MTESformer: Multi-Scale Temporal and Enhance Spatial Transformer for Traffic Flow Prediction

Abstract

Traffic flow prediction has become an important component of intelligent transportation systems. However, high-precision traffic flow prediction (especially long-term prediction) is still very challenging due to the complex spatial-temporal dependences of urban traffic data. In this paper, a novel Multi-scale Temporal and Enhance Spatial Transformer (MTESformer) model is proposed to capture complex dynamic spatial-temporal dependencies. MTESformer provides a reasonable feature embedding of periodic characteristics of traffic; it can recognize different temporal feature patterns and capture long-term dependencies, and efficiently focuses on two different node-space dependencies (long-range and neighboring nodes dependencies). Specifically, we develop a special multi-scale convolution unit that unites temporal self-attention to capture a wider range of dynamic temporal dependencies from a multi-receptive field and identify different temporal feature patterns. Secondly, we design a novel Enhance Spatial Transformer module, which can better focus on the dynamic spatial dependencies among nodes by fusing their neighborhood information. Experimental results on the public transportation network datasets METR-LA, PEMS-BAY, PEMS04, and PEMS08 data show that our proposed method outperforms most of the baseline models and outperforms the state-of-the-art models in long-term prediction. (The MAE of 60min prediction of our model on METR-LA, PEMS-BAY dataset is 3.37, 1.87, and the MAPE is 9.62%, 4.35%, respectively, and all of them outperform the PDFormer on PEMS04 and PEMS08 datasets.)

Keywords

• Long-term traffic flow prediction
• multi-scale convolution
• spatial-temporal dependency
• transformer