Abstract
The incomplete traffic data in urban road networks greatly affect the quality and performance of traffic information platform. The importance of effectively imputing the missing values emerges. The paper introduces a three-layer feed-forward neural network to missing traffic index prediction, where the group search optimization (GSO) is proposed to optimize the connection weights and thresholds to solve the problem of falling into the local optima. It is the first time to apply the technique to missing data imputation. Simulations are presented to demonstrate the accuracies and effectiveness of the proposed methodologies. This reveals that it is a promising approach in missing data prediction.
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