Spatial–temporal attention fusion for traffic speed prediction
Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term pr...
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Gespeichert in:
| Autor*in: |
Zhang, Anqin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 26(2021), 2 vom: 19. Nov., Seite 695-707 |
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volume:26 ; year:2021 ; number:2 ; day:19 ; month:11 ; pages:695-707 |
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10.1007/s00500-021-06521-7 |
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| 520 | |a Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. | ||
| 650 | 4 | |a Traffic speed prediction | |
| 650 | 4 | |a Temporal attention convolutional network | |
| 650 | 4 | |a Spatial attention mechanism | |
| 650 | 4 | |a Spatial–temporal features | |
| 700 | 1 | |a Liu, Qizheng |4 aut | |
| 700 | 1 | |a Zhang, Ting |4 aut | |
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10.1007/s00500-021-06521-7 doi (DE-627)OLC2077782188 (DE-He213)s00500-021-06521-7-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Anqin verfasserin (orcid)0000-0003-1572-9585 aut Spatial–temporal attention fusion for traffic speed prediction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. Traffic speed prediction Temporal attention convolutional network Spatial attention mechanism Spatial–temporal features Liu, Qizheng aut Zhang, Ting aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2021), 2 vom: 19. Nov., Seite 695-707 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2021 number:2 day:19 month:11 pages:695-707 https://doi.org/10.1007/s00500-021-06521-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2021 2 19 11 695-707 |
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10.1007/s00500-021-06521-7 doi (DE-627)OLC2077782188 (DE-He213)s00500-021-06521-7-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Anqin verfasserin (orcid)0000-0003-1572-9585 aut Spatial–temporal attention fusion for traffic speed prediction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. Traffic speed prediction Temporal attention convolutional network Spatial attention mechanism Spatial–temporal features Liu, Qizheng aut Zhang, Ting aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2021), 2 vom: 19. Nov., Seite 695-707 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2021 number:2 day:19 month:11 pages:695-707 https://doi.org/10.1007/s00500-021-06521-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2021 2 19 11 695-707 |
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10.1007/s00500-021-06521-7 doi (DE-627)OLC2077782188 (DE-He213)s00500-021-06521-7-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Anqin verfasserin (orcid)0000-0003-1572-9585 aut Spatial–temporal attention fusion for traffic speed prediction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. Traffic speed prediction Temporal attention convolutional network Spatial attention mechanism Spatial–temporal features Liu, Qizheng aut Zhang, Ting aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2021), 2 vom: 19. Nov., Seite 695-707 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2021 number:2 day:19 month:11 pages:695-707 https://doi.org/10.1007/s00500-021-06521-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2021 2 19 11 695-707 |
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10.1007/s00500-021-06521-7 doi (DE-627)OLC2077782188 (DE-He213)s00500-021-06521-7-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Zhang, Anqin verfasserin (orcid)0000-0003-1572-9585 aut Spatial–temporal attention fusion for traffic speed prediction 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. Traffic speed prediction Temporal attention convolutional network Spatial attention mechanism Spatial–temporal features Liu, Qizheng aut Zhang, Ting aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 26(2021), 2 vom: 19. Nov., Seite 695-707 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:26 year:2021 number:2 day:19 month:11 pages:695-707 https://doi.org/10.1007/s00500-021-06521-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 26 2021 2 19 11 695-707 |
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Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract Accurate vehicle speed prediction is of great significance to the urban traffic intelligent control system. However, in terms of traffic speed prediction, the modules that integrate temporal and spatial features in the existing traffic speed prediction methods are effective in short-term prediction, but the medium-term or long-term prediction errors are relatively large. In order to reduce the errors of existing methods in short-term prediction and predict the medium-term and long-term traffic speed, this paper proposes a traffic speed prediction method that combines attention and Spatial–temporal features, referred to as ASTCN. Specifically, unlike previous methods, ASTCN can use the temporal attention convolutional network (ATCN) to separately extract temporal features from the traffic speed features collected by each sensor, and use the spatial attention mechanism to extract spatial features and then perform spatial–temporal feature fusion. Experiments on three real-world datasets show that the proposed ASTCN model outperforms the state-of-the-art baselines. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| title_short |
Spatial–temporal attention fusion for traffic speed prediction |
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https://doi.org/10.1007/s00500-021-06521-7 |
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Liu, Qizheng Zhang, Ting |
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Liu, Qizheng Zhang, Ting |
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10.1007/s00500-021-06521-7 |
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2024-07-03T17:16:40.530Z |
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