Robust urban flow prediction is crucial for transportation planning and management in urban areas. Although recent advances in modeling spatio-temporal correlations have shown potential, most models fail to adequately consider the complex spatio-temporal semantic information present in real-world scenarios. We summarize the following three primary limitations in existing models: a) The majority of existing models project overall time periods into the same latent space, neglecting the diverse temporal semantics between different time intervals. b) Existing models tend to capture spatial dependencies from a locale perspective such as surroundings but do not pay attention to the global influence factors. c) Beyond the spatio-temporal properties, the dynamics and instability of the data sequences introduce perturbations to the prediction results, potentially leading to model degradation. To address these issues, we propose a dual-track spatial-temporal learning module named DualST for accurate urban flow inference. To more effectively differentiate semantic information in the time dimension, we assign the overall time scales into closeness and periodicity. The dual-track module, which includes temporal causality inference and temporal contextual inference, simultaneously exploits the dynamic evolutionary trends and periodic traffic patterns, respectively. The proposed DualST captures global spatial features in a self-supervised manner which not only enriches the spatial semantics but also avoids introducing additional prior knowledge. To eliminate the instability caused by dynamics, we first adopt spatio-temporal adaptive normalization to learn appropriate data sequence normalization. We evaluate the proposed DualST on two typical urban flow datasets. The experiment results show that our model not only exhibits a consistent superiority over various state-of-the-art baselines but also has remarkable generalization capability.

稳健的城市流量预测对于城市地区的交通规划和管理至关重要。尽管时空相关性建模的最新进展已显示出潜力，但大多数模型未能充分考虑现实场景中存在的复杂时空语义信息。我们总结了现有模型中的以下三个主要局限性：a）大多数现有模型将整个时间段投影到同一潜在空间中，忽略了不同时间间隔之间的不同时间语义。b）现有模型倾向于从环境角度捕捉空间依赖性，但没有关注全局影响因素。c) 除了时空特性之外，数据序列的动态性和不稳定性会给预测结果带来扰动，可能导致模型退化。为了解决这些问题，我们提出了一种名为 DualST 的双轨时空学习模块，用于准确的城市流量推理。为了更有效地区分时间维度上的语义信息，我们将整体时间尺度分配为紧密度和周期性。双轨模块包括时间因果推理和时间上下文推理，分别同时利用动态演化趋势和周期性流量模式。所提出的 DualST 以自我监督的方式捕获全局空间特征，这不仅丰富了空间语义，而且避免了引入额外的先验知识。为了消除动态引起的不稳定性，我们首先采用时空自适应归一化来学习适当的数据序列归一化。我们在两个典型的城市流量数据集上评估了所提出的 DualST。实验结果表明，我们的模型不仅表现出相对于各种最先进基线的一致优越性，而且还具有显着的泛化能力。