In major domestic cities, the development of urban expressway is network oriented. The traffic flow forecasting system is the important prerequisite and foundation for realizing real-time traffic management and control. However, the traffic flow forecasting research is mainly based on highways. Research and application of short-term traffic forecasting for urban expressway is severely insufficient. Therefore, the study of urban expressway flow forecasting is discussed and a short-term traffic flow forecasting system for urban expressway based on k-NN nonparametric regression model is proposed in this study. First, the study analyzes the characteristics and needs of the urban expressway traffic flow, introduces the k-NN nonparametric regression model, and designs the short-term traffic flow forecasting system based on k-NN overall. Then, the short-term urban expressway flow forecasting system based on k-NN is established in three aspects: the historical database, the search mechanism and algorithm parameters, and the forecasting plan. Finally, a short-term traffic forecasting for urban expressway based on k-NN nonparametric regression model is developed in the VS2010 VC++ platform. Utilizing the Shanghai urban expressway section measured traffic flow data, the comparison of average and weighted k-NN nonparametric regression model is discussed and the reliability of the forecasting result is analyzed. Results show that the accuracy of the proposed method, under the five-minute interval, is over 90%, which best proves the reasonableness of the proposed forecasting model based on the k-NN nonparametric model.


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    Titel :

    An Improved k-NN Nonparametric Regression-Based Short-Term Traffic Flow Forecasting Model for Urban Expressways


    Beteiligte:
    Zhang, Lun (Autor:in) / Rao, Qian (Autor:in) / Yang, Wenchen (Autor:in) / Zhang, Meng (Autor:in)

    Kongress:

    Fourth International Conference on Transportation Engineering ; 2013 ; Chengdu, China


    Erschienen in:

    ICTE 2013 ; 1214-1223


    Erscheinungsdatum :

    2013-10-09




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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