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:交通流量预测之所以具有挑战性,是因为交通流量数据存在复杂的时空关系。 现有的基于变压器的方法通常将交通流量预测作为多变时间序列( MTS) 的预测处理。 但是,太多的传感器可能造成一个尺寸大于800的矢量, 难以在没有信息损失的情况下处理。 此外, 这些方法设计了复杂的机制, 以捕捉多边贸易体系的空间依赖性, 导致预测速度缓慢。 为了解决上述问题, 我们提议在本文中建立一个快速的纯质变换器网络( FPTN ) 。 首先, 交通流量数据被分为传感器维度的序列, 而不是时间维度。 然后, 为了充分代表复杂的时空关系, 提议将三种类型的嵌入方式用于将这些矢量投射到合适的矢量空间中。 之后, 为了同时捕捉这些矢量中复杂的空间- 空间依赖性关系, 我们使用变压器编码并用几个层次堆叠它。 首先, 用4个真实的数据集和13个基线来进行广泛的实验, 这表明, FPTNTN比用过的四分之一的计算模型的州- 基模型比用量小。</s>