论文标题
自适应图形时空变压器网络，用于交通流量预测
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
论文作者
论文摘要
图表上的交通流量预测在许多字段中都具有现实世界应用，例如运输系统和计算机网络。由于复杂的时空相关性和非线性流量模式，交通预测可能会极具挑战性。现有的作品主要是通过分别考虑空间相关性和时间相关性来对这种时空依赖性进行建模，并且无法对直接的空间相关性进行建模。受到图形域中变形金刚最近成功的启发，在本文中，我们建议使用局部多头自我攻击直接建模在时空图上的跨空间相关性。为了降低时间复杂性，我们将注意力接收场设置为空间相邻的节点，还引入了自适应图以捕获隐藏的时空依赖性。基于这些注意机制，我们提出了一种新型的自适应图时空变压器网络（ASTTN），该网络堆叠了多个时空的注意层以在输入图上应用自我注意力，然后是线性层进行预测。公共交通网络数据集，Metr-La PEMS-Bay，PEMSD4和PEMSD7的实验结果证明了我们的模型的出色性能。
Traffic flow forecasting on graphs has real-world applications in many fields, such as transportation system and computer networks. Traffic forecasting can be highly challenging due to complex spatial-temporal correlations and non-linear traffic patterns. Existing works mostly model such spatial-temporal dependencies by considering spatial correlations and temporal correlations separately and fail to model the direct spatial-temporal correlations. Inspired by the recent success of transformers in the graph domain, in this paper, we propose to directly model the cross-spatial-temporal correlations on the spatial-temporal graph using local multi-head self-attentions. To reduce the time complexity, we set the attention receptive field to the spatially neighboring nodes, and we also introduce an adaptive graph to capture the hidden spatial-temporal dependencies. Based on these attention mechanisms, we propose a novel Adaptive Graph Spatial-Temporal Transformer Network (ASTTN), which stacks multiple spatial-temporal attention layers to apply self-attention on the input graph, followed by linear layers for predictions. Experimental results on public traffic network datasets, METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of our model.
