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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting

February 1, 2023

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Authors

Zulong Diao

The State University of New York


Xin Wang

Stony Brook University


Dafang Zhang

Hunan University


Yingru Liu

Stony Brook University


Kun Xie

Hunan University


Shaoyao He

Hunan University


DOI:

10.1609/aaai.v33i01.3301890


Abstract:

Graph convolutional neural networks (GCNN) have become an increasingly active field of research. It models the spatial dependencies of nodes in a graph with a pre-defined Laplacian matrix based on node distances. However, in many application scenarios, spatial dependencies change over time, and the use of fixed Laplacian matrix cannot capture the change. To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting. The core of our deep learning framework is the finding of the change of Laplacian matrix with a dynamic Laplacian matrix estimator. To enable timely learning with a low complexity, we creatively incorporate tensor decomposition into the deep learning framework, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationship and a local component that captures the traffic fluctuations. We propose a novel design to estimate the dynamic Laplacian matrix of the graph with above two components based on our theoretical derivation, and introduce our design basis. The forecasting performance is evaluated with two realtime traffic datasets. Experiment results demonstrate that our network can achieve up to 25% accuracy improvement.

Topics: AAAI

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HOW TO CITE:

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting Proceedings of the AAAI Conference on Artificial Intelligence (2019) 890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting AAAI 2019, 890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He (2019). Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He. 2019. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. "Proceedings of the AAAI Conference on Artificial Intelligence". 890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He. (2019) "Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting", Proceedings of the AAAI Conference on Artificial Intelligence, p.890-897

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He, "Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting", AAAI, p.890-897, 2019.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He. "Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He. "Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 890-897.

Zulong Diao||Xin Wang||Dafang Zhang||Yingru Liu||Kun Xie||Shaoyao He. Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. AAAI[Internet]. 2019[cited 2023]; 890-897.


ISSN: 2374-3468


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