  Planning a public transit network is a challenging optimization problem, but
essential in order to realize the benefits of autonomous buses. We propose a
novel algorithm for planning networks of routes for autonomous buses. We first
train a graph neural net model as a policy for constructing route networks, and
then use the policy as one of several mutation operators in a evolutionary
algorithm. We evaluate this algorithm on a standard set of benchmarks for
transit network design, and find that it outperforms the learned policy alone
by up to 20% and a plain evolutionary algorithm approach by up to 53% on
realistic benchmark instances.
