Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses. We propose a novel algorithm for planning networks of routes for autonomous buses. We first train a graph neural net model as a policy for constructing route networks, and then use the policy as one of several mutation operators in a evolutionary algorithm. We evaluate this algorithm on a standard set of benchmarks for transit network design, and find that it outperforms the learned policy alone by up to 20\% and a plain evolutionary algorithm approach by up to 53\% on realistic benchmark instances.

翻译：公共公交网络规划是一个具有挑战性的优化问题，但对于实现自动驾驶公交车的优势至关重要。我们提出了一种用于规划自动驾驶公交车线路网络的新算法。首先，我们训练一个图神经网络模型，作为构建线路网络的策略，然后将其作为进化算法中多个变异算子之一。我们在交通网络设计的标准基准测试集上评估该算法，发现其在现实基准测试实例上，比仅使用学习策略的方法性能提升高达20%，比纯进化算法方法性能提升高达53%。