A Low-rank Tensor Model for Imputation of Missing Vehicular Traffic Volume
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2018-09
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Mcode
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Language
en
Pages
6
8934 - 8938
8934 - 8938
Series
IEEE Transactions on Vehicular Technology, Volume 67, issue 9
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.Description
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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Citation
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