Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting
With rapid urbanization and technological advancements, increasing population density has posed significant challenges for urban traffic management. Traffic flow forecasting is essential for optimizing traffic signals, regulating traffic flow, and improving overall transportation efficiency. Recent...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11036171/ |
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| _version_ | 1850114115083698176 |
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| author | Liyuan Wang Jiafeng Zhuang Shuo Ma Hai Lin |
| author_facet | Liyuan Wang Jiafeng Zhuang Shuo Ma Hai Lin |
| author_sort | Liyuan Wang |
| collection | DOAJ |
| description | With rapid urbanization and technological advancements, increasing population density has posed significant challenges for urban traffic management. Traffic flow forecasting is essential for optimizing traffic signals, regulating traffic flow, and improving overall transportation efficiency. Recent advancements in deep learning, particularly Graph Convolutional Networks (GCNs), offer more effective solutions for traffic forecasting. However, existing approaches face challenges in modeling traffic trends, periodicity, and randomness. To address these issues, this paper proposes a Frequency-enhanced dynamic Graph Convolutional Network (FGCN) that incorporates sequence decomposition and reconstruction to extract periodic and trend components. Additionally, the proposed method integrates the Fourier transform into the loss function to mitigate label autocorrelation and improve long-term forecasting accuracy. Furthermore, this paper employs a dynamic graph convolution method to separately model spatial and temporal dependencies, capturing traffic flow variations more effectively. Experimental results on four open datasets demonstrate that FGCN not only outperforms state-of-the-art methods but also provides interpretable insights into the dynamic spatial relationships between road segments, while significantly improving forecasting accuracy. |
| format | Article |
| id | doaj-art-50d4cce679764b0ab63e18f8e6fe96a2 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-50d4cce679764b0ab63e18f8e6fe96a22025-08-20T02:36:59ZengIEEEIEEE Access2169-35362025-01-011310345110346210.1109/ACCESS.2025.357933411036171Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow ForecastingLiyuan Wang0Jiafeng Zhuang1Shuo Ma2https://orcid.org/0009-0003-3665-9739Hai Lin3https://orcid.org/0000-0003-1495-7121CCCC Second Highway Consultants Company Ltd., Wuhan, ChinaCCCC Second Highway Consultants Company Ltd., Wuhan, ChinaSchool of Cyber Science and Engineering, Wuhan University, Wuhan, ChinaSchool of Cyber Science and Engineering, Wuhan University, Wuhan, ChinaWith rapid urbanization and technological advancements, increasing population density has posed significant challenges for urban traffic management. Traffic flow forecasting is essential for optimizing traffic signals, regulating traffic flow, and improving overall transportation efficiency. Recent advancements in deep learning, particularly Graph Convolutional Networks (GCNs), offer more effective solutions for traffic forecasting. However, existing approaches face challenges in modeling traffic trends, periodicity, and randomness. To address these issues, this paper proposes a Frequency-enhanced dynamic Graph Convolutional Network (FGCN) that incorporates sequence decomposition and reconstruction to extract periodic and trend components. Additionally, the proposed method integrates the Fourier transform into the loss function to mitigate label autocorrelation and improve long-term forecasting accuracy. Furthermore, this paper employs a dynamic graph convolution method to separately model spatial and temporal dependencies, capturing traffic flow variations more effectively. Experimental results on four open datasets demonstrate that FGCN not only outperforms state-of-the-art methods but also provides interpretable insights into the dynamic spatial relationships between road segments, while significantly improving forecasting accuracy.https://ieeexplore.ieee.org/document/11036171/Traffic flow forecastingdeep learningspatiotemporal modelinggraph convolutional networks |
| spellingShingle | Liyuan Wang Jiafeng Zhuang Shuo Ma Hai Lin Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting IEEE Access Traffic flow forecasting deep learning spatiotemporal modeling graph convolutional networks |
| title | Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting |
| title_full | Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting |
| title_fullStr | Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting |
| title_full_unstemmed | Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting |
| title_short | Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting |
| title_sort | frequency enhanced dynamic graph convolutional networks for traffic flow forecasting |
| topic | Traffic flow forecasting deep learning spatiotemporal modeling graph convolutional networks |
| url | https://ieeexplore.ieee.org/document/11036171/ |
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