Support-conditioned sensor-adaptive meta-graph learning (SC-SAMG) is proposed, which derives target-node representations and spatial dependencies from a short support period and consistently outperforms fine-tuned gated recurrent unit (GRU), adaptive graph convolutional recurrent network (AGCRN), and diffusion convolut...
Can Wang, Zhiyu Wang, Weijie Wang et al.· Italian National Conference...· 0 citations
This study develops an integrated framework combining multi-output prediction, NSGA-II multi-objective optimization, SHAP-based interpretation, LOWESS nonlinear analysis, and DirectLiNGAM causal inference that provides evidence for multidimensional accident-consequence category prediction and differentiated traffic saf...
Yanni Ju, Wanqiu Li, Di Tang et al.· Applied Sciences· 0 citations
The results highlight the robustness and versatility of the proposed framework in real-world traffic scenarios, demonstrating its potential to enhance prediction accuracy and scalability through key-node identification and selective coverage analysis.
Jing Gan, Dongmei Yan, Yue Wang et al.· Systems· 0 citations
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