Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution
Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-08-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023173/ |
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| _version_ | 1841533661519806464 |
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| author | Xiongtao ZHANG Jingyu ZHENG Qing SHEN Danfeng SUN Yunliang JIANG |
| author_facet | Xiongtao ZHANG Jingyu ZHENG Qing SHEN Danfeng SUN Yunliang JIANG |
| author_sort | Xiongtao ZHANG |
| collection | DOAJ |
| description | Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets. |
| format | Article |
| id | doaj-art-b1565a27cabb4c9ea010c04ce0ee9d96 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-b1565a27cabb4c9ea010c04ce0ee9d962025-01-15T02:58:12ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-08-01399711059561008Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolutionXiongtao ZHANGJingyu ZHENGQing SHENDanfeng SUNYunliang JIANGAiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023173/The National Natural Science Foundation of ChinaThe “Pioneer” and “Leading Goose” Research ＆ Development Program of Zhejiang Provinceintelligent transportationdynamic graph convolutionmulti-head attention |
| spellingShingle | Xiongtao ZHANG Jingyu ZHENG Qing SHEN Danfeng SUN Yunliang JIANG Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution Dianxin kexue The National Natural Science Foundation of China The “Pioneer” and “Leading Goose” Research ＆ Development Program of Zhejiang Province intelligent transportation dynamic graph convolution multi-head attention |
| title | Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution |
| title_full | Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution |
| title_fullStr | Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution |
| title_full_unstemmed | Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution |
| title_short | Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution |
| title_sort | multi channel spatial temporal traffic flow prediction based on hybrid static dynamic graph convolution |
| topic | The National Natural Science Foundation of China The “Pioneer” and “Leading Goose” Research ＆ Development Program of Zhejiang Province intelligent transportation dynamic graph convolution multi-head attention |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023173/ |
| work_keys_str_mv | AT xiongtaozhang multichannelspatialtemporaltrafficflowpredictionbasedonhybridstaticdynamicgraphconvolution AT jingyuzheng multichannelspatialtemporaltrafficflowpredictionbasedonhybridstaticdynamicgraphconvolution AT qingshen multichannelspatialtemporaltrafficflowpredictionbasedonhybridstaticdynamicgraphconvolution AT danfengsun multichannelspatialtemporaltrafficflowpredictionbasedonhybridstaticdynamicgraphconvolution AT yunliangjiang multichannelspatialtemporaltrafficflowpredictionbasedonhybridstaticdynamicgraphconvolution |