Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.

翻译：城市不同时段交通流量的稳健预测在智能交通系统中扮演着关键角色。尽管先前的工作已投入大量精力建模时空相关性，现有方法仍存在两个主要限制：i) 大多数模型对所有区域的流量进行统一预测，未考虑空间异质性（即不同区域的交通流量分布可能存在偏差）。ii) 这些模型未能捕捉由时变交通模式导致的时间异质性，因为它们通常使用所有时段共享的参数化空间来建模时间相关性。为应对这些挑战，我们提出了一种新颖的时空自监督学习（ST-SSL）交通预测框架，该框架通过辅助自监督学习范式，增强交通模式表征以同时反映空间和时间异质性。具体而言，我们的ST-SSL构建于集成模块之上，该模块包含时间和空间卷积以编码跨时空信息。为实现自适应时空自监督学习，ST-SSL首先在属性层面和结构层面对交通流图数据进行自适应增强。在增强后的交通图上，构建了两个SSL辅助任务，通过空间和时间异质性感知的增强来补充主要交通预测任务。在四个基准数据集上的实验表明，ST-SSL始终优于各种最先进的基线模型。由于时空异质性在实际数据集中普遍存在，所提出的框架也可能为其他时空应用带来启示。模型实现请访问 https://github.com/Echo-Ji/ST-SSL。