  Autonomous trucks are expected to fundamentally transform the freight
transportation industry. In particular, Autonomous Transfer Hub Networks
(ATHNs), which combine autonomous trucks on middle miles with human-driven
trucks on the first and last miles, are seen as the most likely deployment
pathway for this technology. This paper presents a framework to optimize ATHN
operations and evaluate the benefits of autonomous trucking. By exploiting the
problem structure, this paper introduces a flow-based optimization model for
this purpose that can be solved by blackbox solvers in a matter of hours. The
resulting framework is easy to apply and enables the data-driven analysis of
large-scale systems. The power of this approach is demonstrated on a system
that spans all of the United States over a four-week horizon. The case study
quantifies the potential impact of autonomous trucking and shows that ATHNs can
have significant benefits over traditional transportation networks.
