Aug 2026· International Journal for Global Academic & Scientific Research· Vol 5, pp. 157-180· 0 citations· 34 references
TL;DR
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.
Abstract
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.
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
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
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.
Norman Bereczki, Vilmos Simon· International Journal of Int...· 0 citations
Experimental results on four real-world datasets demonstrate that the proposed MGSTFN achieves superior performance compared to state-of-the-art methods, and the computational efficiency analysis shows that it maintains a favorable balance between prediction accuracy and computational cost, indicating its suitability for large-scale traffic forecasting scenarios.
Yu-Ling Hong, Jiaqi Zhang· Journal of Supercomputing· 0 citations
Precise traffic flow prediction functions as the fundamental cornerstone for the efficient, safe, and reliable operation of intelligent transportation systems (ITS). It not only provides data-driven support for key applications, for instance, real-time traffic signal regulation, proactive congestion mitigation, and personalized route optimization, but also exerts a critical effect on reducing traffic accidents and improving overall urban travel efficiency. However, the traffic system belongs to a complex system, with spatio-temporal dynamics that are both intricate and variable, ranging from predictable fluctuations during morning and evening peak hours to localized propagation effects caused by accidents, as well as seasonal variations and significant nonlinear characteristics. These factors collectively pose substantial challenges to building accurate and reliable prediction models, creating a long-standing technical bottleneck in this field. With the aim of solving the dilemma that existing methods are hardly able to capture traffic flow’s spatio-temporal dependence effectively, we advance an adaptive spatial–temporal diffusion graph convolutional network (ASTD-GCN) for a traffic flow prediction model that integrates adaptive graph learning, diffusion convolution, and bi-directional long short-term memory network (Bi-LSTM) with attention mechanism. The model dynamically constructs the correlation between the nodes of the transportation network through the adaptive graph learning module and accurately describes the spatial topology. The diffusion convolution module realizes multi-order spatial information diffusion based on graph structure, which realizes the effective extraction of the traffic flow’s spatial dependence features. The Bi-LSTM module incorporating the attention mechanism captures the historical and future context information of traffic flow simultaneously through the bidirectional loop structure and the temporal attention mechanism, and strengthens the key time step features. Experimental results on -world traffic datasets PEMS03, PEMS04, PEMS07, and PEMS08 indicate that our proposed model exhibits better predictive precision in traffic flow forecasting tasks than baseline counterparts.
Urban traffic congestion remains a critical challenge for smart city development, particularly in the context of real-time monitoring and prediction for intelligent transportation systems. To address this issue, this study proposes a Digital Twin-based urban traffic prediction framework using a lightweight Diffusion Convolutional Recurrent Neural Network (DCRNN-Lite). The proposed model integrates spatial dependencies among road segments through diffusion convolution and temporal traffic dynamics through recurrent modeling, enabling effective spatiotemporal traffic forecasting with reduced computational complexity. Experiments were conducted on the real-world METR-LA dataset, consisting of traffic speed data from 207 sensors deployed across the Los Angeles highway network. The experimental results demonstrate that DCRNN-Lite achieves stable prediction performance, as reflected by low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), consistent convergence behavior, and strong correlation between predicted and actual traffic speeds at both node and city-wide levels. Despite its simplified architecture, the model effectively captures local traffic variations and global mobility trends, making it suitable for real-time deployment. The findings indicate that the proposed approach provides a favorable balance between accuracy and efficiency, highlighting its potential as a core component for digital twin-enabled smart cities and metaverse-based urban traffic management systems.
H. Awad· International Journal Resear...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
