Recurrent Graph Neural Network Hybrid Model for Spatio-Temporal Traffic Flow Prediction in Intelligent Transportation Systems

Abstract

Traffic congestion is a growing challenge in urban environments, driven by increasing population and vehicle density, leading to significant economic and societal impacts. Accurate traffic forecasting is a key component of Cooperative Intelligent Transportation Systems (C-ITS), enabling proactive traffic management strategies such as adaptive signal control and dynamic routing. However, existing approaches often struggle to capture spatial dependencies in road networks and long-range temporal dynamics in traffic data. This paper proposes TETRA, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM). By incorporating matrix-based memory and memory mixing, xLSTM enables the model to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models. The proposed approach is evaluated on a real-world urban traffic dataset and the widely used METR-LA benchmark. Experimental results show that TETRA outperforms or matches established baseline models representative of the main spatio-temporal paradigms, with the most pronounced gains at medium- and long-term horizons, achieving up to 13.0% lower MAE, 20.0% lower RMSE, and 6.0% higher $$R^2$$ on a real-world dataset relative to the strongest investigated baseline at each horizon. Statistical analysis confirms that these improvements are robust across prediction horizons. Additional evaluations, including ablation and sensitivity analyses, demonstrate the effectiveness and scalability of the proposed model.