2025· Proceedings of the 3rd International Conference on Data Analysis and Machine Learning· 0 citations· 13 references
TL;DR
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.
Abstract
: With the progress of the system of intelligent transportation, traffic flow forecasting is essential to achieve efficient traffic management and control. It can not only optimize real-time traffic flow and improve travel efficiency, but also provide a basis for long-term road network planning and reduce carbon emissions. However, present models are unable to acquire the complicated spatio-temporal dependency of data, leading to low prediction accuracy. Therefore, this research uses a prediction model that incorporates multiple attention mechanisms with spatiotemporal graph convolutional networks (HASTGCN). This model combines channel attention, spatial attention, and temporal attention mechanisms, and designs a spatiotemporal map convolution module to collaboratively model the dynamic spatiotemporal connection of traffic flow collaboratively model. The trial results display the performance of HASTGCN on the PEMS04 dataset (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) indicators) performed better than other models which proving its effectiveness in capturing spatiotemporal characteristics and improving prediction accuracy.
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
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
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
The proposed position-aware spatio-temporal modeling strategy provides a practical reference for information fusion and dynamic state estimation in large-scale wireless sensing networks and electromagnetic signal-driven monitoring systems, supporting future intelligent perception and communication infrastructures.
J. Sun, Y. Liu, Y. Dou et al.· Advanced Electromagnetics· 0 citations
Traffic congestion has become a major obstacle to urban mobility and quality of life due to rapid urbanization and an increase in car ownership. Accurate traffic flow prediction is important for some intelligent transportation systems such as traffic guidance and signal control. The traditional methods including ARIMA and support vector machines cannot deal well with the complicated spatial and temporal correlations in large-scale traffic data. At present, most spatiotemporal graph neural networks (STGNNs) mainly depend on fixed graph structures which are not adaptive to the varying traffic conditions. We suggest a Dynamic Adaptive Attention Graph Learning based STGNN Transformer model (DASTGNN-Transformer) for multi-step traffic flow prediction. The model uses a multi-step Transformer decoder to identify global long-term temporal dependencies and obtains local spatiotemporal features by means of spatiotemporal graph convolutional encoders. Attention maps are generated from real-time data by a dynamic adaptive graph learning module to reflect the changing location relationships. Experiments conducted on PeMSD4 (containing 307 nodes) and PeMSD8 (with 170 nodes) datasets indicate that DASTGNN-Transformer outperforms the mainstream baselines such as STGCN, adaptive graph networks and iTransformer in terms of RMSE of 0.072–0.085, MAE of 0.061–0.079 and MAPE of 8.2%–9.5%. The ablation studies have confirmed the contributions of each module: the RMSE increases to 0.12–0.20 when the dynamic graph module is omitted. Furthermore, the robustness tests have demonstrated consistent performance even in the presence of noise (the RMSE only increases to 0.142 at a noise level of 0.5) and across different datasets. This research provides an integrated approach for dynamic spatiotemporal dependency modeling which significantly enhances the multi-step prediction accuracy.
Xiao-Li Wang· Engineering Research Express· 0 citations
Predicting traffic flow is crucial to optimizing transportation systems and improving urban mobility. Many graph convolution-based models have been proposed to extract spatial-temporal features and predict traffic flow. However, most focus on spatial-temporal and semantic correlation in topological relationships. There are two primary problems to address. Firstly, the convolutional structure in the model focuses on utilizing static spatial dependencies and spatial-temporal relationships in topological structures, while neglecting the different information propagation delays between adjacent nodes in the convolution. Secondly, these methods often stack a large number of complex structures, resulting in a substantial increase in computational time during the model training phase, thereby disregarding the model's requirements for timeliness. In this paper, we propose a novel network called the Attention-Based Spatial-Temporal Fusion Graph Convolution Network (A-STFGCN). We design a spatial-temporal fusion block to extract the spatial-temporal feature correlations with propagation delay errors removed and to capture both long-term and short-term temporal characteristics of the data within a multi-head self-attention mechanism based on a mask matrix. Extensive experiments on five real-world datasets demonstrate that our method achieves the best overall performance while having good computation and data utilization efficiency compared with the eight baseline methods.
Jinpeng Chen, Ziyue Yu, Tao Wang et al.· 0 citations