Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems. The complex and dynamic spatial-temporal dependencies make the traffic flow prediction quite challenging. Although existing spatial-temporal graph neural networks hold prominent, they often encounter challenges such as (1) ignoring the fixed graph that limits the predictive performance of the model, (2) insufficiently capturing complex spatial-temporal dependencies simultaneously, and (3) lacking attention to spatial-temporal information at different time lengths. In this paper, we propose a Multi-Scale Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN, which consists of two different recurrent neural networks: the single-step gate recurrent unit and the multi-step gate recurrent unit to fully capture the complex spatial-temporal information in the traffic data under different time windows. Moreover, we propose a spatial-temporal synchronous attention mechanism that integrates adaptive position graph convolutions into the self-attention mechanism to achieve synchronous capture of spatial-temporal dependencies. We conducted extensive experiments on four real traffic datasets and demonstrated that our model achieves the best prediction accuracy with non-trivial margins compared to all the twenty baseline methods.

翻译：交通流量预测是智能交通系统中最基本的任务之一。复杂且动态的时空依赖性使得交通流量预测极具挑战性。尽管现有的时空图神经网络表现突出，但它们常面临以下问题：(1) 忽略固定图结构限制了模型的预测性能；(2) 未能同时充分捕获复杂的时空依赖性；(3) 缺乏对不同时间跨度下时空信息的关注。本文提出一种用于交通流量预测的多尺度时空循环网络（MSSTRN），该网络包含两种不同的循环神经网络：单步门控循环单元和多步门控循环单元，以全面捕获不同时间窗口内交通数据中的复杂时空信息。此外，我们提出一种时空同步注意力机制，该机制将自适应位置图卷积集成到自注意力机制中，以实现时空依赖性的同步捕获。我们在四个真实交通数据集上进行了大量实验，结果表明，与所有二十种基准方法相比，我们的模型以显著优势取得了最佳预测精度。