The complex spatial-temporal correlations in transportation networks make the
traffic forecasting problem challenging. Since transportation system inherently
possesses graph structures, much research efforts have been put with graph
neural networks. Recently, constructing adaptive graphs to the data has shown
promising results over the models relying on a single static graph structure.
However, the graph adaptations are applied during the training phases, and do
not reflect the data used during the testing phases. Such shortcomings can be
problematic especially in traffic forecasting since the traffic data often
suffers from the unexpected changes and irregularities in the time series. In
this study, we propose a novel traffic forecasting framework called Progressive
Graph Convolutional Network (PGCN). PGCN constructs a set of graphs by
progressively adapting to input data during the training and the testing
phases. Specifically, we implemented the model to construct progressive
adjacency matrices by learning trend similarities among graph nodes. Then, the
model is combined with the dilated causal convolution and gated activation unit
to extract temporal features. With residual and skip connections, PGCN performs
the traffic prediction. When applied to four real-world traffic datasets of
diverse geometric nature, the proposed model achieves state-of-the-art
performance with consistency in all datasets. We conclude that the ability of
PGCN to progressively adapt to input data enables the model to generalize in
different study sites with robustness.

本研究提出了一种新的交通预测框架 - Progressive Graph Convolutional Network（PGCN），它通过在训练和测试阶段逐步适应输入数据来构建一组图形，并与扩张因果卷积和门控激活单元相结合，提取时间特征。PGCN 具有逐步适应输入数据的能力，使模型在不同的研究场所具有稳健性，并在各种几何性质的实际交通数据集上实现了最先进的性能。