Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

Chao Song · Youfang Lin · Shengnan Guo · Huaiyu Wan

1,732 citations0/0 files storedProceedings of the AAAI Conference on Artificial Intelligence

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Traffic Prediction and Management Techniques Spatial and Panel Data Analysis Advanced Graph Neural Networks

Abstract

Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms 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 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

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2022Graph neural network for traffic forecasting: A survey 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting 2021Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting 2021Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey

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