Traffic Speed Data Imputation Method Based on Tensor Completion
Joint Authors
Wang, Wu-hong
Feng, Jianshuai
Liu, Ying
Tan, Huachun
Ran, Bin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-03-03
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Traffic speed data plays a key role in Intelligent Transportation Systems (ITS); however, missing traffic data would affect the performance of ITS as well as Advanced Traveler Information Systems (ATIS).
In this paper, we handle this issue by a novel tensor-based imputation approach.
Specifically, tensor pattern is adopted for modeling traffic speed data and then High accurate Low Rank Tensor Completion (HaLRTC), an efficient tensor completion method, is employed to estimate the missing traffic speed data.
This proposed method is able to recover missing entries from given entries, which may be noisy, considering severe fluctuation of traffic speed data compared with traffic volume.
The proposed method is evaluated on Performance Measurement System (PeMS) database, and the experimental results show the superiority of the proposed approach over state-of-the-art baseline approaches.
American Psychological Association (APA)
Ran, Bin& Tan, Huachun& Feng, Jianshuai& Liu, Ying& Wang, Wu-hong. 2015. Traffic Speed Data Imputation Method Based on Tensor Completion. Computational Intelligence and Neuroscience،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1057687
Modern Language Association (MLA)
Ran, Bin…[et al.]. Traffic Speed Data Imputation Method Based on Tensor Completion. Computational Intelligence and Neuroscience No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1057687
American Medical Association (AMA)
Ran, Bin& Tan, Huachun& Feng, Jianshuai& Liu, Ying& Wang, Wu-hong. Traffic Speed Data Imputation Method Based on Tensor Completion. Computational Intelligence and Neuroscience. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1057687
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057687