Short-term traffic speed prediction is an important technique for advanced traffic management systems. In this paper, a deep architecture combining convolutional neural networks (CNNs) and restricted Boltzmann machines (RBMs) with Gaussian units (GRBM-CNN) is proposed to predict short-term traffic speed; it combines the advantages of RBMs and CNNs and achieves a powerful capability to learn complex features of traffic data. Furthermore, this paper uses a graphics processing unit (GPU) to accelerate the learning process. Experimental results show that the proposed deep architecture has a lot of advantages over state-of-arts on the short-term traffic speed prediction task.


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    Title :

    A Deep Architecture Combining CNNS and GRBMS for Traffic Speed Prediction


    Contributors:
    Tan, Huachun (author) / Zhong, Zhiyu (author) / Wu, Yuankai (author) / Chen, Xiaoxuan (author) / Zhang, Jian (author)

    Conference:

    17th COTA International Conference of Transportation Professionals ; 2017 ; Shanghai, China


    Published in:

    CICTP 2017 ; 310-319


    Publication date :

    2018-01-18




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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