SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction
Deep learning based subway passenger flow prediction was widely employed to promote prediction accuracy, which is crucial for subway management and commercial infrastructure planning. However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extra...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001773 |
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| author | Shiyuan Jin Changfeng Jing Sheng Yao Yushan Zhang Pu Zhao Jinlong Zhang |
| author_facet | Shiyuan Jin Changfeng Jing Sheng Yao Yushan Zhang Pu Zhao Jinlong Zhang |
| author_sort | Shiyuan Jin |
| collection | DOAJ |
| description | Deep learning based subway passenger flow prediction was widely employed to promote prediction accuracy, which is crucial for subway management and commercial infrastructure planning. However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extract passengers and enhance prediction accuracy. In this work, a Semantic-Augmented Spatio-temporal Graph Convolutional Network (SASTGCN) model was proposed, which considered semantic similarity, spatiotemporal correlations and spatial heterogeneity to realize the passenger inflow and outflow prediction. The station function was derived from travel characteristics of passengers by data-driven method. The spatiotemporal block including Topology Adaptive Graph Convolutional Network (TAGCN) and ConvNeXt, constructed adaptive spatial topology, depthwise separable convolution and expanded receptive fields to capture spatiotemporal correlations and spatial heterogeneity. The SASTGCN model was validated with the card swiping data in Shanghai, the prediction ability and error analysis results demonstrated the performance outperform nine baseline methods, and the accuracy was improved by approximately 21%. The proposed model can provide inspiration for the follow-up research of passenger flow prediction, traffic pattern recognition and dynamic scheduling. |
| format | Article |
| id | doaj-art-30cf4d32d6b546f6a2a4a62d893078cd |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-30cf4d32d6b546f6a2a4a62d893078cd2025-08-20T03:49:42ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910453010.1016/j.jag.2025.104530SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow predictionShiyuan Jin0Changfeng Jing1Sheng Yao2Yushan Zhang3Pu Zhao4Jinlong Zhang5Langfang Integrated Natural Resources Survey Center, China Geological Survey, Langfang 065000, China; School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China; School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; Corresponding author at: China University of Geoscience Beijing, China.School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, ChinaLangfang Integrated Natural Resources Survey Center, China Geological Survey, Langfang 065000, ChinaLangfang Integrated Natural Resources Survey Center, China Geological Survey, Langfang 065000, ChinaLangfang Integrated Natural Resources Survey Center, China Geological Survey, Langfang 065000, ChinaDeep learning based subway passenger flow prediction was widely employed to promote prediction accuracy, which is crucial for subway management and commercial infrastructure planning. However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extract passengers and enhance prediction accuracy. In this work, a Semantic-Augmented Spatio-temporal Graph Convolutional Network (SASTGCN) model was proposed, which considered semantic similarity, spatiotemporal correlations and spatial heterogeneity to realize the passenger inflow and outflow prediction. The station function was derived from travel characteristics of passengers by data-driven method. The spatiotemporal block including Topology Adaptive Graph Convolutional Network (TAGCN) and ConvNeXt, constructed adaptive spatial topology, depthwise separable convolution and expanded receptive fields to capture spatiotemporal correlations and spatial heterogeneity. The SASTGCN model was validated with the card swiping data in Shanghai, the prediction ability and error analysis results demonstrated the performance outperform nine baseline methods, and the accuracy was improved by approximately 21%. The proposed model can provide inspiration for the follow-up research of passenger flow prediction, traffic pattern recognition and dynamic scheduling.http://www.sciencedirect.com/science/article/pii/S1569843225001773Subway passenger flow predictionFunctional semanticsGraph convolution networksMulti-component |
| spellingShingle | Shiyuan Jin Changfeng Jing Sheng Yao Yushan Zhang Pu Zhao Jinlong Zhang SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction International Journal of Applied Earth Observations and Geoinformation Subway passenger flow prediction Functional semantics Graph convolution networks Multi-component |
| title | SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction |
| title_full | SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction |
| title_fullStr | SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction |
| title_full_unstemmed | SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction |
| title_short | SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction |
| title_sort | sastgcn semantic augmented spatio temporal graph convolutional network for subway flow prediction |
| topic | Subway passenger flow prediction Functional semantics Graph convolution networks Multi-component |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001773 |
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