Transit agencies world-wide face tightening budgets and declining ridership. 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 achieve new state-of-the-art results the challenging Mumford benchmark. They also improve upon a simulation of the real transit network in the city of Laval, Canada, by as much as 52% and 25% on two key metrics, and offer cost savings of up to 19% over the city's existing transit network.
View on arXiv@article{holliday2025_2404.05894,
title={ Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning },
author={ Andrew Holliday and Ahmed El-Geneidy and Gregory Dudek },
journal={arXiv preprint arXiv:2404.05894},
year={ 2025 }
}