Spatiotemporal traffic data imputation (STDI), estimating the missing data
from partially observed traffic data, is an inevitable and challenging task in
data-driven intelligent transportation systems (ITS). Due to traffic data's
multidimensional and spatiotemporal properties, we treat the missing data
imputation as a tensor completion problem. Many studies have been on STDI based
on tensor decomposition in the past decade. However, how to use spatiotemporal
correlations and core tensor sparsity to improve the imputation performance
still needs to be solved. This paper reshapes a 3rd/4th order Hankel tensor and
proposes an innovative manifold regularized Tucker decomposition (ManiRTD)
model for STDI. Expressly, we represent the sensory traffic state data as the
3rd/4th tensors by introducing Multiway Delay Embedding Transforms. Then,
ManiRTD improves the sparsity of the Tucker core using a sparse regularization
term and employs manifold regularization and temporal constraint terms of
factor matrices to characterize the spatiotemporal correlations. Finally, we
address the ManiRTD model through a block coordinate descent framework under
alternating proximal gradient updating rules with convergence-guaranteed.
Numerical experiments are conducted on real-world spatiotemporal traffic
datasets (STDs). Our results demonstrate that the proposed model outperforms
the other factorization approaches and reconstructs the STD more precisely
under various missing scenarios.

本论文提出了一个创新的流形正则化 Tucker 分解（ManiRTD）模型，通过引入多元时延嵌入变换，将感官交通状态数据表示为三阶 / 四阶张量，并利用稀疏正则化项改善 Tucker 核的稀疏性以及使用流形正则化和时间约束条件特征化其时空关联，最终在交替近端梯度更新规则下通过块坐标下降框架解决 ManiRTD 模型，并在实际交通数据集上进行数字实验，结果表明所提出的模型在各种缺失情况下优于其他分解方法，并更精确地重建了 STD。