Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
The authors use deep reinforcement learning with graph neural networks to learn low-level heuristics for an evolutionary algorithm, which improves the algorithm's results on benchmark synthetic cities and obtains state-of-the-art results when optimizing operating costs. The learned heuristics 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.
Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services
Redesigning multimodal transit networks with Shared Autonomous Mobility Services optimizes costs and enhances service.
A Novel Algorithm for Autonomous Transit Network Design
The authors propose a novel algorithm combining neural networks and evolutionary algorithms to optimize autonomous transit network design, outperforming traditional methods.
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