Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both temporal and spatial correlations. The generalization ability of spatiotemporal models has received considerable attention in recent scholarly discourse. However, no substantive datasets specifically addressing traffic out-of-distribution (OOD) scenarios have been proposed. Existing ST-OOD methods are either constrained to testing on extant data or necessitate manual modifications to the dataset. Consequently, the generalization capacity of current spatiotemporal models in OOD scenarios remains largely underexplored. In this paper, we investigate state-of-the-art models using newly proposed traffic OOD benchmarks and, surprisingly, find that these models experience a significant decline in performance. Through meticulous analysis, we attribute this decline to the models' inability to adapt to previously unobserved spatial relationships. To address this challenge, we propose a novel Mixture of Experts (MoE) framework, which learns a set of graph generators (i.e., graphons) during training and adaptively combines them to generate new graphs based on novel environmental conditions to handle spatial distribution shifts during testing. We further extend this concept to the Transformer architecture, achieving substantial improvements. Our method is both parsimonious and efficacious, and can be seamlessly integrated into any spatiotemporal model, outperforming current state-of-the-art approaches in addressing spatial dynamics.

翻译：近年来，时空图神经网络（ST-GNNs）和Transformer通过有效捕捉时空相关性，在交通流量预测领域展现出巨大潜力。时空模型的泛化能力已成为近期学术讨论的重要议题。然而，目前尚未出现专门针对交通分布外（OOD）场景的实质性数据集。现有ST-OOD方法要么局限于在现有数据上进行测试，要么需要对数据集进行人工修改。因此，当前时空模型在OOD场景中的泛化能力仍未得到充分探索。本文基于新提出的交通OOD基准测试对前沿模型进行研究，意外发现这些模型性能出现显著下降。通过细致分析，我们将此归因于模型无法适应先前未观测到的空间关系。为应对这一挑战，我们提出一种新颖的专家混合（MoE）框架，该框架在训练过程中学习一组图生成器（即图函数），并根据新环境条件自适应组合这些生成器以构建新图，从而处理测试期间的空间分布偏移。我们进一步将该概念拓展至Transformer架构，实现了显著性能提升。本方法兼具简洁性与高效性，可无缝集成至任意时空模型中，在应对空间动态变化方面超越了当前最先进方法。