ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data
Aiming at the challenges of spatio-temporal heterogeneity and dynamic correlation in the prediction of travel origins and destinations for urban residents, this study invokes a spatio-temporal graph attention network (ST-GAT) OD prediction model based on cell phone signaling data. This model innovat...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11028587/ |
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| _version_ | 1849336900405428224 |
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| author | Xianguang Jia Weijie Fang Yingying Lyu Jinke Zhang Mengyi Guo Dong Li Jie Qu Fengxiang Guo |
| author_facet | Xianguang Jia Weijie Fang Yingying Lyu Jinke Zhang Mengyi Guo Dong Li Jie Qu Fengxiang Guo |
| author_sort | Xianguang Jia |
| collection | DOAJ |
| description | Aiming at the challenges of spatio-temporal heterogeneity and dynamic correlation in the prediction of travel origins and destinations for urban residents, this study invokes a spatio-temporal graph attention network (ST-GAT) OD prediction model based on cell phone signaling data. This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. The data cleaning innovatively uses the DTW data cleaning model, and MSE, MAE, RMSE are used as evaluation indexes. In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. The research in this paper provides an effective solution for the application of Intelligent Transportation System (ITS) and demonstrates the potential of Deep Learning (DL) in urban transportation planning. |
| format | Article |
| id | doaj-art-dfadad3105e04762ad58ed96ba3b6afe |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dfadad3105e04762ad58ed96ba3b6afe2025-08-20T03:44:51ZengIEEEIEEE Access2169-35362025-01-011310092010093310.1109/ACCESS.2025.357761411028587ST-GAT Resident OD Prediction Model Based on Mobile Signaling DataXianguang Jia0Weijie Fang1https://orcid.org/0009-0005-1076-7445Yingying Lyu2Jinke Zhang3Mengyi Guo4Dong Li5Jie Qu6Fengxiang Guo7Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, ChinaAiming at the challenges of spatio-temporal heterogeneity and dynamic correlation in the prediction of travel origins and destinations for urban residents, this study invokes a spatio-temporal graph attention network (ST-GAT) OD prediction model based on cell phone signaling data. This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. The data cleaning innovatively uses the DTW data cleaning model, and MSE, MAE, RMSE are used as evaluation indexes. In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. The research in this paper provides an effective solution for the application of Intelligent Transportation System (ITS) and demonstrates the potential of Deep Learning (DL) in urban transportation planning.https://ieeexplore.ieee.org/document/11028587/OD predictioncell phone signaling dataST-GAN modeldeep learning |
| spellingShingle | Xianguang Jia Weijie Fang Yingying Lyu Jinke Zhang Mengyi Guo Dong Li Jie Qu Fengxiang Guo ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data IEEE Access OD prediction cell phone signaling data ST-GAN model deep learning |
| title | ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data |
| title_full | ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data |
| title_fullStr | ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data |
| title_full_unstemmed | ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data |
| title_short | ST-GAT Resident OD Prediction Model Based on Mobile Signaling Data |
| title_sort | st gat resident od prediction model based on mobile signaling data |
| topic | OD prediction cell phone signaling data ST-GAN model deep learning |
| url | https://ieeexplore.ieee.org/document/11028587/ |
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