Urban Traffic Flow Forecasting Based on Graph Structure Learning
The transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. The city-level traffic flow forecasting can effectively reflect the flow changes of the traffic system and provide practical guidance for the for...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/atr/7878081 |
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| _version_ | 1850076235842977792 |
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| author | Guangyu Huo Yong Zhang Yimei Lv Hao Ren Baocai Yin |
| author_facet | Guangyu Huo Yong Zhang Yimei Lv Hao Ren Baocai Yin |
| author_sort | Guangyu Huo |
| collection | DOAJ |
| description | The transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. The city-level traffic flow forecasting can effectively reflect the flow changes of the traffic system and provide practical guidance for the formulation of traffic rules. Recent city-level traffic flow forecasting works rely on accurate prior knowledge of graphs (i.e., the spatial relationships between roads), which hinders their effectiveness and application in the real world. We propose a novel framework for urban traffic flow forecasting, which simultaneously infers and utilizes the relationship between time series. In our model, the graph structure learning module dynamically captures the correlation and causation between the different time series and infers a potentially fully connected graph. At the same time, the temporal convolution network captures the temporal correlation between a single time series. The graph neural network uses the graph for forecasting. Our model no longer relies on accurate graph priors and achieves better forecasting results than previous work. Experiments on two public datasets verify that the proposed model is very competitive. |
| format | Article |
| id | doaj-art-da32eb7dc80f4cee810b3ccae792e4ce |
| institution | DOAJ |
| issn | 2042-3195 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-da32eb7dc80f4cee810b3ccae792e4ce2025-08-20T02:46:04ZengWileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/atr/7878081Urban Traffic Flow Forecasting Based on Graph Structure LearningGuangyu Huo0Yong Zhang1Yimei Lv2Hao Ren3Baocai Yin4School of Information Science and TechnologyBeijing Key Laboratory of Multimedia and Intelligent Software TechnologyQingdao Engineering Vocational CollegeChina Electronics Technology Group Taiji Co. Ltd.Beijing Key Laboratory of Multimedia and Intelligent Software TechnologyThe transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. The city-level traffic flow forecasting can effectively reflect the flow changes of the traffic system and provide practical guidance for the formulation of traffic rules. Recent city-level traffic flow forecasting works rely on accurate prior knowledge of graphs (i.e., the spatial relationships between roads), which hinders their effectiveness and application in the real world. We propose a novel framework for urban traffic flow forecasting, which simultaneously infers and utilizes the relationship between time series. In our model, the graph structure learning module dynamically captures the correlation and causation between the different time series and infers a potentially fully connected graph. At the same time, the temporal convolution network captures the temporal correlation between a single time series. The graph neural network uses the graph for forecasting. Our model no longer relies on accurate graph priors and achieves better forecasting results than previous work. Experiments on two public datasets verify that the proposed model is very competitive.http://dx.doi.org/10.1155/atr/7878081 |
| spellingShingle | Guangyu Huo Yong Zhang Yimei Lv Hao Ren Baocai Yin Urban Traffic Flow Forecasting Based on Graph Structure Learning Journal of Advanced Transportation |
| title | Urban Traffic Flow Forecasting Based on Graph Structure Learning |
| title_full | Urban Traffic Flow Forecasting Based on Graph Structure Learning |
| title_fullStr | Urban Traffic Flow Forecasting Based on Graph Structure Learning |
| title_full_unstemmed | Urban Traffic Flow Forecasting Based on Graph Structure Learning |
| title_short | Urban Traffic Flow Forecasting Based on Graph Structure Learning |
| title_sort | urban traffic flow forecasting based on graph structure learning |
| url | http://dx.doi.org/10.1155/atr/7878081 |
| work_keys_str_mv | AT guangyuhuo urbantrafficflowforecastingbasedongraphstructurelearning AT yongzhang urbantrafficflowforecastingbasedongraphstructurelearning AT yimeilv urbantrafficflowforecastingbasedongraphstructurelearning AT haoren urbantrafficflowforecastingbasedongraphstructurelearning AT baocaiyin urbantrafficflowforecastingbasedongraphstructurelearning |