Multi dynamic temporal representation graph convolutional network for traffic flow prediction
Abstract Traffic flow prediction is fundamental to the dynamic control and application of Intelligent Transportation Systems (ITS), which play a crucial role in alleviating road congestion. However, existing approaches have not fully exploited the inherent dynamic and multifaceted spatiotemporal fea...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01157-1 |
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| author | Zuojun Wu Xiaojun Liu Xiaoling Zhang |
| author_facet | Zuojun Wu Xiaojun Liu Xiaoling Zhang |
| author_sort | Zuojun Wu |
| collection | DOAJ |
| description | Abstract Traffic flow prediction is fundamental to the dynamic control and application of Intelligent Transportation Systems (ITS), which play a crucial role in alleviating road congestion. However, existing approaches have not fully exploited the inherent dynamic and multifaceted spatiotemporal features within traffic data, posing significant challenges in achieving accurate traffic flow predictions. To address this issue, we propose a novel Multi Dynamic Temporal Representation Graph Convolutional Network (MDTRGCN). Specifically, we introduce a dynamic graph construction method that learns the time‒space dependencies specific to road segments. On the basis of this method, we develop a dynamic graph convolution module that aggregates the hidden states of neighboring nodes to a focal node by propagating messages across a dynamic adjacency matrix. Moreover, a multiaspect fusion module is presented, which combines auxiliary hidden states learned from traffic volume with primary hidden states derived from traffic speed. Finally, we propose a temporal representation module that infers the content of masked subsequences from small portions of unmasked subsequences and their temporal context. The experimental results on real-world datasets demonstrate that the proposed method not only achieves state-of-the-art predictive performance but also provides clear and interpretable insights into the dynamic spatial relationships of road segments. |
| format | Article |
| id | doaj-art-ab294a32d23a4cffaf90ba873693d1c7 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ab294a32d23a4cffaf90ba873693d1c72025-08-20T01:51:28ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01157-1Multi dynamic temporal representation graph convolutional network for traffic flow predictionZuojun Wu0Xiaojun Liu1Xiaoling Zhang2College of Intelligent Manufacturing and Control Engineering, Shandong Institute of Petroleum and Chemical TechnologyCollege of Intelligent Manufacturing and Control Engineering, Shandong Institute of Petroleum and Chemical TechnologyCollege of Intelligent Manufacturing and Control Engineering, Shandong Institute of Petroleum and Chemical TechnologyAbstract Traffic flow prediction is fundamental to the dynamic control and application of Intelligent Transportation Systems (ITS), which play a crucial role in alleviating road congestion. However, existing approaches have not fully exploited the inherent dynamic and multifaceted spatiotemporal features within traffic data, posing significant challenges in achieving accurate traffic flow predictions. To address this issue, we propose a novel Multi Dynamic Temporal Representation Graph Convolutional Network (MDTRGCN). Specifically, we introduce a dynamic graph construction method that learns the time‒space dependencies specific to road segments. On the basis of this method, we develop a dynamic graph convolution module that aggregates the hidden states of neighboring nodes to a focal node by propagating messages across a dynamic adjacency matrix. Moreover, a multiaspect fusion module is presented, which combines auxiliary hidden states learned from traffic volume with primary hidden states derived from traffic speed. Finally, we propose a temporal representation module that infers the content of masked subsequences from small portions of unmasked subsequences and their temporal context. The experimental results on real-world datasets demonstrate that the proposed method not only achieves state-of-the-art predictive performance but also provides clear and interpretable insights into the dynamic spatial relationships of road segments.https://doi.org/10.1038/s41598-025-01157-1Traffic flow predictionGraph convolutional networksGraph constructionSpatiotemporal characteristics |
| spellingShingle | Zuojun Wu Xiaojun Liu Xiaoling Zhang Multi dynamic temporal representation graph convolutional network for traffic flow prediction Scientific Reports Traffic flow prediction Graph convolutional networks Graph construction Spatiotemporal characteristics |
| title | Multi dynamic temporal representation graph convolutional network for traffic flow prediction |
| title_full | Multi dynamic temporal representation graph convolutional network for traffic flow prediction |
| title_fullStr | Multi dynamic temporal representation graph convolutional network for traffic flow prediction |
| title_full_unstemmed | Multi dynamic temporal representation graph convolutional network for traffic flow prediction |
| title_short | Multi dynamic temporal representation graph convolutional network for traffic flow prediction |
| title_sort | multi dynamic temporal representation graph convolutional network for traffic flow prediction |
| topic | Traffic flow prediction Graph convolutional networks Graph construction Spatiotemporal characteristics |
| url | https://doi.org/10.1038/s41598-025-01157-1 |
| work_keys_str_mv | AT zuojunwu multidynamictemporalrepresentationgraphconvolutionalnetworkfortrafficflowprediction AT xiaojunliu multidynamictemporalrepresentationgraphconvolutionalnetworkfortrafficflowprediction AT xiaolingzhang multidynamictemporalrepresentationgraphconvolutionalnetworkfortrafficflowprediction |