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

Giancarlo Pastor Figueroa

    Research output: Contribution to journalArticleScientificpeer-review

    29 Citations (Scopus)
    194 Downloads (Pure)

    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.

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

    Keywords

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

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