Traffic Data Imputation and Prediction: An Efficient Realization of Deep Learning

In this paper, we study the prediction of traffic flow in the presence of missing information from data set. Specifically, we adopt three different patterns to model the missing data structure, and apply two types of approaches for the imputation. In consequence, a forecasting model via deep learning based methods is proposed to predict the traffic flow from the recovered data set. The experiments demonstrate the effectiveness of using deep learning based imputation in improving the accuracy of traffic flow prediction. Based on the experimental results, we conduct a thorough discussion on the appropriate methods to predict traffic flow under various missing data conditions, and thus shedding the light for a practical design.

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