crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional 'zero-inflated' model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using real-world datasets highlight STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios.

翻译：对于交通管理至关重要。然而，传统的时空深度学习模型难以解决高分辨率OD矩阵中的稀疏性和长尾特征，也无法量化预测不确定性。这一困境源于矩阵中大量存在的零值和过度离散的出行需求模式，这些特征挑战了确定性深度学习模型固有的高斯假设。为应对这些挑战，我们提出了一种新方法：时空Tweedie图神经网络（STTD）。STTD引入Tweedie分布作为传统"零膨胀"模型的有力替代方案，并利用空间和时间嵌入来参数化出行需求分布。基于真实数据集的评估结果表明，STTD在高分辨率场景下在提供准确预测和精确置信区间方面具有显著优越性。