Research in deep learning models to forecast traffic intensities has gained
great attention in recent years due to their capability to capture the complex
spatio-temporal relationships within the traffic data. However, most
state-of-the-art approaches have designed spatial-only (e.g. Graph Neural
Networks) and temporal-only (e.g. Recurrent Neural Networks) modules to
separately extract spatial and temporal features. However, we argue that it is
less effective to extract the complex spatio-temporal relationship with such
factorized modules. Besides, most existing works predict the traffic intensity
of a particular time interval only based on the traffic data of the previous
one hour of that day. And thereby ignores the repetitive daily/weekly pattern
that may exist in the last hour of data. Therefore, we propose a Unified
Spatio-Temporal Graph Convolution Network (USTGCN) for traffic forecasting that
performs both spatial and temporal aggregation through direct information
propagation across different timestamp nodes with the help of spectral graph
convolution on a spatio-temporal graph. Furthermore, it captures historical
daily patterns in previous days and current-day patterns in current-day traffic
data. Finally, we validate our work's effectiveness through experimental
analysis, which shows that our model USTGCN can outperform state-of-the-art
performances in three popular benchmark datasets from the Performance
Measurement System (PeMS). Moreover, the training time is reduced significantly
with our proposed USTGCN model.

本文提出了一种统一的 Spatio-Temporal Graph Convolution Network (USTGCN) 模型用于交通预测，该模型具有通过频谱计算实现的时空图表达和历史模式的捕捉等优点，并在实验中证明了其超越了现有模型，同时减少了培训时间。

使用图神经网络进行交通预测的统一时空建模

Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph  Neural Network

Variations of human body skeletons may be considered as dynamic graphs, which
are generic data representation for numerous real-world applications. In this
paper, we propose a spatio-temporal graph convolution (STGC) approach for
assembling the successes of local convolutional filtering and sequence learning
ability of autoregressive moving average. To encode dynamic graphs, the
constructed multi-scale local graph convolution filters, consisting of matrices
of local receptive fields and signal mappings, are recursively performed on
structured graph data of temporal and spatial domain. The proposed model is
generic and principled as it can be generalized into other dynamic models. We
theoretically prove the stability of STGC and provide an upper-bound of the
signal transformation to be learnt. Further, the proposed recursive model can
be stacked into a multi-layer architecture. To evaluate our model, we conduct
extensive experiments on four benchmark skeleton-based action datasets,
including the large-scale challenging NTU RGB+D. The experimental results
demonstrate the effectiveness of our proposed model and the improvement over
the state-of-the-art.

本文提出了一种称为 Spatio-Temporal Graph Convolution (STGC) 的方法，用于动作识别中的骨架，该方法组合了本地卷积过滤器的成功和自回归移动平均的序列学习能力，通过递归地进行多尺度局部图卷积过滤器构建。实验表明 STGC 模型的有效性和优于现有技术的改进。