Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network
Existing traffic flow prediction methods generally only consider the spatiotemporal characteristics of traffic flow. However, in addition to the spatiotemporal characteristics, the interference of various external factors needs to be considered in traffic flow prediction, including severe weather, m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/2348375 |
| _version_ | 1811181777163649024 |
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| author | Shun Wang Yimei Lv Yuan Peng Xinglin Piao Yong Zhang |
| author_facet | Shun Wang Yimei Lv Yuan Peng Xinglin Piao Yong Zhang |
| author_sort | Shun Wang |
| collection | DOAJ |
| description | Existing traffic flow prediction methods generally only consider the spatiotemporal characteristics of traffic flow. However, in addition to the spatiotemporal characteristics, the interference of various external factors needs to be considered in traffic flow prediction, including severe weather, major events, traffic control, and metro failures. The current research still cannot fully use the information contained in these external factors. To address this issue, we propose a novel metro traffic flow prediction method (KGR-STGNN) based on knowledge graph representation learning. We construct a knowledge graph that stores factors related to metro traffic networks. Through the knowledge graph representation learning technology, we can learn the influence representation of external factors from the traffic knowledge graph, which can better incorporate the influence of external factors into the prediction model based on the spatiotemporal graph neural network. Experimental results demonstrate the effectiveness of our proposed model. |
| first_indexed | 2024-04-11T09:21:58Z |
| format | Article |
| id | doaj.art-ecf17ebbae6849f0819e458ce2a50621 |
| institution | Directory Open Access Journal |
| issn | 2042-3195 |
| language | English |
| last_indexed | 2024-04-11T09:21:58Z |
| publishDate | 2022-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj.art-ecf17ebbae6849f0819e458ce2a506212022-12-22T04:32:09ZengHindawi-WileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/2348375Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural NetworkShun Wang0Yimei Lv1Yuan Peng2Xinglin Piao3Yong Zhang4Beijing Artificial Intelligence InstituteQingdao Engineering Vocational CollegeChina Electronics Technology GroupBeijing Artificial Intelligence InstituteBeijing Artificial Intelligence InstituteExisting traffic flow prediction methods generally only consider the spatiotemporal characteristics of traffic flow. However, in addition to the spatiotemporal characteristics, the interference of various external factors needs to be considered in traffic flow prediction, including severe weather, major events, traffic control, and metro failures. The current research still cannot fully use the information contained in these external factors. To address this issue, we propose a novel metro traffic flow prediction method (KGR-STGNN) based on knowledge graph representation learning. We construct a knowledge graph that stores factors related to metro traffic networks. Through the knowledge graph representation learning technology, we can learn the influence representation of external factors from the traffic knowledge graph, which can better incorporate the influence of external factors into the prediction model based on the spatiotemporal graph neural network. Experimental results demonstrate the effectiveness of our proposed model.http://dx.doi.org/10.1155/2022/2348375 |
| spellingShingle | Shun Wang Yimei Lv Yuan Peng Xinglin Piao Yong Zhang Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network Journal of Advanced Transportation |
| title | Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network |
| title_full | Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network |
| title_fullStr | Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network |
| title_full_unstemmed | Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network |
| title_short | Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network |
| title_sort | metro traffic flow prediction via knowledge graph and spatiotemporal graph neural network |
| url | http://dx.doi.org/10.1155/2022/2348375 |
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