Reliable forecasting of traffic flow requires efficient modeling of traffic data. Indeed, different correlations and influences arise in a dynamic traffic network, making modeling a complicated task. Existing literature has proposed many different methods to capture traffic networks' complex underlying spatial-temporal relations. However, given the heterogeneity of traffic data, consistently capturing both spatial and temporal dependencies presents a significant challenge. Also, as more and more sophisticated methods are being proposed, models are increasingly becoming memory-heavy and, thus, unsuitable for low-powered devices. To this end, we propose Spatio-Temporal Lightweight Graph GRU, namely STLGRU, a novel traffic forecasting model for predicting traffic flow accurately. Specifically, our proposed STLGRU can effectively capture dynamic local and global spatial-temporal relations of traffic networks using memory-augmented attention and gating mechanisms in a continuously synchronized manner. Moreover, instead of employing separate temporal and spatial components, we show that our memory module and gated unit can successfully learn the spatial-temporal dependencies with reduced memory usage and fewer parameters. Extensive experimental results on three real-world public traffic datasets demonstrate that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Our code is available at https://github.com/Kishor-Bhaumik/STLGRU

翻译：可靠的交通流量预测需要对交通数据进行高效建模。事实上，动态交通网络中会产生不同的相关性与影响，使得建模成为一项复杂任务。现有文献已提出多种方法以捕捉交通网络复杂的潜在时空关系。然而，由于交通数据的异质性，同时一致地捕获空间和时间依赖关系仍面临重大挑战。此外，随着越来越多复杂方法的提出，模型的存储占用日益增大，因此不适用于低功耗设备。为此，我们提出了时空轻量级图GRU（即STLGRU），一种用于精确预测交通流量的新型交通预测模型。具体而言，我们提出的STLGRU能够通过内存增强的注意力机制和门控机制，以持续同步的方式有效捕捉交通网络的动态局部和全局时空关系。此外，我们证明，与采用独立的时空组件不同，所提出的内存模块和门控单元能够以更少的内存占用和参数数量成功学习时空依赖关系。在三个真实世界公共交通数据集上的大量实验结果表明，我们的方法不仅能实现最先进的性能，还展现出有竞争力的计算效率。我们的代码可在 https://github.com/Kishor-Bhaumik/STLGRU 获取。