A Low-Rank Tensor Model for Imputation of Missing Vehicular Traffic Volume

This paper presents a low-rank tensor model for vehicular traffic volume data. Contrarily to previous works, the authors capitalize on a definition of rank, called the tensor train, that is as effective as possible; so that it exploits all the correlation between local structures that are present in the multiple modes, but practical enough that efficient optimization algorithms still hold. From the authors' model, a formulation to find balanced (higher order) tensors is derived. The resulting optimally-balanced tensor improves the imputation accuracy of the tensor train rank. Then, the authors design specific experiments, which are numerically evaluated using real-world traffic data from Tampere city, Finland. The experimental results are promising, the authors proposed approach outperforms existing algorithms in both imputation accuracy and, in some instances, computation time.

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  • English

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  • Accession Number: 01684522
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Oct 18 2018 2:07PM