Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network
Abstract Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01769-6 |
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| _version_ | 1823861510717308928 |
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| author | Xiang Yin Junyang Yu Xiaoyu Duan Lei Chen Xiaoli Liang |
| author_facet | Xiang Yin Junyang Yu Xiaoyu Duan Lei Chen Xiaoli Liang |
| author_sort | Xiang Yin |
| collection | DOAJ |
| description | Abstract Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic conditions of nearby nodes. In order to solve this problem, this paper proposes a model based on Dynamic Diffusion Spatial-Temporal Graph Convolutional Network. It first combines the dynamic generation matrix and the static distance matrix to grasp real-time traffic conditions, and then introduces the diffusion random walk strategy to capture the correlation of spatial nodes. Finally, the convolutional LSTM module is used to mine the spatiotemporal dependence of traffic data to improve the accuracy of traffic prediction. Compared to several baseline models, the experimental results show that the model is 7% better than other models on several metrics and demonstrates the necessity of the module through ablation experiments. |
| format | Article |
| id | doaj-art-df6d1d1cec884f2da03ef260de6bd954 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-df6d1d1cec884f2da03ef260de6bd9542025-02-09T13:01:23ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111211210.1007/s40747-024-01769-6Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional networkXiang Yin0Junyang Yu1Xiaoyu Duan2Lei Chen3Xiaoli Liang4School of Software, Intelligent Data Processing Engineering Research Center of Henan Province, Henan UniversitySchool of Software, Henan UniversitySchool of Computer Science, Wuhan UniversitySchool of Software, Henan UniversitySchool of Software, Henan UniversityAbstract Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic conditions of nearby nodes. In order to solve this problem, this paper proposes a model based on Dynamic Diffusion Spatial-Temporal Graph Convolutional Network. It first combines the dynamic generation matrix and the static distance matrix to grasp real-time traffic conditions, and then introduces the diffusion random walk strategy to capture the correlation of spatial nodes. Finally, the convolutional LSTM module is used to mine the spatiotemporal dependence of traffic data to improve the accuracy of traffic prediction. Compared to several baseline models, the experimental results show that the model is 7% better than other models on several metrics and demonstrates the necessity of the module through ablation experiments.https://doi.org/10.1007/s40747-024-01769-6Traffic forecastingGraph convolutional networkSpatial-temporalDynamic generation |
| spellingShingle | Xiang Yin Junyang Yu Xiaoyu Duan Lei Chen Xiaoli Liang Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network Complex & Intelligent Systems Traffic forecasting Graph convolutional network Spatial-temporal Dynamic generation |
| title | Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network |
| title_full | Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network |
| title_fullStr | Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network |
| title_full_unstemmed | Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network |
| title_short | Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network |
| title_sort | short term urban traffic forecasting in smart cities a dynamic diffusion spatial temporal graph convolutional network |
| topic | Traffic forecasting Graph convolutional network Spatial-temporal Dynamic generation |
| url | https://doi.org/10.1007/s40747-024-01769-6 |
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