Jul 2026· International Journal of Intelligent Transportation Systems Research· 0 citations· 14 references
TL;DR
TETRA is proposed, a hybrid spatio-temporal traffic forecasting model that integrates Graph Convolutional Networks (GCNs) with Extended Long Short-Term Memory (xLSTM) to capture complex multi-timescale temporal patterns, including congestion propagation and delayed recovery dynamics, which are not well represented by conventional recurrent models.
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.
This framework introduces an adaptive graph learning module that dynamically infers meaningful connectivity relationships among traffic sensors—not relying on fixed geographic or distance-based assumptions—but instead leveraging real-time traffic correlations and node-level embeddings, enabling effective modeling of both localized spatial interactions and multi-scale temporal dependencies across varying prediction horizons.
Zhengxu Luan, Huan Wang, Miaobowen Wang et al.· Computers and artificial int...· 0 citations
A robust focused comparative evaluation of seven traffic forecasting approaches suggests that traffic forecasting models should be assessed not only by clean-data accuracy but also by their robustness under degraded sensing conditions before deployment in real intelligent transportation systems.
Shreya N. Desai, Kasim Ishaque Ghanchi, Ali Mehdi Mirza et al.· International journal of res...· 0 citations
As transportation networks grow increasingly complex and data-rich, the need for intelligent, adaptive routing mechanisms has become essential for efficient and resilient mobility operations. This study presents a deep learning–enabled framework for real-time dynamic route optimization in logistics systems, addressing fundamental limitations of traditional static routing and heuristic-based decision approaches. The proposed architecture integrates long short-term memory (LSTM) networks with spatio- temporal graph convolutional networks (ST-GCN) to model nonlinear temporal evolution and spatial dependencies in traffic flows, GPS trajectories, meteorological conditions, and road network structures. By capturing these complex patterns, the predictive module generates highly accurate short-term forecasts of congestion levels and delivery delays, which are subsequently incorporated into an adaptive routing engine that continuously updates vehicle paths in response to evolving network conditions. Comprehensive preprocessing of multimodal traffic and environmental datasets, advanced feature engineering, and supervised training of the LSTM and ST-GCN models are employed. Model performance is assessed via mean absolute error (MAE), root mean square error (RMSE), and ROC–AUC. Experimental results show substantial gains over baseline predictors and conventional routing: a 45.6% reduction in MAE, a 39.5% reduction in RMSE, and an ROC–AUC of 0.91 for delay prediction, while enabling an estimated 12.3% reduction in carbon emissions. These improvements translate into measurable reductions in travel time and fuel consumption, underscoring the system’s potential to enhance operational resilience, environmental sustainability, and decision efficiency.
Ahmed Abdel-Wahab Rakha, Mohammed S. A. Elsersy· Informatica· 0 citations
Spatio-temporal traffic forecasting, with reliable temporal and spatial information, is a crucial component of any urban transportation network for intelligent transportation systems and the management of mobility. This study proposes an Adaptive Spatio-Temporal Forecasting (ASTF) framework based on Graph Neural Networks (GNNs), Temporal Convolutional Networks (TCNs) and an adaptive attention mechanism. The GNN models spatial relationships between interconnected traffic sensors and TCN models temporal patterns and changing traffic conditions. Adaptive attention additionally enhances prediction by putting more weight on important sensor positions. The framework is tested on the well-known METR-LA and PEMS-BAY benchmark datasets that includes measurements of traffic speed from urban road networks. The results show the effectiveness of spatial graph learning, temporal convolution and adaptive attention in forecasting traffic speeds across the benchmark urban transportation datasets and provide a promising way to apply the proposed method in real scenarios.
Unknown authors· International Journal for Gl...· 0 citations
A prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN) and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model is used.
Chu-xia Chen· Proceedings of the 3rd Inter...· 0 citations
Accurate urban traffic flow prediction is essential for intelligent transportation systems. Traditional time series models and conventional recurrent neural networks (RNNs) often struggle to capture complex nonlinear and long-term temporal dependencies while maintaining computational efficiency. To address this issue, this paper proposes an improved reservoir computing model, termed I-ICM-RC, in which a Simplified Continuous Coupled Neural Network (SCCNN) is employed as the reservoir module. By incorporating bio-inspired integrate-and-fire dynamics and structured local coupling, the proposed model enhances the representation of spatiotemporal patterns in traffic flow. Experiments conducted on real-world traffic datasets evaluate the proposed method under multi-step forecasting scenarios. The results show that the proposed model generally achieves better performance than the standard Echo State Network (ESN), particularly in short-and medium-term prediction tasks, while maintaining low computational cost. These findings indicate that the proposed approach provides an efficient and robust alternative for traffic flow prediction.
Xinyu Shi, Jiatai Cheng, Tailai Bai et al.· International Conference on...· 0 citations