Project Summary: We propose to assess the expected efficiency and affordability of automated vehicle (AV) passenger services, such as Waymo and Cruise, in rural and underserved communities.  Our methodology involves applying an innovative model to predict the human-driven ride-hailing demand (Uber and Lyft) for every Census tract in the Southeast, assessing the potential for AVs to reduce the cost of providing equivalent service, and predicting how the demand would change in response to cost reductions.  We have already developed the predictive model, and via this CR2C2 REEG grant, we will quickly scale it up for large scale application and test its spatial transferability against observed data in rural areas. The results will enable transportation policy makers to engage with the AV companies in an informed way as the companies continue their roll-out in the Southeast.