Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. The authors systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.

• Record URL:
  • https://doi.org/10.1016/j.trc.2013.05.008
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X13001095
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier.
• Authors:
  • Li, Li
  • Li, Yuebiao
  • Li, Zhiheng
• Publication Date: 2013-9

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References;
• Pagination: pp 108-120
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 34
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Data fusion; Data quality; Mathematical methods; Time series; Traffic data; Traffic estimation; Traffic flow; Traffic forecasting
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I71: Traffic Theory;

Filing Info

• Accession Number: 01492114
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 3 2013 12:30PM