Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation
This work addresses the problem of missing data recovery in intelligent transportation systems, offering an incremental improvement by better capturing spatiotemporal correlations for high-level missing scenarios.
The paper tackles traffic data imputation by proposing a spatiotemporal regularized Tucker decomposition method that avoids setting tensor ranks and uses manifold regularization and temporal constraints, achieving superior performance over baselines in real-world datasets under various missing scenarios.
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.