Nov 2026· Journal of Transportation Engineering Part A Systems· 0 citations· 23 references
TL;DR
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.
Abstract
Recent research typically formulates traffic forecasting tasks as spatiotemporal graph modeling problems. For spatial correlations, researchers typically use predefined graphs to learn short-range spatial dependencies among traffic data, neglecting the learning of long-rang spatial features that are crucial for accurately capturing the dependencies among traffic data. For temporal correlations, studies tend to focus on local continuous correlations while overlooking global temporal correlations. To this end, this paper presents an in-depth study on long-range spatial dependencies and global temporal continuity. Specifically, a novel method called adaptive diffused spatiotemporal graph convolution network (ADSTGCN) is proposed for accurate traffic flow prediction. In this approach, an adaptive adjacency matrix–based graph convolution operation is introduced to learn local spatial features. Building on this, global spatial interdependencies between traffic series are represented through graph diffusion convolution, which also relies on the adaptive adjacency matrix. Additionally, a novel module for learning continuous temporal correlations is introduced to effectively capture the sequential patterns in traffic flow data. Additionally, multihead attention is employed to model global temporal dependencies among the traffic series. The experimental results show that the proposed approach achieves superior performance compared to other state-of-the-art methods.
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
Accurate traffic forecasting is essential for proactive resource management in edge computing, where service demand evolves dynamically across both space and time. In practical cellular edge systems, traffic exhibits strong spatial correlations among neighboring service regions and long-range temporal dependencies driven by user mobility and application behavior. Existing recurrent forecasting approaches can capture short-term dynamics but often struggle to model long-horizon traffic evolution under non-stationary conditions. To address this challenge, we propose a spatiotemporal graph Transformer framework that jointly models spatial interactions and temporal dependencies for traffic forecasting in edge computing. The framework employs graph neural networks to capture spatial correlations among service regions and leverages Transformer-based self-attention to learn long-range temporal patterns from historical traffic observations. By decoupling spatial representation learning from temporal reasoning, the proposed approach provides an effective mechanism for large-scale spatiotemporal traffic modeling. Extensive experiments on a real-world cellular network dataset demonstrate that the proposed graph Transformer consistently outperforms recurrent graph-based baselines, including GCN-RNN, GCN-LSTM, and GCN-GRU models, across multiple forecasting horizons. The resulting forecasts enable more effective proactive resource provisioning and reduce overload risk compared with reactive management strategies. These results highlight the potential of graph-enhanced attention mechanisms for building intelligent and adaptive edge computing systems.
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
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
Traffic flow forecasting requires joint modeling of temporal nonstationarity and diverse evolution patterns of traffic states under non-Euclidean road network constraints. Although recent spatiotemporal graph forecasting methods incorporate dynamic graph learning to alleviate the limitations of static topology, their spatial structure evolution is still largely driven by feature similarity or latent variables. An explicit temporal-prior-modulated mechanism for structure generation remains absent. Temporal modeling and spatial structure learning are often conducted independently at different levels, limiting the ability to capture periodically driven evolution of spatiotemporal dependencies. To address these limitations, a Tensor-Evolving Graph with Temporal Separation Network (TEG-TSNet) for traffic flow forecasting is proposed, which constructs a unified spatial prior via graph Laplacian spectral embedding. A node-level gating mechanism is then applied to enable differentiable temporal decoupling between trend and seasonal components. Next, a tensor-evolving graph encoder embeds time, source nodes, and target nodes into a unified multilinear tensor representation. This design dynamically generates structure-aware adjacency relations for different time slices and models time-varying spatial dependencies via diffusion graph convolution. During decoding, spatiotemporal attention fusion and a sparse expert routing mechanism are employed to strengthen representations of multimodal traffic states. Unlike prior studies that loosely combine temporal modeling and dynamic graph learning, the proposed framework introduces a time-conditioned structure generation paradigm, where temporal priors explicitly govern the evolution of spatial dependencies through a unified tensor formulation. This establishes a tightly coupled spatiotemporal modeling mechanism rather than a simple component-level integration. Extensive experiments on multiple real-world traffic datasets demonstrate stable and competitive forecasting performance and validate effectiveness and generalization. Related codes are available at https://github.com/Haku-zx/TEG-TSNet.
Hongyan Wang, Linlong Chen· Journal of King Saud Univers...· 0 citations
Traffic forecasting plays a crucial role in real-world applications such as traffic management and urban planning. Recent studies have mainly focused on spatio-temporal graph neural networks (STGNNs) and attention-based methods, which have shown promising results. Nevertheless, both approaches model spatial information implicitly, which limits their ability to generalize across different traffic networks. In this paper, we propose Spatio-Temporal Unified Network (STUNet), a framework to explicitly encode spatial features into unified representations and integrate them with temporal information effectively. To obtain spatial representations explicitly, we design a spatial tokenizer that segments the adjacency matrix of the relation graph into patches to serve as spatial tokens. Furthermore, to effectively integrate spatial and temporal representations, we introduce query-aggregate attention, which simulates the process of tracing upstream and downstream nodes and aggregating their information, thereby capturing complex spatio-temporal dependencies. Extensive experiments on traffic benchmarks demonstrate that STUNet achieves generalization across different traffic networks with competitive performance. Code is available at https://github.com/JimmyChen6/STUNet.
Yujun Chen, Shihao Tu, Wenyu Ding et al.· Proceedings of the 32nd ACM...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.