双分支空间-时间自监督表征用于增强道路网络学习
Dual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning
AI总结：
DST通过双分支空间-时间自监督框架提升道路网络学习，结合动态道路关系和交通模式差异，增强零样本学习能力。
摘要：
道路网络表示学习（RNRL）已受到研究者和实践者越来越多的关注，因为各种时空任务不断涌现。最近的先进方法利用图神经网络（GNNs）和对比学习来以自监督的方式表征道路段的空间结构。然而，道路网络的空间异质性和时间动态性对自监督GNN的邻域平滑机制提出了严峻挑战。为了解决这些问题，我们提出了一种双分支空间-时间自监督表征框架，用于增强道路表示，称为DST。一方面，DST设计了一个混合跳转过渡矩阵用于图卷积，以纳入从轨迹中获得的动态道路关系。此外，DST以空间自监督的方式将普通道路网络的路表示与超图的路表示进行对比。超图是基于三种类型的超边新建的，以捕捉长距离关系。另一方面，DST在基于因果Transformer的交通动态序列上执行下一个标记预测作为时间自监督任务，并通过区分工作日和周末的交通模式进行正则化。广泛的实验验证了我们所提出框架的优越性。此外，全面的空间-时间建模使DST在零样本学习场景中表现出色。
英文摘要：
Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks (GNNs) and contrastive learning to characterize the spatial structure of road segments in a self-supervised paradigm. However, spatial heterogeneity and temporal dynamics of road networks raise severe challenges to the neighborhood smoothing mechanism of self-supervised GNNs. To address these issues, we propose a $\textbf{D}$ual-branch $\textbf{S}$patial-$\textbf{T}$emporal self-supervised representation framework for enhanced road representations, termed as DST. On one hand, DST designs a mix-hop transition matrix for graph convolution to incorporate dynamic relations of roads from trajectories. Besides, DST contrasts road representations of the vanilla road network against that of the hypergraph in a spatial self-supervised way. The hypergraph is newly built based on three types of hyperedges to capture long-range relations. On the other hand, DST performs next token prediction as the temporal self-supervised task on the sequences of traffic dynamics based on a causal Transformer, which is further regularized by differentiating traffic modes of weekdays from those of weekends. Extensive experiments against state-of-the-art methods verify the superiority of our proposed framework. Moreover, the comprehensive spatiotemporal modeling facilitates DST to excel in zero-shot learning scenarios.
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