Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

Mengzhang Li · Zhanxing Zhu

1,035 citations0/0 files storedProceedings of the AAAI Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Traffic control and management Advanced Graph Neural Networks

Abstract

Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks usually utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. Furthermore, existing methods were out at elbows when solving complicated spatial-temporal data: they usually utilize separate modules for spatial and temporal correlations, or they only use independent components capturing localized or global heterogeneous dependencies. To overcome those limitations, our paper proposes a novel Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. First, a data-driven method of generating “temporal graph” is proposed to compensate several genuine correlations that spatial graph may not reflect. STFGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, treated for different time periods in parallel. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer parallelly, STFGNN could handle long sequences by learning more spatial-temporal dependencies with layers stacked. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.

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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 2020Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting 2020ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed

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2022Graph neural network for traffic forecasting: A survey 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey 2023Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting 2023Graph Neural Networks for Intelligent Transportation Systems: A Survey 2022Graph Neural Controlled Differential Equations for Traffic Forecasting

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