Memory attention enhanced graph convolution long short‐term memory network for traffic forecasting. Issue 9 (10th February 2022)

• Record Type: Journal Article
• Authors: Qin, Yanjun; Zhao, Fang; Fang, Yuchen; Luo, Haiyong; Wang, Chenxing
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 9(2022)
• Page Start: 6555
• Page End: 6576
• Publication Date: 2022-02-10
• Subjects: attention mechanism -- graph convolution network -- intelligent transportation systems -- long short‐term memory -- traffic forecasting; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22855
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: In recent years, traffic forecasting has gradually attracted attention in data mining because of the increasing availability of large‐scale traffic data. However, it faces substantial challenges of complex temporal‐spatial correlations in traffic. Recent studies mainly focus on modeling the local spatial correlations by utilizing graph neural networks and neglect the influence of long‐distance spatial correlations. Besides, most existing works utilize recurrent neural networks‐based encoder–decoder architecture to forecast multistep traffic volume and suffer from accumulative errors in recurrent neural networks. To deal with these issues, we propose the memory attention (MA) enhanced graph convolution long short‐term memory network (MAEGCLSTM), a novel deep learning model for traffic forecasting. Specifically, MAEGCLSTM combines the MA and the vanilla graph convolution long short‐term memory to capture global and local spatio‐temporal dependencies, respectively. Then MAEGCLSTM utilizes a simplified GCLSTM to effectively fuse the global and local information. Moreover, we integrate the MAEGCLSTM into an encoder–decoder architecture to forecast multistep traffic volume. Besides MAEGCLSTM, we add the convolution neural network and encoder–decoder attention into the decoder to ease accumulative errors caused by iterative prediction and gain whole historical information from the encoder. Experiments on four real‐world traffic data sets show that our model significantly outperforms by up to 6.07 % $6.07 \% $ improvement in L 1 $L1$ measure over 14 baselines.