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

Rongzhou Huang · Chuyin Huang · Yubao Liu · Genan Dai · Weiyang Kong

287 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Data Management and Algorithms Advanced Graph Neural Networks

Abstract

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.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2019Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2019Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2020GMAN: A Graph Multi-Attention Network for Traffic Prediction 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2019Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

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2023A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting

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