies are representative of urban, suburban, and rural areas throughout the county. Furthermore, I conclude that spatial variability in each community will influence how policy and planning shape the path for autonomous vehicle development. Through analyzing the fundamental differences between demographics, geography, and transportation behaviors in each study area, I conclude that local policymakers and planners should account for spatial variability when crafting tools to manage autonomous vehicle development in each neighborhood.