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

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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.

Details

Original languageEnglish
Pages (from-to)8934 - 8938
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Volume67
Issue number9
Early online date29 Jun 2018
Publication statusPublished - Sep 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Crowdsensing, crowdsourcing, data imputation, Indexes, missing data, Monitoring, Optimization, Roads, Solid modeling, Tensile stress, tensor completion, transportation systems, Urban areas

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