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

원문 초록

Planning a network of public transit routes is a challenging optimization problem. Metaheuristic algorithms search through the space of possible transit networks by applying heuristics that randomly alter routes in a network. The design of these heuristics has a major impact on the quality of the result. In this paper, we use deep reinforcement learning to train a graph neural net to provide heuristics for an evolutionary algorithm. These neural heuristics improve the algorithm's results on benchmark synthetic cities with 70 nodes or more, and achieve new state-of-the-art results on the challenging Mumford benchmark. They also improve upon a simulation of the real transit network in the city of Laval, Canada, by 52% and 25% on two key metrics, and offer cost savings of up to 19% over the city's existing transit network.