
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.


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learning-heuristics-for-transit-network-design-and-improvement-with-deep-reinforcement-learning


Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning


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Redesigning multimodal transit networks with Shared Autonomous Mobility Services optimizes costs and enhances service.


redesigning-large-scale-multimodal-transit-networks-with-shared-autonomous-mobility-services


Redesigning Large-Scale Multimodal Transit Networks with Shared Autonomous Mobility Services



The authors propose a novel algorithm combining neural networks and evolutionary algorithms to optimize autonomous transit network design, outperforming traditional methods.


a-novel-algorithm-for-autonomous-transit-network-design


A Novel Algorithm for Autonomous Transit Network Design
