Hybrid Graph Models for Traffic Prediction
Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static ad...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/15/8673 |
| _version_ | 1797587078725238784 |
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| author | Renyi Chen Huaxiong Yao |
| author_facet | Renyi Chen Huaxiong Yao |
| author_sort | Renyi Chen |
| collection | DOAJ |
| description | Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static adjacency matrix or a dynamic adjacency matrix to aggregate spatial information between nodes, which cannot fully represent the topological information. In this paper, we propose a Hybrid Graph Model (HGM) for accurate traffic prediction. The HGM constructs a static graph and a dynamic graph to represent the topological information of the traffic network, which is beneficial for mining potential and obvious spatial correlations. The proposed method combines a graph neural network, convolutional neural network, and attention mechanism to jointly extract complex spatial–temporal features. The HGM consists of two different sub-modules, called spatial–temporal attention module and dynamic graph convolutional network, to fuse complex spatial–temporal information. Furthermore, the proposed method designs a novel gated function to adaptively fuse the results from spatial–temporal attention and dynamic graph convolutional network to improve prediction performance. Extensive experiments on two real datasets show that the HGM outperforms comparable state-of-the-art methods. |
| first_indexed | 2024-03-11T00:32:10Z |
| format | Article |
| id | doaj.art-43f580139a4441e9b710bd116a8acb2d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:32:10Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-43f580139a4441e9b710bd116a8acb2d2023-11-18T22:35:58ZengMDPI AGApplied Sciences2076-34172023-07-011315867310.3390/app13158673Hybrid Graph Models for Traffic PredictionRenyi Chen0Huaxiong Yao1Computer School, Central China Normal University, Wuhan 430079, ChinaComputer School, Central China Normal University, Wuhan 430079, ChinaObtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static adjacency matrix or a dynamic adjacency matrix to aggregate spatial information between nodes, which cannot fully represent the topological information. In this paper, we propose a Hybrid Graph Model (HGM) for accurate traffic prediction. The HGM constructs a static graph and a dynamic graph to represent the topological information of the traffic network, which is beneficial for mining potential and obvious spatial correlations. The proposed method combines a graph neural network, convolutional neural network, and attention mechanism to jointly extract complex spatial–temporal features. The HGM consists of two different sub-modules, called spatial–temporal attention module and dynamic graph convolutional network, to fuse complex spatial–temporal information. Furthermore, the proposed method designs a novel gated function to adaptively fuse the results from spatial–temporal attention and dynamic graph convolutional network to improve prediction performance. Extensive experiments on two real datasets show that the HGM outperforms comparable state-of-the-art methods.https://www.mdpi.com/2076-3417/13/15/8673traffic predictiongraph neural networkattention |
| spellingShingle | Renyi Chen Huaxiong Yao Hybrid Graph Models for Traffic Prediction Applied Sciences traffic prediction graph neural network attention |
| title | Hybrid Graph Models for Traffic Prediction |
| title_full | Hybrid Graph Models for Traffic Prediction |
| title_fullStr | Hybrid Graph Models for Traffic Prediction |
| title_full_unstemmed | Hybrid Graph Models for Traffic Prediction |
| title_short | Hybrid Graph Models for Traffic Prediction |
| title_sort | hybrid graph models for traffic prediction |
| topic | traffic prediction graph neural network attention |
| url | https://www.mdpi.com/2076-3417/13/15/8673 |
| work_keys_str_mv | AT renyichen hybridgraphmodelsfortrafficprediction AT huaxiongyao hybridgraphmodelsfortrafficprediction |