Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 508-519· 0 citations· 48 references
Abstract
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.
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
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, Shao-Bao Li et al.· Journal of Transportation En...· 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
Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting. Existing approaches have developed increasingly sophisticated graph, attention, and decomposition architectures, while the influence of the underlying nonlinear function approximator has received comparatively less attention. In this work, we propose STKAN, a spatio-temporal forecasting architecture that introduces Taylor-polynomial Kolmogorov--Arnold Network modules into spatial and temporal token mixing. STKAN first constructs high-level spatial representations through a learnable soft node-group assignment mechanism, applies group-wise spatial mixing, and subsequently models temporal dependencies over the compressed sequence. Spatial and temporal self-attention layers are further employed to capture long-range interactions. Experiments on five traffic forecasting benchmarks show that STKAN achieves competitive performance and performs better than the evaluated MLP-based variant in the tested settings. These results suggest that the design of nonlinear function approximators can serve as a useful complement to architectural design in spatio-temporal 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.
Accurate traffic flow forecasting hinges on modeling coupled spatio-temporal dependencies rather than treating space and time in isolation. Many prior methods process spatial and temporal features separately — either in series or in parallel — and then fuse them with simple operators, which weakens their ability to capture intrinsic space–time interactions. We propose multi-graph transformer for traffic flow forecasting (MGTTP), a framework with an innovatively designed bidirectional spatio-temporal interaction mechanism: temporal signals guide multi-graph spatial fusion, while spatial context guides attention-based temporal aggregation. It addresses the limitations of static spatial fusion in existing multi-graph models and the serial spatio-temporal modeling paradigm in vanilla transformer baselines, achieving deep coupled modeling of spatio-temporal features. First, MGTTP builds three complementary graphs — adjacency, reachability, and similarity — and applies temporal feature-guided attention to dynamically fuse their multi-dimensional spatial representations. Subsequently, a transformer encoder captures long-term temporal dependencies, with spatial feature-guided attention to aggregate the time series. Finally, a gated fusion module realizes the ultimate fusion of spatio-temporal features for prediction. Extensive experiments on four public real-world traffic datasets demonstrate that MGTTP outperforms all compared mainstream baseline models across all evaluation metrics, with statistically significant performance gaps, validating the effectiveness of the proposed bidirectional spatio-temporal interaction mechanism.
Xiaolong 小龙 Fan 范, J. He 何· 0 citations
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