基于多通道Transformer的交通量预测方法

目前，我国高速公路拥堵程度居高不下，而交通流预测作为实现智能交通系统的重要一环，若能对其实现高精度的预测，那么将能够高效地管理交通，从而缓解拥堵。针对该问题，提出了一种考虑时空关联的多通道交通流预测方法（MCST-Transformer）。首先，将Transformer结构用于不同数据的内在规律提取，然后引入空间关联模块对不同数据间的关联特征进行挖掘，最后，借助通道注意力整合优化全局信息。采用广东省高速公路数据，实现了两小时内92个收费站的高精度流量预测。结果表明：MCST-Transformer优于传统机器学习方法以及部分基于注意力机制的时间序列模型，在120 min预测跨度下，相比贝叶斯回归，MAPE降低了5.1%；对比Seq2Seq-Att以及Seq2Seq这些深度学习算法，所提方法的总体MAPE也能降低0.5%，说明通过多通道的方式能够区分不同数据的特性，进而更好地预测。

Traffic flow prediction method based on multi-channel Transformer

At present, the congestion of highways in China is severe. Traffic flow prediction plays an important role in the intelligent transportation system. If it can achieve high-precision prediction, it will be able to efficiently manage traffic and alleviate congestion. To solve this issue, this paper proposed a multi-channel traffic flow prediction method(MCST-Transformer) considering spatiotemporal correlation. Firstly, it used Transformer to extract the internal laws of different data, and then introduced a spatial correlation module to mine the association features of different data. Finally, it integrated global information through channel attention. Using the data of Guangdong province highway, the proposed method realized the traffic flow prediction of 92 toll stations within two hours with high precision. The results show that: MCST-Transformer is superior to traditional machine learning methods and time series models based on the attention mechanism. Under the prediction horizon of 120 min, MAPE decreases by 5.1% compared with Bayesian regression. Compared with deep learning algorithms like Seq2Seq-Att and Seq2Seq, the overall MAPE of the proposed method can also be reduced by 0.5%. It indicates that the multi-channel approach can distinguish the characteristics of different data, so as to acquire better performance.