In the field of road traffic management, fuzzy techniques have already been used for traffic control. In this paper, we use fuzzy methods for traffic data analysis. The results of the data analysis are classification and prediction systems. Our work is focused on fuzzy clustering methods. The known clustering models are extended to: constrained prototypes, the use of a mix of different prototypes for one data set, partial supervision of the clustering, and the estimation of the number of clusters by cluster merging. Two successful application examples are given. The first one is the classification of traffic jam on a German autobahn, and the second application is a long-term prediction of traffic volume.


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    Title :

    Classification and prediction of road traffic using application-specific fuzzy clustering


    Contributors:
    Stutz, C. (author) / Runkler, T.A. (author)

    Published in:

    Publication date :

    2002


    Size :

    12 Seiten, 25 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English