Open AccessMTESformer: Multi-scale Temporal and Enhance Spatial Transformer for Traffic Flow Prediction
Author(s) -
Wanbo Zhao,
Xinhua Dong,
Hongmu Han,
Zhanyi Zhu,
Hui Zhang
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3381987
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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 dependence 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 dataset).