New roads are being constructed all the time. However, the capabilities of
previous deep forecasting models to generalize to new roads not seen in the
training data (unseen roads) are rarely explored. In this paper, we introduce a
novel setup called a spatio-temporal (ST) split to evaluate the models'
capabilities to generalize to unseen roads. In this setup, the models are
trained on data from a sample of roads, but tested on roads not seen in the
training data. Moreover, we also present a novel framework called Spatial
Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to
extract latent features from unseen roads during inference time. This spatial
encoder is pre-trained using contrastive learning. During inference, the
spatial encoder only requires two days of traffic data on the new roads and
does not require any re-training. We also show that the output from the spatial
encoder can be used effectively to infer latent node embeddings on unseen roads
during inference time. The SCPT framework also incorporates a new layer, named
the spatially gated addition (SGA) layer, to effectively combine the latent
features from the output of the spatial encoder to existing backbones.
Additionally, since there is limited data on the unseen roads, we argue that it
is better to decouple traffic signals to trivial-to-capture periodic signals
and difficult-to-capture Markovian signals, and for the spatial encoder to only
learn the Markovian signals. Finally, we empirically evaluated SCPT using the
ST split setup on four real-world datasets. The results showed that adding SCPT
to a backbone consistently improves forecasting performance on unseen roads.
More importantly, the improvements are greater when forecasting further into
the future.

本论文介绍了一种新的设置，称为时空分裂，用于评估模型推广到未出现在训练数据中的新道路的能力，并提出了一种名为空间对比预训练的新型框架来从未见过的道路中提取潜在特征。 SCPT 框架不需要重新训练，在四个真实的数据集上实验结果表明，将 SCPT 添加到神经网络骨干中可以提高预测性能，特别是在进一步预测未来时提高更为显著。

利用空间对比预训练进行在训练数据中看不见的新道路的交通预测

Traffic Forecasting on New Roads Unseen in the Training Data Using  Spatial Contrastive Pre-Training

Graph neural networks (GNNs) have gained traction over the past few years for
their superior performance in numerous machine learning tasks. Graph
Convolutional Neural Networks (GCN) are a common variant of GNNs that are known
to have high performance in semi-supervised node classification (SSNC), and
work well under the assumption of homophily. Recent literature has highlighted
that GCNs can achieve strong performance on heterophilous graphs under certain
"special conditions". These arguments motivate us to understand why, and how,
GCNs learn to perform SSNC. We find a positive correlation between similarity
of latent node embeddings of nodes within a class and the performance of a GCN.
Our investigation on underlying graph structures of a dataset finds that a
GCN's SSNC performance is significantly influenced by the consistency and
uniqueness in neighborhood structure of nodes within a class.

本文研究了 Graph Convolutional Neural Networks 在半监督节点分类中的表现。实验发现，节点的相似度和 GCN 的性能有正相关关系，并且 GCN 在同一类中节点邻居的一致性和唯一性对性能影响显著。