Missing Data Imputation Considering Multi-Mode Variations

Missing traffic data are inevitable due to detector or communication malfunctions which adversely affect the performance of intelligent transportation systems and make the requirement of missing traffic data imputation more important. In this paper, a novel method based on tensor completion is proposed to estimate the missing traffic data. Compared with previous tensor-based methods, systematic variations encoded with total variation are used to mine the traffic intrinsic properties. By minimizing the total variation norm, the approach can keep the systematic variations of traffic volume while inheriting the advantage of mining the multi-dimensional correlations of traffic data from the tensor pattern. Experimental results on Performance Monitoring System (PeMS) database show the proposed method achieves a better imputation performance than the state-of-the-art missing traffic data imputation approaches.

• Record URL:
  https://doi.org/10.1061/9780784413623.047
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784413623
• Supplemental Notes:
  • © 2014 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Tan, Huachun
  • Yao, Qi
  • Cheng, Bin
  • Wang, Wuhong
  • Ran, Bin
• Conference:
  • 14th COTA International Conference of Transportation Professionals
  • Location: Changsha , China
  • Date: 2014-7-4 to 2014-7-7
• Publication Date: 2014-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 478-489
• Monograph Title: CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems

Subject/Index Terms

• TRT Terms: Data mining; Data quality; Intelligent transportation systems; Multimodal transportation; Tensor analysis; Traffic data; Traffic volume
• Identifier Terms: Performance Monitoring System (PeMS)
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01536185
• Record Type: Publication
• ISBN: 9780784413623
• Files: TRIS, ASCE
• Created Date: Aug 28 2014 9:12AM