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：规划公共交通网络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效益至关重要。我们提出了一种新颖的算法，用于规划自动驾驶公交车的路线网络。我们首先训练一个图神经网络模型作为构建路线网络的策略，然后将该策略作为进化算法中多个变异算子之一使用。我们在公共交通网络设计的一组标准基准测试上评估该算法，发现在现实的基准测试实例中，其性能比单独使用学习策略高出多达20%，比普通进化算法方法高出多达53%。