This paper presents a Hidden Markov Model (HMM) based fuzzy model of short-term traffic forecasting. The model uses three sequential phases. Firstly, the HMM is used to partition the traffic flow data according to the ordering of the calculated log-likelihood values. Then, a recursive top-down algorithm is used to generate the minimum number of rules for accurate forecasting. Finally, a gradient descent method is applied to fine-tune the model parameters. To verify the method in real situation, an experimental model is constructed to produce traffic flow forecasting of freeway in CA, USA based on actual data.


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    Title :

    HMM Based Fuzzy Model for Short-Term Traffic Forecasting


    Contributors:
    Chang, Gang (author) / Zhang, Yi (author) / Yao, Danya (author) / Yue, Yun (author)

    Conference:

    First International Conference on Transportation Information and Safety (ICTIS) ; 2011 ; Wuhan, China


    Published in:

    ICTIS 2011 ; 1856-1862


    Publication date :

    2011-06-16




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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