Spatiotemporal traffic data imputation (STDI), estimating the missing value from partially observed traffic data, is an inevitable and challenging task in data-driven intelligent transportation systems (ITS). Due to the traffic data's multidimensionality, we transform the traffic matrix into the 3rd-order tensor and propose an innovative manifold regularized Tucker decomposition (ManiRTD) model for STDI. ManiRTD considers the sparsity of the Tucker core tensor to constrain the low rankness and employs manifold regularization and the Toeplitz matrix to enhance the model performance. We address the ManiRTD model through a block coordinate descent framework under alternating proximal gradient updating rules with convergence-guaranteed. Numerical experiments on real-world spatiotemporal traffic datasets (STDs) demonstrate that our proposed model is superior to the other baselines under various missing scenarios.

翻译：时空交通数据插补（STDI）旨在从部分观测的交通数据中估计缺失值，是数据驱动型智能交通系统（ITS）中一项不可避免且具有挑战性的任务。鉴于交通数据的多维特性，我们将交通矩阵转换为三阶张量，并提出一种创新的流形正则化塔克分解（ManiRTD）模型用于STDI。ManiRTD通过利用塔克核张量的稀疏性约束低秩性，并采用流形正则化和托普利茨矩阵来提升模型性能。我们基于块坐标下降框架，结合交替邻近梯度更新规则解决ManiRTD模型，且该算法具有收敛保证。在真实世界时空交通数据集（STDs）上的数值实验表明，我们的模型在不同缺失场景下均优于其他基线方法。