Data Imputation Using Least Squares Support Vector Machines in Urban Arterial Streets

Abstract

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.