March 14, 2024, 4:42 a.m. | Andrew Holliday, Gregory Dudek

cs.LG updates on

arXiv:2403.07917v1 Announce Type: cross
Abstract: 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 …

abstract algorithm arxiv autonomous benefits cs.lg design graph network networks neural net novel optimization planning policy public routes train transit type

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