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.

本文研究通过提出一种快速的 Transformer 网络来预测交通流量，该网络能够准确地捕捉交通数据中的复杂时空依赖关系，同时大大降低了计算时间和计算资源的要求。