Because the neuro-fuzzy system (NFS) combines the learning capability of neural networks and the decision structure of fuzzy inference systems, it is very useful in the modeling, control, and forecasting of complex systems such as traffic systems. This paper proposes a form of neuro-fuzzy systems (NFS) and applies it to forecast short-term traffic flows. Different learning algorithms for the NFS have been tested and evaluated using actual traffic data collected from the Loop 3 Freeway in Beijing, China. These test results indicate that the NFS based approach is an effective method for short-tern traffic flow forecasting. To demonstrate the advantage of the proposed approach, a comparison with a typical neural network based approach has been made.


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    Title :

    A neuro-fuzzy system approach for forecasting short-term freeway traffic flows


    Contributors:
    Long Chen, (author) / Fei-Yue Wang, (author)


    Publication date :

    2002-01-01


    Size :

    340131 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A NEURO-FUZZY SYSTEM APPROACH FOR FORECASTING SHORT-TERM FREEWAY TRAFFIC FLOWS

    Chen, L. / Wang, F.-Y. / IEEE | British Library Conference Proceedings | 2002


    Hybrid Neuro-Fuzzy Application in Short-Term Freeway Traffic Volume Forecasting

    Park, B. / Transportation Research Board | British Library Conference Proceedings | 2002



    Hybrid Neuro-Fuzzy Application in Short-Term Freeway Traffic Volume Forecasting

    Park, Byungkyu “Brian” | Transportation Research Record | 2002