MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction
Abstract With the development of intelligent transportation technology, road travel time prediction has become an important research direction. Owing to the complex periodic dependence and non‐linear features of road travel time series, achieving accurate and effective predictions remains a challeng...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12440 |
| _version_ | 1827381592259035136 |
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| author | Zhihua Zhao Li Chao Xue Zhang Nengfu Xie Qingtian Zeng |
| author_facet | Zhihua Zhao Li Chao Xue Zhang Nengfu Xie Qingtian Zeng |
| author_sort | Zhihua Zhao |
| collection | DOAJ |
| description | Abstract With the development of intelligent transportation technology, road travel time prediction has become an important research direction. Owing to the complex periodic dependence and non‐linear features of road travel time series, achieving accurate and effective predictions remains a challenging task. Most existing traffic sequence prediction methods lack modelling of the dynamic correlation between multiple period information, resulting in unsatisfactory prediction results. To address this, a multi‐component attention graph convolutional network (MCAGCN) is proposed for road travel time prediction. First, the spatial‐temporal features of three historical components (hourly, daily and weekly) are modelled individually. A skip attention layer is then used to fuse multi‐scale spatial‐temporal features to enhance the model's feature extraction capabilities. Secondly, a component attention layer is proposed to calculate the correlation between different components using the temporal features of the prediction moment, to achieve dynamic modelling between different period information. The experimental results on the Tianchi, METR‐LA, and PeMS‐BAY datasets, which are real‐world traffic forecasting datasets, demonstrate the superiority of MCAGCN. |
| first_indexed | 2024-03-08T13:58:31Z |
| format | Article |
| id | doaj.art-e31abcf463814809bad56d7fe0d272fb |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-03-08T13:58:31Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-e31abcf463814809bad56d7fe0d272fb2024-01-15T09:12:38ZengWileyIET Intelligent Transport Systems1751-956X1751-95782024-01-0118113915310.1049/itr2.12440MCAGCN: Multi‐component attention graph convolutional neural network for road travel time predictionZhihua Zhao0Li Chao1Xue Zhang2Nengfu Xie3Qingtian Zeng4College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaCollege of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaCollege of Computer Science and Engineering Shandong University of Science and Technology Qingdao ChinaKey Laboratory of Agricultural Blockchain Application Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing ChinaCollege of Electronic and Information Engineering Shandong University of Science and Technology Qingdao ChinaAbstract With the development of intelligent transportation technology, road travel time prediction has become an important research direction. Owing to the complex periodic dependence and non‐linear features of road travel time series, achieving accurate and effective predictions remains a challenging task. Most existing traffic sequence prediction methods lack modelling of the dynamic correlation between multiple period information, resulting in unsatisfactory prediction results. To address this, a multi‐component attention graph convolutional network (MCAGCN) is proposed for road travel time prediction. First, the spatial‐temporal features of three historical components (hourly, daily and weekly) are modelled individually. A skip attention layer is then used to fuse multi‐scale spatial‐temporal features to enhance the model's feature extraction capabilities. Secondly, a component attention layer is proposed to calculate the correlation between different components using the temporal features of the prediction moment, to achieve dynamic modelling between different period information. The experimental results on the Tianchi, METR‐LA, and PeMS‐BAY datasets, which are real‐world traffic forecasting datasets, demonstrate the superiority of MCAGCN.https://doi.org/10.1049/itr2.12440data analysisdata mininggraph theory |
| spellingShingle | Zhihua Zhao Li Chao Xue Zhang Nengfu Xie Qingtian Zeng MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction IET Intelligent Transport Systems data analysis data mining graph theory |
| title | MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction |
| title_full | MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction |
| title_fullStr | MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction |
| title_full_unstemmed | MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction |
| title_short | MCAGCN: Multi‐component attention graph convolutional neural network for road travel time prediction |
| title_sort | mcagcn multi component attention graph convolutional neural network for road travel time prediction |
| topic | data analysis data mining graph theory |
| url | https://doi.org/10.1049/itr2.12440 |
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