Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network
Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/5221362 |
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| _version_ | 1832567084103499776 |
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| author | Wenhao Jiang Yunpeng Xiao Yanbing Liu Qilie Liu Zheng Li |
| author_facet | Wenhao Jiang Yunpeng Xiao Yanbing Liu Qilie Liu Zheng Li |
| author_sort | Wenhao Jiang |
| collection | DOAJ |
| description | Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction. |
| format | Article |
| id | doaj-art-263eb6aa199246469536b6999eab3535 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-263eb6aa199246469536b6999eab35352025-02-03T01:02:20ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5221362Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural NetworkWenhao Jiang0Yunpeng Xiao1Yanbing Liu2Qilie Liu3Zheng Li4School of Computer Science and TechnologySchool of Communication and Information EngineeringSchool of Computer Science and TechnologySchool of Communication and Information EngineeringSchool of Communication and Information EngineeringBecause traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction.http://dx.doi.org/10.1155/2022/5221362 |
| spellingShingle | Wenhao Jiang Yunpeng Xiao Yanbing Liu Qilie Liu Zheng Li Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network Journal of Advanced Transportation |
| title | Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network |
| title_full | Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network |
| title_fullStr | Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network |
| title_full_unstemmed | Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network |
| title_short | Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network |
| title_sort | bi grcn a spatio temporal traffic flow prediction model based on graph neural network |
| url | http://dx.doi.org/10.1155/2022/5221362 |
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