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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. [ABSTRACT FROM AUTHOR]