TY  - JOUR
PY  - 2024//
TI  - Tucker factorization-based tensor completion for robust traffic data imputation
JO  - Transportation research part C: emerging technologies
A1  - Lyu, Cheng
A1  - Lu, Qing-Long
A1  - Wu, Xinhua
A1  - Antoniou, Constantinos
SP  - e104502
EP  - e104502
VL  - 160
IS  - 
N2  - Missing values are prevalent in spatio-temporal traffic data, undermining the quality of data-driven analysis. While prior works have demonstrated the promise of tensor completion methods for imputation, their performance remains limited for complicated composite missing patterns. This paper proposes a novel imputation framework combining tensor factorization and rank minimization, which is effective in capturing key traffic dynamics and eliminates the need for exhaustive rank tuning. The framework is further supplemented with time series decomposition to account for trends, spatio-temporal correlations, and outliers, with the intention of improving the robustness of imputation results. A Bregman ADMM algorithm is designed to solve the resulting multi-block nonconvex optimization efficiently. Experiments on four real-world traffic state datasets suggest that the proposed framework outperforms state-of-the-art imputation methods, including the context of complex missing patterns with high missing rates, while maintaining reasonable computation efficiency. Furthermore, the robustness of our model in extreme missing data scenarios, as well as under perturbation in hyperparameters, has been validated. These results also underscore the potential benefits of incorporating temporal modeling for more reliable imputation.<p />  <p>Language: en</p>
LA  - en
SN  - 0968-090X
UR  - http://dx.doi.org/10.1016/j.trc.2024.104502
ID  - ref1
ER  -