论文标题
关系融合网络：道路网络的图形卷积网络
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
论文作者
论文摘要
机器学习技术在道路网络设置中的应用有可能促进许多重要的智能运输应用。图形卷积网络（GCN）是能够利用网络结构的神经网络。但是，GCN的许多隐性假设不适用于道路网络。我们介绍了关系融合网络（RFN），这是一种专门为道路网络设计的新型GCN。特别是，我们提出的方法在道路网络中的两个机器学习任务上胜过最先进的GCN 21％-40％。此外，我们表明，最先进的GCN可能无法有效利用道路网络结构，并且可能无法很好地推广到其他道路网络。
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks. We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road networks. In particular, we propose methods that outperform state-of-the-art GCNs by 21%-40% on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs may fail to effectively leverage road network structure and may not generalize well to other road networks.
