Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
<|reference_start|>Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning: Transit agencies world-wide face tightening budgets. To maintain quality of service while cutting costs, efficient transit network design is essential. But planning a network of public transit routes is a challenging optimization problem. The most successful approaches to date use metaheuristic algorithms to search through the space of possible transit networks by applying low-level heuristics that randomly alter routes in a network. The design of these low-level heuristics has a major impact on the quality of the result. In this paper we use deep reinforcement learning with graph neural nets to learn low-level heuristics for an evolutionary algorithm, instead of designing them manually. These learned heuristics improve the algorithm's results on benchmark synthetic cities with 70 nodes or more, and obtain state-of-the-art results when optimizing operating costs. They also improve upon a simulation of the real transit network in the city of Laval, Canada, by as much as 54% and 18% on two key metrics, and offer cost savings of up to 12% over the city's existing transit network.<|reference_end|>
@article{holliday2024learning,
    title={Learning Heuristics for Transit Network Design and Improvement with Deep
  Reinforcement Learning},
    author={Andrew Holliday, Ahmed El-Geneidy, Gregory Dudek},
    journal={arXiv preprint arXiv:2404.05894},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05894},
    primaryClass={cs.LG cs.AI cs.NE}
}