Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow
Understanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. Crude oil is the most imported energy in the world. Investigating the crude oil maritime network status and pred...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9709828/ |
| _version_ | 1818329660096774144 |
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| author | Qi Ouyang Tengda Sun Yuanyuan Xue Zhehui Liu |
| author_facet | Qi Ouyang Tengda Sun Yuanyuan Xue Zhehui Liu |
| author_sort | Qi Ouyang |
| collection | DOAJ |
| description | Understanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. Crude oil is the most imported energy in the world. Investigating the crude oil maritime network status and predicting the crude oil traffic flow changes has great significance for the global trade, especially for key crude oil importing/exporting regions and countries. To address this, a system-based approach using long short-term memory and graph convolution network for the crude oil traffic flow forecasting named LGCOTFF is introduced. The LGCOTFF approach constructs a maritime transportation network firstly, and then calculates and predicts the node traffic flow based on trajectory data and crude oil berth geographical position. Firstly, we construct a maritime crude oil transportation network based on supply-demand relationship, ship trajectory and route information. Then, we design an approach to calculate how many crude oil ships finished up-load/offtake tasks in a single week for each port, and gather this data to countries and regions. Finally, we design a deep learning neural network named long short-term memory and graph convolution network (L-GCN) to extract the temporal and spatial characteristics of crude oil transportation, and predict the node traffic flow. We evaluate the proposed model on China, Russia, Middle East and America respectively and observe consistent improvement of more than 10% over state-of-the-art baselines. |
| first_indexed | 2024-12-13T12:51:35Z |
| format | Article |
| id | doaj.art-c95906ffa4504980a3b7f92291108a43 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T12:51:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c95906ffa4504980a3b7f92291108a432022-12-21T23:45:19ZengIEEEIEEE Access2169-35362022-01-0110189221893210.1109/ACCESS.2022.31508529709828Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic FlowQi Ouyang0https://orcid.org/0000-0002-8695-7115Tengda Sun1https://orcid.org/0000-0003-1540-0798Yuanyuan Xue2Zhehui Liu3China Transport Telecommunications and Information Center, Beijing, ChinaChina Transport Telecommunications and Information Center, Beijing, ChinaChina Transport Telecommunications and Information Center, Beijing, ChinaChina Transport Telecommunications and Information Center, Beijing, ChinaUnderstanding maritime network structure and traffic flow changes is a challenging task that must incorporate economic, energy, geopolitics, maritime transportation, and network sciences. Crude oil is the most imported energy in the world. Investigating the crude oil maritime network status and predicting the crude oil traffic flow changes has great significance for the global trade, especially for key crude oil importing/exporting regions and countries. To address this, a system-based approach using long short-term memory and graph convolution network for the crude oil traffic flow forecasting named LGCOTFF is introduced. The LGCOTFF approach constructs a maritime transportation network firstly, and then calculates and predicts the node traffic flow based on trajectory data and crude oil berth geographical position. Firstly, we construct a maritime crude oil transportation network based on supply-demand relationship, ship trajectory and route information. Then, we design an approach to calculate how many crude oil ships finished up-load/offtake tasks in a single week for each port, and gather this data to countries and regions. Finally, we design a deep learning neural network named long short-term memory and graph convolution network (L-GCN) to extract the temporal and spatial characteristics of crude oil transportation, and predict the node traffic flow. We evaluate the proposed model on China, Russia, Middle East and America respectively and observe consistent improvement of more than 10% over state-of-the-art baselines.https://ieeexplore.ieee.org/document/9709828/Crude oil transportation networktraffic flowgraph convolution network |
| spellingShingle | Qi Ouyang Tengda Sun Yuanyuan Xue Zhehui Liu Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow IEEE Access Crude oil transportation network traffic flow graph convolution network |
| title | Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow |
| title_full | Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow |
| title_fullStr | Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow |
| title_full_unstemmed | Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow |
| title_short | Long Short-Term Memory and Graph Convolution Network for Forecasting the Crude Oil Traffic Flow |
| title_sort | long short term memory and graph convolution network for forecasting the crude oil traffic flow |
| topic | Crude oil transportation network traffic flow graph convolution network |
| url | https://ieeexplore.ieee.org/document/9709828/ |
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