WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting
Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. Thi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096825000205 |
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| author | Theodoros Theodoropoulos Angelos-Christos Maroudis Uwe Zdun Antonios Makris Konstantinos Tserpes |
| author_facet | Theodoros Theodoropoulos Angelos-Christos Maroudis Uwe Zdun Antonios Makris Konstantinos Tserpes |
| author_sort | Theodoros Theodoropoulos |
| collection | DOAJ |
| description | Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims to expand upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions and the populations that traverse them to establish a more efficient prediction model. The end-product of this scientific endeavor is a novel spatio-temporal graph neural network architecture for regional traffic forecasting referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution significantly outperforms its competitors in an experimental evaluation of 19 forecasting models across several datasets. Finally, an additional ablation study determined that each component of the proposed solution enhances its overall performance. |
| format | Article |
| id | doaj-art-eb2fbeb123f343d6bbbd40b13dd3a117 |
| institution | OA Journals |
| issn | 2667-0968 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj-art-eb2fbeb123f343d6bbbd40b13dd3a1172025-08-20T02:05:17ZengElsevierInternational Journal of Information Management Data Insights2667-09682025-06-015110033810.1016/j.jjimei.2025.100338WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecastingTheodoros Theodoropoulos0Angelos-Christos Maroudis1Uwe Zdun2Antonios Makris3Konstantinos Tserpes4Harokopio University of Athens, Omirou 9, Athens, 17778, Greece; University of Vienna, Software Architecture Research Group, Vienna, 1090, Austria; Corresponding author at: Harokopio University of Athens, Omirou 9, Athens, 17778, Greece.National Technical University of Athens, Heroon Polytechniou 9, Athens, 15780, GreeceUniversity of Vienna, Software Architecture Research Group, Vienna, 1090, AustriaNational Technical University of Athens, Heroon Polytechniou 9, Athens, 15780, Greece; Harokopio University of Athens, Omirou 9, Athens, 17778, GreeceNational Technical University of Athens, Heroon Polytechniou 9, Athens, 15780, Greece; Harokopio University of Athens, Omirou 9, Athens, 17778, GreeceRegional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims to expand upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions and the populations that traverse them to establish a more efficient prediction model. The end-product of this scientific endeavor is a novel spatio-temporal graph neural network architecture for regional traffic forecasting referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution significantly outperforms its competitors in an experimental evaluation of 19 forecasting models across several datasets. Finally, an additional ablation study determined that each component of the proposed solution enhances its overall performance.http://www.sciencedirect.com/science/article/pii/S2667096825000205Graph neural networksTraffic forecastingUrban mobility |
| spellingShingle | Theodoros Theodoropoulos Angelos-Christos Maroudis Uwe Zdun Antonios Makris Konstantinos Tserpes WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting International Journal of Information Management Data Insights Graph neural networks Traffic forecasting Urban mobility |
| title | WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting |
| title_full | WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting |
| title_fullStr | WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting |
| title_full_unstemmed | WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting |
| title_short | WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting |
| title_sort | west gcn lstm weighted stacked spatio temporal graph neural networks for regional traffic forecasting |
| topic | Graph neural networks Traffic forecasting Urban mobility |
| url | http://www.sciencedirect.com/science/article/pii/S2667096825000205 |
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