Accurate vessel traffic flow prediction is crucial for smart port operations and navigational safety. However, maritime traffic flow data are often highly sparse with intermittent bursts, making robust forecasting challenging. Under such conditions, conventional spatio-temporal graph neural networks (ST-GNNs) can degrade toward conservative near-zero predictions and fail to capture non-zero activity. Although zero-inflated negative binomial (ZINB) models partially address excess zeros, their two-part formulation can still remain conservative around abrupt transitions. To address these issues, we propose a model-agnostic learnable Tweedie head that can be attached as a plug-and-play output module to arbitrary ST-GNN backbones. Instead of likelihood-based Tweedie training, which typically requires surrogate objectives, our approach optimizes the closed-form Tweedie unit deviance and predicts the mean for point forecasting while learning a node-level variance power to capture heterogeneous variability across port areas. Experiments on a maritime traffic graph constructed from real-world AIS data in the Port of Los Angeles and Long Beach show that the proposed head consistently improves RMSE across multiple ST-GNN backbones, especially on non-zero events, leading to more reliable forecasts for practical maritime traffic control.

翻译：精准的船舶交通流预测对于智慧港口运营与航行安全至关重要。然而，海上交通流数据常呈现高度稀疏性并伴随间歇性突发特征，这使得鲁棒预测极具挑战性。在此类条件下，传统时空图神经网络易退化为保守的趋近零预测，无法捕捉非零活动。尽管零膨胀负二项模型可部分解决过度零值问题，但其双分量结构在突变过渡区间仍可能保持保守性。针对上述问题，我们提出一种模型无关的可学习Tweedie头，可作为即插即用输出模块附加于任意时空图神经网络架构。不同于通常需替代目标的似然基Tweedie训练方法，本方法优化闭式Tweedie单位偏差，通过预测均值实现点预测，并学习节点级方差幂参数以捕获港口区域间的异质性变异。基于洛杉矶-长滩港真实自动识别系统数据构建的海上交通图实验表明，所提方法在多个时空图神经网络骨干模型上持续提升均方根误差，尤其针对非零事件，可为实际海上交通管控提供更可靠的预测结果。