Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network

Abstract In the multimedia system for Internet of Vehicles (IoVs), accurate traffic flow information processing and feedback can give drivers guidance. In traditional information processing for IoVs, few researches deal with traffic flow information processing by deep learning. Specially, most of the existing prediction technologies adopt shallow neural network, and their models for chaotic time series are prone to be restricted by multiple parameters. Over the last few years, the dawning of the big data era creates opportunities for the intelligent traffic control and management. In this paper, we take Restricted Boltzmann Machine (RBM) as the method for traffic flow prediction, which is a typical algorithm based on deep learning architecture. Considering traffic big data aggregation in IoVs, multimedia technologies provide enough real sample data for model training. RBM constructs the long-term model of polymorphic for chaotic time series, using phase space reconstruction to recognize the data. To the best of our knowledge, it is the first time apply RBM model to short-term traffic flow prediction, which can improve the performance of multimedia system in IoVs. Moreover, experimental results show that the proposed method has superior performance than traditional shallow neural network prediction methods.

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