Prevailing short-term traffic flow prediction models concentrate on using black-box type of artificial intelligence (AI) algorithms without explicit knowledge of traffic flow data. In this paper, a novel short-term traffic flow prediction method Ml-k-NN was developed using multilinear analysis. The model recognizes the lane flow distribution within the traffic flow data and uses the k-nearest neighbor to predict traffic flow. The proposed multilinear analysis technique employs a dynamic tensor form of traffic flow data and uses tensor decomposition to combine several characteristics of traffic flow data. With the tensor decomposition, we can not only find the spatial-temporal information and lane distribution of traffic flow pattern, but also acquire the short-term traffic prediction by applying the k-nearest neighbor method on the generated features. Experiments on real traffic data acquired from 10 locations on 4-lane freeway are provided to validate and evaluate the proposed approach. Experimental results show that the proposed method has the promising performance in predicting traffic flow.


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    Titel :

    Short-Term Traffic Flow Prediction Based on Multilinear Analysis and k-Nearest Neighbor Regression


    Beteiligte:
    Wu, Yuankai (Autor:in) / Tan, Huachun (Autor:in) / Peter, Jin (Autor:in) / Shen, Bin (Autor:in) / Ran, Bin (Autor:in)

    Kongress:

    15th COTA International Conference of Transportation Professionals ; 2015 ; Beijing, China


    Erschienen in:

    CICTP 2015 ; 556-569


    Erscheinungsdatum :

    13.07.2015




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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