Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models. However, due to structural limitations, existing STGNNs can only utilize short-range traffic flow data; therefore, the models cannot adequately learn the complex trends and periodic features in traffic flow. Besides, it is challenging to extract the key temporal information from the long historical traffic series and obtain a compact representation. To solve the above problems, we propose a novel LSTTN (Long-Short Term Transformer-based Network) framework comprehensively considering the long- and short-term features in historical traffic flow. First, we employ a masked subseries Transformer to infer the content of masked subseries from a small portion of unmasked subseries and their temporal context in a pretraining manner, forcing the model to efficiently learn compressed and contextual subseries temporal representations from long historical series. Then, based on the learned representations, long-term trend is extracted by using stacked 1D dilated convolution layers, and periodic features are extracted by dynamic graph convolution layers. For the difficulties in making time-step level prediction, LSTTN adopts a short-term trend extractor to learn fine-grained short-term temporal features. Finally, LSTTN fuses the long-term trend, periodic features and short-term features to obtain the prediction results. Experiments on four real-world datasets show that in 60-minute-ahead long-term forecasting, the LSTTN model achieves a minimum improvement of 5.63\% and a maximum improvement of 16.78\% over baseline models. The source code is available at https://github.com/GeoX-Lab/LSTTN.

翻译：精确的交通预测是智能交通系统中的基本问题，而通过时空图神经网络（STGNNs）学习具有关键信息的长程交通表示是当前交通流预测模型的基本假设。然而，由于结构限制，现有STGNNs仅能利用短时交通流数据，导致模型无法充分学习交通流中复杂的趋势和周期性特征。此外，从长历史交通序列中提取关键时间信息并获得紧凑表示具有挑战性。为解决上述问题，我们提出一种新颖的LSTTN（长短时Transformer网络）框架，综合考量历史交通流中的长时与短时特征。首先，采用掩码子序列Transformer，通过预训练方式从少量未掩码子序列及其时间上下文中推断掩码子序列的内容，迫使模型从长历史序列中高效学习压缩且上下文感知的子序列时间表示。然后，基于学习到的表示，利用堆叠的一维膨胀卷积层提取长期趋势，通过动态图卷积层提取周期性特征。针对时间步级别预测的难点，LSTTN采用短时趋势提取器学习细粒度的短时时间特征。最后，LSTTN融合长期趋势、周期性特征与短时特征以获得预测结果。在四个真实数据集上的实验表明，在60分钟超前长期预测中，LSTTN模型相较基线模型实现最低5.63%至最高16.78%的性能提升。源代码已发布在https://github.com/GeoX-Lab/LSTTN。