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.
Abstract
To address the inadequacy of existing models in capturing spatial position information and multi-scale periodic patterns in traffic flow, this paper proposes a Position-Aware Spatio-Temporal Graph Convolutional Network (PASTGCN). The model incorporates a spatio-temporal position embedding module that encodes geographic coordinates and topological attributes into spatial embeddings, combined with sinusoidal encoding for periodic temporal representations. A dual graph learning mechanism fuses a static distance graph based on a Gaussian kernel with an attention-based dynamic graph, while spatial-aware dilated causal convolution enables temporally modulated spatial feature injection. Multigranularity periodic branches further enhance the modeling of daily and weekly patterns. Extensive experiments on the METR-LA dataset show that PASTGCN achieves an RMSE of 2.53 (veh/5 min) and an R2 of 0.982, outperforming several contemporary baselines including DCRNN, STGCN, Graph WaveNet, ASTGCN, AGCRN, and STAEformer. Ablation studies confirm the contribution of each module, with the dynamic graph learning alone reducing RMSE by 0.34. The model also maintains high efficiency with an inference latency of 18.7 ms and a throughput of 53.5 samples/second, demonstrating its suitability for real-time urban traffic prediction. Beyond intelligent transportation applications, 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. This high accuracy and low-latency capability also makes PASTGCN particularly promising for supporting intelligent logistics scheduling in time-sensitive industries.
The ability to forecast traffic conditions in urban environments is essential for intelligent transport systems because it provides proactive congestion management, traffic control and informed urban mobility planning. However, due to the extreme spatial and temporal volatility of traffic flow patterns, conventional statistical, machine learning, and sequence-based deep learning approaches either fail to account for the spatial relationships between highway segments or fail to sufficiently model long-range temporal dynamics. To address these gaps, the present study uses a Spatial-Temporal Graph Neural Network (STGNN) framework that can learn a combination of spatial and temporal relationships in road networks and changing time-varying patterns in traffic flow. The model has a graph-based architecture where graph-convolutional layers are combined with gated recurrent units and transformer-based attention units, thus creating a hybrid architecture that is capable of multi-scale spatio-temporal dependencies. The METR-LA dataset was used in experiments, and it was observed that STGNN had smaller Mean Absolute Percentage Error(MAPE), Mean Absolute Error(MAE), and Root Mean Squared Error(RMSE) at 15, 30, and 60 minute horizons of prediction than the baseline and the implemented models: Sequence to Sequence (Seq2Seq) and Temporal Graph Convolutional Network (T-GCN). The model achieved the best results for the 15-minute interval with an MAE of 2.71, an RMSE of 5.17, and a MAPE of 7.08. The results imply that adaptive spatial learning, together with the temporal sequence modeling, can produce much better forecasting stability, which underlines the potential STGNN-based traffic prediction systems have to contribute to real-time traffic control.
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
Urban traffic flow is difficult to forecast accurately because its evolution is non-linear and governed by dependencies that operate over different spatial and temporal ranges. This paper introduces the Multi-Perspective Spatio-Temporal Feature Fusion Model (MPSTFFM) to describe these dependencies through complementary views. The temporal signal is separated into a slowly varying trend and a residual fluctuation, while the spatial structure is represented by four graphs: first-order adjacency, second-order in-degree, second-order out-degree, and a data-adaptive graph. These graphs respectively encode physical road connectivity, common inflow sources, common outflow destinations, and latent spatial associations. Whereas the first three are constructed from the known network topology, the adaptive graph is learned together with the prediction model and can therefore identify correlations not expressed by physical links. Within each spatio-temporal view, self-attention captures dependencies over long ranges, and convolutional operations extract local patterns. The features learned from all views are subsequently fused into a high-dimensional representation used to predict future flow. Experiments on real-world datasets compare MPSTFFM with twelve methods published during the preceding five years. On these benchmarks MPSTFFM outperforms every baseline, lowering the average MAE, RMSE, and MAPE across the four datasets by 13.04%, 5.28%, and 9.59%, respectively, relative to the best baseline on each one.
A. Marakhimov, Rustem Jalelov, J.K. Kudaybergenov et al.· Italian National Conference...· 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
: 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.
Chu-xia Chen· Proceedings of the 3rd Inter...· 0 citations
A novel method called adaptive diffused spatiotemporal graph convolution network (ADSTGCN) is proposed for accurate traffic flow prediction and achieves superior performance compared to other state-of-the-art methods.
Xiao Luo, Shanshan Wang, Shaobao Li et al.· Journal of Transportation En...· 0 citations