The Structure-Guided Spatiotemporal Attention Graph Neural Network is proposed, offering a mechanistic account of the model's decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise.
Abstract
Deep spatiotemporal models integrating graph convolutions and attention mechanisms have demonstrated excellent performance in network-level traffic flow prediction, owing to their exceptional ability to capture complex spatiotemporal dependencies. Despite their predictive success, deployment of such models in safety-critical urban systems remains constrained by their inherent lack of transparency. Existing post-hoc diagnostic methods often struggle with spurious correlations and fail to unveil the intrinsic decision-making mechanisms governing traffic dynamics, resulting in suboptimal interpretability and limited operational trustworthiness. To address these challenges, this paper proposes the Structure-Guided Spatiotemporal Attention Graph Neural Network (SGSAN). Departing from traditional architectures that rely on unconstrained adaptive graphs, SGSAN explicitly learns a static Directed Dependency Graph (DDG) to identify the invariant macroscopic propagation paths of traffic states. We further introduce an InfoNCE-based soft-coupling mechanism that anchors the model's dynamic spatiotemporal attention to this structural prior, offering a mechanistic account of the model's decision-making process while ensuring robust forecasting by aligning attention-based reasoning with identified macroscopic dependencies and preventing over-reliance on ephemeral local noise. Furthermore, a decoupled two-stage optimization framework is developed to resolve the fundamental conflict between structural discovery and predictive error minimization. Extensive experiments on multiple real-world datasets demonstrate that SGSAN achieves state-of-the-art predictive accuracy while providing built-in interpretability that organically aligns with the physical logic of traffic networks.
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
Much of the recent work on traffic forecasting relies on two implicit simplifications: representing the road network as an undirected graph and adopting temporally symmetric encoders within the historical observation window. These choices may obscure asymmetric predictive relations among sensors and the chronological ordering of intermediate temporal representations. CausalST addresses these limitations jointly. For spatial modeling, separate source and destination embeddings are assigned to each sensor to construct a learned directed adjacency under road-topology constraints. On the temporal side, dilated causal convolutions serve as a temporal inductive bias, ensuring that the representation at each historical position depends only on current and earlier observations. Delay-aware graph propagation aggregates current and lagged neighbor states over predefined discrete lags. A parallel spatial attention branch captures nonlocal dependencies beyond the physical topology, while a gated fusion unit adaptively integrates the temporal, graph-propagation, and spatial-attention representations. Experiments on PeMS03, PeMS04, and PeMS08 show that CausalST achieves the lowest MAPE among all compared methods while remaining competitive in MAE and RMSE. Ablation results reveal non-additive interactions between directed weighting and delayed aggregation, with the full configuration attaining the lowest MAE on both evaluated datasets.
A novel spatiotemporal forecasting framework, termed Memory-augmented Diffusion Convolutional LSTM network (MDC-LSTM), which integrates a MemBART-inspired memory mechanism, diffusion convolution, regional attention, and LSTM-based temporal modeling and effectively captures both localized spatial patterns and deep inter-temporal relationships.
Xi Chen, Jiajia Chen· Applied Artificial Intellige...· 0 citations
Traffic network partitioning studies show that road networks can be divided into spatially connected subregions with relatively homogeneous traffic states, whose composition may evolve with congestion. However, many hierarchical traffic forecasting models rely on offline or globally shared node-to-region relationships that remain fixed across input windows. We propose DH-STGCN, an input-conditioned dynamic hierarchical spatiotemporal graph convolutional network for traffic flow prediction. The model generates a window-specific soft node-to-region assignment from current spatiotemporal node representations, constructs corresponding regional representations and a regional graph, performs spatiotemporal learning at both sensor and regional scales, and feeds regional information back to the sensor scale. Aggregation consistency and region balancing are used as auxiliary structural regularizers. Experiments on PeMSD3, PeMSD4, PeMSD7, and PeMSD8 show that DH-STGCN reduces average MAE by 3.5–8.2% relative to STGCN and by 2.3–8.2% relative to HGCN. Relative to the strongest competing models, performance is dataset-dependent: Graph WaveNet achieves lower mean MAE on PeMSD3, PeMSD4, and PeMSD7, whereas DH-STGCN performs better on PeMSD8. Controlled hierarchy comparisons favor the window-conditioned assignment over fixed-uniform, static-hard, globally shared, and alternative differentiable assignments. Diagnostic analyses further show that the learned memberships are diffuse yet measurably nonuniform, change across consecutive input windows, and produce distinguishable regional representations. Overall, DH-STGCN provides a flexible input-conditioned hierarchical representation for multistep traffic flow prediction.
Jinghao Hu, Yan He, Runkui Li et al.· Applied Sciences· 0 citations
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.
Chu-xia Chen· Proceedings of the 3rd Inter...· 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.-J. Liu, Y.-L. Dou et al.· Advanced Electromagnetics· 0 citations
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