Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction
The daily long-term traffic prediction is an important urban computing issue, and can give users a global insight into traffic. Accurate traffic prediction is conducive to rational route planning and efficient traffic resource allocation. However, it is challenging to capture the global spatial-temp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822002993 |
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| _version_ | 1849325025089290240 |
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| author | Song Zhang Yanbing Liu Yunpeng Xiao Rui He |
| author_facet | Song Zhang Yanbing Liu Yunpeng Xiao Rui He |
| author_sort | Song Zhang |
| collection | DOAJ |
| description | The daily long-term traffic prediction is an important urban computing issue, and can give users a global insight into traffic. Accurate traffic prediction is conducive to rational route planning and efficient traffic resource allocation. However, it is challenging to capture the global spatial-temporal correlations for daily long-term traffic prediction. In this paper, we propose a spatial-temporal upsampling graph convolutional network (STUGCN) for daily long-term traffic speed prediction. STUGCN uses an innovative upsampling method to capture the global spatial-temporal correlations. Specifically, in spatial dimension, we construct an upsampled road network by adding virtual nodes to the original road network to capture local and global spatial correlations. In temporal dimension, we build a time graph to capture the temporal correlations among adjacent time steps. Besides, we construct a knowledge base, and the global temporal correlations can be captured by upsampling the current day from the knowledge base. Therefore, STUGCN not only preserves the local spatial-temporal correlations, but also has the ability to learn global spatial-temporal correlations. The experimental results on two real-world datasets demonstrate that our approach is approximately 16.4%–17.1%, 14.1%–17.0% and 17.4%–22.4% better than the state-of-the-art in terms of MAE, RMSE and MAPE metrics, respectively. |
| format | Article |
| id | doaj-art-8566d276ed3845bd8f14996c7fb43900 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-8566d276ed3845bd8f14996c7fb439002025-08-20T03:48:31ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-11-0134108996901010.1016/j.jksuci.2022.08.025Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed predictionSong Zhang0Yanbing Liu1Yunpeng Xiao2Rui He3School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Medical Information, Chongqing Medical University, Chongqing 400065, China; Corresponding author.School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaThe daily long-term traffic prediction is an important urban computing issue, and can give users a global insight into traffic. Accurate traffic prediction is conducive to rational route planning and efficient traffic resource allocation. However, it is challenging to capture the global spatial-temporal correlations for daily long-term traffic prediction. In this paper, we propose a spatial-temporal upsampling graph convolutional network (STUGCN) for daily long-term traffic speed prediction. STUGCN uses an innovative upsampling method to capture the global spatial-temporal correlations. Specifically, in spatial dimension, we construct an upsampled road network by adding virtual nodes to the original road network to capture local and global spatial correlations. In temporal dimension, we build a time graph to capture the temporal correlations among adjacent time steps. Besides, we construct a knowledge base, and the global temporal correlations can be captured by upsampling the current day from the knowledge base. Therefore, STUGCN not only preserves the local spatial-temporal correlations, but also has the ability to learn global spatial-temporal correlations. The experimental results on two real-world datasets demonstrate that our approach is approximately 16.4%–17.1%, 14.1%–17.0% and 17.4%–22.4% better than the state-of-the-art in terms of MAE, RMSE and MAPE metrics, respectively.http://www.sciencedirect.com/science/article/pii/S1319157822002993Long-term traffic speed predictionSpatial-temporal upsamplingGraph convolutional networkIntelligent transportation system |
| spellingShingle | Song Zhang Yanbing Liu Yunpeng Xiao Rui He Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction Journal of King Saud University: Computer and Information Sciences Long-term traffic speed prediction Spatial-temporal upsampling Graph convolutional network Intelligent transportation system |
| title | Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction |
| title_full | Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction |
| title_fullStr | Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction |
| title_full_unstemmed | Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction |
| title_short | Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction |
| title_sort | spatial temporal upsampling graph convolutional network for daily long term traffic speed prediction |
| topic | Long-term traffic speed prediction Spatial-temporal upsampling Graph convolutional network Intelligent transportation system |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822002993 |
| work_keys_str_mv | AT songzhang spatialtemporalupsamplinggraphconvolutionalnetworkfordailylongtermtrafficspeedprediction AT yanbingliu spatialtemporalupsamplinggraphconvolutionalnetworkfordailylongtermtrafficspeedprediction AT yunpengxiao spatialtemporalupsamplinggraphconvolutionalnetworkfordailylongtermtrafficspeedprediction AT ruihe spatialtemporalupsamplinggraphconvolutionalnetworkfordailylongtermtrafficspeedprediction |