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Tensor based missing traffic data completion with spatialtemporal spatialtemporal correlation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tan, Hua-Chun Wu, Yuankai Jin, Peter J. |
| Copyright Year | 2015 |
| Abstract | Missing and suspicious traffic data is a major problem for intelligent transportation system, which adversely affects a diverse variety of transportation applications. Several missing traffic data imputationmethods had been proposed in the last decade. It is still an open problem of how tomake full use of spatial information from upstream/downstream detectors to improve imputing performance. In this paper, a tensor based method considering the full spatial–temporal information of traffic flow, is proposed to fuse the traffic flow data from multiple detecting locations. The traffic flow data is reconstructed in a 4-way tensor pattern, and the low-n-rank tensor completion algorithm is applied to imputemissing data. This novel approach not only fully utilizes the spatial information from neighboring locations, but also can impute missing data in different locations under a unified framework. Experiments demonstrate that the proposed method achieves a better imputation performance than the method without spatial information. The experimental results show that the proposed method can address the extreme case where the data of a long period of one or several weeks are completely missing. © 2015 Elsevier B.V. All rights reserved. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://static.tongtianta.site/paper_pdf/7bfb1ade-8c1d-11e9-8b54-00163e08bb86.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
