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...
Saved in:
| Main Authors: | Theodoros Theodoropoulos, Angelos-Christos Maroudis, Uwe Zdun, Antonios Makris, Konstantinos Tserpes |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096825000205 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A novel encrypted traffic detection model based on detachable convolutional GCN-LSTM
by: Xiaogang Yuan, et al.
Published: (2025-07-01) -
Linear attention based spatiotemporal multi graph GCN for traffic flow prediction
by: Yanping Zhang, et al.
Published: (2025-03-01) -
Spatio-temporal prediction of terrorist attacks based on GCN-LSTM
by: Yingjie Du, et al.
Published: (2025-06-01) -
TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting
by: Xiaxia He, et al.
Published: (2024-11-01) -
Deep spatio-temporal dependent convolutional LSTM network for traffic flow prediction
by: Jie Tang, et al.
Published: (2025-04-01)