Missing data detection and imputation for urban ANPR system using an iterative tensor decomposition approach

The automatic number plate recognition (ANPR) system has been widely implemented as an important part of intelligent transportation system (ITS). However, similar to other traffic monitoring devices, missing data is a common and critical problem in the ANPR system. To solve the missing data problem, numerous tensor-based methods have been proposed in previous studies. Most of them, however, assume that where and when missing data occur in the dataset are known. This would be impractical, because missing data may occur randomly. In this study, the authors propose a novel tensor-based algorithm, specifically, an iterative tensor decomposition (ITD) approach, that utilizes multidimensional inherent correlation of traffic data to detect and impute missing data in the ANPR system. The proposed algorithm is tested with a real-world ANPR system dataset. The experimental results show that missing data from the ANPR system can be classified into three cases, i.e., no missing, random elements missing, and extreme missing. The proposed ITD can accurately detect and correct missing data under different missing cases. Furthermore, ITD is also compared with other state-of-the-art methods and the results show that ITD outperforms the existing methods.

• Record URL:
  https://doi.org/10.1016/j.trc.2019.08.013
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X18315596
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Han
  • Chen, Peng
  • Zheng, Jianfeng
  • Zhu, Jingqing
  • Yu, Guizhen
  • Wang, Yunpeng
  • Liu, Henry X
• Publication Date: 2019-10

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: pp 337-355
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 107
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Algorithms; Automatic license plate readers; Case studies; Data collection; Data files; Intelligent transportation systems; Tensor analysis; Traffic data; Urban highways
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01717451
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2019 3:07PM