FastDTW-Encoded Spatial-temporal Attention Dual Graph Convolutional Network for Traffic Flow Prediction
Addressing the insufficient extraction of traffic network topology features and inadequate topological feature extraction and spatial-temporal dependency modeling in traffic flow prediction, we construct a dual graph convolutional network algorithm based on fast Dynamic Time Warping (fastDTW) and sp...
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| Veröffentlicht in: | IEEE International Symposium on IT in Medicine and Education S. 720 - 725 |
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13.09.2024
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| ISSN: | 2474-3828 |
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| Abstract | Addressing the insufficient extraction of traffic network topology features and inadequate topological feature extraction and spatial-temporal dependency modeling in traffic flow prediction, we construct a dual graph convolutional network algorithm based on fast Dynamic Time Warping (fastDTW) and spatial-temporal attention mechanism (STADGCN). Firstly, the spatial-temporal attention module is employed to capture the dynamic influence weights of the spatial-temporal dimensions. Secondly, fastDTW is utilized to measure similarity between nodes in the traffic network, enhancing topology-based feature extraction through adjacency matrix encoding. Subsequently, dual graph convolutional and temporal convolutional networks are constructed to algorithm spatial-temporal dependencies. Finally, the prediction performance of the STADGCN algorithm is verified by a weighted fusion of recent, daily, and weekly components based on real highway network detector data. Experimental results demonstrate that compared to ARIMA, VAR, FNN, GAT, GCN, GWNet, STGCN, and ASTGCN, STADGCN exhibits superior performance with MAPE reductions of 81.97%, 64.52%, 78.85%, 69.44%, 54.17%, 8.33%, 8.33%, and 26.67% respectively, on the pems08 dataset. |
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| AbstractList | Addressing the insufficient extraction of traffic network topology features and inadequate topological feature extraction and spatial-temporal dependency modeling in traffic flow prediction, we construct a dual graph convolutional network algorithm based on fast Dynamic Time Warping (fastDTW) and spatial-temporal attention mechanism (STADGCN). Firstly, the spatial-temporal attention module is employed to capture the dynamic influence weights of the spatial-temporal dimensions. Secondly, fastDTW is utilized to measure similarity between nodes in the traffic network, enhancing topology-based feature extraction through adjacency matrix encoding. Subsequently, dual graph convolutional and temporal convolutional networks are constructed to algorithm spatial-temporal dependencies. Finally, the prediction performance of the STADGCN algorithm is verified by a weighted fusion of recent, daily, and weekly components based on real highway network detector data. Experimental results demonstrate that compared to ARIMA, VAR, FNN, GAT, GCN, GWNet, STGCN, and ASTGCN, STADGCN exhibits superior performance with MAPE reductions of 81.97%, 64.52%, 78.85%, 69.44%, 54.17%, 8.33%, 8.33%, and 26.67% respectively, on the pems08 dataset. |
| Author | Chen, Linlong Shen, Bingqi Yang, Nan |
| Author_xml | – sequence: 1 givenname: Bingqi surname: Shen fullname: Shen, Bingqi email: bk99_2022@163.com organization: Guiyang Institute of Humanities and Technology,College of Big Data and Information Engineering,Guiyang,China,550025 – sequence: 2 givenname: Linlong surname: Chen fullname: Chen, Linlong organization: Guiyang Institute of Humanities and Technology,College of Big Data and Information Engineering,Guiyang,China,550025 – sequence: 3 givenname: Nan surname: Yang fullname: Yang, Nan organization: Guiyang Institute of Humanities and Technology,College of Big Data and Information Engineering,Guiyang,China,550025 |
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| SubjectTerms | Attention mechanisms Convolution Correlation FastDTW Feature extraction Graph convolutional networks Heuristic algorithms Network topology Prediction algorithms Predictive models Spatial-temporal dependency modeling Time series analysis Traffic flow prediction |
| Title | FastDTW-Encoded Spatial-temporal Attention Dual Graph Convolutional Network for Traffic Flow Prediction |
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