Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism
Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Yan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study - Dai, Jiamiao ELSEVIER, 2022, europhysics journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:608 ; year:2022 ; day:15 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.physa.2022.128274 |
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ELV059708786 |
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| 520 | |a Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. | ||
| 520 | |a Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. | ||
| 650 | 7 | |a Deep learning |2 Elsevier | |
| 650 | 7 | |a Gated recurrent unit |2 Elsevier | |
| 650 | 7 | |a Graph convolutional network |2 Elsevier | |
| 650 | 7 | |a Traffic flow prediction |2 Elsevier | |
| 700 | 1 | |a Wang, Shengyou |4 oth | |
| 700 | 1 | |a Dong, Chunjiao |4 oth | |
| 700 | 1 | |a Li, Wenquan |4 oth | |
| 700 | 1 | |a Zheng, Wen |4 oth | |
| 700 | 1 | |a Yu, Jingcai |4 oth | |
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10.1016/j.physa.2022.128274 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001981.pica (DE-627)ELV059708786 (ELSEVIER)S0378-4371(22)00832-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zheng, Yan verfasserin aut Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Deep learning Elsevier Gated recurrent unit Elsevier Graph convolutional network Elsevier Traffic flow prediction Elsevier Wang, Shengyou oth Dong, Chunjiao oth Li, Wenquan oth Zheng, Wen oth Yu, Jingcai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:608 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.physa.2022.128274 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 608 2022 15 1215 0 |
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10.1016/j.physa.2022.128274 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001981.pica (DE-627)ELV059708786 (ELSEVIER)S0378-4371(22)00832-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zheng, Yan verfasserin aut Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Deep learning Elsevier Gated recurrent unit Elsevier Graph convolutional network Elsevier Traffic flow prediction Elsevier Wang, Shengyou oth Dong, Chunjiao oth Li, Wenquan oth Zheng, Wen oth Yu, Jingcai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:608 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.physa.2022.128274 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 608 2022 15 1215 0 |
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10.1016/j.physa.2022.128274 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001981.pica (DE-627)ELV059708786 (ELSEVIER)S0378-4371(22)00832-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zheng, Yan verfasserin aut Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Deep learning Elsevier Gated recurrent unit Elsevier Graph convolutional network Elsevier Traffic flow prediction Elsevier Wang, Shengyou oth Dong, Chunjiao oth Li, Wenquan oth Zheng, Wen oth Yu, Jingcai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:608 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.physa.2022.128274 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 608 2022 15 1215 0 |
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10.1016/j.physa.2022.128274 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001981.pica (DE-627)ELV059708786 (ELSEVIER)S0378-4371(22)00832-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zheng, Yan verfasserin aut Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Deep learning Elsevier Gated recurrent unit Elsevier Graph convolutional network Elsevier Traffic flow prediction Elsevier Wang, Shengyou oth Dong, Chunjiao oth Li, Wenquan oth Zheng, Wen oth Yu, Jingcai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:608 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.physa.2022.128274 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 608 2022 15 1215 0 |
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10.1016/j.physa.2022.128274 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001981.pica (DE-627)ELV059708786 (ELSEVIER)S0378-4371(22)00832-9 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Zheng, Yan verfasserin aut Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. Deep learning Elsevier Gated recurrent unit Elsevier Graph convolutional network Elsevier Traffic flow prediction Elsevier Wang, Shengyou oth Dong, Chunjiao oth Li, Wenquan oth Zheng, Wen oth Yu, Jingcai oth Enthalten in North Holland Publ. Co Dai, Jiamiao ELSEVIER Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study 2022 europhysics journal Amsterdam (DE-627)ELV00892340X volume:608 year:2022 day:15 month:12 pages:0 https://doi.org/10.1016/j.physa.2022.128274 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 608 2022 15 1215 0 |
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Enthalten in Effects of psychiatric disorders on ultrasound measurements and adverse perinatal outcomes in Chinese pregnant women: A ten-year retrospective cohort study Amsterdam volume:608 year:2022 day:15 month:12 pages:0 |
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urban road traffic flow prediction: a graph convolutional network embedded with wavelet decomposition and attention mechanism |
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Urban road traffic flow prediction: A graph convolutional network embedded with wavelet decomposition and attention mechanism |
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Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. |
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Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. |
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Urban road traffic flow prediction is the key basis for the development of Intelligent Transportation System. The complex urban structure leads to irregular shape and layout of the road network, which poses a challenge to capture the spatio-temporal correlation of traffic flow at different nodes in the region. In this study, a graph convolutional network model framework embedded with wavelet decomposition and attention mechanism (WDA-GCN) is proposed to predict the traffic flow of each traffic monitor at the regional level by exploring the spatio-temporal correlation among traffic monitors. Specifically, the spatial correlation between different monitors is encoded into two graphs by Graph Convolutional Network (GCN): geographical neighbor graph and functional similarity graph. The Gated Recurrent Unit (GRU) is used to learn the spatial features extracted by GCN, and the attention mechanism is added to improve the prediction accuracy. Finally, the time series data and spatio-temporal correlation of traffic flow are input into the encoder–decoder based on GRU to realize regional traffic flow prediction. The model is validated and compared with the real traffic monitor data in Daxing District of Beijing, China, and the results show that the prediction accuracy of WDA-GCN model can reach 81.03% after embedding wavelet decomposition and attention mechanism, which is better than the traditional time series prediction methods and deep learning methods. |
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Wang, Shengyou Dong, Chunjiao Li, Wenquan Zheng, Wen Yu, Jingcai |
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