A Neural-Evolutionary Algorithm for Autonomous Transit Network Design
<|reference_start|>A Neural-Evolutionary Algorithm for Autonomous Transit Network Design: 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.<|reference_end|>
@article{holliday2024a,
    title={A Neural-Evolutionary Algorithm for Autonomous Transit Network Design},
    author={Andrew Holliday, Gregory Dudek},
    journal={arXiv preprint arXiv:2403.07917},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07917},
    primaryClass={cs.NE cs.LG}
}