Missing traffic flow data prediction using least squares support vector machines in urban arterial streets

Accurate traffic parameters such as traffic flow, travel speeds and occupancies, are crucial to effective management of intelligent transportation systems (ITS). Some traffic data from loop detectors settled in arterial streets are incomplete, and the importance of effectively imputing the missing values emerges. In this paper, a technique called least squares support vector machines (LS-SVMs) is introduced to predict missing traffic flow based on spatio-temporal analysis in urban arterial streets. To the best of our knowledge, it is the first time to apply the rising computational intelligence (CI) technique incorporating with state space approach in missing traffic data imputation. Having good generalization ability and guaranteeing global minima ensure its well performance in the area. A baseline imputation technique, expectation maximization/data augmentation (EM/DA), is selected for comparison because of its proved effectiveness in missing data recovery. Through persuasive comparisons of the techniques, the proposed model is proved to be more applicable and performs better in stability and adaptability, which reveals that it is a promising approach in missing data prediction.