Traffic speed forecasting is one of the core problems in transportation
systems. For a more accurate prediction, recent studies started using not only
the temporal speed patterns but also the spatial information on the road
network through the graph convolutional networks. Even though the road network
is highly complex due to its non-Euclidean and directional characteristics,
previous approaches mainly focused on modeling the spatial dependencies using
the distance only. In this paper, we identify two essential spatial
dependencies in traffic forecasting in addition to distance, direction and
positional relationship, for designing basic graph elements as the fundamental
building blocks. Using the building blocks, we suggest DDP-GCN (Distance,
Direction, and Positional relationship Graph Convolutional Network) to
incorporate the three spatial relationships into deep neural networks. We
evaluate the proposed model with two large-scale real-world datasets, and find
positive improvements for long-term forecasting in highly complex urban
networks. The improvement can be larger for commute hours, but it can be also
limited for short-term forecasting.

本研究针对交通速度预测问题，通过基于距离、方向和位置关系的图卷积网络构建基本图元素，成功地将空间依赖关系引入深度神经网络， 在高度复杂的城市网络中实现了长期预测并获得了积极的改进。

DDP-GCN：用于时空交通预测的多图卷积网络

DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic  Forecasting

We propose a new network architecture, Gated Attention Networks (GaAN), for
learning on graphs. Unlike the traditional multi-head attention mechanism,
which equally consumes all attention heads, GaAN uses a convolutional
sub-network to control each attention head's importance. We demonstrate the
effectiveness of GaAN on the inductive node classification problem. Moreover,
with GaAN as a building block, we construct the Graph Gated Recurrent Unit
(GGRU) to address the traffic speed forecasting problem. Extensive experiments
on three real-world datasets show that our GaAN framework achieves
state-of-the-art results on both tasks.

该文提出了一种新的网络架构 —— 门控注意力网络（GaAN），用于学习图形。该架构使用卷积子网络来控制每个注意力头的重要性，证明了其在归纳节点分类问题和交通速度预测问题上的有效性，并在三个真实世界数据集上取得了最先进的结果。