Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting.
Abstract
In dynamic Vehicular Edge Computing (VEC) environments, rapidly changing vehicle mobility and traffic lead to fluctuating edge resource demands, challenging task offloading and scheduling. Accurate prediction of future traffic flow and traffic-state-derived workload representations is thus crucial for proactive resource management. To address the limitations of existing methods in short-term dynamic characterization, complex spatial interaction modeling, and heterogeneous target prediction, this paper proposes a Spatio-Temporal Graph Attention Network (TA-STGAT). The proposed model constructs multi-dimensional RSU-level state sequences from simulated trajectories generated on a real-world road network and separately forecasts vehicle flow within RSU coverage areas and the associated traffic-state-derived workload representation under a unified spatio-temporal modeling framework. By integrating gated dilated temporal convolutions with a topology-constrained multi-head graph attention mechanism, the model captures multi-scale temporal dependencies and nonlinear spatial correlations. Experimental results show that, compared with the best-performing baseline in terms of RMSE for each forecasting task, TA-STGAT reduces RMSE by 10.89% and 11.29% in workload-representation prediction and traffic flow prediction, respectively, demonstrating its effectiveness for short-term edge-state forecasting.
A spatiotemporal graph Transformer framework that jointly models spatial interactions and temporal dependencies for traffic forecasting in edge computing and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations is proposed.
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
Network-scale traffic speed prediction plays a central role in signal timing, routing, traffic control and congestion management. However, existing methods face challenges in capturing complex spatiotemporal dependencies among road segments, and in achieving computational efficiency in large-scale urban networks. To address these challenges, we propose an enhanced Dynamic Spatiotemporal Residual Network (DST-ResNet) framework for network-scale traffic speed prediction. First, the method employs a multi-scale grid partitioning strategy to segment urban road networks at varying levels of granularity, enabling precise predictions at both local and global scales. Second, a dynamic convolution mechanism is introduced to integrate real-time traffic data with static road network features, allowing the model to adaptively capture the dynamic and heterogeneous characteristics of urban traffic. Then, a spatiotemporal attention mechanism is incorporated to capture sequential dependencies in traffic patterns, improving the accuracy and robustness of long-term traffic speed prediction. Last, we conduct experiments based on GPS trajectory data from taxis in Xi’an city, China. The results demonstrate that DST-ResNet outperforms five baseline models. The model maintains stable performance across five spatial resolutions, and a mask-aware retraining strategy reduces the average MSE by 49.65% under missing observations. Ablation studies confirm the contributions of dynamic convolution and attention mechanisms, while residual units mitigate the vanishing gradient problem, facilitating deeper network training.
Recently, traffic prediction, which serves as a fundamental component of intelligent transportation systems (ITS), has become essential for traffic planning and management. Nevertheless, existing traffic prediction methods still face challenges in capturing critical information from traffic data, such as spatial-temporal dependencies, dynamics, periodicity, heterogeneity, etc. Therefore, a novel spatial-temporal graph learning network (ST-GLN) based on multi-domain temporal gated self-attention and attentive spectral graph attention network is proposed in this paper. It can efficiently capturing crucial information mentioned above from traffic data. Specifically, an effective data embedding module is first constructed to project raw traffic data into high-dimensional representations, in which essential positional and periodic information is encoded. Second, we design spatial-temporal encoders to extract critical information from traffic data, namely, spatial-temporal dependencies, dynamics, and heterogeneity. Within each spatial-temporal encoder, we employ a temporal encoder consisting of time-domain gated self-attention and residual frequency-domain gated self-attention to capture temporal dependencies, dynamics, and heterogeneity. Meanwhile, a spatial encoder composed of self-scaled dynamic diffusion spectral graph neural network and multi-view graph self-attention is proposed to learn spatial dependencies, dynamics, and heterogeneity. Thirdly, we adopt a multi-layer perceptron as the output embedding module to strengthen its capability of learning deep representations and generating final prediction results. Finally, we conduct extensive experiments on several representative traffic datasets to demonstrate that our method achieves superior performance against existing state-of-the-art approaches. For example, we boost the MAE, RMSE and MAPE by up to 16.83, 21.95 and 14.15% when we use ST-GLN to predict PEMS08. And the MAE, RMSE and MAPE are enhanced by up to 17.17, 23.34 and 13.11% when we use ST-GLN to predict PEMS04.
Kai Chen, Chen-Yang Huang· Engineering Research Express· 0 citations
A novel adaptive traffic signal control framework by integrating Attention-based Spatio-Temporal Graph Convolutional Networks (ASTGCN) with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed, providing a scalable and data-driven solution for intelligent traffic signal control in urban traffic networks, supporting the development of smart mobility systems.
Jing Wang, Xiaopeng Wang, Yang Mo et al.· Scientific Reports· 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
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