Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/1/238 |
| _version_ | 1797625994191831040 |
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| author | Hui Zeng Chaojie Jiang Yuanchun Lan Xiaohui Huang Junyang Wang Xinhua Yuan |
| author_facet | Hui Zeng Chaojie Jiang Yuanchun Lan Xiaohui Huang Junyang Wang Xinhua Yuan |
| author_sort | Hui Zeng |
| collection | DOAJ |
| description | Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines. |
| first_indexed | 2024-03-11T10:04:18Z |
| format | Article |
| id | doaj.art-ca1fe1e9b69545f597d52a74275f86ec |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T10:04:18Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ca1fe1e9b69545f597d52a74275f86ec2023-11-16T15:12:59ZengMDPI AGElectronics2079-92922023-01-0112123810.3390/electronics12010238Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow ForecastingHui Zeng0Chaojie Jiang1Yuanchun Lan2Xiaohui Huang3Junyang Wang4Xinhua Yuan5Department of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaDepartment of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaTraffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines.https://www.mdpi.com/2079-9292/12/1/238long short-term spatial-temporal dependenciesspatial-temporal graph convolutiontraffic flow forecasting |
| spellingShingle | Hui Zeng Chaojie Jiang Yuanchun Lan Xiaohui Huang Junyang Wang Xinhua Yuan Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting Electronics long short-term spatial-temporal dependencies spatial-temporal graph convolution traffic flow forecasting |
| title | Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting |
| title_full | Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting |
| title_fullStr | Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting |
| title_full_unstemmed | Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting |
| title_short | Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting |
| title_sort | long short term fusion spatial temporal graph convolutional networks for traffic flow forecasting |
| topic | long short-term spatial-temporal dependencies spatial-temporal graph convolution traffic flow forecasting |
| url | https://www.mdpi.com/2079-9292/12/1/238 |
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