WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting

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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Bibliographic Details
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:
Graph neural networks
Traffic forecasting
Urban mobility
Online Access:http://www.sciencedirect.com/science/article/pii/S2667096825000205
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