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 language | English |
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Pages (from-to) | 8934 - 8938 |
Number of pages | 6 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 67 |
Issue number | 9 |
Early online date | 29 Jun 2018 |
DOIs | |
Publication status | Published - Sept 2018 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Crowdsensing
- crowdsourcing
- data imputation
- Indexes
- missing data
- Monitoring
- Optimization
- Roads
- Solid modeling
- Tensile stress
- tensor completion
- transportation systems
- Urban areas