A Neural-Evolutionary Algorithm for Autonomous Transit Network Design
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 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.
Cite
@article{arxiv.2403.07917,
title = {A Neural-Evolutionary Algorithm for Autonomous Transit Network Design},
author = {Andrew Holliday and Gregory Dudek},
journal= {arXiv preprint arXiv:2403.07917},
year = {2024}
}
Comments
Copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. arXiv admin note: text overlap with arXiv:2306.00720