Reliable forecasting of traffic flow requires efficient modeling of traffic data. Different correlations and influences arise in a dynamic traffic network, making modeling a complicated task. Existing literature has proposed many different methods to capture the complex underlying spatial-temporal relations of traffic networks. However, methods still struggle to capture different local and global dependencies of long-range nature. Also, as more and more sophisticated methods are being proposed, models are increasingly becoming memory-heavy and, thus, unsuitable for low-powered devices. In this paper, we focus on solving these problems by proposing a novel deep learning framework - STLGRU. Specifically, our proposed STLGRU can effectively capture both local and global spatial-temporal relations of a traffic network using memory-augmented attention and gating mechanism. Instead of employing separate temporal and spatial components, we show that our memory module and gated unit can learn the spatial-temporal dependencies successfully, allowing for reduced memory usage with fewer parameters. We extensively experiment on several real-world traffic prediction datasets to show that our model performs better than existing methods while the memory footprint remains lower. Code is available at \url{https://github.com/Kishor-Bhaumik/STLGRU}.

翻译：可靠的交通流预测需要高效建模交通数据。动态交通网络中产生的不同相关性与影响使得建模成为一项复杂任务。现有文献提出了多种方法来捕捉交通网络复杂的时空关系，但这些方法仍难以捕获长程性质的局部与全局依赖性。此外，随着日益复杂的方法被提出，模型逐渐变得高内存占用，因而难以适用于低功耗设备。本文聚焦解决上述问题，提出一种新型深度学习框架——STLGRU。具体而言，所提出的STLGRU通过记忆增强注意力机制与门控机制，能够有效捕获交通网络的局部与全局时空关系。我们证明，无需采用独立的时空组件，记忆模块与门控单元即可成功学习时空依赖性，从而以更少参数降低内存占用。在多个真实交通预测数据集上的广泛实验表明，本文模型在保持更低内存占用的同时，性能优于现有方法。代码开源于\url{https://github.com/Kishor-Bhaumik/STLGRU}。