Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 16, Issue 5, Pages 414-417Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2009.2016451
Keywords
Data imputation; least squares support vector machines (LS-SVMs); urban arterial streets
Categories
Funding
- National Science Technology [2006BAJ18B02]
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Some traffic data from loop detectors settled in urban arterial streets are incomplete. The importance of effectively imputing the missing values emerges. The letter introduces least squares support vector machines (LS-SVMs) to missing traffic flow prediction based on spatio-temporal analysis. It is the first time to apply the technique to missing data imputation. A baseline imputation technique, expectation maximization/data augmentation (EM/DA), is selected for comparison because of its proved effectiveness. Experimental results demonstrate that our method is more applicable and performs better at relatively high missing data rates. This reveals that it is a promising approach in the field.
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