Multi-attention gated temporal graph convolution neural Network for traffic flow forecasting

Abstract

Real-time and accurate traffic flow forecasting plays a crucial role in transportation systems and holds great significance for urban traffic planning, traffic management, traffic control, and more. The most difficult challenge is the extraction of temporal features and spatial correlations of nodes in traffic flow forecasting. Meanwhile, graph convolutional networks has shown good performance in extracting relational spatial dependencies in existing methods. However, it is difficult to accurately mine the hidden spatial-temporal features of the traffic network by using graph convolution alone. In this paper, we propose a multi-attention gated temporal graph convolution network (MATGCN) for accurately forecasting the traffic flow. Firstly, we propose a gated multi-modal temporal convolution(MTCN) to handle the long-term series of the raw traffic data. Then, we use an efficient channel attention module(ECA) to extract temporal features. For the complexity of the spatial structure of traffic roads, we develop multi-attention graph convolution module (MAGCN)including graph convolution and graph attention to further extract the spatial features of a road network. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods.