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该论文提出 GCRN+（图卷积循环网络），利用稀疏固定传感器（仅覆盖 15.6% 路段）结合历史探针车数据监督，在达拉斯-沃思堡高速网络上实现全网当前及未来一小时车速的估计与预测，性能显著优于历史均值及 STID、STAEformer 等基线模型。
This empirical study finds that joint upper-bound coverage of traffic speed forecasts can be substantially improved via calibration, yet improved coverage does not translate into better route-choice utility in frozen offline proxy tasks on Beijing and Chengdu road-speed data.
这篇论文通过分层诊断研究探讨了交通预测精度提升为何未能改善信号控制决策，发现预测价值取决于时间可观测性、动作可识别性、动力学一致性和目标对齐，并提出了一套实用的诊断协议。
AdaST introduces an adaptive spatial-temporal forecasting framework that decomposes inputs into components with distinct coupling regimes using heterogeneity-aware experts, then recomposes them with a correlation-informed module to outperform state-of-the-art baselines on traffic, climate, and energy forecasting.
The paper proposes ADNet, an adaptive decomposition network for multi-step traffic forecasting that learns to disentangle heterogeneous traffic dynamics into dominant and residual components via spectral decomposition, achieving superior performance on the TraffiDent dataset.
This paper proposes a spatiotemporal graph Transformer framework for traffic forecasting in edge computing, combining graph neural networks for spatial correlations and Transformer self-attention for long-range temporal dependencies. Experiments on real-world cellular data show it outperforms recurrent graph-based baselines like GCN-LSTM and GCN-GRU.
EMAGN is a new efficient multi-attention graph network for traffic forecasting that linearizes self-attention via learned clustering, reducing complexity from quadratic to linear while maintaining accuracy close to full-attention models, with significant reductions in training time, inference time, and GPU memory.
This paper introduces STKAN, a spatio-temporal forecasting architecture that integrates Taylor-polynomial Kolmogorov-Arnold Network modules for spatial and temporal token mixing. Experiments on five traffic benchmarks show competitive performance, suggesting nonlinear function approximators can complement architectural design.
This paper investigates whether complex transformer-based attention is necessary for global spatial information extraction in traffic forecasting, finding that simple global aggregation operators achieve comparable performance with lower computational complexity.
This paper introduces regime-stratified evaluation for time series foundation models, revealing that aggregate metrics hide severe failures during traffic regime transitions, and proposes bimodal mixture augmentation to improve coverage while preserving overall accuracy.
PatchSTG introduces a patch-based spatiotemporal graph Transformer for traffic forecasting on irregular sensor networks, achieving near-linear complexity while maintaining competitive performance.
Proposes GC-MoE, a graph-conditioned mixture of experts framework for traffic forecasting that assigns each node a personalized combination of frozen pretrained spatio-temporal GNN experts based on graph topology and recent input, training only a lightweight routing module (∼17K parameters) and achieving competitive performance on four benchmarks.
Proposes a Global-Local Graph Attention Network (GLGAT) with pairwise encoding and event-based adjacency matrix for traffic forecasting, effectively capturing spatio-temporal correlations and achieving competitive performance on real-world datasets.