The paper proposes a new method for transforming traffic flow time series into the vector symbolization series based on data mining technique. With consideration of the complexity and randomness of traffic system, the linearization smooth of discrete traffic flows is developed to filter stochastic flows by least square approximation, and the extensive meaning regularity and knowledge of traffic data are drawn. It segments the time series of traffic flows into linear segments that are well applied for their shape expression. Then online clustering analysis on the segmented traffic flow time series is conducted step by step with the similarity threshold and the improved K-Means algorithm to adapt the characteristics of traffic data. The measure of shape similarity is also proposed in this paper. Effectiveness of this method has been verified by the vector symbolization series of express highway traffic flows. The influencing factors of experimental result are discussed, some research to be taken in the future is proposed.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on vector symbolization of traffic flow time series by data mining oriented method


    Contributors:
    Yi Zhang, (author) / Guo-hui Zhang, (author) / Jiang-tao Ren, (author) / Zuo Zhang, (author)


    Publication date :

    2003-01-01


    Size :

    364831 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Research on Vector Symbolization of Traffic Flow Time Series by Data Mining Oriented Method

    Zhang, Y. / Zhang, G.-h. / Ren, J.-t. et al. | British Library Conference Proceedings | 2003



    Traffic flow time series decomposition method

    WANG WEI / ZHOU WEI / WANG YUJIE et al. | European Patent Office | 2020

    Free access

    Traffic Flow Management: Data Mining Update

    Grabbe, Shon R. | NTRS | 2012


    Calibration of microscopic traffic flow models against time-series data

    Montanino, M. / Ciuffo, B. / Punzo, V. | IEEE | 2012