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Title	Long-Term Spatio-Temporal Graph Attention Network For Traffic Forecasting
ID_Doc	35590
Authors	Remmouche B.; Boukraa D.; Zakharova A.; Bouwmans T.; Taffar M.
Year	2025
Published	Expert Systems with Applications, 288
DOI	http://dx.doi.org/10.1016/j.eswa.2025.128244
Abstract	Accurate traffic flow prediction is a critical component of intelligent transportation systems and smart cities, playing an essential role in traffic control, transportation planning, and infrastructure development. Numerous recent research studies highlight the need to enhance prediction accuracy by addressing complex temporal and spatial dependencies. However, due to the complexity of these spatio-temporal patterns, achieving accurate traffic predictions is still a main challenge in long-term scenarios. In this context, we first provide a comprehensive overview of the traffic forecasting to locate where research is going on. Then, we develop a Long-Term Spatio-Temporal Graph Attention Network (LSTGAN) architecture designed to analyze long-term historical data to address the above issue. This architecture encodes several previous time steps and extracts temporal patterns using convolutional layers. These features are then combined with the spatial features captured by a spatial attention module and a graph convolution layer to be processed by a temporal attention decoder responsible for making predictions. Experiments on METR-LA and PEMS-BAY datasets show that our proposed architecture outperforms most existing state-of-the-art baselines. © 2025 The Author(s)
Author Keywords	GCN; Long-term dependency; Spatial/temporal attention; Traffic forecasting

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