PLU-MCN: Perturbation learning enhanced U-shaped multi-graph convolutional network for traffic flow prediction

Abstract

Traffic flow prediction is a challenging task in intelligent transportation systems. To improve the accuracy of traffic flow prediction, graph convolutional neural networks and traffic networks are usually combined to reveal dynamic spatial-temporal correlations. However, the existing graph convolution methods need to repeatedly preprocess data of the same type due to network fluctuations, hardware failures, and other troublesome factors. To address this challenge, a novel Perturbation Learning enhanced U-shaped Multi-graph Convolutional Network (PLU-MCN) is proposed. Specifically, PLU-MCN consists of a Perturbation Learning (PL) module and a U-shaped Multi-graph Convolutional Network (U-MCN) based on encoder-decoder architecture. The PL module is designed based on residual convolution and residual transpose convolution to learn perturbation-resistant features from historical time series in the presence of perturbations. The U-MCN consists of multi-graph convolutional modules based on U-Net to extract dynamic spatial-temporal features from the internal and external information of the perturbation-resistant features and output the final predicted values. Compared with existing state-of-the-art models, the PLU-MCN model demonstrated excellently predictive accuracy and reliability in experiments based on five real-world datasets. The key source code and data are available at https://github.com/Bounger2/PLUMCN.