  In intelligent transportation systems, traffic data imputation, estimating
the missing value from partially observed data is an inevitable and challenging
task. Previous studies have not fully considered traffic data's
multidimensionality and spatiotemporal correlations, but they are vital to
traffic data recovery, especially for high-level missing scenarios. To address
this problem, we propose a novel spatiotemporal regularized Tucker
decomposition method. First, the traffic matrix is converted into a third-order
tensor. Then, based on Tucker decomposition, the tensor is approximated by
multiplying non-negative factor matrices with a sparse core tensor. Notably, we
do not need to set the tensor rank or determine it through matrix nuclear-norm
minimization or tensor rank minimization. The low rankness is characterized by
the $l_1$-norm of the core tensor, while the manifold regularization and
temporal constraint are employed to capture spatiotemporal correlations and
further improve imputation performance. We use an alternating proximal gradient
method with guaranteed convergence to address the proposed model. Numerical
experiments show that our proposal outperforms matrix-based and tensor-based
baselines on real-world spatiotemporal traffic datasets in various missing
scenarios.
