Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction
Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. However, prior approaches often overlook the temporal-scale differentiation of spatial-temporal features, limiting their ability to extract complex st...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/8256907 |
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| _version_ | 1849697785183469568 |
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| author | Xin Zan Jasmine Siu Lee Lam |
| author_facet | Xin Zan Jasmine Siu Lee Lam |
| author_sort | Xin Zan |
| collection | DOAJ |
| description | Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. However, prior approaches often overlook the temporal-scale differentiation of spatial-temporal features, limiting their ability to extract complex structural information. In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific scale. Besides, we design the spatial-temporal feature fusion (STFF) module to refine the prediction results. Based on the multibranch complementary features, the module adopts a coarse-to-fine fusion strategy, incorporating different spatial-temporal scale features to obtain recalibrated prediction results. Finally, we evaluate the MBAF-GCN using two real-world traffic datasets. Experimentally, the newly designed multibranch can efficaciously utilize the prior information of different temporal scales. Our MBAF-GCN achieved better performance in the comparative model, indicating its potential and validity. |
| format | Article |
| id | doaj-art-721846a63c564697bcc0d6c3790243ae |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-721846a63c564697bcc0d6c3790243ae2025-08-20T03:19:07ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/8256907Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow PredictionXin Zan0Jasmine Siu Lee Lam1Antai College of Economics & ManagementSchool of Civil and Environmental EngineeringUrban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. However, prior approaches often overlook the temporal-scale differentiation of spatial-temporal features, limiting their ability to extract complex structural information. In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific scale. Besides, we design the spatial-temporal feature fusion (STFF) module to refine the prediction results. Based on the multibranch complementary features, the module adopts a coarse-to-fine fusion strategy, incorporating different spatial-temporal scale features to obtain recalibrated prediction results. Finally, we evaluate the MBAF-GCN using two real-world traffic datasets. Experimentally, the newly designed multibranch can efficaciously utilize the prior information of different temporal scales. Our MBAF-GCN achieved better performance in the comparative model, indicating its potential and validity.http://dx.doi.org/10.1155/2023/8256907 |
| spellingShingle | Xin Zan Jasmine Siu Lee Lam Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction Journal of Advanced Transportation |
| title | Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction |
| title_full | Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction |
| title_fullStr | Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction |
| title_full_unstemmed | Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction |
| title_short | Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction |
| title_sort | multibranch adaptive fusion graph convolutional network for traffic flow prediction |
| url | http://dx.doi.org/10.1155/2023/8256907 |
| work_keys_str_mv | AT xinzan multibranchadaptivefusiongraphconvolutionalnetworkfortrafficflowprediction AT jasminesiuleelam multibranchadaptivefusiongraphconvolutionalnetworkfortrafficflowprediction |