LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks

By Rongzhou Huang, Chuyin Huang, Yubao Liu, Genan Dai, Weiyang Kong. International Joint Conference on Artificial Intelligence, 2020

309 citations recorded by the source corpus.

Traffic prediction is a classical spatial-temporal prediction problem with many real-world applications such as intelligent route planning, dynamic traffic management, and smart location-based applications. Due to the high nonlinearity and complexity of traffic data, deep learning approaches have attracted much interest in recent years. However, few methods are satisfied with both long and short-term prediction tasks. Target at the shortcomings of existing studies, in this paper, we propose a novel deep learning framework called Long Short-term Graph Convolutional Networks (LSGCN) to tackle both traffic prediction tasks. In our framework, we propose a new graph attention network called cosAtt, and integrate both cosAtt and graph convolution networks (GCN) into a spatial gated block. By the spatial gated block and gated linear units convolution (GLU), LSGCN can efficiently capture complex spatial-temporal features and obtain stable prediction results. Experiments with three real-world traffic datasets verify the effectiveness of LSGCN.

Subfields: Building and Construction, Neurology, Urban Studies

View Paper: https://www.semanticscholar.org/paper/c04b328d50dd48ae09ae3c087aa6c97b54178eff

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Citations in the corpus

Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution (2021, methodology): “STGCN [34] Distance-based graph GCN CNN Time ASTGCN [9] Binary graph GCN + Attention CNN + Attention Time STSGCN [23] Binary graph GCN GCN Time LSGCN [12] Binary graph GCN + Attention CNN Time Graph WaveNet [30] Adaptive graph + Distance-based graph GCN CNN Time SLCNN [36] Adaptive graph GCN CNN Tim”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “The dataset used in [221], [222], and [220] This data is collected via inductive loop detectors in Seattle.”
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction (2021, background): “This is because, on the one hand, Autoformer focuses on modeling seasonal patterns and conducts self-attention at the sub-series level (instead of the raw data), which is much better in extracting long-term temporal patterns than vanilla Transformer-based methods.”
Graph Neural Network for Traffic Forecasting: A Survey (2021, unknown): “Problem Relevant Studies Road Traffic Flow [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62],”
Graph Neural Controlled Differential Equations for Traffic Forecasting (2021, background): “…Cheng et al. 2018a,b; Hossain et al. 2015; Ren et al. 2021; Tekin et al. 2021; Li et al. 2018; Yu, Yin, and Zhu 2018; Wu et al. 2019; Guo et al. 2019; Bai et al. 2019; Song et al. 2020; Huang et al. 2020; Bai et al. 2020; Li and Zhu 2021; Chen, Segovia-Dominguez, and Gel 2021; Fang et al. 2021).”