Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting. However, too many sensors can cause a vector with a dimension greater than 800, which is difficult to process without information loss. In addition, these methods design complex mechanisms to capture spatial dependencies in MTS, resulting in slow forecasting speed. To solve the abovementioned problems, we propose a Fast Pure Transformer Network (FPTN) in this paper. First, the traffic flow data are divided into sequences along the sensor dimension instead of the time dimension. Then, to adequately represent complex spatio-temporal correlations, Three types of embeddings are proposed for projecting these vectors into a suitable vector space. After that, to capture the complex spatio-temporal correlations simultaneously in these vectors, we utilize Transformer encoder and stack it with several layers. Extensive experiments are conducted with 4 real-world datasets and 13 baselines, which demonstrate that FPTN outperforms the state-of-the-art on two metrics. Meanwhile, the computational time of FPTN spent is less than a quarter of other state-of-the-art Transformer-based models spent, and the requirements for computing resources are significantly reduced.

翻译：交通流预测因数据中复杂的时空相关性而颇具挑战性。现有基于Transformer的方法通常将交通流预测视为多变量时间序列预测。然而，过多的传感器会导致向量维度超过800，这难以在无信息损失的情况下进行处理。此外，这些方法设计了复杂机制来捕捉多变量时间序列中的空间依赖关系，导致预测速度缓慢。为解决上述问题，本文提出了一种快速纯Transformer网络（FPTN）。首先，交通流数据沿传感器维度而非时间维度划分为序列。然后，为充分表示复杂的时空相关性，提出了三种类型的嵌入方法将这些向量投影到合适的向量空间中。接着，为同时捕捉这些向量中的复杂时空相关性，我们采用Transformer编码器并将其堆叠多层。使用4个真实世界数据集和13个基线模型进行了广泛实验，结果表明FPTN在两个指标上均优于现有最先进方法。同时，FPTN的计算时间不到其他最先进的基于Transformer模型所用时间的四分之一，且计算资源需求显著降低。