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A Low-rank Tensor Model for Imputation of Missing Vehicular Traffic Volume
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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6
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IEEE Transactions on Vehicular Technology, Volume 67, issue 9, pp. 8934 - 8938
Abstract
This paper presents a low-rank tensor model for vehicular traffic volume data. Contrarily to previous works, we 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 our 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, we design specific experiments which are numerically evaluated using real-world traffic data from Tampere city, Finland. The experimental results are promising, our proposed approach outperforms existing algorithms in both imputation accuracy and, in some instances, computation time.
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Pastor Figueroa, G 2018, 'A Low-rank Tensor Model for Imputation of Missing Vehicular Traffic Volume', IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8934 - 8938. https://doi.org/10.1109/TVT.2018.2833505