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Title	Enhancing Traffic Prediction With Spatio-Temporal Deep Learning: A Gcn-Lstm Hybrid Model
ID_Doc	24041
Authors	Ghosh A.; Giacobbe M.; Rafiq M.T.; Puliafito A.; Giorgi R.
Year	2025
Published	2025 14th Mediterranean Conference on Embedded Computing, MECO 2025 - Proceedings
DOI	http://dx.doi.org/10.1109/MECO66322.2025.11049131
Abstract	This paper presents a hybrid deep learning model that combines Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) networks for spatio-temporal traffic speed prediction. By leveraging the strengths of GCN in capturing spatial dependencies and LSTM in modeling temporal patterns, the model is capable of accurately predicting traffic speeds across a network of sensors. We evaluate the model on two distinct datasets: METR-LA and PEMS-BAY, demonstrating its effectiveness in both short-term and long-term forecasting. Additionally, the introduction of an attention mechanism further enhances the model's predictive accuracy by focusing on the most relevant sensors. © 2025 IEEE.
Author Keywords	cyber-physical systems; deep learning; embedded computing; graph convolutional networks; smart cities; traffic prediction

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